Websites for booklovers as meeting places

Purpose – The study aims to report on a research project that analyzed social websites for booklovers. These sites represent a service that is promising for public libraries in their efforts to find new ways in promoting reading and literature. At the same time the growth of such sites is another example of how technological developments challenge librarianship. Many of these sites are established and run independently from the library field. Design/methodology/approach – This paper reports from a research comparing two such websites – the Norwegian Bokelskere.no and the Hungarian Moly.hu. A questionnaire was published on the two websites in mid September 2010. It was accessible for approximately 20 days. A total of 777 users filled in and returned the questionnaire. Findings – As the typical user of Moly/Bokelskere is a young, ethnic Hungarian or Norwegian, well educated, female from the bigger cities the complexity and pluralism of society is not reflected in the websites in the same way as it is in physical libraries. They are not heavy library users, and they have a relatively low trust concerning libraries in comparison with other sources of information. The sites are mainly used as information sources and not as places where one can meet with others. Although the social dimension of reading appears, it is related mainly to the family or friends and not to strangers. Research limitations/implications – It would be inaccurate to claim that the study gives a comprehensive overview on social sites for booklovers. The relatively high number of respondents from the two analyzed websites provides an extensive, but not comprehensive, sample. Self-recruitment of respondents might cause biases compared with a randomly drawn sample. Practical implications – The study on which the paper is based is a part of the PLACE project, which aims at exploring the role of public libraries as meeting places. The study generates knowledge on the potential and role of virtual meeting places that is relevant for public libraries in their efforts to adapt to a new reality. Social implications – The study generates knowledge that can be of importance for developing libraries and library policies in relation to digital meeting places. Originality/value – There are few studies analyzing literary websites for booklovers and the study contributes in developing a new research field in library and information science

Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibility to directly measure velocity. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is inevitable and must be dealt with. Sensors must be capable of detecting, or even mitigating the harmful effects of interference, which include a decreased detection sensitivity. In this paper, we evaluate a Convolutional Neural Network (CNN)-based approach for interference mitigation on real-world radar measurements. We combine real measurements with simulated interference in order to create input-output data suitable for training the model. We analyze the performance to model complexity relation on simulated and measurement data, based on an extensive parameter search. Further, a finite sample size performance comparison shows the effectiveness of the model trained on either simulated or real data as well as for transfer learning. A comparative performance analysis with the state of the art emphasizes the potential of CNN-based models for interference mitigation and denoising of real-world measurements, also considering resource constraints of the hardware.Comment: 2020 IEEE International Radar Conference (RADAR

Haptoglobin Polymorphism: A Novel Genetic Risk Factor for Celiac Disease Development and Its Clinical Manifestations

Background: Haptoglobin (Hp) α-chain alleles 1 and 2 account for 3 phenotypes that may influence the course of inflammatory diseases via biologically important differences in their antioxidant, scavenging, and immunomodulatory properties. Hp1-1 genotype results in the production of small dimeric, Hp2-1 linear, and Hp2-2 cyclic polymeric haptoglobin molecules. We investigated the haptoglobin polymorphism in patients with celiac disease and its possible association to the presenting symptoms. Methods: We studied 712 unrelated, biopsy-proven Hungarian celiac patients (357 children, 355 adults; severe malabsorption 32.9%, minor gastrointestinal symptoms 22.8%, iron deficiency anemia 9.4%, dermatitis herpetiformis 15.6%, silent disease 7.2%, other 12.1%) and 384 healthy subjects. We determined haptoglobin phenotypes by gel electrophoresis and assigned corresponding genotypes. Results: Hp2-1 was associated with a significant risk for celiac disease (P = 0.0006, odds ratio [OR] 1.54, 95% CI 1.20–1.98; prevalence 56.9% in patients vs 46.1% in controls). It was also overrepresented among patients with mild symptoms (69.2%) or silent disease (72.5%). Hp2-2 was less frequent in patients than in controls (P = 0.0023), but patients having this phenotype were at an increased risk for severe malabsorption (OR 2.21, 95% CI 1.60–3.07) and accounted for 45.3% of all malabsorption cases. Celiac and dermatitis herpetiformis patients showed similar haptoglobin phenotype distributions. Conclusions: The haptoglobin polymorphism is associated with susceptibility to celiac disease and its clinical presentations. The predominant genotype in the celiac population was Hp2-1, but Hp2-2 predisposed to a more severe clinical course. The phenotype-dependent effect of haptoglobin may result from the molecule’s structural and functional properties

Dealing with the effects of sensor displacement in wearable activity recognition

Most wearable activity recognition systems assume a predefined sensor deployment that remains unchanged during runtime. However, this assumption does not reflect real-life conditions. During the normal use of such systems, users may place the sensors in a position different from the predefined sensor placement. Also, sensors may move from their original location to a different one, due to a loose attachment. Activity recognition systems trained on activity patterns characteristic of a given sensor deployment may likely fail due to sensor displacements. In this work, we innovatively explore the effects of sensor displacement induced by both the intentional misplacement of sensors and self-placement by the user. The effects of sensor displacement are analyzed for standard activity recognition techniques, as well as for an alternate robust sensor fusion method proposed in a previous work. While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements.This work was supported by the High Performance Computing (HPC)-Europa2 project funded by the European Commission-DG Research in the Seventh Framework Programme under grant agreement No. 228398 and by the EU Marie Curie Network iCareNet under grant No. 264738. This work was also supported by the Spanish Comision Interministerial de Ciencia y Tecnologia (CICYT) Project SAF2010-20558, Junta de Andalucia Project P09-TIC-175476 and the FPU Spanish grant, AP2009-2244

nucleAIzer : A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information.Peer reviewe