KRAN: Knowledge Refining Attention Network for Recommendation

Recommender algorithms combining knowledge graph and graph convolutional network are becoming more and more popular recently. Specifically, attributes describing the items to be recommended are often used as additional information. These attributes along with items are highly interconnected, intrinsically forming a Knowledge Graph (KG). These algorithms use KGs as an auxiliary data source to alleviate the negative impact of data sparsity. However, these graph convolutional network based algorithms do not distinguish the importance of different neighbors of entities in the KG, and according to Pareto’s principle, the important neighbors only account for a small proportion. These traditional algorithms can not fully mine the useful information in the KG. To fully release the power of KGs for building recommender systems, we propose in this article KRAN, a Knowledge Refining Attention Network, which can subtly capture the characteristics of the KG and thus boost recommendation performance. We first introduce a traditional attention mechanism into the KG processing, making the knowledge extraction more targeted, and then propose a refining mechanism to improve the traditional attention mechanism to extract the knowledge in the KG more effectively. More precisely, KRAN is designed to use our proposed knowledge-refining attention mechanism to aggregate and obtain the representations of the entities (both attributes and items) in the KG. Our knowledge-refining attention mechanism first measures the relevance between an entity and it’s neighbors in the KG by attention coefficients, and then further refines the attention coefficients using a “richer-get-richer” principle, in order to focus on highly relevant neighbors while eliminating less relevant neighbors for noise reduction. In addition, for the item cold start problem, we propose KRAN-CD, a variant of KRAN, which further incorporates pre-trained KG embeddings to handle cold start items. Experiments show that KRAN and KRAN-CD consistently outperform state-of-the-art baselines across different settings.

Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project

Methods to measure physical activity and sedentary behaviors typically quantify the amount of time devoted to these activities. Among patients with chronic diseases, these methods can provide interesting behavioral information, but generally do not capture detailed body motion and fine movement behaviors. Fine detection of motion may provide additional information about functional decline that is of clinical interest in chronic diseases. This perspective paper highlights the need for more developed and sophisticated tools to better identify and track the decomposition, structuration, and sequencing of the daily movements of humans. The primary goal is to provide a reliable and useful clinical diagnostic and predictive indicator of the stage and evolution of chronic diseases, in order to prevent related comorbidities and complications among patients.

Demographic and Life History Characteristics of Black Bullheads Ameiurus melas in a North Temperate USA Lake

Black bullheads Ameiurus melas are an environmentally tolerant omnivorous fish species that are found throughout much of North America and parts of Europe. Despite their prevalence, black bullheads are an infrequently studied species making their biology, ecology, and life history poorly understood. Although limited information has been published on black bullheads, evidence suggests that bullheads can dominate the fish biomass and have profound influences on the fish community in some north temperate USA lakes. The goal of our study was to provide additional information on black bullhead population demographics, growth rates, life history characteristics, and seasonal diet preferences in a northern Wisconsin lake. Using common fish collection gears (fyke netting, electrofishing), fish aging protocols, fecundity assessments, and diet indices, our results suggested that black bullheads exhibited relatively fast growth rates, early ages at maturity, moderate fecundity, and a diverse omnivorous diet. Due to these demographic and life history characteristics, black bullheads have the potential to dominate fish community biomass in their native and introduced range. Results from our study may inform the management of black bullhead as native and invasive species.

A DFTB-Based Molecular Dynamics Investigation of an Explicitly Solvated Anatase Nanoparticle

We performed a self-consistent charge density functional tight-binding molecular dynamics (SCC DFTB-MD) simulation of an explicitly solvated anatase nanoparticle. From the 2 ps trajectory, we were able to calculate both dynamic and static properties, such as the energies of interaction and the formation of water layers at the surface, and compare them to the observed behaviour reported elsewhere. The high degree of agreement between our simulation and other sources, and the additional information gained from employing this methodology, highlights the oft-overlooked viability of DFTB-based methods for electronic structure calculations of large systems.

Impact of Condensation on the System Performance of a Fuel Cell Turbocharger

A mobile fuel cell systems power output can be increased by pressure amplification using an electric turbocharger. These devices are subject to frequent transient manoeuvres due to a multitude of load changes during the mission in automotive applications. In this paper, the authors describe a simulation approach for an electric turbocharger, considering the impact of moist air and condensation within the cathode gas supply system. Therefore, two simulation approaches are used: an iterative simulation method and one based on a set of ordinary differential equations. Additional information is included from turbine performance maps taking into account condensation using Euler–Lagrange CFD simulations, which are presented. The iterative calculation approach is well suited to show the impact of condensation and moist air on the steady state thermodynamic cycle and yields a significant shift of the steady state operating line towards the surge line. It is shown that a substantial risk of surge occurs during transient deceleration manoeuvres triggered by a load step.

A modified resistance to compression (RtC) test for evaluation of natural fiber softness

Comfort is a key feature of any clothing that relates significantly to softness of the fiber, yarn and fabric from which is it constructed. A known softness assessment method for fibers is the resistance to compression test. This traditional test only provides a single force value for the resistance of a loose fiber sample using a fixed mass under compression. In this research, a modified resistance to compression test was introduced to show the effects of repeated compression, providing more information about the softness and resilience of selected fibers. Three different natural fiber types, including wool, cotton and alpaca were compared using this new approach. The results showed compression profiles were quite different for different fiber types as well as for the same fibers with different diameters. While the diameters of the wool and alpaca samples were similar (18.5 μm), the modified resistance to compression values were significantly higher for wool (with a peak value at 9.5 kPa compared to 2.1 kPa for alpaca). Cotton was different from wool and alpaca but showed a similar modified resistance to compression value (10.4 kPa) to wool. During cycles of compression, modified resistance to compression peak values decreased slightly and then tended to be constant. Even though the structures of wool, cotton and alpaca were quite different, there was no significant difference in the magnitude of decline in modified resistance to compression peak values. This means that the modified resistance to compression test is able to provide additional information on the resilience characteristics of different natural fibers, and can reveal the resistance behavior of fiber samples during cyclic compression.

Picture description in the assessment of connected speech intelligibility in Parkinson's disease: A pilot study

Introduction: Assessment of intelligibility in dysarthria tends to rely on oral reading of sentences or words. However, self-generated utterances are closer to a clients’ natural speech. This study investigated how transcription of utterances elicited by picture description can be used in the assessment of intelligibility in speakers with Parkinson’s disease. Methods: Speech samples from eleven speakers with Parkinson’s disease and six neurologically healthy persons were audio-recorded. Forty-two naive listeners completed transcriptions of self-generated sentences from a picture description task and orally read sentences from the Swedish Test of Intelligibility, as well as scaled ratings of narrative speech samples. Results: Intelligibility was higher in orally read than self-generated sentences and higher for content words than for the whole sentence in self-generated sentences for most of the speakers, although these within-group differences were not statistically significant at group level. Adding contextual leads for the listeners increased intelligibility in self-generated utterances significantly, but with individual variation. Although correlations between the intelligibility measures were at least moderate or strong, there was a considerable inter- and intra-speaker variability in intelligibility scores between tasks for the speakers with Parkinson’s disease, indicating individual variation of factors that impact intelligibility. Intelligibility scores from neurologically healthy speakers were generally high across tasks with no significant differences between the conditions. Discussion/Conclusion: Within-speaker variability support literature recommendations to use multiple methods and tasks when assessing intelligibility. The inclusion of transcription of self-generated utterances elicited by picture description to the intelligibility assessment has the potential to provide additional information to assessment methods based on oral reading of pre-scripted sentences, and to inform the planning of interventions.

Convolutional Neural Networks Used to Date Photographs

Nowadays, the information provided by digital photographs is very complete and very relevant in different professional fields, such as scientific or forensic photography. Taking this into account, it is possible to determine the date when they were taken, as well as the type of device that they were taken with, and thus be able to locate the photograph in a specific context. This is not the case with analog photographs, which lack any information regarding the date they were taken. Extracting this information is a complicated task, so classifying each photograph according to the date it was taken is a laborious task for a human expert. Artificial intelligence techniques make it possible to determine the characteristics and classify the images automatically. Within the field of artificial intelligence, convolutional neural networks are one of the most widely used methods today. This article describes the application of convolutional neural networks to automatically classify photographs according to the year they were taken. To do this, only the photograph is used, without any additional information. The proposed method divides each photograph into several segments that are presented to the network so that it can estimate a year for each segment. Once all the segments of a photograph have been processed, a general year for the photograph is calculated from the values generated by the network for each of its segments. In this study, images taken between 1960 and 1999 were analyzed and classified using different architectures of a convolutional neural network. The computational results obtained indicate that 44% of the images were classified with an error of less than 5 years, 20.25% with a marginal error between 5 and 10 years, and 35.75% with a higher marginal error of more than 10 years. Due to the complexity of the problem, the results obtained are considered good since 64.25% of the photographs were classified with an error of less than 10 years. Another important result of the study carried out is that it was found that the color is a very important characteristic when classifying photographs by date. The results obtained show that the approach given in this study is an important starting point for this type of task and that it allows placing a photograph in a specific temporal context, thus facilitating the work of experts dedicated to scientific and forensic photography.

Digitization, Prediction, and Market Efficiency: Evidence from Book Publishing Deals

Digitization has given creators direct access to consumers as well as a plethora of new data for suppliers of new products to draw on. We study how this affects market efficiency in the context of book publishing. Using data on about 50,000 license deals over more than 10 years, we identify the effects of digitization from quasi-experimental variation across book types. Consistent with digitization generating additional information for predicting product appeal, we show that the size of license payments more accurately reflects a product’s ex post success, and more so for publishers that invest more in data analytics. These effects cannot be fully explained by changes in bargaining power or in demand. We estimate that efficiency gains are worth between 10% and 18% of publishers’ total investments in book deals. Thus, digitization can have large impacts on the allocation of resources across products of varying qualities in markets in which product appeal has traditionally been difficult to predict ex ante. This paper was accepted by Joshua Gans, business strategy.

Infant urinary metabolomic profile in a fatal acute methadone intoxication

A case report suspicious for a Sudden Infant Death Syndrome is here described. Pathological findings were consistent with an acute respiratory failure while toxicological analysis revealed an elevated blood methadone concentration. Death was then ascribed to an acute methadone intoxication. In addition to the routinary approach, the urinary sample collected at autopsy was investigated with a 1H NMR metabolomic approach and the identified metabolomic profile was challenged with the urinary metabolomic profiles previously obtained from 10 newborns who experienced perinatal asphyxia and 16 healthy control newborns. Intriguingly, the urinary profile of the methadone intoxicated infant was very similar to those belonging to the perinatal asphyxia newborns, especially to those belonging to the newborns characterised by the worst outcome. The results offer several hints on a shared metabolic derangement between different mechanisms of asphyxia/hypoxia. To the best of the authors’ knowledge, this is the first report of the use of a metabolomic approach in a pathological case, in which metabolomics offers useful additional information regarding the mechanism and the cause of death.