RTG/science instrument radiation interactions for deep space probes, phase 2, 3, and 4

Assessment of interference to scientific instruments onboard RTG powered spacecraft caused by radiation emanating from RTG unit with application to Pioneer F/G space probe

GEMRec: A graph-based emotion-aware music recommendation approach

© Springer International Publishing AG 2016. Music recommendation has gained substantial attention in recent times. As one of the most important context features,user emotion has great potential to improve recommendations,but this has not yet been sufficiently explored due to the difficulty of emotion acquisition and incorporation. This paper proposes a graph-based emotion-aware music recommendation approach (GEMRec) by simultaneously taking a user’s music listening history and emotion into consideration. The proposed approach models the relations between user,music,and emotion as a three-element tuple (user,music,emotion),upon which an Emotion Aware Graph (EAG) is built,and then a relevance propagation algorithm based on random walk is devised to rank the relevance of music items for recommendation. Evaluation experiments are conducted based on a real dataset collected from a Chinese microblog service in comparison to baselines. The results show that the emotional context from a user’s microblogs contributes to improving the performance of music recommendation in terms of hitrate,precision,recall,and F1 score

Ontology-based identification of music for places

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-36309-2_37Proceedings of the International Conference on Information and Communication Technologies in Tourism in Innsbruck, Austria, January 22-25, 2013Place is a notion closely linked with the wealth of human experience, and invested by values, attitudes, and cultural influences. In particular, many places are strongly linked to music, which contributes to shaping the perception and the meaning of a place. In this paper we propose a computational approach for identifying musicians and music suited for a place of interest (POI). We present a knowledge-based framework built upon the DBpedia ontology, and a graph-based algorithm that scores musicians with respect to their semantic relatedness to a POI and suggests the top scoring ones. We found that users appreciate and judge as valuable the musician suggestions generated by the proposed approach. Moreover, users perceived compositions of the suggested musicians as suited for the POIs

Liquid Hydrogen Target Experience at SLAC

Liquid hydrogen targets have played a vital role in the physics program at SLAC for the past 40 years. These targets have ranged from small "beer can" targets to the 1.5 m long E158 target that was capable of absorbing up to 800 W without any significant density changes. Successful use of these targets has required the development of thin-wall designs, liquid hydrogen pumps, remote positioning and alignment systems, safety systems, control and data acquisition systems, cryogenic cooling circuits and heat exchangers. Detailed operating procedures have been created to ensure safety and operational reliability.This paper surveys the evolution of liquid hydrogen targets at SLAC and discusses advances in several of the enabling technologies that made these targets possible

AntRS: Recommending Lists through a Multi-Objective Ant Colony System

International audienceWhen people use recommender systems, they generally expect coherent lists of items. Depending on the application domain, it can be a playlist of songs they are likely to enjoy in their favorite online music service, a set of educational resources to acquire new competencies through an intelligent tutoring system, or a sequence of exhibits to discover from an adaptive mobile museum guide. To make these lists coherent from the users' perspective, recommendations must find the best compromise between multiple objectives (best possible precision, need for diversity and novelty). We propose to achieve that goal through a multi-agent recommender system, called AntRS. We evaluated our approach with a music dataset with about 500 users and more than 13,000 sessions. The experiments show that we obtain good results as regards to precision, novelty and coverage in comparison with typical state-of-the-art single and multi-objective algorithms

Discovering the Unfindable: The Tension Between Findability and Discoverability in a Bookshop Designed for Serendipity

Serendipity is a key aspect of user experience, particularly in the context of information acquisition - where it is known as information encountering. Unexpectedly encountering interesting or useful information can spark new insights while surprising and delighting. However, digital environments have been designed primarily for goal-directed seeking over loosely-directed exploration, searching over discovering. In this paper we examine a novel physical environment - a bookshop designed primarily for serendipity - for cues as to how information encountering might be helped or hindered by digital design. Naturalistic observations and interviews revealed it was almost impossible for participants to find specific books or topics other than by accident. But all unexpectedly encoun-tered interesting books, highlighting a tension between findability and discoverability. While some of the bookshop’s design features enabled information en-countering, others inhibited it. However, encountering was resilient, as it occurred despite participants finding it hard to understand the purpose of even those features that did enable it. Findings suggest the need to consider how transparent or opaque the purpose of design features should be and to balance structure and lack of it when designing digital environments for findability and discoverability

Alleviating the new user problem in collaborative filtering by exploiting personality information

The final publication is available at Springer via http://dx.doi.org/10.1007/s11257-016-9172-zThe new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3-40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.This work was supported by the Spanish Ministry of Economy and Competitiveness (TIN2013-47090-C3). We thank Michal Kosinski and David Stillwell for their attention regarding the dataset

Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

yesDrug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables

Physiologic upper limit of pore size in the blood-tumor barrier of malignant solid tumors

<p>Abstract</p> <p>Background</p> <p>The existence of large pores in the blood-tumor barrier (BTB) of malignant solid tumor microvasculature makes the blood-tumor barrier more permeable to macromolecules than the endothelial barrier of most normal tissue microvasculature. The BTB of malignant solid tumors growing outside the brain, in peripheral tissues, is more permeable than that of similar tumors growing inside the brain. This has been previously attributed to the larger anatomic sizes of the pores within the BTB of peripheral tumors. Since in the physiological state <it>in vivo </it>a fibrous glycocalyx layer coats the pores of the BTB, it is possible that the effective physiologic pore size in the BTB of brain tumors and peripheral tumors is similar. If this were the case, then the higher permeability of the BTB of peripheral tumor would be attributable to the presence of a greater number of pores in the BTB of peripheral tumors. In this study, we probed <it>in vivo </it>the upper limit of pore size in the BTB of rodent malignant gliomas grown inside the brain, the orthotopic site, as well as outside the brain in temporalis skeletal muscle, the ectopic site.</p> <p>Methods</p> <p>Generation 5 (G5) through generation 8 (G8) polyamidoamine dendrimers were labeled with gadolinium (Gd)-diethyltriaminepentaacetic acid, an anionic MRI contrast agent. The respective Gd-dendrimer generations were visualized <it>in vitro </it>by scanning transmission electron microscopy. Following intravenous infusion of the respective Gd-dendrimer generations (Gd-G5, N = 6; Gd-G6, N = 6; Gd-G7, N = 5; Gd-G8, N = 5) the blood and tumor tissue pharmacokinetics of the Gd-dendrimer generations were visualized <it>in vivo </it>over 600 to 700 minutes by dynamic contrast-enhanced MRI. One additional animal was imaged in each Gd-dendrimer generation group for 175 minutes under continuous anesthesia for the creation of voxel-by-voxel Gd concentration maps.</p> <p>Results</p> <p>The estimated diameters of Gd-G7 dendrimers were 11 ± 1 nm and those of Gd-G8 dendrimers were 13 ± 1 nm. The BTB of ectopic RG-2 gliomas was more permeable than the BTB of orthotopic RG-2 gliomas to all Gd-dendrimer generations except for Gd-G8. The BTB of both ectopic RG-2 gliomas and orthotopic RG-2 gliomas was not permeable to Gd-G8 dendrimers.</p> <p>Conclusion</p> <p>The physiologic upper limit of pore size in the BTB of malignant solid tumor microvasculature is approximately 12 nanometers. In the physiologic state <it>in vivo </it>the luminal fibrous glycocalyx of the BTB of malignant brain tumor and peripheral tumors is the primary impediment to the effective transvascular transport of particles across the BTB of malignant solid tumor microvasculature independent of tumor host site. The higher permeability of malignant peripheral tumor microvasculature to macromolecules smaller than approximately 12 nm in diameter is attributable to the presence of a greater number of pores underlying the glycocalyx of the BTB of malignant peripheral tumor microvasculature.</p