Miracle-dz/UniFAR-FTUs huggingface dataset:
Breadth of Tuning and Taste Coding in Mammalian Taste Buds

A longstanding question in taste research concerns taste coding and, in particular, how broadly are individual taste bud cells tuned to taste qualities (sweet, bitter, umami, salty, and sour). Taste bud cells express G-protein-coupled receptors for sweet, bitter, or umami tastes but not in combination. However, responses to multiple taste qualities have been recorded in individual taste cells. We and others have shown previously there are two classes of taste bud cells directly involved in gustatory signaling: “receptor” (type II) cells that detect and transduce sweet, bitter, and umami compounds, and “presynaptic” (type III) cells. We hypothesize that receptor cells transmit their signals to presynaptic cells. This communication between taste cells could represent a potential convergence of taste information in the taste bud, resulting in taste cells that would respond broadly to multiple taste stimuli. We tested this hypothesis using calcium imaging in a lingual slice preparation. Here, we show that receptor cells are indeed narrowly tuned: 82% responded to only one taste stimulus. In contrast, presynaptic cells are broadly tuned: 83% responded to two or more different taste qualities. Receptor cells responded to bitter, sweet, or umami stimuli but rarely to sour or salty stimuli. Presynaptic cells responded to all taste qualities, including sour and salty. These data further elaborate functional differences between receptor cells and presynaptic cells, provide strong evidence for communication within the taste bud, and resolve the paradox of broad taste cell tuning despite mutually exclusive receptor expression.
International Journal of Behavioral Nutrition and Physical Activity Reliability and Validity of the Healthy Home Survey: a Tool to Measure Factors within Homes Hypothesized to Relate to Overweight in Children

Background: The contribution of the environment to the obesity epidemic is well recognized. Parents have control over their home environment and can, therefore, support healthy dietary and activity habits in their children by manipulating factors such as access to energy-dense foods, availability of physical activity equipment, and restricting screen time. This paper describes the development of the Healthy Home Survey and its reliability and validity. The Healthy Home Survey was designed to assess characteristics of the home environment that are hypothesized to influence healthy weight behaviors in children including diet and physical activity.
Online non-feedback image re-ranking via dominant data selection

Image re-ranking aims at improving the precision of keyword-based image retrieval, mainly by introducing visual features to re-rank. Many existing approaches require offline training for every keyword, which are unsuitable for online image search. Other real-time approaches demand user interaction, which are inappropriate for large-scale image collection. To improve the accuracy of web image retrieval in a practicable manner, we propose a novel re-ranking algorithm to explore the cluster information of the image set. First, we build spectral graph on images that retrieved bysearch engine, and remove isolated nodes as noisy images. Then, we select positive samples from the most dominant cluster in initial top-ranked images, and the samples are used for semi-supervised learning and ranking. Our algorithm is online and non-feedback. Experiments on two public databases demonstrate that our algorithm outperforms the state-of-the-art approaches.
Towards the Reusability and Compositionality of Causal Representations

Causal Representation Learning (CRL) aims at identifying high-level causal factors and their relationships from high-dimensional observations, e.g., images. While most CRL works focus on learning causal representations in a single environment, in this work we instead propose a first step towards learning causal representations from temporal sequences of images that can be adapted in a new environment, or composed across multiple related environments. In particular, we introduce DECAF, a framework that detects which causal factors can be reused and which need to be adapted from previously learned causal representations. Our approach is based on the availability of intervention targets, that indicate which variables are perturbed at each time step. Experiments on three benchmark datasets show that integrating our framework with four state-of-the-art CRL approaches leads to accurate representations in a new environment with only a few samples.
Automated semantic annotation of rare disease cases: a case study

Motivation: As the number of clinical reports in the peer-reviewed medical literature keeps growing, there is an increasing need for online search tools to find and analyze publications on patients with similar clinical characteristics. This problem is especially critical and challenging for rare diseases, where publications of large series are scarce. Through an applied example, we illustrate how to automatically identify new relevant cases and semantically annotate the relevant literature about patient case reports to capture the phenotype of a rare disease named cerebrotendinous xanthomatosis. Results: Our results confirm that it is possible to automatically identify new relevant case reports with a high precision and to annotate them with a satisfactory quality (74% F-measure). Automated annotation with an emphasis to entirely describe all phenotypic abnormalities found in a disease may facilitate curation efforts by supplying phenotype retrieval and assessment of their frequency. Availability and Supplementary information: http://www.usc. es/keam/Phenotype Annotation/. Database URL: http://www.usc. es/keam/PhenotypeAnnotation/
Performance Effects Related to the Sequence of Integration of Healthcare Technologies

There is a natural order to most events in life: Everything from learning to read to DNA sequences in molecular biology follows some predetermined, structured methodology that has been refined to yield improved results. Likewise, it would seem that firms could benefit by adopting and implementing technologies in some logical way so as to increase their overall performance. In this study of 555 hospitals, we investigate the order in which medical technologies are transformed into information technologies through a process of converting them from stand‐alone technologies to interoperable, integrated information systems and whether certain configurations of sequences of integration yield additional value. We find that sequence does matter and that hospitals that integrated foundational technologies first—which in this case are known to be more complex—tend to perform better. Theoretical and practical implications of this finding and others are discussed.
A comparative performance analysis of n-cubes and star graphs

Many theoretical-based comparison studies, relying on the graph theoretical viewpoints with using structural and algorithmic properties, have been conducted for the hypercube and the star graph. None of these studies, however, considered real working conditions and implementation limits. We have compared the performance of the star and hypercube networks for different message length and virtual channels and considered two implementation constraints, namely the constant bisection bandwidth and constant node pin-out. We use two accurate analytical models already proposed for the star graph and hypercube and implement the parameter changes imposed by technological implementation constraints. The comparison results reveal that the star graph has a better performance compared to the equivalent hypercube under light traffic loads while the opposite conclusion is reached for heavy traffic loads. The hypercube with more channels compared to its equivalent star graph saturates later showing that it can bear heavier traffic loads
Clustered TDB: A Clustered Triple Store for Jena

This paper describes the design of Clustered TDB, a clustered triple store designed to store and query very large quantities of Resource Description Framework (RDF) data. It presents an evaluation of an initial prototype, showing that Clustered TDB offers excellent scaling characteristics with respect to load times and query throughput. Design decisions are justified in the context of a literature review on Database Management System (DBMS) and RDF store clustering, and it is shown that many techniques created during the course of DBMS research are applicable to the problem of storing RDF data.
Recovery of acronyms, out-of-lattice words and pronunciations from parallel multilingual speech

In this work we present a set of techniques which explore information from multiple, different language versions of the same speech, to improve Automatic Speech Recognition (ASR) performance. Using this redundant information we are able to recover acronyms, words that cannot be found in the multiple hypotheses produced by the ASR systems, and pronunciations absent from their pronunciation dictionaries. When used together, the three techniques yield a relative improvement of 5.0% over the WER of our baseline system, and 24.8% relative when compared with standard speech recognition, in an Europarl Committee dataset with three different languages (Portuguese, Spanish and English). One full iteration of the system has a parallel Real Time Factor (RTF) of 3.08 and a sequential RTF of 6.44.
Empirical evaluation of pareto efficient multi-objective regression test case prioritisation

The aim of test case prioritisation is to determine an ordering of test cases that maximises the likelihood of early fault revelation. Previous prioritisation techniques have tended to be single objective, for which the additional greedy algorithm is the current state-of-the-art. Unlike test suite minimisation, multi objective test case prioritisation has not been thoroughly evaluated. This paper presents an extensive empirical study of the effectiveness of multi objective test case prioritisation, evaluating it on multiple versions of five widely-used benchmark programs and a much larger real world system of over 1 million lines of code. The paper also presents a lossless coverage compaction algorithm that dramatically scales the performance of all algorithms studied by between 2 and 4 orders of magnitude, making prioritisation practical for even very demanding problems.
Modeling Quantified Things Using a Multi-Agent System

The Internet of Things (IoT) aims at empowering anything to connect to the Internet and to collect data about itself and the environment in which it is situated. Therefore, the IoT has been producing a wide range of data. Our aim is to deliver a way of incorporating the small data generated by each thing into larger datasets to get more meaning out of these data. A thing, equipped with sensors, can be monitored and can record specific features regarding its behavior. Then it can insert data into a cloud database in order to provide individual-and collective-level analyses. We call this process "Quantified Things," and we present it as a new area for the application of "Quantified-Self" and "Quantified-Community" that are approaches of the IoT. In this paper, we provide an overview of the Quantified Things (QT) concept and the key requirements for the creation of QT applications. In addition, we present an agent-based model as a solution to meet such requirements. To illustrate the use of this model, we derived an example from one of the Quantified Things applications: "Quantified Fruit. " Using the "Quantified Fruit" concept, some fruit storage use sensors to monitor environmental conditions, such as temperature, relative humidity, lighting and some gases that may affect fruit ripening. In turn, they insert these collected data into a cloud database and consequently enable knowledge sharing. Through these collective experiences, fruit storage may provide an advanced informative perspective on fruit shelf life based on local environmental conditions.
Blur Identification by Multilayer Neural Network Based on Multivalued Neurons

A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones.
Simplified intersubject averaging on the cortical surface using SUMA

Task and group comparisons in functional magnetic resonance imaging (fMRI) studies are often accomplished through the creation of intersubject average activation maps. Compared with traditional volume‐based intersubject averages, averages made using computational models of the cortical surface have the potential to increase statistical power because they reduce intersubject variability in cortical folding patterns. We describe a two‐step method for creating intersubject surface averages. In the first step cortical surface models are created for each subject and the locations of the anterior and posterior commissures (AC and PC) are aligned. In the second step each surface is standardized to contain the same number of nodes with identical indexing. An anatomical average from 28 subjects created using the AC–PC technique showed greater sulcal and gyral definition than the corresponding volume‐based average. When applied to an fMRI dataset, the AC–PC method produced greater maximum, median, and mean t‐statistics in the average activation map than did the volume average and gave a better approximation to the theoretical‐ideal average calculated from individual subjects. The AC–PC method produced average activation maps equivalent to those produced with surface‐averaging methods that use high‐dimensional morphing. In comparison with morphing methods, the AC–PC technique does not require selection of a template brain and does not introduce deformations of sulcal and gyral patterns, allowing for group analysis within the original folded topology of each individual subject. The tools for performing AC–PC surface averaging are implemented and freely available in the SUMA software package. Hum Brain Mapp, 2005. © 2005 Wiley‐Liss, Inc.
Travel motivation: linking theory to practice

Purpose – The purpose of this paper is to review the relevant literature on travel motivation and provides a scenario‐based discussion on the applications of travel motivation theories in the industry practice. The paper offers an invaluable practical interpretation of travel motivation and related management issues.Design/methodology/approach – A comprehensive literature review on travel motivation is conducted to acknowledge the major theories. Two scenarios are presented and discussed by linking two motivation conceptual schemes, Maslow's hierarchy of needs theory and Plog's allocentrism/psychocentrism model, to practical issues in travel management.Findings – The literature review discloses that travel motivation theories have evolved over time. By linking theories to industry practice, the paper advocates that travel managers be more conscious of travelers' internal psychological needs when designing travel services/products.Originality/value – This paper offers a good understanding of travel motivat.
Driver head pose and gaze estimation based on multi-template ICP 3-D point cloud alignment

Head movements, combined with the line of gaze, play a fundamental role in predicting the driver's actions and in inferring his intention. However, a gaze tracking system for automotive applications needs to satisfy high demands: It must not disturb the driver in his freedom of movements, it must cover large and fast head turns in yaw and pitch, be resistant to changing illumination conditions, be fast enough to recognize fast mirror checks, which are performed almost exclusively through eye rather than head movements, and be accurate and reliable enough to derive high quality information for driver assistance systems relying on their output. In this work a multi-template, ICP-based gaze tracking system is introduced. The system determines the head pose and subsequently estimates the driver's line of gaze by analyzing the angles of the eyes. Due to a fast search of correspondences, and switching between point-to-point and point-to-plane alignment, real-time performance and high accuracy can be achieved. The system is compared with other state of the art head pose estimation systems based on a publicly available benchmark database, where a classification rate of 92% at a tolerance of 10 degrees in yaw could be achieved. We further show in the experiments section, that head rotations up to 4 radians per seconds can be handled. Taking the angles of the eyes into account, rather than the head pose only, the driver's line of sight could be successfully mapped to particular regions of interest.
Global Nomads' Network and Mobile Sociality: Exploring New Media Uses on the Move

This paper explores the convergence of communication and travel and the emergence of a mobile and network sociality, through investigating new communication practices based on the internet and mobile phone enacted by backpackers while on the move. These global nomads produce and maintain mobile spaces of sociality, founded on a complex intersection of face-to-face interaction and mediated communication, co-presence and virtual proximity, corporeal travel and virtual mobilities. Personal communities become a mobile phenomenon, relocalized in a plurality of online and offline social spaces. It is thus argued that network relationships are reshaped and mobilized through reconfigurations of co-presence, proximity and distance in relation to the use of new media. Exploring new media uses on the move can thus provide a useful insight into the emerging social model of the network and mobile society.
Taking stock: parents' reasons for and against having a third child

In developed countries with below-replacement fertility, the proportion of people who have at least three children make a substantial difference to the aggregate level of fertility. This study, based on 40 in-depth interviews with Australian parents of two children, analyses what factors influence the decision to have a third child. Using a grounded theory method of qualitative analysis, the study finds that parents who have decided to stop at two children are more able to articulate their reasons than are parents who are considering having a third child. The reasons for stopping include age and health; work and finances; and the capacity to parent another child. The weighing up of multiple factors is evidence of parents taking stock of personal and financial resources when making complex family formation decisions. The reasons for having a third child are expressed with far less elaboration and are more guarded and personal. We conclude that there is less shared or familiar language for articulating the value of family relationships. The contemporary context is one in which parents are attempting to manage risks related to having children, including the personal and financial implications of time out of the workforce. This study finds a persistent story of limited resources among parents of two children. Policies aimed at increasing fertility need to address this perception of limited resources through direct measures like affordable childcare and more generally through greater government and community support for families.
Psychological mindedness and adult attachment styles

The aims of this study were to examine the relationship between psychological mindedness and adult attachment styles, and to assess the effects of a match or mismatch on these concepts between therapists and their clients. A critical review of the literature highlighted ambiguities in definition and measurement, which this study also aimed to clarify. A pilot study confirmed the validity of the measures chosen for the study and provided preliminary data. The main study consisted of a comparison between a therapist group and a client group on the Psychological Mindedness Scale (PMS), Toronto Alexithymia Scale (TAS- 20), Adult Attachment Scale (AAS), and the Hazan and Shaver attachment questionnaire (1987). The attachment dimensions on the AAS were also converted into Bartholomew's (1990) four-category model. A third group provided an unmatched control for the client group. The methodological limitations of the study, particularly due to the low response rate, further attrition from the client and control groups on follow-up after six months, and confounding factors associated with the groups, meant that only very tentative conclusions could be reached. Psychological mindedness and secure attachment were found to be positively correlated, and alexithymia and insecure attachment (particularly fearfulavoidant) negatively correlated. The therapist group had high levels of psychological mindedness and were mostly securely attached. The literature on 'wounded healers' was discussed in relation to those therapists who were insecurely attached. The client group were more insecurely attached than any of the other groups. Predictions about therapist and client matching on psychological mindedness and attachment styles were unable to be properly tested. The relevance of this study for psychotherapeutic practice, particularly in relation to the impact of similar or different attachment styles in therapists and clients, was discussed. A speculative model suggesting common pathways in the development of psychological mindedness and attachment styles was proposed.