Miracle-dz/UniFAR-FTUs huggingface dataset:
Eurographics/ Ieee-vgtc Symposium on Visualization (2006) Evaluating the Effectiveness of Tree Visualization Systems for Knowledge Discovery

User studies, evaluations, and comparisons of tree visualization systems have so far focused on questions that can readily be answered by simple, automated queries without needing visualization. Studies are lacking on the actual use of tree visualization in discovering intrinsic, hidden, non-trivial and potentially valuable knowledge. We have thus formulated a set of tree exploration tasks not previously considered and have performed user studies and analysis to determine how visualization helps users to perform these tasks. In our study, we evaluated three systems: RINGS (a node-link representation), Treemap (a containment representation), and Windows Explorer. Our findings suggest a few ways that tree visualization helps users to perceive different aspects of hierarchical structured information. We then explain how these visual representations are able to trigger human perception to make these discoveries.
Adaptive Actuator Failure Compensation of Uncertain Nonlinear Systems with Dead-Zone Inputs

As is well known, dead-zone nonlinearity often exists in practical actuators and unknown actuator failures may occur during system operation. However, available results based on adaptive approaches to compensate unknown failures of such actuators are still very limited. In this paper, we address such a problem by considering a class of uncertain nonlinear systems with dead-zone hysteresis nonlinearities inputs. An adaptive control scheme is proposed by using backstepping technology to compensate the uncertainties caused by unknown failures of dead-zone actuators. The boundedness of all signals is established for the closed loop system and the desired output tracking performance is maintained for any unknown actuator failure.
Getting our head in the clouds: toward evaluation studies of tagclouds

Tagclouds are visual presentations of a set of words, typically a set of "tags" selected by some rationale, in which attributes of the text such as size, weight, or color are used to represent features, such as frequency, of the associated terms. This note describes two studies to evaluate the effectiveness of differently constructed tagclouds for the various tasks they can be used to support, including searching, browsing, impression formation and recognition. Based on these studies, we propose a paradigm for evaluating tagclouds and ultimately guidelines for tagcloud construction.
- dependent regulation of low-shear flow-induced NF-B activity : role of nitric oxide

Sumathy Mohan,1 Masao Hamuro,1 George P. Sorescu,5 Koichi Koyoma,1 Eugene A. Sprague,2 Hanjoong Jo,5 Anthony J. Valente,3 Thomas J. Prihoda,1 and Mohan Natarajan4 Departments of 1Pathology, 2Radiology, 3Medicine, and 4Radiation Oncology, University of Texas Health Science Center, San Antonio, Texas 78229; and 5Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia 30322
Circadian Gene Expression Regulates Pulsatile Gonadotropin-Releasing Hormone (GnRH) Secretory Patterns in the Hypothalamic GnRH-Secreting GT1-7 Cell Line

Although it has long been established that episodic secretion of gonadotropin-releasing hormone (GnRH) from the hypothalamus is required for normal gonadotropin release, the molecular and cellular mechanisms underlying the synchronous release of GnRH are primarily unknown. We used the GT1-7 mouse hypothalamic cell line as a model for GnRH secretion, because these cells release GnRH in a pulsatile pattern similar to that observed in vivo. To explore possible molecular mechanisms governing secretory timing, we investigated the role of the molecular circadian clock in regulation of GnRH secretion. GT1-7 cells express many known core circadian clock genes, and we demonstrate that oscillations of these components can be induced by stimuli such as serum and the adenylyl cyclase activator forskolin, similar to effects observed in fibroblasts. Strikingly, perturbation of circadian clock function in GT1-7 cells by transient expression of the dominant-negative Clock-Δ19 gene disrupts normal ultradian patterns of GnRH secretion, significantly decreasing mean pulse frequency. Additionally, overexpression of the negative limb clock gene mCry1 in GT1-7 cells substantially increases GnRH pulse amplitude without a commensurate change in pulse frequency, demonstrating that an endogenous biological clock is coupled to the mechanism of neurosecretion in these cells and can regulate multiple secretory parameters. Finally, mice harboring a somatic mutation in the Clock gene are subfertile and exhibit a substantial increase in estrous cycle duration as revealed by examination of vaginal cytology. This effect persists in normal light/dark (LD) cycles, suggesting that a suprachiasmatic nucleus-independent endogenous clock in GnRH neurons is required for eliciting normal pulsatile patterns of GnRH secretion.
Face recognition from one example view

To create a pose-invariant face recognizer, one strategy is the view-based approach, which uses a set of real example views at different poses. But what if we only have one real view available, such as a scanned passport photo-can we still recognize faces under different poses? Given one real view at a known pose, it is still possible to use the view-based approach by exploiting prior knowledge of faces to generate virtual views, or views of the face as seen from different poses. To represent prior knowledge, we use 2D example views of prototype faces under different rotations. We develop example-based techniques for applying the rotation seen in the prototypes to essentially "rotate" the single real view which is available. Next, the combined set of one real and multiple virtual views is used as example views for a view-based, pose-invariant face recognizer. Oar experiments suggest that among the techniques for expressing prior knowledge of faces, 2D example-based approaches should be considered alongside the more standard 3D modeling techniques. <<ETX>>
Feeding a Protective Hydrolysed Casein Diet to Young Diabetes-prone BB Rats Affects Oxidation of L[U−C14] glutamine in Islets and Peyer's Patches, Reduces Abnormally High Mitotic Activity in Mesenteric Lymph Nodes, Enhances Islet Insulin and Tends to Normalize NO Production

The present studies were undertaken to examine concomitant diet-induced changes in pancreatic islets and cells of the gut immune system of diabetes-prone BB rats in the period before classic insulitis. Diabetes-prone (BBdp) and control non-diabetes prone (BBc) BB rats were fed for ~ 17 days either a mainly plant-based standard laboratory rodent diet associated with high diabetes frequency, NIH-07 (NIH) or a protective semipurified diet with hydrolyzed casein (HC) as the amino acid source. By about 7 weeks of age, NIH-fed BBdp rats had lower plasma insulin and insulin/glucose ratio, lower insulin content of isolated islets, lower basal levels of NO but higher responsiveness of NO production to IL-1β in cultured islets, and higher Con A response and biosynthetic activities in mesenteric lymphocytes than control rats fed the same diet. In control rats, the HC diet caused only minor changes in most variables, except for a decrease in oxidation of L-[U−C14]glutamine in Peyer's patch (PP) cells and an increase in protein biosynthesis in mesenteric lymphocytes. In BBdp rats, however, the HC diet increased plasma insulin concentration, islet insulin/ protein ratio, and tended to normalize the basal and IL-1β-stimulated NO production by cultured islets. The HC diet decreased oxidation of L-[U−C14]glutamine in BBdp pancreatic islets, whereas oxidation of L-[U−C14]glutamine in PP cells was increased, and the basal [Methyl-H3] thymidine incorporation in mesenteric lymphocytes was decreased. These findings are compatible with the view that alteration of nutrient catabolism in islet cells as well as key cells of the gut immune system, particularly changes in mitotic and biosynthetic activities in mesenteric lymphocytes, as well as basal and IL-1β stimulated NO production, participate in the sequence of events leading to autoimmune diabetes in BB rats. Thus, the protection afforded by feeding a hydrolysed casein-based diet derives from alterations in both the target islet tissue and key cells of the gut immune system in this animal model of type 1 diabetes.
The impact of elite frames and motivated reasoning on beliefs in a global warming conspiracy: The promise and limits of trust

Given the potential attitudinal and behavioral impact of Anthropogenic Global Warming (AGW) conspiracy beliefs, it is important to understand their causes and moderators. Here, two explanations for the variation in these beliefs are engaged: the first is the choice among elites to frame AGW using the phrase ‘global warming’ (GW) as opposed to ‘climate change’ (CC); the second is partisan motivated reasoning. A theory is then developed about the role of trust in moderating the impact of the two frames on AGW conspiracy beliefs. In the case of CC, which is perceived as less severe than GW (and is therefore less identity threatening among Republicans), it is hypothesized that trust will moderate hoax beliefs among Republicans. In the case of GW, where the implications of existence beliefs have policy consequences that are more unpleasant, motivated reasoning will ‘win out’, and trust will not moderate conspiracy endorsement among Republicans. The results from an original question framing experiment are consistent with the author’s hypotheses. Whilst trust is a welcome commodity to those looking to persuade citizens to support AGW-ameliorating policies, it is not a cure-all, especially in the face of elite partisan cues that edify pre-existing attitudes/identities and arouse a strong desire to engage in motivated reasoning.
1 Modeling Social Interactions : Identification , Empirical Methods and Policy Implications

Social interactions occur when agents in a network affect other agents’ choices directly, as opposed to via the intermediation of markets. The study of such interactions and the resultant outcomes has long been an area of interest across a wide variety of social sciences. With the advent of electronic media that facilitate and record such interactions, this interest has grown sharply in the business world as well. In this paper, we provide a brief summary of what is known so far, discuss the main challenges for researchers interested in this area, and provide a common vocabulary that will hopefully engender future (cross disciplinary) research. The paper considers the challenges of distinguishing actual causal social interactions from other phenomena that may lead to a false inference of causality. Further, we distinguish between two broadly defined types of social interactions that relate to how strongly interactions spread through a network. We also provide a very selective review of how insights from other disciplines can improve and inform modeling choices. Finally, we discuss how models of social interaction can be used to provide guidelines for marketing policy and conclude with thoughts on future research directions.
Topological Features of Online Social Networks

The importance of modeling and analyzing Social Networks is a consequence of the success of Online Social Networks during last years. Several models of networks have been proposed, reflecting the different characteristics of Social Networks. Some of them fit better to model specific phenomena, such as the growth and the evolution of the Social Networks; others are more appropriate to capture the topological characteristics of the networks. Because these networks show unique and different properties and features, in this work we describe and exploit several models in order to capture the structure of popular Online Social Networks, such as Arxiv, Facebook, Wikipedia and YouTube. Our experimentation aims at verifying the structural characteristics of these networks, in order to understand what model better depicts their structure, and to analyze the inner community structure, to illustrate how members of these Online Social Networks interact and group together into smaller communities.
Intensive toilet training targeting defecation for a child with Autism Spectrum Disorder

ABSTRACTIndependent toileting is a critical functional life skill. Unfortunately, acquisition of this skill is often delayed in children with Autism Spectrum Disorder. Interventions based on behavioural principles are often used to toilet train children. Methods that incorporate behavioural components, including systematic prompting and reinforcement contingencies, have yielded positive results for teaching toileting skills to children with disabilities. The purpose of the present study was to determine whether selected procedures based upon a behavioural model would be effective in teaching an 8-year-old boy with autism independent toileting skills, including in-toilet defecation. A secondary goal of the intervention was to reduce interfering behaviours associated with toileting. In-toilet defecation increased as a result of the intervention.
On mapping emotional states and implicit gestures to sonification output from the 'Intangible Musical Instrument'

Sonification is an interdisciplinary field of research, aiming at generating sound from data based on systematic, objective and reproducible transformations. Towards this direction, expressive gestures play an important role in music performances facilitating the artistic perception by the audience. Moreover, emotions are linked with music, as sound has the ability to evoke emotions. In this vein, a combinatory approach which aims at gesture and emotion sonification in the context of music composition and performance is presented here. The added value of the proposed system is that both gesture and emotion are able to continuously manipulate the reproduced sound in real-time.
Deep Transfer Network: Unsupervised Domain Adaptation

Domain adaptation aims at training a classifier in one dataset and applying it to a related but not identical dataset. One successfully used framework of domain adaptation is to learn a transformation to match both the distribution of the features (marginal distribution), and the distribution of the labels given features (conditional distribution). In this paper, we propose a new domain adaptation framework named Deep Transfer Network (DTN), where the highly flexible deep neural networks are used to implement such a distribution matching process. This is achieved by two types of layers in DTN: the shared feature extraction layers which learn a shared feature subspace in which the marginal distributions of the source and the target samples are drawn close, and the discrimination layers which match conditional distributions by classifier transduction. We also show that DTN has a computation complexity linear to the number of training samples, making it suitable to large-scale problems. By combining the best paradigms in both worlds (deep neural networks in recognition, and matching marginal and conditional distributions in domain adaptation), we demonstrate by extensive experiments that DTN improves significantly over former methods in both execution time and classification accuracy.
Comparative feedbacks under incomplete information

Comparative feedbacks, that is messages providing personalized information about how one's behavior compares to that of relevant others, are nowadays widely used-sometimes at a very large scale-in order to change people's behaviors. Yet there are several possible reasons why such feedbacks may affect behaviors, which makes welfare analysis a very challenging task. This paper uses an online experiment to disentangle the role played by incomplete information and the role played by peer pressure. Incomplete information is shown to be a necessary condition for comparative feedbacks to effectively affect participants' choices, suggesting peer pressure was unlikely to play a major role in our specific setting. Nevertheless comparative feedbacks triggered greater changes in behaviors than other informative-only feedbacks. A possible explanation may be found in cognitive costs, as comparative feedbacks more effectively conveyed to participants the idea that it should not be too difficult for them to reach a better outcome.
Transcriptome of the Australian Mollusc Dicathais orbita Provides Insights into the Biosynthesis of Indoles and Choline Esters

Dicathais orbita is a mollusc of the Muricidae family and is well known for the production of the expensive dye Tyrian purple and its brominated precursors that have anticancer properties, in addition to choline esters with muscle-relaxing properties. However, the biosynthetic pathways that produce these secondary metabolites in D. orbita are not known. Illumina HiSeq 2000 transcriptome sequencing of hypobranchial glands, prostate glands, albumen glands, capsule glands, and mantle and foot tissues of D. orbita generated over 201 million high quality reads that were de novo assembled into 219,437 contigs. Annotation with reference to the Nr, Swiss-Prot and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases identified candidate-coding regions in 76,152 of these contigs, with transcripts for many enzymes in various metabolic pathways associated with secondary metabolite biosynthesis represented. This study revealed that D. orbita expresses a number of genes associated with indole, sulfur and histidine metabolism pathways that are relevant to Tyrian purple precursor biosynthesis, and many of which were not found in the fully annotated genomes of three other molluscs in the KEGG database. However, there were no matches to known bromoperoxidase enzymes within the D. orbita transcripts. These transcriptome data provide a significant molecular resource for gastropod research in general and Tyrian purple producing Muricidae in particular.
Identifying the consequences of dynamic treatment strategies

We formulate the problem of learning and comparing the effects of dynamic treatment strategies in a probabilistic decision-theoretic framework, and in particular show how Robins’s “G-computation” formula arises naturally. Careful attention is paid to the mathematical and substantive conditions necessary to justify use of this formula. Probabilistic influence diagrams are used to simplify manipulations. We compare our approach with formulations based on causal DAGs and on potential response models. Some key words and phrases : Causal inference; G-computation; Influence diagram; Observational study; Potential response; Sequential decision theory; Stability.
Analyses of phenotype and ARGOS and ASY1 expression in a ploidy Chinese cabbage series derived from one haploid

The aim of this research was to improve our understanding of how ploidy level influences phenotype and gene expression in Chinese cabbage (Brassica rapa L. ssp. pekinensis). Haploid plants (2n = 10) was induced by 0.2% colchicine to produce diploid (2n = 20) and tetraploid plants (2n = 40). The aneuploid (2n = 24) was also obtained by hybridization between diploid plants as the female and tetraploid plants. The ploidy levels of all plants were identified through chromosome counts and flow cytometry. Leaves and petals became larger as the ploidy level increased from haploid to diploid, and from aneuploid to tetraploid. Similarly, expression of ARGOS was regulated by genome size, increasing in parallel with the level of ploidy. Among the four ploidy types, expression was stronger in the floral buds than in the leaves. Expression by ASY1 also differed according to ploidy level, being highest in diploid plants, followed in order by tetraploids. Expression was similar between haploids and aneuploids at two stages—prior to and after meiosis—but was higher in the haploids during meiosis. When buds were compared within the same ploidy type at different stages, ASY1 expression was obviously higher during meiosis than either before or after. Our study demonstrated the generation and phenotype of a ploidy Chinese cabbage series derived from one haploid. Expression of genes ARGOS and ASY1 were modulated by genome size in this ploidy series, and the regulated patterns of the two genes was different.
How to Title Electronic Documents Using Text Mining Techniques

: Automatic titling of text is a task allowing to determine a well formed word group able to represent the text in a relevant way. The main difficulty of this task is to determine a title having morpho-syntactic characteristics close to titles written by concerned people. Our approach has to be relevant for all type of text (e.g. news, emails, fora, and so forth). Our automatic titling method is developed in four stages: Corpus acquisition, candidate sentences determination for titling, noun phrase extraction in the candidate sentences, and finally, selecting a particular noun phrase to play the role of the text title (ChTITRES approach). Evaluation shows that titles determined by our methods are relevant.
Category Specificity in Early Perception: Face and Word N170 Responses Differ in Both Lateralization and Habituation Properties

N170 event-related potential (ERP) responses to both faces and visual words raises questions about category specific processing mechanisms during early perception and their neural basis. Topographic differences across word and face N170s suggests a form of category specific processing in early perception – the word N170 is consistently left-lateralized, while less consistent evidence supports a right-lateralization for the face N170. Additionally, the face N170 shows a reduction in amplitude across consecutive individual faces, a form of habituation that might differ across studies thereby helping to explain inconsistencies in lateralization. This effect remains unexplored for visual words. The current study directly contrasts N170 responses to words and faces within the same subjects, examining both category-level habituation and lateralization effects. ERP responses to a series of different faces and words were collected under two contexts: blocks that alternated faces and words vs. pure blocks of a single category designed to induce category-level habituation. Global and occipito-temporal measures of N170 amplitude demonstrated an interaction between category (words, faces) and block context (alternating categories, same category). N170 amplitude demonstrated class-level habituation for faces but not words. Furthermore, the pure block context diminished the right-lateralization of the face N170, pointing to class-level habituation as a factor that might drive inconsistencies in findings of right-lateralization across different paradigms. No analogous effect for the word N170 was found, suggesting category specificity for this form of habituation. Taken together, topographic and habituation effects suggest distinct forms of perceptual processing drive the face N170 and the visual word form N170.