Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Relevance Feedback for Image Retrieval with Asymmetric Sampling

Relevance feedback is a quite effective approach to improve performance for image retrieval. Recently, active learning method has attracted much attention due to its capability of alleviating the burden of labeling in relevance feedback. However, most of the traditional studies focus on single sample selection in each feedback which needs heavy computational cost in practice. In this paper, we presents a novel batch mode active learning method for informative sample selection. Inspired by graph propagation, we consider the certainty of labels as asymmetric propagation information on graph, and formulate the correlation between labeled samples and unlabeled samples in an united scheme. Extensive experiments on publicly available data sets show that the proposed method is promising.
How Local Should a Learning Method Be?

We consider the question of why modern machine learning methods like support vector machines outperform earlier nonparametric techniques like kNN. Our approach investigates the locality of learning methods, i.e., the tendency to focus mainly on the close-by part of the training set when constructing a new guess at a particular location. We show that, on the one hand, we can expect all consistent learning methods to be local in some sense; hence if we consider consistency a desirable property then a degree of locality is unavoidable. On the other hand, we also claim that earlier methods like k-NN are local in a more strict manner which implies performance limitations. Thus, we argue that a degree of locality is necessary but that this should not be overdone. Support vector machines and related techniques strike a good balance in this matter, which we suggest may partially explain their good performance in practice.
Keeping faith : factors contributing to spiritual transformation, identity, and maturity in church-attending adolescents

The current research study investigated factors associated with spiritual development among church-attending adolescents. Eighty five participants (12 – 19 years) attending church youth groups in the Albuquerque area were asked to complete a questionnaire measuring spiritual transformation, identity, and maturity as well as demographics, personality, religious social support, and personal devotional activities. The purpose of this research was to understand how religious social support (family, friends, church, mentorship) and personal devotional activities (prayer, worship, study of sacred texts) were related to a spiritual transformation experience, spiritual identity, and spiritual maturity. It was hypothesized that adolescents who reported having had a positive spiritual transformation and who demonstrated higher levels of spiritual identity and maturity would be more connected to religious support systems and would engage more in personal devotional activities. These hypotheses were supported, as were several others. In particular, those who reported stronger levels of spiritual identity were more actively engaged in prayer, worship, and Bible study and had more religious support from v friends, parents, church, and mentorship. Youth who had higher levels of spiritual identity also reported a desire both to be mentored by, and to mentor others. Active engagement in a greater religious community was related to having had a spiritual transformation experience. Age, relationship status, personality, personal identity, and religious coping were also associated with levels of spiritual identity and maturity. These findings may enable mental health professionals, religious leaders, and parents to better understand how to support adolescents with religious and spiritual interests in their journey of spiritual development.
Motivations for avoiding wheat consumption in Australia: results from a population survey

Abstract Objective To assess the prevalence of and explanations for wheat avoidance, including reported symptoms, diagnoses and information sources influencing the decision to avoid wheat, and to investigate potential psychological predictors of this behaviour. Design Cross-sectional population survey. Setting The study was conducted in Australia, using a nationwide postal omnibus survey. Subjects Adults aged 18 years and over (n 1184; 52·9 % female) selected at random from the Australian Electoral Roll. Results With cases of stated and suspected coeliac disease (1·2 %) excluded, 7·3 % of the sample reported adverse physiological effects, predominantly gastrointestinal, that they associated with wheat consumption. Few among this group (5·7 %) claimed a formally diagnosed intolerance or allergy requiring avoidance of wheat-based foods. Symptomatic wheat avoidance was highly correlated with dairy avoidance and predicted by gender (female), lesser receptiveness to conventional medicine and greater receptiveness to complementary medicine, but not by neuroticism, reasoning style or tendency to worry about illness. Conclusions The data indicate that many adult Australians are consciously avoiding consumption of wheat foods, predominantly without any formal diagnosis. Reported symptoms suggest a physiological but not allergenic basis to this behaviour. Questions to be answered concern whether symptoms are attributed correctly to wheat, the agents (wheat components, dietary factors or additives) and physiological mechanism(s) involved, the nutritional adequacy of avoiders’ diets, and the clinical and psychosocial processes that lead a substantial number of adults to avoid consuming wheat (or any other dietary factor) apparently independently of a medical diagnosis.
Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network

The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems. This is done by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoid function making their outputs not restricted to the sigmoid function output. Also, we introduce a dynamic back propagation learning algorithm to train the new proposed network parameters. The simulation results showed that the (SDRNN) is more efficient and accurate than the DRNN in both the identification and adaptive control of nonlinear dynamical systems.
Using Program Analysis Infrastructure for Software Maintenance

Enabling the reuse of available techniques and tools for software maintenance is a major topic. However, research focuses mostly on two topics: parsing and tool interoperability. In the future, more sophisticated approaches to maintenance will be needed and dataflow analysis has to be used. As building dataflow analyzers for real languages is expensive, we must start to provide reusable dataflow analysis infrastructures for software maintenance. This paper first reports on our experience in building program analysis based maintenance tools. From that perspective, we formulate specific requirements for reusable program analysis infrastructures and then take a look at some program analysis infrastructures from compiler optimization research to evaluate if they are (re)usable for software maintenance tools.
Gross Cystic Disease Fluid Protein-15/Prolactin-Inducible Protein as a Biomarker for Keratoconus Disease

Keratoconus (KC) is a bilateral degenerative disease of the cornea characterized by corneal bulging, stromal thinning, and scarring. The etiology of the disease is unknown. In this study, we identified a new biomarker for KC that is present in vivo and in vitro. In vivo, tear samples were collected from age-matched controls with no eye disease (n = 36) and KC diagnosed subjects (n = 17). Samples were processed for proteomics using LC-MS/MS. In vitro, cells were isolated from controls (Human Corneal Fibroblasts-HCF) and KC subjects (Human Keratoconus Cells-HKC) and stimulated with a Vitamin C (VitC) derivative for 4 weeks, and with one of the three transforming growth factor-beta (TGF-β) isoforms. Samples were analyzed using real-time PCR and Western Blots. By using proteomics analysis, the Gross cystic disease fluid protein-15 (GCDFP-15) or prolactin-inducible protein (PIP) was found to be the best independent biomarker able to discriminate between KC and controls. The intensity of GCDFP-15/PIP was significantly higher in healthy subjects compared to KC-diagnosed. Similar findings were seen in vitro, using a 3D culture model. All three TGF-β isoforms significantly down-regulated the expression of GCDFP-15/PIP. Zinc-alpha-2-glycoprotein (AZGP1), a protein that binds to PIP, was identified by proteomics and cell culture to be highly regulated. In this study by different complementary techniques we confirmed the potential role of GCDFP-15/PIP as a novel biomarker for KC disease. It is likely that exploring the GCDFP-15/PIP-AZGP1 interactions will help better understand the mechanism of KC disease.
Does the Type of Assessment Feedback I Give Make a Difference?: The Impact of Qualitative and Quantitative Assessment Feedback

Feedback provided to postgraduate students about their assessment tasks influences the way in which they reflect on their learning and themselves personally. In particular, the nature of the feedback and sequence and timing of its dissemination can further impact how students incorporate, or don't incorporate, assessment advice into their future learning, a process referred to by Duncan (2007) as "feed-forward". Despite the value placed on assessment feedback by academic teaching staff, it often has minimal impact on students' learning (Sadler, 2010). Past research into the impact of qualitative and quantitative feedback on student learning established that quantitative feedback (for example, marks, grades, scores) impacts students' egos whereas qualitative feedback (for example, advicegiving comments) impacts students' understanding of the learning task (Butler, 1987, 1988; Butler & Nisan, 1986). The impact of assessment feedback continues to be investigated in higher education (The Higher Education Academy, 2012) but it remains a contentious issue (Boud & Molloy, 2013). Pulfrey, Darnon and Butera (2013) suggest that the practice of giving grades to students continues to be an area of disagreement among educators. The reported project extends on previous research about the nature of assessment feedback and its impact on students' learning and self-perceptions. This study investigates students’ responses to assessment feedback (Bell, Mladenovic, & Price, 2013), specifically focusing on postgraduates enrolled in online courses. The study investigates how these students responded to the sequenced release of qualitative and quantitative assessment feedback using online assessment tools within the institution's Learning Management System (LMS). Students responded to a series of online prompts that elicited their comments about what they did and how they felt when they received, firstly, qualitative feedback and, secondly, quantitative feedback about their assessment tasks. To gather holistic data and in-depth information about each case (Flick, 2004), a qualitative research approach was adopted in which students' responses to varied types of data were gathered. These data were then analysed through a process of constant comparison to determine how each type of feedback influenced the students' perceptions of their learning and of themselves. The findings of the study revealed that students focused on themselves and their learning, depending on the feedback they were given about their assessment tasks. Furthermore, the type of feedback received influenced the nature and specificity of their responses to the feedback. The findings of this study have implications for how: a) assessment tasks are designed; and b) assessment feedback is provided to students. Findings from this research provides ways to promote deeper engagement of students through assessment feedback.
Searching the wikipedia with contextual information

We propose a framework for searching the Wikipedia with contextual information. Our framework extends the typical keyword search, by considering queries of the type (q,p), where q is a set of terms (as in classical Web search), and p is a source Wikipedia document. The query terms q represent the information that the user is interested in finding, and the document p provides the context of the query. The task is to rank other documents in Wikipedia with respect to their relevance to the query terms q given the context document p. By associating a context to the query terms, the search results of a search initiated in a particular page can be made more relevant. We suggest a number of features that extend the classical query-search model so that the context document p is considered. We then use RankSVM (Joachims 2002) to learn weights for the individual features given suitably constructed training data. Documents are ranked at query time using the inner product of the feature and the weight vectors. The experiments indicate that the proposed method considerably improves results obtained by a more traditional approach that does not take the context into account.
Making use of drivers' glances onto the screen for explicit gaze-based interaction

Interaction with communication and infotainment systems in the car is common while driving. Our research investigates modalities and techniques that enable interaction with interactive applications while driving without compromising safety. In this paper we present the results of an experiment where we use eye-gaze tracking in combination with a button on the steering wheel as explicit input substituting the interaction on the touch screen. This approach combines the advantages of direct interaction on visual displays without the drawbacks of touch screens. In particular the freedom of placement for the screen (even out of reach from the user) and that both hands remain on the steering wheel are the main advantages. The results show that this interaction modality is slightly slower and more distracting than a touch screen but it is significantly faster than automated speech interaction.
The single ring theorem

We study the empirical measure LAn of the eigenvalues of non-normal square matrices of the form An = UnTnVn with Un;Vn independent Haar distributed on the unitary group and Tn real diagonal. We show that when the empirical measure of the eigenvalues of Tn converges, and Tn satisfies some technical conditions, LAn converges towards a rotationally invariant measure µ on the complex plane whose support is a single ring. In particular, we provide a complete proof of Feinberg-Zee single ring theorem [6]. We also consider the case where Un;Vn are independent Haar distributed on the orthogonal group.
Traumatic Stress after Childbirth: The Role of Obstetric Variables

In a sample of 1550 recently delivered women, traumatic stress after childbirth was studied in relation to obstetric variables A post-traumatic stress disorder (PTSD) symptom profile and traumatic stress symptoms were assessed by means of the Traumatic Event Scale (TES) Obstetric data comprised delivery mode, duration of the second stage of labor (the time from cervical dilation of 10 cm to partus) and the use of analgesia/anesthesia Traumatic stress symptoms and having a PTSD symptom profile were both significantly related to the experience of an emergency cesarean section or an instrumental vaginal delivery It is of clinical importance, however that most women with a PTSD symptom profile were found in the normal vaginal delivery group (NVD) This implies that a normal vaginal delivery can be experienced as traumatic, lust as an emergency cesarian section is not necessarily traumatic Traumatic stress symptoms were neither substantially correlated to the duration of the second stage of labor, nor to the use of analgesia/anesthesia
Sequencing of the rpoB Gene and Flanking Spacers for Molecular Identification of Acinetobacter Species

ABSTRACT Acinetobacter species are defined on the basis of several phenotypic characters, results of DNA-DNA homology, and more recently, similarities or dissimilarities in 16S rRNA gene sequences. However, the 16S rRNA gene is not polymorphic enough to clearly distinguish all Acinetobacter species. We used an RNA polymerase β-subunit gene (rpoB)-based identification scheme for the delineation of species within the genus Acinetobacter, and towards that end, we determined the complete rpoB gene and flanking spacer (rplL-rpoB and rpoB-rpoC) sequences of the 17 reference strains of Acinetobacter species and 7 unnamed genomospecies. By using complete gene sequences (4,089 bp), we clearly separated all species and grouped them into different clusters. A phylogenetic tree constructed using these sequences was supported by bootstrap values higher than those obtained with 16S rRNA or the gyrB or recA gene. Four pairs of primers enabled us to amplify and sequence two highly polymorphic partial sequences (350 and 450 bp) of the rpoB gene. These and flanking spacers were designed and tested for rapid identification of the 17 reference strains of Acinetobacter species and 7 unnamed genomospecies. Each of these four variable sequences enabled us to delineate most species. Sequences of at least two polymorphic sequences should be used to distinguish Acinetobacter grimontii, Acinetobacter junii, Acinetobacter baylyi, and genomic species 9 from one another. Finally, 21 clinical isolates of Acinetobacter baumannii were tested for intraspecies relationships and assigned correctly to the same species by comparing the partial sequences of the rpoB gene and its flanking spacers.
Anisotropic MAP defined by eigenvoices for large vocabulary continuous speech recognition

A general method is examined, which unifies the eigenvoice approach and MAP adaptation. The a priori distribution for MAP is chosen to be anisotropic with the eigenvoices as preferred directions while still allowing adaptation into all other directions. This allows the exploitation of a priori knowledge about typical speaker variability within the MAP framework. This approach has two advantages: long term adaptation leads to the same good results as the MAP method, whereas for ultra-short adaptation in the range of 1-2 seconds an overfitting as for maximum likelihood techniques is avoided. The method is applied to large vocabulary continuous speech recognition. Results are to be compared with our transfer of the maximum likelihood eigenvoice method to LVCSR (Botterweck 2000). Even after only one recognized word significant improvements of the WER of up to 6% relative are observed for gender independent recognition. 14% improvements are obtained after 5 seconds.
Hough Transform-based Mouth Localization for Audio-visual Speech Recognition

We present a novel method for mouth localization in the context of multimodal speech recognition where audio and visual cues are fused to improve the speech recognition accuracy. While facial feature points like mouth corners or lip contours are commonly used to estimate at least scale, position, and orientation of the mouth, we propose a Hough transform-based method. Instead of relying on a predefined sparse subset of mouth features, it casts probabilistic votes for the mouth center from several patches in the neighborhood and accumulates the votes in a Hough image. This makes the localization more robust as it does not rely on the detection of a single feature. In addition, we exploit the different shape properties of eyes and mouth in order to localize the mouth more efficiently. Using the rotation invariant representation of the iris, scale and orientation can be efficiently inferred from the localized eye positions. The superior accuracy of our method and quantitative improvements for audio-visual speech recognition over monomodal approaches are demonstrated on two datasets.
c-Jun N-Terminal Kinase 1/c-Jun Activation of the p53/MicroRNA 34a/Sirtuin 1 Pathway Contributes to Apoptosis Induced by Deoxycholic Acid in Rat Liver

ABSTRACT MicroRNAs (miRs) are increasingly associated with metabolic liver diseases. We have shown that ursodeoxycholic acid, a hydrophilic bile acid, counteracts the miR-34a/sirtuin 1 (SIRT1)/p53 pathway, activated in the liver of nonalcoholic steatohepatitis (NASH) patients. In contrast, hydrophobic bile acids, particularly deoxycholic acid (DCA), activate apoptosis and are increased in NASH. We evaluated whether DCA-induced apoptosis of rat hepatocytes occurs via miR-34a-dependent pathways and whether they connect with c-Jun N-terminal kinase (JNK) induction. DCA enhanced miR-34a/SIRT1/p53 proapoptotic signaling in a dose- and time-dependent manner. In turn, miR-34a inhibition and SIRT1 overexpression significantly rescued targeting of the miR-34a pathway and apoptosis by DCA. In addition, p53 overexpression activated the miR-34a/SIRT1/p53 pathway, further induced by DCA. DCA increased p53 expression as well as p53 transcriptional activation of PUMA and miR-34a itself, providing a functional mechanism for miR-34a activation. JNK1 and c-Jun were shown to be major targets of DCA, upstream of p53, in engaging the miR-34a pathway and apoptosis. Finally, activation of this JNK1/miR-34a proapoptotic circuit was also shown to occur in vivo in the rat liver. These results suggest that the JNK1/p53/miR-34a/SIRT1 pathway may represent an attractive pharmacological target for the development of new drugs to arrest metabolism- and apoptosis-related liver pathologies.
Optimal caching placement for cache-enabled mobile social networks

Caching at user devices is one promising solution to alleviate the heavy base station burden in mobile social networks. In this paper, we consider both physical coupling and social relationships among users, and then devise the optimal content placement scheme to maximize the offloading probability for mobile social networks. In particular, because the problem is NP-complete, we first propose a weighted fast unfolding algorithm to divide the users into communities according to users' social ties, locations and communication radius in the area. We then prove the optimal problem based on the proposed algorithm to be monotone submodular maximization subject to matroid constraint and a community based greedy algorithm is then presented. Simulation results indicate the proposed caching placement scheme achieves a great performance gain over existing approaches, and show that user devices should cache most popular files while there are seldom social ties in the network, otherwise users in the same community should store the same contents and store different contents for different communities.