Miracle-dz/UniFAR-FTUs huggingface dataset:
Leaf litter of invasive Chinese tallow (Triadica sebifera) negatively affects hatching success of an aquatic breeding anuran, the Southern Leopard Frog (Lithobates sphenocephalus).

Chinese tallow (Triadica sebifera (L.) Small) is an aggressive invasive tree species that can be abundant in parts of its non-native range. This tree species has the capability of producing monocultures, by outcompeting native trees, which can be in or near wetlands that are utilized by breeding amphibians. Existing research suggests that leaf litter from invasive Chinese tallow reduces survival in larval anurans. The purpose of this study was to determine the effects of Chinese tallow leaf litter on anuran eggs. We exposed eggs of the Southern Leopard Frog (Lithobates sphenocephalus (Cope, 1886)) at various stages of development to different concentrations of Chinese tallow leaf litter to determine survival. Eggs in the earliest stages of development that we exposed to tallow leaf litter died, regardless of concentration; however, some more-developed eggs exposed to tallow leaf litter did hatch. We determined that the greater the concentration of tallow leaf litter, the lower the dissolved oxygen and pH .
Implementation Science Implementation Science: a Role for Parallel Dual Processing Models of Reasoning?

Background: A better theoretical base for understanding professional behaviour change is needed to support evidence-based changes in medical practice. Traditionally strategies to encourage changes in clinical practices have been guided empirically, without explicit consideration of underlying theoretical rationales for such strategies. This paper considers a theoretical framework for reasoning from within psychology for identifying individual differences in cognitive processing between doctors that could moderate the decision to incorporate new evidence into their clinical decision-making.
Semi-automatic Integration of Web Service Interfaces

Modern information systems may exploit numerous web services for communication. Each web service may exploit its own XML format for data representation which causes problems with their integration and evolution. Manual integration and management of evolution of the XML formats may be very hard. In this paper, we present a novel method which exploits a conceptual diagram. We introduce an algorithm which helps a domain expert to map the XML formats to the conceptual diagram. It measures similarities between the XML formats and the diagram and adjusts them on the base of the input from the expert. The result is a precise mapping. The diagram then integrates the XML formats and facilitates their evolution - a change can be made only once in the diagram and propagated to the XML formats.
The role of feminine and masculine norms in college women's alcohol use

Title of Thesis: THE ROLE OF FEMININE AND MASCULINE NORMS IN COLLEGE WOMEN’S ALCOHOL USE Aylin Esra Kaya, Master of Arts, 2016 Thesis directed by: Dr. Derek Kenji Iwamoto, Department of Psychology Current literature suggests not only that men and women can conform to both feminine and masculine norms, but that women who adhere to certain masculine norms may be at greater risk for problematic alcohol use. This study examined conformity to both masculine and feminine norms, and how conformity to distinct norms influenced heavy episodic drinking and alcohol-related problems among a sample of underage college women (N= 645). Results demonstrated that the masculine norms risk-taking and emotional control were associated with increased HED, while the masculine norm power over women was associated with a decrease in HED. Traditional feminine norms, including modesty and sexual fidelity, were associated with a decrease in HED and alcohol-related problems. The feminine norm relational was associated with increased HED, while the norms thinness and appearance were associated with increased alcohol-related problems. The study’s theoretical and clinical implications are discussed. THE ROLE OF FEMININE AND MASCULINE NORMS IN COLLEGE WOMEN’S ALCOHOL USE
High‐frequency sampling of a continuous‐time ARMA process

Continuous‐time autoregressive moving average (CARMA) processes have recently been used widely in the modelling of non‐uniformly spaced data and as a tool for dealing with high‐frequency data of the form ,n = 0, 1, 2,…, where Δ is small and positive. Such data occur in many fields of application, particularly in finance and in the study of turbulence. This article is concerned with the characteristics of the process , when Δ is small and the underlying continuous‐time process is a specified CARMA process.
Combined Inhibition of Complement and CD14 Attenuates Bacteria-Induced Inflammation in Human Whole Blood More Efficiently Than Antagonizing the Toll-like Receptor 4–MD2 Complex

Background. Single inhibition of the Toll-like receptor 4 (TLR4)–MD2 complex failed in treatment of sepsis. CD14 is a coreceptor for several TLRs, including TLR4 and TLR2. The aim of this study was to investigate the effect of single TLR4-MD2 inhibition by using eritoran, compared with the effect of CD14 inhibition alone and combined with the C3 complement inhibitor compstatin (Cp40), on the bacteria-induced inflammatory response in human whole blood. Methods. Cytokines were measured by multiplex technology, and leukocyte activation markers CD11b and CD35 were measured by flow cytometry. Results. Lipopolysaccharide (LPS)–induced inflammatory markers were efficiently abolished by both anti-CD14 and eritoran. Anti-CD14 was significantly more effective than eritoran in inhibiting LPS-binding to HEK-293E cells transfected with CD14 and Escherichia coli–induced upregulation of monocyte activation markers (P < .01). Combining Cp40 with anti-CD14 was significantly more effective than combining Cp40 with eritoran in reducing E. coli–induced interleukin 6 (P < .05) and monocyte activation markers induced by both E. coli (P < .001) and Staphylococcus aureus (P < .01). Combining CP40 with anti-CD14 was more efficient than eritoran alone for 18 of 20 bacteria-induced inflammatory responses (mean P < .0001). Conclusions. Whole bacteria–induced inflammation was inhibited more efficiently by anti-CD14 than by eritoran, particularly when combined with complement inhibition. Combined CD14 and complement inhibition may prove a promising treatment strategy for bacterial sepsis.
Detecting and Evading Wormholes in Mobile Ad-hoc Wireless Networks

Mobile ad-hoc wireless networks are established in improvised environments through the mutual cooperation of its participating nodes. These nodes often operate in a physically insecure environment and, as a result, are vulnerable to capture and compromise. In addition, the nature of the wireless communication medium restricts enforcement of rigourous node memberships and so a number of malicious nodes may also participate in the network. These nodes, in order to snoop or sabotage, can undertake a variety of attacks against the network. Among these, wormhole attacks have unusual significance primarily due to their modus operandi and peculiar attack pattern. In such attacks, two or more malicious colluding nodes create a higher level virtual tunnel in the network, which is employed to transport packets between the tunnel endpoints. These tunnels emulate shorter links in the network and so act as bait to unsuspecting network nodes which, by default, seek shorter routes. The benefit gained by the malicious nodes is that they are able to conduct a variety of attacks against the tunnelled traffic. In this paper, we present a novel trust-based scheme for identifying and isolating nodes that create a wormhole in the network, without engaging any cryptographic means. With the help of extensive simulations, we demonstrate that our scheme functions effectively in the presence of malicious colluding nodes and does not impose any unnecessary conditions upon the network establishment and operation phase.
The N domain of Smad7 is essential for specific inhibition of transforming growth factor-β signaling

Inhibitory Smads (I-Smads) repress signaling by cytokines of the transforming growth factor-β (TGF-β) superfamily. I-Smads have conserved carboxy-terminal Mad homology 2 (MH2) domains, whereas the amino acid sequences of their amino-terminal regions (N domains) are highly divergent from those of other Smads. Of the two different I-Smads in mammals, Smad7 inhibited signaling by both TGF-β and bone morphogenetic proteins (BMPs), whereas Smad6 was less effective in inhibiting TGF-β signaling. Analyses using deletion mutants and chimeras of Smad6 and Smad7 revealed that the MH2 domains were responsible for the inhibition of both TGF-β and BMP signaling by I-Smads, but the isolated MH2 domains of Smad6 and Smad7 were less potent than the full-length Smad7 in inhibiting TGF-β signaling. The N domains of I-Smads determined the subcellular localization of these molecules. Chimeras containing the N domain of Smad7 interacted with the TGF-β type I receptor (TβR-I) more efficiently, and were more potent in repressing TGF-β signaling, than those containing the N domain of Smad6. The isolated N domain of Smad7 physically interacted with the MH2 domain of Smad7, and enhanced the inhibitory activity of the latter through facilitating interaction with TGF-β receptors. The N domain of Smad7 thus plays an important role in the specific inhibition of TGF-β signaling.
Design space exploration of systolic realization of QR factorization on a runtime reconfigurable platform

In the world of high performance computing huge efforts have been put to accelerate Numerical Linear Algebra (NLA) kernels like QR Decomposition (QRD) with the added advantage of reconfigurability and scalability. While popular custom hardware solution in form of systolic arrays can deliver high performance, they are not scalable, and hence not commercially viable. In this paper, we show how systolic solutions of QRD can be realized efficiently on REDEFINE, a scalable runtime reconfigurable hardware platform. We propose various enhancements to REDEFINE to meet the custom need of accelerating NLA kernels. We further do the design space exploration of the proposed solution for any arbitrary application of size n×n. We determine the right size of the sub-array in accordance with the optimal pipeline depth of the core execution units and the number of such units to be used per sub-array.
Monocular car viewpoint estimation with circular regression forests

Determining the viewpoint of traffic participants provides valuable high-level attributes to constrain the interpretation of their movement, and thus allows more specific predictions of alert behavior. We present a monocular object viewpoint estimation approach that is realized by a random regression forest. In particular, we address the circular and continuous structure of the problem for training the decision trees. Our approach builds on a 2D deformable part based object detector. Using detected cars on the KITTI vision benchmark, we demonstrate performance for continuous viewpoint estimation, ground point estimation, and their integration into a high-dimensional particle filtering framework. Besides location and viewpoint of cars, the filter framework considers full monocular egomotion information of the observing platform. This demonstrates the versatility of using only monocular information processing with appropriate machine learning.
Challenging Personalized Video Recommendation

The online videos are generated at an unprecedented speed in recent years. As a result, how to generate personalized recommendation from the large volume of videos becomes more and more challenging. In this paper, we propose to extract the non-textual contents from the videos themselves to enhance the personalized video recommendation. The change of the content types makes us study three issues in this paper. The first issue is what non-textual contents are helpful. Considering the users are attracted by the videos in different aspects, multiple audio and visual features are extracted, encoded and transformed to represent the video contents in the recommender system for the first time. The second issue is how to use the non-textual contents to generate accurate personalized recommendation. We reproduce the existing methods and find that they do not perform well with the non-textual contents due to the mismatch between the features and the learning methods. To address this problem, we propose a new method in this paper. Our experiments show that the proposed method is more accurate whether the video content features are non-textual or textual.
Optimal replica placement strategy for hierarchical data grid systems

Grid computing is an important mechanism for utilizing distributed computing resources. These resources are distributed in different geographical locations, but are organized to provide an integrated service. In order to speed up data access efficiency data grid systems replicate essential data in multiple locations, so that a user can access the data from a site in his vicinity. This paper studies replica placement in data grid systems, taking into account several important issues described below. First, the replicas should be placed in proper server locations so that the workload on each server is balanced. Second, we choose the optimal number of replicas to balance the data access efficiency, and the expensive maintenance costs for multiple copies of data. Clearly, optimizing access cost of data requests and reducing the cost of replication are two conflicting goals. Finding a good balance between them is a challenging task. We propose efficient algorithms for selecting optimal locations for placing the replicas so that the workload among these replicas is balanced. Also when given the data usage from each user site and the maximum workload allowed for each replica server, our algorithm efficiently determines the minimum number of replicas required, as well as their locations.
The Role Five Personality Factors with Narcissism and Psychopathy in Prediction of Dark Triad

The aim of present study was to investigate the relationship of main traits with narcissism and psychopathy of dark triad traits. In this manner, 351 male and female college students in the different universities of Tabriz who educated in 2013-14, were selected by multi-stage cluster sampling method. Data was gathered by NEO Five-Factor of Personality Inventory by Costa and McCrae, Defense Styles Questionnaire by Andrews et al., and Dark Triad Traits Scale by Jonason and Webster and analyzed by Pearson's correlation test, stepwise multivariate regression analysis, ANOVA and Chi square by SPSS-18. Results indicated there was a significant relationship between main traits of personality and narcissism and psychopathy (p<0.01).
Individual versus group behavior and the role of the decision making procedure in gift-exchange experiments

We test for behavioral differences between groups and individuals in gift-exchange experiments. Related studies in economics establish group behavior as often closer to the standard game-theoretic equilibrium under the assumptions of rationality and selfishness. We show that this result may depend crucially on the decision making procedure within groups and the nature of the task. A novel experimental decision making protocol opens the black box of group decision making and allows tracking important features of the group interaction process. We are also able to show that acting in a group may shift initial individual choices.
Action Recognition by Local Space-Time Features and Least Square Twin SVM (LS-TSVM)

In this research a new approach ffor human action recognition is proposed. At first, local spaace-time features extracted which recently becomes a popular video representation. Feature extraction is done wwith use of Harris detector algorithm and Histogram of Optiical Flow (HOF) descriptor. Then we apply a new extendedd SVM classifier called least square Twin SVM (LS-TSVM)). LS-TSVM is a binary classifier that does classification by use of two non¬parallel hyperplanes and it is four times faster than the classical SVM while the precision is better. WWe investigate the performance of LS-TSVM method on a totall of 25 persons on KTH dataset. Our experiments on the standdard KTH action dataset shown that our method improvees state-of-the-art results by achieving 95.8%, 96.3% and 97.2%% accuracy in case of 1-fold , 5-fold and 10-fold cross validation.
The effects of primordial non-Gaussianity on the cosmological reionization

We investigate the effects of non-Gaussianity in the primordial density field on the reionization history. We rely on a semi-analytic method to describe the processes acting on the intergalactic medium (IGM), relating the distribution of the ionizing sources to that of dark matter haloes. Extending previous work in the literature, we consider models in which the primordial non-Gaussianity is measured by the dimensionless non-linearity parameter f NL , using the constraints recently obtained from cosmic microwave background data. We predict the ionized fraction and the optical depth at different cosmological epochs assuming two different kinds of non-Gaussianity characterized by a scale-independent and a scale-dependent f NL and comparing the results to those for the standard Gaussian scenario. We find that a positive f NL enhances the formation of high-mass haloes at early epochs when reionization begins, and, as a consequence, the IGM ionized fraction can grow by a factor of up to 5 with respect to the corresponding Gaussian model. The increase of the filling factor has a small impact on the reionization optical depth and is of the order of ∼ 10 per cent if a scale-dependent non-Gaussianity is assumed. Our predictions for non-Gaussian models are in agreement with the latest Wilkinson Microwave Anisotropy Probe results within the error bars, but a higher precision is required to constrain the scale dependence of non-Gaussianity.
A Study of Evaluation Metrics for Recommender Algorithms

There are inherent problems with evaluating the accuracy of recommender systems. Commonly-used metrics for recommender systems depend on the number of recommendations produced and the number of hidden items withheld, making it difficult to directly compare one system with another. In this paper we compare recommender algorithms using two datasets; the standard MovieLens set and an e-commerce dataset that has implicit ratings based on browsing behaviour. We introduce a measure that aids in the comparison and show how to compare results with baseline predictions based on random recommendation selections.
New method robust video coding based on compressive sensing

Compressed sensing theory (Compressed Sensing, CS) can break through the Nyquist sampling theorem limit for efficient, high-precision sampling and reconstruction of signals. It is a new signal acquisition and processing theory which developed only in recent years. Main idea of this theory use sparsity or compressibility of signals, reconstruct the signals accurately or approximately through non-related measurement of sampling data in low dimension. Compressed sensing theory provides a new way of thinking to signal processing, it has gain wild attention from is proposed, many research institutions and researchers have conducted in depth research. In this article we proposed a robust method video coding based on Compressive sampling matching pursuit (CoSaMP). The simulation results show that proposed approach can achieve a higher quality than other exiting compressive video coding schemes.
Vision-based recognition for robot localization

Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. As autonomous robots, possessing visual acquisition capability is very crucial to explore unknown environments reliably, SIFT (Scale Invariant Feature Transform) key points are powerful in detecting objects under various imaging conditions, robot can use the recognized object as landmarks to navigate and localize itself. This paper presents a method to reduce the size, complexity and matching time of SIFT features in robot SLAM context. Experimental results demonstrate the effectiveness of the proposed algorithm.
Fundamental Frequency Estimation for Noisy Speech Using Entropy-Weighted Periodic and Harmonic Features

SUMMARY This paper proposes a robust method for estimating the fundamental frequency (F0) in real environments. It is assumed that the spectral structure of real environmental noise varies momentarily and its energy does not distribute evenly in the time-frequency domain. Therefore, segmenting a spectrogram of speech mixed with environmental noise into narrow time-frequency regions will produce low-noise regions in which the signal-to-noise ratio is high. The proposed method estimates F0 from the periodic and harmonic features that are clearly observed in the low-noise regions. It first uses two kinds of spectrogram, one with high frequency resolution and another with high temporal resolution, to represent the periodic and harmonic features corresponding to F0. Next, the method segments these two kinds of feature plane into narrow time-frequency regions, and calculates the probability function of F0 for each region. It then utilizes the entropy of the probability function as weight to emphasize the probability function in the low-noise region and to enhance noise robustness. Finally, the probability functions are grouped in each time, and F0 is obtained as the frequency with the highest probability of the function. The experimental results showed that, in comparison with other approaches such as the cepstrum method and the autocorrelation method, the developed method can more robustly estimate F0s from speech in the presence of band-limited noise and car noise.
Dynamic Functional Organization of Language

One of the oldest questions in cognitive science is whether cognitive operations are modular or distributed across domains. We propose that fMRI has made a unique contribution to this question by elucidating the nature of structure–function relations. We focus our discussion on language, which is the classic domain for arguments in favor of domain specificity and a fixed neural architecture. We argue that fMRI has provided evidence for the idea that there is a dynamic functional architecture, rather than a fixed neural architecture, that emerges across the lifespan, pursuant to injury and in response to language experience. We use empirical examples to highlight how fMRI has helped restructure theory by shedding light on how functionally distinct modular components of the grammar can recruit some of the same neural regions, how areas considered to be domain-specific may be recruited in a domain-general fashion, and how language network specialization and left lateralization dynamically emerge in response to experience. fMRI provides a window into neural plasticity and dynamic functional organization not easily afforded by behavior alone.