Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Quantitative Genetics of Mixture Characters

Finite mixture models are helpful for uncovering heterogeneity due to hidden structure. Quantitative genetics issues of continuous characters having a finite mixture of Gaussian components as statistical distribution are explored in this article. The partition of variance in a mixture, the covariance between relatives under the supposition of an additive genetic model, and the offspring–parent regression are derived. Formulas for assessing the effect of mass selection operating on a mixture are given. Expressions for the genetic and phenotypic correlations between mixture and Gaussian traits and between two mixture traits are presented. It is found that, if there is heterogeneity in a population at the genetic or environmental level, then genetic parameters based on theory treating distributions as homogeneous can lead to misleading interpretations. Some peculiarities of mixture characters are: heritability depends on the mean values of the component distributions, the offspring–parent regression is nonlinear, and genetic or phenotypic correlations cannot be interpreted devoid of the mixture proportions and of the parameters of the distributions mixed.
Force guided assemblies using a novel parallel manipulator

In this paper we present recent experimental results using a novel parallel manipulator operating under natural admittance control performing difficult mechanical assemblies. The example assemblies are components from an automotive automatic transmission. Success depends on appropriate force responsiveness, rather than precise position control. The robotic assemblies are shown to be gentler than human assemblies while competitive with human assembly rates. The results show the potential for robotic automation in application areas that are currently limited to manual assembly.
Modified Multiphase Level Set Image Segmentation Search for Energy Formulation with Bias Correction

: Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized . The purpose of this paper is the level set approach to simultaneous tissue segmentation and bias correction of Magnetic Resonance Imaging (MRI) images. A modified level set approach to joint segmentation and bias correction of images with intensity in homogeneity. A sliding window is used to transform the gradient intensity domain to another domain, where the distribution overlap between different tissues is significantly suppressed. Tissue segmentation and bias correction are simultaneously achieved via a multiphase level set evolution process. The proposed methods are very robust to initialization, and are directly compatible with any type of level set implementation. Experiments on images of various modalities demonstrated the superior performance over state-of-the-art methods.
IMPACT OF FOREIGN DIRECT INVESTMENT ON INDIAN ECONOMY: A SECTORAL LEVEL ANALYSIS 1

The main objective of this paper is to examine the long-run relationship of Foreign Direct Investment (FDI) with the Gross Output (GO), Export (EX) and Labour Productivity (LPR) in the Indian economy at the sectoral level by using the annual data from 1990-91 to 2000-01. The study uses the Panel co-integration (PCONT) test and the results demonstrate that the flow of FDI into the sectors has helped to raise the output, labour productivity and export in some sectors but a better role of FDI at the sectoral level is still expected. Results also reveal that there is no significant cointegrating relationship among the variables like FDI, GO, EX and LPR in core sectors of the economy. This implies that when there is an increase in the output, export or labour productivity of the sectors it is not due to the advent of FDI. Thus, it could be concluded that the advent of FDI has not helped to wield a positive impact on the Indian economy at the sectoral level. Thus, in the eve of India's plan for further opening up of the economy, it is advisable to open up the export oriented sectors so that a higher growth of the economy could be achieved through the growth of these sectors.
The impact of knowledge management on Slovenian innovativeness

Results of many studies show that innovative firms are capable today to create target profits for their further growth. The level of innovativeness of Slovenian firms is low. A quarter of Slovenian firms have not introduced any new product or service in the last two years. If knowledge management is positively influenced by the search for innovations, the investments in the development of new knowledge may propel companies into new business and more rewarding markets. The main goal of a study described in the paper was to analyze the whole new product development process in Slovenian firms in order to reveal the critical success factors of Slovenian new products. One of the significant results was the finding that poor knowledge management in Slovenian firms was important reason for failure of Slovenian new products. Results supporting this conclusion are also presented in this paper.
Matchmaking through semantic annotation and similarity measurement

The proposed work briefly describes an approach to automatically extract structured information from semi-structured documents to match the document creators and users in order to find the best similarities between them and connect them for further collaborations. The general idea is to employ a semantic annotation technique and similarity measurement approach by using the ontology to find best matches between web documents. The proposed approach uses ontologies to annotate the extracted information and for the measuring the similarity between each pair of documents. GATE (General Architecture for Text Engineering) as one of the most famous annotation tools has been utilized to annotate semi-structure documents. A novel algorithm is proposed to update the supported ontology for extraction purpose in GATE by using a training data set. Furthermore, specific domain-based metrics are also utilized to measure semantic similarities between documents with regard to semantic annotations which are implemented in an ontology-based approach. These metrics can be used in order to find the most similar web documents among documents corpus.
The Game-Theoretic Interaction Index on Social Networks with Applications to Link Prediction and Community Detection

Measuring similarity between nodes has been an issue of extensive research in the social network analysis literature. In this paper, we construct a new measure of similarity between nodes based on the game-theoretic interaction index (Grabisch and Roubens, 1997). Despite the fact that, in general, this index is computationally challenging, we show that in our network application it can be computed in polynomial time. We test our measure on two important problems, namely link prediction and community detection, given several real-life networks. We show that, for the majority of those networks, our measure outperforms other local similarity measures from the literature.
'I Just Did 400 Million Event Studies' – A Study of Market Model Robustness and Deterioration in Times of Crisis

During the financial crisis, stock returns became more correlated, increasing the R-square of market models estimated during that time. However, the overall increase in volatility also increased the standard error of these models. As a result, conventional event studies tend to find too many significant days, especially when looking further out of sample. In particular, I find that on the average day in September 2008, over 45% of stocks on the S&P 500 showed significant returns at the 5% level based on a market model estimated over the year ending September 2007. As such, I propose calculating new critical values (“RADS” critical values) using the distribution of the t-statistics of excess returns for a particular event study date and market model sample, such that only 5% of stocks in the index would be regarded as “significant”. These critical values, when calculated from S&P 500 companies, may also be applicable to other markets. In particular, I find that applying S&P 500 RADS to event studies on FTSE 100 companies leads to a tighter distribution of the proportion of companies with significant returns on each day around 5% regardless of the time period or the number of days the event is outside the market model sample, and results in a more meaningful determination of significance.
Time analysable synchronisation techniques for parallelised hard real-time applications

In this paper we present synchronisation techniques for hard real-time (HRT) capable execution of parallelised applications on embedded multi-core processors. We show how commonly used software synchronisation techniques can be implemented in a time analysable way based on the proposed hardware primitives. We choose to implement the hardware synchronisation primitives in the memory controller for two reasons. Firstly, we remove pessimism in the WCET analysis of parallelised HRT applications. Secondly, we enable that the implementation of synchronisation techniques is mostly independent of the chosen instruction set architecture (ISA) which allows to use the existing ISAs without enhancements. We analyse the presented synchronisation techniques with the static worst-case execution time (WCET) analysis tool OTAWA. In summary, our specifically engineered synchronisation techniques yield a tremendous gain on the WCET of parallelised HRT applications.
Confidential messages to a cooperative relay

We extend the broadcast channel with confidential messages to the situation where the receiver of the secret message also serves as a relay. We analyze the fundamental cooperation versus secrecy trade-offs for discrete memoryless channels and obtain the exact rate-equivocation region in this case. For the Gaussian channel, we consider various strategies leading to different levels of secrecy. Our study highlights the fundamental role of jamming as a means to increase secrecy rates, but also emphasizes the importance of carefully designed relaying strategies.
Clearing the sky : understanding SLA elements in cloud computing

Issues characterizing cloud computing such as security, interoperability, and vendor lock-in are likely to act as inhibitors of the widespread adoption of cloud computing in organizations. These issues call for relational and contractual mechanisms to articulate the desired outcomes of service provisioning and acceptable behavior of service recipients and providers. In this paper we focus on the contractual mechanisms for this purpose and, more specifically, Service Level Agreements (SLA). We have conducted a qualitative study interviewing industry experts to understand the extent to which SLA specifications in traditional IT services outsourcing can be applied in cloud computing, identifying elements becoming redundant or not applicable, and new elements capturing the specificity of the business relationships entailed by cloud computing. Our study results in a detailed SLA specification for different cloud computing deployment models, i.e. private and public cloud, and in a framework investigating the role of SLA elements in the decision making process for cloud computing outsourcing.
SurpriseMe: an integrated tool for network community structure characterization using Surprise maximization

SUMMARY Detecting communities and densely connected groups may contribute to unravel the underlying relationships among the units present in diverse biological networks (e.g. interactomes, coexpression networks, ecological networks). We recently showed that communities can be precisely characterized by maximizing Surprise, a global network parameter. Here, we present SurpriseMe, a tool that integrates the outputs of seven of the best algorithms available to estimate the maximum Surprise value. SurpriseMe also generates distance matrices that allow visualizing the relationships among the solutions generated by the algorithms. We show that the communities present in small- and medium-sized networks, with up to 10 000 nodes, can be easily characterized: on standard PC computers, these analyses take less than an hour. Also, four of the algorithms may rapidly analyze networks with up to 100 000 nodes, given enough memory resources. Because of its performance and simplicity, SurpriseMe is a reference tool for community structure characterization. AVAILABILITY AND IMPLEMENTATION SurpriseMe is implemented in Perl and C/C++. It compiles and runs on any UNIX-based operating system, including Linux and Mac OS/X, using standard libraries. The source code is freely and publicly available under the GPL 3.0 license at http://github.com/raldecoa/SurpriseMe/releases.
Antibodies against Listerial Protein 60 Act as an Opsonin for Phagocytosis of Listeria monocytogenes by Human Dendritic Cells

ABSTRACT Human-monocyte-derived dendritic cells (MoDC) are very efficient in the uptake of Listeria monocytogenes, a gram-positive bacterium which is an important pathogen in humans and animals causing systemic infections with symptoms such as septicemia and meningitis. In this work, we analyzed the influence of blood plasma on the internalization of L. monocytogenes into human MoDC and compared the uptake of L. monocytogenes with that ofSalmonella enterica serovar Typhimurium and Yersinia enterocolitica. While human plasma did not significantly influence the uptake of serovar Typhimurium and Y. enterocolitica by human MoDC, the efficiency of the uptake ofL. monocytogenes by these phagocytes was strongly enhanced by human plasma. In plasma-free medium the internalization of L. monocytogenes was very low, whereas the addition of pooled human immunoglobulins resulted in the internalization of these bacteria to a degree comparable to the highly efficient uptake observed with human plasma. All human plasma tested contained antibodies against the 60-kDa extracellular protein of L. monocytogenes (p60), and anti-p60 antibodies were also found in the commercially available pooled immunoglobulins. Strikingly, in contrast to L. monocytogenes wild type, an iap deletion mutant (totally deficient in p60) showed only a minor difference in the uptake by human MoDC in the presence or the absence of human plasma. These results support the assumption that antibodies against the listerial p60 protein may play an important role in Fc-receptor-mediated uptake of L. monocytogenes by human MoDC via opsonization of the bacteria. This process may have a major impact in preventing systemic infection in L. monocytogenes in immunocompetent humans.
AIS Electronic Library (AISeL)

This paper reports a cross-cultural analysis of Wikipedia communities of practice (CoPs). First, this paper argues that Wikipedia communities can be analyzed and understood as CoPs. Second, the similarities and differences in norms of behaviors across three different languages (English, Hebrew, and Japanese) and on three types of discussion spaces (Talk, User Talk, and Wikipedia Talk) are identified. These are explained by Hofstede’s dimensions of cultural diversity, the size of the community, and the role of each discussion area. This paper expands the research on online CoPs, which have not performed in-depth examinations of cultural variations across multiple CoPs.
Release of Luminal Exosomes Contributes to TLR4-Mediated Epithelial Antimicrobial Defense

Exosomes are membranous nanovesicles released by most cell types from multi-vesicular endosomes. They are speculated to transfer molecules to neighboring or distant cells and modulate many physiological and pathological procedures. Exosomes released from the gastrointestinal epithelium to the basolateral side have been implicated in antigen presentation. Here, we report that luminal release of exosomes from the biliary and intestinal epithelium is increased following infection by the protozoan parasite Cryptosporidium parvum. Release of exosomes involves activation of TLR4/IKK2 signaling through promoting the SNAP23-associated vesicular exocytotic process. Downregulation of let-7 family miRNAs by activation of TLR4 signaling increases SNAP23 expression, coordinating exosome release in response to C. parvum infection. Intriguingly, exosomes carry antimicrobial peptides of epithelial cell origin, including cathelicidin-37 and beta-defensin 2. Activation of TLR4 signaling enhances exosomal shuttle of epithelial antimicrobial peptides. Exposure of C. parvum sporozoites to released exosomes decreases their viability and infectivity both in vitro and ex vivo. Direct binding to the C. parvum sporozoite surface is required for the anti-C. parvum activity of released exosomes. Biliary epithelial cells also increase exosomal release and display exosome-associated anti-C. parvum activity following LPS stimulation. Our data indicate that TLR4 signaling regulates luminal exosome release and shuttling of antimicrobial peptides from the gastrointestinal epithelium, revealing a new arm of mucosal immunity relevant to antimicrobial defense.
Accelerated Parallelizable Neural Network Learning Algorithm for Speech Recognition

We describe a set of novel, batch-mode algorithms we developed recently as one key component in scalable, deep neural network based speech recognition. The essence of these algorithms is to structure the singlehidden-layer neural network so that the upper-layer’s weights can be written as a deterministic function of the lower-layer’s weights. This structure is effectively exploited during training by plugging in the deterministic function to the least square error objective function while calculating the gradients. Accelerating techniques are further exploited to make the weight updates move along the most promising directions. The experiments on TIMIT frame-level phone and phonestate classification show strong results. In particular, the error rate is strictly monotonically dropping as the minibatch size increases. This demonstrates the potential for the proposed batch-mode algorithms in large scale speech recognition since they are easily parallelizable across computers.
Regulators of Vps4 ATPase Activity at Endosomes Differentially Influence the Size and Rate of Formation of Intralumenal Vesicles

Disassembly of ESCRT-III requires Vps4 ATPase activity under the control of regulatory proteins. Described here are distinct spatiotemporal functions for Vps4 regulators, with Did2 playing a unique role in regulating MVB lumenal vesicle size and Vtal-Vps60 promoting efficient membrane scission and delivery of vesicles into the endosome lumen.
System Design Features and Repeated Use of Electronic Data Exchanges

Oftentimes researchers may not only generalize across a population, but may also extrapolate research findings across time. While either assumption can introduce difficulties, generalizing results in one time frame to another time frame may be especially perilous. We study a data exchange, and find that interventions designed to improve exchange features at two points in time have markedly varying effects, from an initial transaction use (time one) to a second transaction occurring two weeks later (time two). Our research objective is to test whether two system design features have the same effects on the intent to continue using an exchange in time two as they had in time one. The two features are control transparency (the availability of information cues) and interim shipping outcome feedback. These effects are mediated, in varying degrees, by perceived information quality. We use social exchange theory and social cognition theory to develop hypotheses regarding changes between time one (the first user transaction) and time two (the second transaction). These are tested using a combined experiment and survey. Supporting the theory, outcome feedback matters at time two even though it did not matter at time one. While control transparency has direct effects on a user's intent to continue use of the exchange in time one, its effects are reduced in time two if negative outcome feedback is communicated to the user. Outcome feedback's effects grow stronger from time one to time two vis-à-vis control transparency's effects. This underscores how critical it is to examine such phenomena at more than one period of time. The study also suggests different strategies for managing data exchanges based on the time frame of use. At the initial transaction use, the exchange should make transparent high-quality information cues to its user. At the next transaction, it should provide feedback showing properly fulfilled orders. These findings have implications for both future research examining effective data exchange design and for professionals who wish to enrich electronic data exchange interactions.
The relation between 3d loop quantum gravity and combinatorial quantisation: Quantum group symmetries and observables

We relate three-dimensional loop quantum gravity to the combinatorial quantisation formalism based on the Chern-Simons formulation for three-dimensional Lorentzian and Euclidean gravity with vanishing cosmological constant. We compare the construction of the kinematical Hilbert space and the implementation of the constraints. This leads to an explicit and very interesting relation between the associated operators in the two approaches and sheds light on their physical interpretation. We demonstrate that the quantum group symmetries arising in the combinatorial formalism, the quantum double of the three-dimensional Lorentz and rotation group, are also present in the loop formalism. We derive explicit expressions for the action of these quantum groups on the space of cylindrical functions associated with graphs. This establishes a direct link between the two quantisation approaches and clarifies the role of quantum group symmetries in three-dimensional gravity.
StoryPivot: Comparing and Contrasting Story Evolution

As the world evolves around us, so does the digital coverage of it. Events of diverse types, associated with different actors and various locations, are continuously captured by multiple information sources such as news articles, blogs, social media etc. day by day. In the digital world, these events are represented through information snippets that contain information on the involved entities, a description of the event, when the event occurred, etc. In our work, we observe that events (and their corresponding digital representations) are often inter-connected, i.e., they form stories which represent evolving relationships between events over time. Take as an example the plane crash in Ukraine in July 2014 which involved multiple entities such as "Ukraine", "Malaysia", and "Russia" and multiple events ranging from the actual crash to the incident investigation and the presentation of the investigator's findings. In this demonstration we present StoryPivot, a framework that helps its users to detect evolving stories in event datasets over time. To resolve stories, we differentiate between story identification, the problem of connecting events over time within a source, and story alignment, the problem of integrating stories across sources. The goal of this demonstration is to present an interactive exploration of both these problems and how events can be dynamically interpreted and put into context in real-world datasets.