Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Truth Existence in Truth Discovery

When integrating information from multiple sources, it is common to encounter conflicting answers to the same question. Truth discovery is to infer the most accurate and complete integrated answers from conflicting sources. In some cases, there exist questions for which the true answers are excluded from the candidate answers provided by all sources. Without any prior knowledge, these questions, named no-truth questions, are difficult to be distinguished from the questions that have true answers, named has-truth questions. In particular, these no-truth questions degrade the precision of the answer integration system. We address such a challenge by introducing source quality, which is made up of three fine-grained measures: silent rate, false spoken rate and true spoken rate. By incorporating these three measures, we propose a probabilistic graphical model, which simultaneously infers truth as well as source quality without any a priori training involving ground truth answers. Moreover, since inferring this graphical model requires parameter tuning of the prior of truth, we propose an initialization scheme based upon a quantity named truth existence score, which synthesizes two indicators, namely, participation rate and consistency rate. Compared with existing methods, our method can effectively filter out no-truth questions, which results in more accurate source quality estimation. Consequently, our method provides more accurate and complete answers to both has-truth and no-truth questions. Experiments on three real-world datasets illustrate the notable advantage of our method over existing state-of-the-art truth discovery methods.
Wait-Time Predictors for Customer Service Systems with Time-Varying Demand and Capacity

We develop new, improved real-time delay predictors for many-server service systems with a time-varying arrival rate, a time-varying number of servers, and customer abandonment. We develop four new predictors, two of which exploit an established deterministic fluid approximation for a many-server queueing model with those features. These delay predictors can be used to make delay announcements. We use computer simulation to show that the proposed predictors outperform previous predictors.
A Randomized Algorithm for Minimizing User Disturbance Due to Changes in Cellular Technology

ABSTRACTWe present a computational approach to a practicalproblem occurring in the mobile telecommunicationsindustry. Due to changes in cellular technology, thereis sometimes the necessity of moving users from onenetwork to another (in this case from TDMA to GSM). In this paper we address the problem of minimizing thenumber of users aﬁected by changes in cellular technol-ogy, subject to the constraint that the selected usersmust be uniformly spread along the covered area. Theresulting problem is shown to be NP-hard and a La-grangian relaxation method is used to ﬂnd approximateresults. We also propose a heuristic algorithm, whichis shown to give good results for some instances.Keywords: Telecommunications, Cellular Telephony,Heuristics, Simulated Annealing, Massive Data Sets. 1. INTRODUCTION In the world of mobile telecommunications, manycompeting technologies are available to be used by cel-lular carriers. Eventually there is a necessity of chang-ing from one technology to another, and at the sametime maintaining the quality of service.In this paper we discuss the problem involving achange from TDMA to GSM technology. The changeis planned to be done progressively, and at ﬂrst just acertain number of users should be selected to change
An Aqueous Extract of Tuberaria lignosa Inhibits Cell Growth, Alters the Cell Cycle Profile, and Induces Apoptosis of NCI-H460 Tumor Cells

Tuberaria lignosa (Sweet) Samp. is found in European regions, and has antioxidant properties due to its composition in ascorbic acid and phenolic compounds. Given its traditional use and antioxidant properties, the tumor cell growth inhibitory potential of aqueous extracts from T. lignosa (prepared by infusion and decoction) was investigated in three human tumor cell lines: MCF-7 (breast adenocarcinoma), NCI-H460 (non-small cell lung cancer), and HCT-15 (human colorectal adenocarcinoma). Both extracts inhibited the growth of these cell lines; the most potent one being the T. lignosa extract obtained by infusion in the NCI-H460 cells (GI50 of approximately 50 μg/mL). Further assays were carried out with this extract in NCI-H460 cells. At 100 μg/mL or 150 μg/mL it caused an increase in the percentage of cells in the G0/G1 phase and a decrease of cells in S phase of the cell cycle. Additionally, these concentrations caused an increase in the percentage of apoptotic cells. In agreement, a decrease in total poly (ADP-ribose) polymerase (PARP) and pro-caspase 3 levels was found. In conclusion, the T. lignosa extract obtained by infusion was more potent in NCI-H460 cells, altering the cell cycle progression and inducing apoptosis. This work highlights the importance of T. lignosa as a source of bioactive compounds with tumor cell growth inhibitory potential.
Human cortical sensorimotor network underlying feedback control of vocal pitch

The control of vocalization is critically dependent on auditory feedback. Here, we determined the human peri-Sylvian speech network that mediates feedback control of pitch using direct cortical recordings. Subjects phonated while a real-time signal processor briefly perturbed their output pitch (speak condition). Subjects later heard the same recordings of their auditory feedback (listen condition). In posterior superior temporal gyrus, a proportion of sites had suppressed responses to normal feedback, whereas other spatially independent sites had enhanced responses to altered feedback. Behaviorally, speakers compensated for perturbations by changing their pitch. Single-trial analyses revealed that compensatory vocal changes were predicted by the magnitude of both auditory and subsequent ventral premotor responses to perturbations. Furthermore, sites whose responses to perturbation were enhanced in the speaking condition exhibited stronger correlations with behavior. This sensorimotor cortical network appears to underlie auditory feedback-based control of vocal pitch in humans.
Lyα Fluorescent Excitation of Fe II in Active Galactic Nuclei

We have calculated Fe II emission-line strengths for active galactic nuclei broad-line regions using precise radiative transfer and Iron Project atomic data. We improve the treatment of all previously considered excitation mechanisms for the Fe II emission, continuum fluorescence, collisional excitation, fluorescence by self-overlap among the iron lines, and fluorescent excitation by Lyα. We demonstrate that Lyα fluorescence is of fundamental importance in determining the strength of the Fe II emission. In addition to enhancing the ultraviolet and optical Fe II flux, Lyα fluorescence also results in significant near-infrared Fe II emission in the 8500-9500 Å wavelength range. New observations are suggested to probe this effect in strong Fe II-emitting quasars.
Imaging structures and measuring forces at nanoscales with atomic force microscopy

Zobrazovanie struktur a meranie sil na nanometrickej urovni atomovo silovou mikroskopiou Interaction between interfaces in solution of chemical reagents is scientifically and technologically significant. Atomic force microscopy has been used to visualise structures of adsorbed surfactant molecules and measure interaction forces between surfaces with adsorbed molecules. This paper presents the results obtained by the author and associates in three major research strands, including imaging structures of surfactants adsorbed at solid-liquid interfaces, measuring interfacial forces between water-repellent (hydrophobic) surfaces and microhydrodynamic forces between an AFM colloid probe and a soft interface. The research increases our understanding of the role of surfactants and interaction forces in particle technology and interface science.
The Convex Relaxation Method on Deconvolution Model withMultiplicative Noise

In this paper, we consider variational approaches to handle the multiplica- tive noise removal and deblurring problem. Based on rather reasonable physical blurring-noisy assumptions, we derive a new variational model for this issue. After the study of the basic properties, we propose to approximate it by a convex relax- ation model which is a balance between the previous non-convex model and a convex model. The relaxed model is solved by an alternating minimization approach. Numer- ical examples arepresentedto illustrate the effectivenessand efficiencyof the proposed method. AMS subject classifications: 52A40, 65K10, 65K15, 90C26
Tetris-XL: A performance-driven spill reduction technique for embedded VLIW processors

As technology has advanced, the application space of Very Long Instruction Word (VLIW) processors has grown to include a variety of embedded platforms. Due to cost and power consumption constraints, many embedded VLIW processors contain limited resources, including registers. As a result, a VLIW compiler that maximizes instruction level parallelism (ILP) without considering register constraints may generate excessive register spills, leading to reduced overall system performance. To address this issue, this article presents a new spill reduction technique that improves VLIW runtime performance by reordering operations prior to register allocation and instruction scheduling. Unlike earlier algorithms, our approach explicitly considers both register reduction and data dependency in performing operation reordering. Data dependency control limits unexpected schedule length increases during subsequent instruction scheduling. Our technique has been evaluated using Trimaran, an academic VLIW compiler, and evaluated using a set of embedded systems benchmarks. Experimental results show that, on average, this technique improves VLIW performance by 10% for VLIW processors with 32 registers and 8 functional units compared with previous spill reduction techniques. Limited improvement is seen versus prior approaches for VLIW processors with 64 registers and 8 functional units.
A componential model of human interaction with graphical display

A componential model of graphical interaction was developed based on analyses of humans interacting with graphical displays. The model, known as the Mixed Arithmetic--Perceptual (MA-P) model, proposes that five component processes underlie graphical interactions: Searching, Encoding, Arithmetic operations, Spatial comparison, and Responding. Experiment 1 demonstrated that the number of components for a given question--graph combination predicted the time to answer that question using that graph. Experiment 2 provided empirical support for two predictions from the model concerning the spatial relations among the elements of the graph. A metric for determining the ease of processing of a graph is outlined and guidelines for design of graphs are presented.
Identifying Functional Transcription Factor Binding Sites in Yeast by Considering Their Positional Preference in the Promoters

Transcription factor binding site (TFBS) identification plays an important role in deciphering gene regulatory codes. With comprehensive knowledge of TFBSs, one can understand molecular mechanisms of gene regulation. In the recent decades, various computational approaches have been proposed to predict TFBSs in the genome. The TFBS dataset of a TF generated by each algorithm is a ranked list of predicted TFBSs of that TF, where top ranked TFBSs are statistically significant ones. However, whether these statistically significant TFBSs are functional (i.e. biologically relevant) is still unknown. Here we develop a post-processor, called the functional propensity calculator (FPC), to assign a functional propensity to each TFBS in the existing computationally predicted TFBS datasets. It is known that functional TFBSs reveal strong positional preference towards the transcriptional start site (TSS). This motivates us to take TFBS position relative to the TSS as the key idea in building our FPC. Based on our calculated functional propensities, the TFBSs of a TF in the original TFBS dataset could be reordered, where top ranked TFBSs are now the ones with high functional propensities. To validate the biological significance of our results, we perform three published statistical tests to assess the enrichment of Gene Ontology (GO) terms, the enrichment of physical protein-protein interactions, and the tendency of being co-expressed. The top ranked TFBSs in our reordered TFBS dataset outperform the top ranked TFBSs in the original TFBS dataset, justifying the effectiveness of our post-processor in extracting functional TFBSs from the original TFBS dataset. More importantly, assigning functional propensities to putative TFBSs enables biologists to easily identify which TFBSs in the promoter of interest are likely to be biologically relevant and are good candidates to do further detailed experimental investigation. The FPC is implemented as a web tool at http://santiago.ee. ncku.edu. tw/FPC/.
The Role of Community Leadership in the Development of Grassroots Innovations

This article focuses on the role of community leadership in the development of grassroots innovations. It asks: When community leaders initiate energy projects, what types of skills and knowledge practices do they utilise to nurture grassroots innovations? Grassroots innovations are usually driven by social and sustainability motives, and developed by civil society groups. Based on a mixed methods approach including research interviews and site visits, the article draws on previous literature on community leadership, grassroots innovations and niche literature. Community leadership is analysed via two in-depth community energy cases in the UK. Research findings show that community leadership can aid the development of grassroots innovations, which operate in niches and require nurturing. Community leadership benefits from being embedded into social networks, shared vision and decision making, but pre-existing skills and tacit knowledge also play a role. Community leaders can also assist niche building by working closely with intermediary actors.
Methodology of the Enterprise Architecture creating and the role of the Enterprise Architecture in rural development.

This contribution deals with the concept of the Enterprise Architecture, the practice that tries to describe and control an organization structure, processes, applications, systems and technology infrastructure, software components, services and roles in enterprise. it links the goals, resources and rule in enterprise with the processes and activities in business organization and creates a large document for planning, investing and the enterprise information strategy. The aim of the article is the methodological approach, the common method for development of the Enterprise Architecture document. What is important in this document? All business managers must make sense of the architecture to optimize their activities according to the specific business goals and to develop a strategy for the innovation and for the introduction improvements in the current business situation and into the future. The Enterprise Architecture can serve as a basis of business knowledge. The description principles should be applied in every area of industry also in the czech agriculture area. The conclusions of the paper evaluate the knowledge context and discuss the necessity of establishing the Enterprise Architecture in
Leaf Gas Exchange and Fluorescence of Two Winter Wheat Varieties in Response to Drought Stress and Nitrogen Supply

Water and nitrogen supply are the two primary factors limiting productivity of wheat (Triticum aestivum L.). In our study, two winter wheat varieties, Xinong 979 and large-spike wheat, were evaluated for their physiological responses to different levels of nitrogen and water status during their seedling stage grown in a phytotron. Our results indicated that drought stress greatly reduced the net photosynthetic rate (Pn), transpiration rate (E), and stomatal conductance (Gs), but with a greater increase in instantaneous water use efficiency (WUE). At the meantime, the nitrogen (N) supply improved photosynthetic efficiency under water deficit. Parameters inferred from chlorophyll a measurements, i.e., photochemical quenching coefficient (qP), the maximum photochemical efficiency (Fv/Fm), the quantum yield of photosystemII(ΦPSII), and the apparent photosynthetic electron transport rate (ETR) decreased under water stress at all nitrogen levels and declined in N-deficient plants. The root–shoot ratio (R/S) increased slightly with water stress at a low N level; the smallest root–shoot ratio was found at a high N level and moderate drought stress treatment. These results suggest that an appropriate nitrogen supply may be necessary to enhance drought resistance in wheat by improving photosynthetic efficiency and relieving photoinhibition under drought stress. However, an excessive N supply had no effect on drought resistance, which even showed an adverse effect on plant growth. Comparing the two cultivars, Xinong 979 has a stronger drought resistance compared with large-spike wheat under N deficiency.
Neuronal Activity and Brain-Derived Neurotrophic Factor Regulate the Density of Inhibitory Synapses in Organotypic Slice Cultures of Postnatal Hippocampus

Hippocampal interneurons inhibit pyramidal neurons through the release of the neurotransmitter GABA. Given the importance of this inhibition for the proper functioning of the hippocampus, the development of inhibitory synapses must be tightly regulated. In this study, the possibility that neuronal activity and neurotrophins regulate the density of GABAergic inhibitory synapses was investigated in organotypic slice cultures taken from postnatal day 7 rats. In hippocampal slices cultured for 13 d in the presence of the GABAA receptor antagonist bicuculline, the density of glutamic acid decarboxylase (GAD) 65-immunoreactive terminals was increased in the CA1 area when compared with control slices. Treatment with the glutamate receptor antagonist 6,7-dinitroquinoxaline-2,3-dione decreased the density of GAD65-immunoreactive terminals in the stratum oriens of CA1. These treatments had parallel effects on the density of GABA-immunoreactive processes. Electron microscopic analysis after postembedding immunogold labeling with antibodies against GABA indicated that bicuculline treatment increased the density of inhibitory but not excitatory synapses. Application of exogenous BDNF partly mimicked the stimulatory effect of bicuculline on GAD65-immunoreactive terminals. Finally, antibodies against BDNF, but not antibodies against nerve growth factor, decrease the density of GAD65-immunoreactive terminals in bicuculline-treated slices. Thus, neuronal activity regulates the density of inhibitory synapses made by postnatal hippocampal interneurons, and BDNF could mediate part of this regulation. This regulation of the density of inhibitory synapses could represent a feedback mechanism aimed at maintaining an appropriate level of activity in the developing hippocampal networks.
Semiotes: a semantics for sharing

MOTIVATION Reliable, automated communication of biological information requires methods to declare the information's semantics. In this paper I describe an approach to semantic declaration intended to permit independent, distributed databases, algorithms, and servers to exchange and process requests for information and computations without requiring coordination or agreement among them on universe of discourse, data model, schema, or implementation. RESULTS This approach uses Glossa, a formal language defining the semantics of biological ideas, information, and algorithms, to executably define the semantics of complex ideas and computations by constructs of semiotes, terms which axiomatically define very simple notions. A database or algorithm wishing to exchange information or computations maintains a set of mappings between its particular notions and semiotes, and a parser to translate between its indigenous ideas and implementation and the semiotes. Requests from other databases or algorithms are issued as semiotic messages, locally interpreted and processed, and the results returned as semiotes to the requesting entity. Thus, semiotes serve as a shared, abstract layer of definitions which can be computably combined by each database or algorithm according to its own needs and ideas. By combining the explicit declaration of semantics with the computation of the semantics of complex ideas, Glossa and its semiotes permit independent computational entities to lightly federate their capabilities as desired while maintaining their unique perspectives on both scientific and technical questions.
The Creation of Social Identity Through

Blogs, as one of the latest emerging tools for communication, are gaining widespread popularity and becoming increasingly common. Despite this, limited academic research exists to help us understand this phenomenon. To overcome some of this gap, we argue that there is a need to examine how the social identity of individual bloggers influences and is being influenced by the blogging community. This issue is explored using participant observation and interviews. The results indicate that different types of blogs are dependent on the blogger, the purpose of the blog, and content within the blog. Bloggers are found to actively select to participate in those communities that match their interests, and they can be members of more than one group. Furthermore, analysis reveals that social identities are created in blogging communities and these have an effect on members as well as on the community in general. The implications for research are identified.
Classification of ECG using some novel features

Heart diseases are frequent reasons of death. Hence, there is always a need to develop systems that can provide prior indication about the state of the heart. This is also required because medical facilities may not be uniform everywhere. In such situation certain innovative approaches using certain signal processing techniques can provide considerable support. As a follow up to such possibilities, system for automatic recognition of cardiac arrhythmias has become necessary and important for diagnosis of cardiac abnormalities. Several algorithms have been proposed to classify cardiac arrhythmias in the literature; however, many of them fail to perform optimally. Here, we have proposed a method for ECG arrhythmia classification using Artificial Neural Network (ANN) and a novel feature set. Fast Fourier Transform is used for pre-processing the ECG recordings. Linear Prediction Coefficients (LPC) and Principal Component Analysis (PCA) are used for extracting some features and then Multi-Layer Perceptron (MLP) ANN performs the classification.
On Tightness of Mutual Dependence Upperbound for Secret-key Capacity of Multiple Terminals

Csiszar and Narayan[3] defined the notion of secret key capacity for multiple terminals, characterized it as a linear program with Slepian-Wolf constraints of the related source coding problem of communication for omniscience, and upper bounded it by some information divergence expression from the joint to the product distribution of the private observations. This paper proves that the bound is tight for the important case when all users are active, using the polymatroidal structure[6] underlying the source coding problem. When some users are not active, the bound may not be tight. This paper gives a counter-example in which 3 out of the 6 terminals are active.