Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of Contributed Service and Market Equilibrium for Participatory Sensing

User-contributed or crowd-sourced information is becoming increasingly common. In this paper, we consider the specific case of participatory sensing whereby people contribute information captured by sensors, typically those on a smartphone, and share the information with others. We propose a new metric called quality of contributed service (QCS) which characterizes the information quality and timeliness of a specific real-time sensed quantity achieved in a participatory manner. Participatory sensing has the problem that contributions are sporadic and infrequent. To overcome this, we formulate a market-based framework for participatory sensing with plausible models of the market participants comprising data contributors, service consumers and a service provider. We analyze the market equilibrium and obtain a closed form expression for the resulting QCS at market equilibrium. Next, we examine the effects of realistic behaviors of the market participants and the nature of the market equilibrium that emerges through extensive simulations. Our results show that, starting from purely random behavior, the market and its participants can converge to the market equilibrium with good QCS within a short period of time.
Bending Fatigue Strength and the Effect of Assembling Stress on Fillet Welded Joints of Catalyst Muffler Flange Pipes

2016 ABSTRACT : Fatigue strength is affected by assembling stress at flanges. Using samples of exhibiting different degrees of flange surface flatness, local stress was measured with strain gauges, and the effect of assembling strain and stress on fatigue strength was verified through fatigue strength examinations. Bending fatigue strengths were inversely proportional to static assembling stress which was proportional to the degree of flange flatness. Factors causing fatigue to failure, such as welding bead shape, metal structure, and crack state were identified through microscope observations and SEM-EDS chemical composition analyses, while assembling deformation and stress concentration were analyzed through FEM computer
The role of non-coding RNAs in the regulation of stem cells and progenitors in the normal mammary gland and in breast tumors

The outlook on stem cell (SC) biology is shifting from a rigid hierarchical to a more flexible model in which the identity and the behavior of adult SCs, far from being fixed, are determined by the dynamic integration of cell autonomous and non-autonomous mechanisms. Within this framework, the recent discovery of thousands of non-coding RNAs (ncRNAs) with regulatory function is redefining the landscape of transcriptome regulation, highlighting the interplay of epigenetic, transcriptional, and post-transcriptional mechanisms in the specification of cell fate and in the regulation of developmental processes. Furthermore, the expression of ncRNAs is often tissue- or even cell type-specific, emphasizing their involvement in defining space, time and developmental stages in gene regulation. Such a role of ncRNAs has been investigated in embryonic and induced pluripotent SCs, and in numerous types of adult SCs and progenitors, including those of the breast, which will be the topic of this review. We will focus on ncRNAs with an important role in breast cancer, in particular in mammary cancer SCs and progenitors, and highlight the ncRNA-based circuitries whose subversion alters a number of the epigenetic, transcriptional, and post-transcriptional events that control “stemness” in the physiological setting.
The near-miss effect in slot-machine gambling: modulation of feedback-related negativity by subjective value

Near-miss plays an important role in the development of gambling addictions. In this study, we measured the neural correlates of the process by which near-miss outcomes are evaluated in simplified, static, slot-machine gambling using event-related potentials. Analysis of event-related potentials revealed that the size of FRN (feedback-related negativity) for a near miss is between the full miss and the win. These results suggest that participants distinguish among near misses, full misses, and wins during the early evaluation phase. The subjective value and objective value of outcome were assessed separately to discuss FRN on outcome evaluation. It is suggested that FRN is mediated not only by the objective value of outcomes but also by the subjective value of feedback.
Construct Virtual Samples for Improving Kernel PCA

Though kernel methods have been widely used for feature extraction, it suffers from the problem that its feature extraction efficiency is in inverse proportion to the size of the training sample set. In order to make kernel-methods-based feature extraction computationally more efficient, we propose a novel improvement to the kernel method. This improvement assumes that the discriminant vector in the feature space can be approximately expressed by a certain linear combination of some constructed virtual sample vectors. We determine these virtual sample vectors one by one by using a very simple and computationally efficient iterative algorithm. The algorithm is simple, robust and competitive. When we determine virtual sample vectors, we need only to set the initial values of the virtual sample vectors to random values. The experiments show that our method can achieve the goal of efficient feature extraction as well as a good and stable classification accuracy.
An analysis of the effect of bit error ratio on signal reconstruction error

When compressed sensing technology is applied to underwater acoustic communication system, signal reconstruction error will be affected more or less by bit error ratio (BER). In this essay, random number of statistics, which obeys the law of standard normal distribution, is used to simulate the generation of bit error. Using the same reconstruction algorithm, reconstruction error with bit error and reconstruction error without bit error are compared. The result of the simulation shows that signal reconstruction error with a bit error ratio of 10−2 is 4 times as much as signal reconstruction error without bit error ratio.
Consumers’ Attitudes towards Organic Products and Sustainable Development: A Case Study of Romania

Organic food consumption has increased during the last years as a consequence of its direct impact on consumer health, life style, and social convenience as well as on the environment and sustainable development. Compared to the European level, the consumption of organic food products is quite low in Romania. This paper investigates the perception and attitudes of the organic food consumers from the North-West Development Region of Romania. Consumers’ perception towards organic food products was measured using 30 items. The data were collected from 568 respondents and analyzed using descriptive and inferential statistics. A factor-cluster approach was used to identify consumer groups. The findings indicated that health concerns, sensory appeal, sustainable consumption and weight concerns are the main reasons for consuming organic food products. Three main groups of organic food consumers were identified: “gourmand”, “environmentally concerned” and “health concerned”.
Expression of S-adenosylmethionine Hydrolase in Tissues Synthesizing Secondary Cell Walls Alters Specific Methylated Cell Wall Fractions and Improves Biomass Digestibility

Plant biomass is a large source of fermentable sugars for the synthesis of bioproducts using engineered microbes. These sugars are stored as cell wall polymers, mainly cellulose and hemicellulose, and are embedded with lignin, which makes their enzymatic hydrolysis challenging. One of the strategies to reduce cell wall recalcitrance is the modification of lignin content and composition. Lignin is a phenolic polymer of methylated aromatic alcohols and its synthesis in tissues developing secondary cell walls is a significant sink for the consumption of the methyl donor S-adenosylmethionine (AdoMet). In this study, we demonstrate in Arabidopsis stems that targeted expression of AdoMet hydrolase (AdoMetase, E.C. 3.3.1.2) in secondary cell wall synthesizing tissues reduces the AdoMet pool and impacts lignin content and composition. In particular, both NMR analysis and pyrolysis gas chromatography mass spectrometry of lignin in engineered biomass showed relative enrichment of non-methylated p-hydroxycinnamyl (H) units and a reduction of dimethylated syringyl (S) units. This indicates a lower degree of methylation compared to that in wild-type lignin. Quantification of cell wall-bound hydroxycinnamates revealed a reduction of ferulate in AdoMetase transgenic lines. Biomass from transgenic lines, in contrast to that in control plants, exhibits an enrichment of glucose content and a reduction in the degree of hemicellulose glucuronoxylan methylation. We also show that these modifications resulted in a reduction of cell wall recalcitrance, because sugar yield generated by enzymatic biomass saccharification was greater than that of wild-type plants. Considering that transgenic plants show no important diminution of biomass yields, and that heterologous expression of AdoMetase protein can be spatiotemporally optimized, this novel approach provides a valuable option for the improvement of lignocellulosic biomass feedstock.
A 200 MHz CMOS phase-locked loop with dual phase detectors

The authors describe a 200-MHz PLL (phase-locked loop) in a 2- mu m CMOS technology employing an untrimmed current-controlled ring oscillator (CCO). Two phase detectors are included: a phase-frequency detector (PFD) for fast acquisition during data preamble (100% pulse density), and a mixer phase detector to lock on actual data (in the presence of missing pulses). Simulation results and experimental data using an external current source suggest that using the bandgap reference, the CCO supply sensitivity will be 4%/V and the CCO temperature coefficient will be about 500 p.p.m. / degrees C. Internal input and output waveforms in lock were measured from buffered test pads with a low-capacitance wideband buffered probe. <<ETX>>
Improving bank-level parallelism for irregular applications

Observing that large multithreaded applications with irregular data access patterns exhibit very low memory bank-level parallelism (BLP) during their execution, we propose a novel loop iteration scheduling strategy built upon the inspector-executor paradigm. A unique characteristic of this strategy is that it considers both bank-level parallelism (from an inter-core perspective) and bank reuse (from an intra-core perspective) in a unified framework. Its primary goal is to improve bank-level parallelism, and bank reuse is taken into account only if doing so does not hurt bank-level parallelism. Our experiments with this strategy using eight application programs on both a simulator and a real multicore system show an average BLP improvement of 46.8% and an average execution time reduction of 18.3%.
Fahrmeir , Kneib : Propriety of Posteriors in Structured Additive Regression Models : Theory and Empirical Evidence

Structured additive regression comprises many semiparametric regression models such as generalized additive (mixed) models, geoadditive models, and hazard regression models within a unified framework. In a Bayesian formulation, nonparametric functions, spatial effects and further model components are specified in terms of multivariate Gaussian priors for high-dimensional vectors of regression coefficients. For several model terms, such as penalised splines or Markov random fields, these Gaussian prior distributions involve rank-deficient precision matrices, yielding partially improper priors. Moreover, hyperpriors for the variances (corresponding to inverse smoothing parameters) may also be specified as improper, e.g. corresponding to Jeffery’s prior or a flat prior for the standard deviation. Hence, propriety of the joint posterior is a crucial issue for full Bayesian inference in particular if based on Markov chain Monte Carlo simulations. We establish theoretical results providing sufficient (and sometimes necessary) conditions for propriety and provide empirical evidence through several accompanying simulation studies.
Knowledge Based Factorized High Order Sparse Learning Models

We propose a scalable knowledge based high order sparse learning framework termed as Group Factorized High order Interactions Model (Group FHIM) for identifying discriminative feature groups and high-order feature group interactions in classification problems. Our factorization technique allows us to incorporate the domain knowledge such as grouping of features directly into the decomposition factors. Unlike previous sparse learning approaches, our model can recover both the discriminative feature groups and the pairwise feature group interactions accurately without enforcing any hierarchical feature constraints. We show that our Group FHIM estimator is asymptotically optimal. Experiments on synthetic and real datasets show that our model outperforms the state-of-the-art sparse learning techniques, and it provides ‘interpretable’ high-order feature group interactions for gene expression prediction and peptide-MHC I binding prediction.
A Fine-Grained Model for Language Identification

Existing state-of-the-art techniques to identify the language of a written text most often use a 3-gram frequency table as basis for ’fingerprinting’ a language. While this approach performs very well in practice (99%-ish accuracy) if the text to be classified is of size, say, 100 characters or more, it cannot be used reliably to classify even shorter input, nor can it detect if the input is a concatenation of text from several languages. The present paper describes a more fine-grained model which aims at reliable classification of input as short as one word. It is heavier than the classic classifiers in that it stores a large frequency dictionary as well as an affix table, but with significant gains in elegance since the classifier is entirely unsupervised. Classifying a short input query in multilingual information retrieval is the target application for which the method was developed, but also tools such as spell-checkers will benefit from recognising occasional interspersed foreign words. It is also acknowledged that a lot of practical applications do not need this fine level of granularity, and thus remain largely unbenefited by the new model. Not having access to real-world multi-lingual query data, we evaluate rigorously, using a 32-language parallel bible corpus, that accuracy is competitive on short input as well as multi-lingual input, and not only for a set of European languages with similar morphological typology.
MULTIGENERATION IMPACTS ON DAPHNIA MAGNA OF CARBON NANOMATERIALS WITH DIFFERING CORE STRUCTURES AND FUNCTIONALIZATIONS

Several classes of contaminants have been shown to have multigenerational impacts once a parental generation has been exposed. Acute and chronic toxicity are described for several types of nanomaterials in the literature; however, no information is available on the impact of nanomaterials on future generations of organisms after the exposure is removed. In the present study, the authors examined the impacts of carbon nanomaterials (CNMs), including fullerenes (C60), single‐walled carbon nanotubes (SWCNTs), and multiwalled carbon nanotubes (MWCNTs) with neutral, positive, and negative functional groups to F1 and F2 generation daphnids after an F0 exposure. Data from the present study indicate that multigenerational toxicity is present with certain nanomaterial exposures and is highly dependent on the surface chemistry of the nanomaterial. Many CNMs that showed toxicity to exposed F0 daphnids in previous experiments did not induce multigenerational toxicity. Certain nanomaterials, however, such as C60‐malonate, SWCNTs, SWCNT‐CONH2, and MWCNTs, caused a significant decrease in either survival or reproduction in F1 daphnids; and SWCNT‐CONH2 decreased reproduction out to the F2 generation. Impacts of nanomaterials on F1 and F2 size were small and lacked clear patterns, indicating that CNMs have minimal multigenerational impacts on size. Industries should take into account how surface chemistry influences nanomaterial toxicity to future generations of organisms to create sustainable nanomaterials that do not harm freshwater ecosystems. Environ Toxicol Chem 2014;33:541–547. © 2013 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC. This is an open access article under the terms of the Creative Commons Attribution–NonCommercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Detection and recognition of traffic signs inside the attentional visual field of drivers

Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.
On the probability distribution of the capacity allocation in optical transport networks

Computer generated topologies are usually employed to perform extensive simulations in telecommunications networks. In a previous work we have proposed a method to generate networks that resemble the topological characteristics of real ones. In this work, we evaluate the amount of capacity required for each link for working and backup paths and compare the probability distribution of real world networks with computer generated ones. Using statistical tests we verified that the networks generated using the proposed method preserves the capacity allocation probability distribution of real optical networks.
Harnessing monocyte‐derived macrophages to control central nervous system pathologies: no longer ‘if’ but ‘how’

The central nervous system (CNS) tissues, including the brain, the eye, and the spinal cord, are immune‐privileged, secluded from the circulation by a complex of barriers, and equipped with their own myeloid cell population, the resident microglia. Based on the classical perspective of immune–brain interactions and on the contribution of such interactions to the progression of multiple sclerosis, an autoimmune inflammatory disease of the CNS, infiltrating macrophages were traditionally viewed as an enemy of the nervous system. However, over the past two decades, research has revealed the pivotal role of monocyte‐derived macrophages in CNS repair, and opened up a new era in understanding and treating CNS pathologies. Here, we gather current knowledge regarding macrophage broad spectrum of activities in the CNS, whose two poles correspond to the classical pro‐inflammatory M1 and the ‘alternatively‐activated’ M2 cells previously described in various non‐CNS pathologies, and their diverse, multi‐functional contribution in various neurological conditions, ranging from acute traumas to neurodegenerative disorders, and autoimmune diseases. The diverse functions are manifested by induction and resolution of inflammation as well as their involvement in neural tissue regeneration and renewal, matrix remodelling, debris clearance, and angiogenesis. A special focus is devoted to current evidence suggesting that resident microglia and infiltrating monocyte‐derived macrophages are functionally non‐redundant cell types. Taken together, these recent advances reveal a dramatic therapeutic opportunity for controlled harnessing of macrophages for repair of the damaged CNS following acute insults, in neurodegenerative conditions, and in psychiatric disorders.
Conductive SU8 Photoresist for Microfabrication

A conductive composite photoresist has been developed for the direct photopatterning of electrodes. It is based on a dispersion of silver nanoparticles in SU8, a non‐conductive, negative‐tone photoresist. Manufactured structures have an electrical conductivity at a low silver content of around 6 vol.‐%.
Adaptive Information Filtering : Learning Drifting Concepts

The task of information filtering is to classify texts from a s tream of documents into relevant and nonrelevant, respectively, with respect to a particular categ ory or user interest, which may change over time. A filtering system should be able to adapt to such concept changes. This p aper explores methods to recognize concept changes and to maintain windows on the training data, whose size is ei ther fixed or automatically adapted to the current extent of concept change. Experiments with two simulated co ncept drift scenarios based on real-world text data and eight learning methods are performed to evaluate three indi cators for concept changes and to compare approaches with fixed and adjustable window sizes, respectively, to eac h other and to learning on all previously seen examples. Even using only a simple window on the data already improves t h performance of the classifiers significantly as compared to learning on all examples. For most of the classifi er , the window adjustments lead to a further increase in performance compared to windows of fixed size. The chosen ind icators allow to reliably recognize concept changes.
Multi-objective Optimization of Placement and Assignment of TSVs in 3D ICs

The advent of 3D IC technology facilitates fabrication of large logic circuits on low area yet high performance chips. For a 3D IC, placement followed by assignment of Through-Silicon-Vias (TSV)s is a challenging problem involving various issues like inter-layer wirelength, power density, congestion and variation in surrounding carrier mobility. Each of the existing techniques for placement of TSVs deals only with a subset of these issues. In this paper, we propose an evolutionary approach MO_TSV to handle this multi-objective optimization problem. Although this method has some similarity with the framework of NSGA-II, several variations have been incorporated so that, on exploration of variety of non-dominated solutions the search process converges to a near-optimum solution in reasonable time. Experimental results on ISCAS'85 and ISCAS'89 benchmarks yield solution quality aswell as convergence times, which are encouraging.
Joint kernel dictionary and classifier learning for sparse coding via locality preserving K-SVD

We present a locality preserving K-SVD (LP-KSVD) algorithm for joint dictionary and classifier learning, and further incorporate kernel into our framework. In LP-KSVD, we construct a locality preserving term based on the relations between input samples and dictionary atoms, and introduce the locality via nearest neighborhood to enforce the locality of representation. Motivated by the fact that locality-related methods works better in a more discriminative and separable space, we map the original feature space to the kernel space, where samples of different classes become more separable. Experimental results show the proposed approach has strong discrimination power and is comparable or outperforms some state-of-the-art approaches on public databases.