Miracle-dz/UniFAR-FTUs huggingface dataset:
The Characteristics of Voice Search

The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries rather than type them. While this kind of voice search is still in its infancy, it is gradually becoming more widespread. In this article, we report a comprehensive voice search query log analysis of a commercial web search engine’s mobile application. We compare voice and text search by various aspects, with special focus on the semantic and syntactic characteristics of the queries. Our analysis suggests that voice queries focus more on audio-visual content and question answering and less on social networking and adult domains. In addition, voice queries are more commonly submitted on the go. We also conduct an empirical evaluation showing that the language of voice queries is closer to natural language than the language of text queries. Our analysis points out further differences between voice and text search. We discuss the implications of these differences for the design of future voice-enabled web search tools.
Further results on H∞ control of switched linear time-delay systems

In this note, we study the problems of stability analysis and H∞ controller synthesis of discrete-time switched systems with time-varying delay. The system under consideration is firstly transformed into an interconnection system. Based on the system transformation and the scaled small gain theorem, the asymptotic stability of the original system is examined via the version of the bounded realness of the transformed forward system. The aim of the proposed approach is to reduce conservatism, which is made possible by a precise approximation of the time-varying delay and the input-output approach. The proposed stability condition is demonstrated to be much less conservative than most existing results. Moreover, the problem of H∞ controller synthesis involving convex optimization is further solved based on the stability condition, whose effectiveness are also illustrated via numerical examples.
An Efficient Storm Identification from Big Rainfall Data Using MapReduce

This paper is part of my doctoral dissertation, “Analysis and Modeling Techniques for Geo-Spatial Datasets,” which focuses on how to summarize, model, and format spatiotemporal data for analysis and mining. The dissertation consists of four main components: (1) spatio-temporal knowledge representation, (2) identifying meaningful concepts from raw data, (3) converting raw data to conceptual data, and (4) analysis and mining of conceptual data. This paper, part of the third component, describes an efficient MapReduce algorithm for converting raw rainfall data into meaningful storm information, which can then be easily analyzed and mined. Our previous work proposed a method to identify relevant storm characteristics from raw rainfall data. The original storm identification system takes too long to produce the summarized storm characteristics, because: (1) the raw rainfall data, which is considered as big data, is stored in a traditional relational database based on CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.) ODM (Observations Data Model), which leads to substantial disk I/O; (2) the storm identification algorithm is based on recursion and regular depth-first-search (DFS), which leads to multiple retrievals for parts of the data. In this paper, we obtain a substantial improvement in performance by utilizing MapReduce. We also utilize the original raw rainfall data text files instead of using the data in the relational database. In our experiments, the performance of the new storm identification system is significantly improved compared to the previous one. With this new system, it will dramatically benefit hydrologists in helping them performing rainfallrelated analysis (both location-specific and storm-specific) such as flood prediction using our identified storms. Keywords-storm analysis; rainfall; big data; MapReduce; distributed computing; CUAHSI
A Hadoop MapReduce Performance Prediction Method

More and more Internet companies rely on large scale data analysis as part of their core services for tasks such as log analysis, feature extraction or data filtering. Map-Reduce, through its Hadoop implementation, has proved to be an efficient model for dealing with such data. One important challenge when performing such analysis is to predict the performance of individual jobs. In this paper, we propose a simple framework to predict the performance of Hadoop jobs. It is composed of a dynamic light-weight Hadoop job analyzer, and a prediction module using locally weighted regression methods. Our framework makes some theoretical cost models more practical, and also well fits for the diversification of the jobs and clusters. It can also help those users who want to predict the cost when applying for an on-demand cloud service. At the end, we do some experiments to verify our framework.
Wavelet neural network for 2D object classification

In this paper, a wavelet neural network (WNN)-based approach for invariant 2D object classification is proposed. The method employs the WNN characterizing the singularities of the object curvature representation and performing the classification at the same time and in an automatic way. The discriminative time-frequency attributes of the singularities on the object boundary are firstly captured by the continuous wavelet transform (CWT) and then stored by the WNN as its initial scale-translation parameters. These parameters are trained to the optimum status during the learning stage. Thus, only a few convolutions at the optimum scale-translation grids are involved during the classification, which makes our method suitable for real-time recognition tasks. Compared with the artificial neural network (ANN)-based approach preceded by a wavelet filter bank with fixed scale-translation parameters as well as the traditional methods like Fourier descriptors and moment invariants, our scheme demonstrates the best discrimination performance under various noisy and affine conditions.
COMPETING THROUGH NEW PRODUCT DEVELOPMENT SUPPORTED BY KNOWLEDGE GAINED FROM INVESTIGATIONS ON SUCCESS FACTORS

The paper discusses the management application of revealed and/or benchmarked success factors of new product development. It couples success factors and recent management concepts on competitiveness. Information related to the reflected knowledge is gained from literature relevant to product innovation management and marketing. Management aspects are emphasized especially through product innovation management models and processes, as well as cost and time saving endeavor with reference on opportunities of the network organization. Marketing aspects are highlighted through focusing on marketing intelligence support to new product development and launch.
Conceptual graph based text classification

Most traditional Wikipedia based methods use only article content information. By organizing Wikipedia articles as a graph, multi-information such as category and structure information can be utilized in our method. In this paper, we propose a novel method to do classification by using knowledge from a conceptual graph which is built from Wikipedia. First, we build a conceptual graph from Wikipedia. Each article is considered as a concept node. Titles, hyperlinks, texts and category information are used as edges to measure the relationship between those concepts. Each text is mapped to its respective set of nodes and Personalized PageRank (random walk) is then used to generate a set of most important node which can represent the text best. Finally the two sets are scored with a measure of vector similarity. We evaluate our techniques on the standard text classification dataset (20newsgroup), the results show the effectiveness of the proposed approach.
Cooperation and Collective Enforcement in Networked Societies

We endogenize social network formation and collective enforcement using a model in which players interact bilaterally and repeatedly along costly links. Cooperation is supported by the threat of collective punishments that spread through the network. Optimal networks are attainable in equilibrium. When the society is homogeneous, the optimal network consists of many separate cliques. Introducing heterogeneous match quality gives rise to more realistic ”small worlds” networks, with connectedness, small distances, and high clustering. (This abstract was borrowed from another version of this item.)
Lack of response to all-trans retinoic acid supplementation in adult dogs following left pneumonectomy.

We showed previously that removing 55-58% of the lung by right pneumonectomy (R-PNX) in adult dogs triggers compensatory growth of the remaining lung, but removing 42-45% of the lung by left PNX (L-PNX) does not. We also showed that, following R-PNX, supplemental all-trans retinoic acid (RA) selectively enhances alveolar capillary endothelial cell volume (Yan X, Bellotto DJ, Foster DJ, Johnson RL, Jr., Hagler HH, Estrera AS, and Hsia CC. J Appl Physiol 96: 1080-1089, 2004). We hypothesized that RA supplementation might enhance compensation following L-PNX and tested this hypothesis by administering RA (2 mg.kg(-1). day(-1), 4 days/wk) or placebo orally to litter-matched adult foxhounds for 4 mo following L-PNX. Resting lung function was measured under anesthesia. Air and tissue volumes of the remaining lung were assessed by high-resolution computed tomography scan and by detailed postmortem morphometric analysis of the fixed lung. There was no significant difference in resting lung function, lung volume, alveolar structure, or septal ultrastructure between RA and placebo treatment groups. We conclude that RA supplementation does not induce post-PNX compensatory lung growth in the absence of existing cellular growth activities initiated by other primary signals.
Urinary Metabolites of Organophosphate and Pyrethroid Pesticides and Behavioral Problems in Canadian Children

Background: Exposure to organophosphate pesticides has been associated with neurobehavioral deficits in children, although data on low levels of exposure experienced by the general population are sparse. Pyrethroids are insecticides rapidly gaining popularity, and epidemiological evidence on their potential effects is lacking. Objective: We examined the association between exposure to organophosphate and pyrethroid pesticides, indicated by urinary metabolites, and parentally reported behavioral problems in children. Methods: We used data on children 6–11 years of age from the Canadian Health Measures Survey (2007–2009). We used logistic regressions to estimate odds ratios (ORs) for high scores on the Strengths and Difficulties Questionnaire (SDQ), which may indicate behavioral problems, in association with concentrations of pyrethroid and organophosphate metabolites in the urine of 779 children, adjusting for covariates (sex, age, race/ethnicity, income, parental education, blood lead levels, maternal smoking during pregnancy, and others). Results: At least one urinary metabolite for organophosphates was detected in 91% of children, and for pyrethroids in 97% of children. Organophosphate metabolites were not significantly associated with high SDQ scores. The pyrethroid metabolite cis-DCCA [3-(2,2-dichlorovinyl)-2,2-dimethylycyclopropane carboxylic acid] was significantly associated with high scores for total difficulties on the SDQ (OR for a 10-fold increase = 2.0; 95% CI: 1.1, 3.6), and there was a nonsignificant association with trans-DCCA (OR = 1.6; 95% CI: 0.9, 3.0). Conclusion: In contrast with previous studies, we did not observe an association between exposure to organophosphate pesticides and behavioral scores in children. However, some pyrethroid urinary metabolites were associated with a high level of parent-reported behavioral problems. Longitudinal studies should be conducted on the potential risks of pyrethroids. Citation: Oulhote Y, Bouchard MF. 2013. Urinary metabolites of organophosphate and pyrethroid pesticides and behavioral problems in Canadian children. Environ Health Perspect 121:1378–1384; http://dx.doi. org/10.1289/ehp.1306667
Expressive Recommender Systems through Normalized Nonnegative Models

We introduce normalized nonnegative models (NNM) for explorative data analysis. NNMs are partial convexifications of models from probability theory. We demonstrate their value at the example of item recommendation. We show that NNM-based recommender systems satisfy three criteria that all recommender systems should ideally satisfy: high predictive power, computational tractability, and expressive representations of users and items. Expressive user and item representations are important in practice to succinctly summarize the pool of customers and the pool of items. In NNMs, user representations are expressive because each user's preference can be regarded as normalized mixture of preferences of stereotypical users. The interpretability of item and user representations allow us to arrange properties of items (e.g., genres of movies or topics of documents) or users (e.g., personality traits) hierarchically.
Sawtooth Software

Introduction: The MaxDiff System is software for obtaining preference/importance scores for multiple items (brand preferences, brand images, product features, advertising claims, etc.) using marketing or social survey research. MaxDiff is a component within Sawtooth Software's SSI Web platform for Web-and CAPI-based interviewing. It may be used designing, fielding, and analyzing:  Method of Paired Comparisons (MPC) experiments (choices from pairs)  Choices from subsets of three items, four items, etc.  MaxDiff (best-worst scaling) experiments Projects may be conducted over the Internet, using computers not connected to the internet (CAPI interviewing), or via paper-and-pencil questionnaires. The Method of Paired Comparisons (MPC) is a very old and well-established approach for eliciting tradeoffs among paired items, dating back at least to the early 1900s (Thurstone 1927), with one author (David 1969) quoting a source as early as the 1800s (Fechner 1860). One can extend the theory of choices from pairs to choices among larger sets (three items, four items, etc.). MaxDiff (Maximum Difference scaling) is a successful, relatively new method for scaling multiple items (Louviere 1991, Finn and Louviere 1992). It may be thought of as a more sophisticated extension of MPC. Our MaxDiff software makes it easy for researchers with only minimal exposure to statistics or advanced statistical methods to conduct sophisticated research involving the scaling of multiple items. For completeness of exposition, this document assumes that the reader has some background in statistics and multivariate methods such as hierarchical Bayes and Latent Class. If you are not familiar with these topics, don't despair. The software manual is much less technical than our explanation here. The trade-off techniques used in the MaxDiff System are very robust and easy to apply (for example, much easier to use than the related conjoint analysis). The resulting item scores are also easy to interpret. You do not need to have any formal training in statistics to use our MaxDiff System well and to achieve good results.
Development of tight junctions de novo in the mouse early embryo: control of assembly of the tight junction-specific protein, ZO-1

Tight junction development during trophectoderm biogenesis in the mouse preimplantation embryo has been examined using monoclonal antibodies recognizing the tight junction-specific peripheral membrane protein, ZO- 1. In immunoblots, mouse embryo ZO-1 had a molecular mass (225 kD) equivalent to that in mouse liver, was barely detectable in four-cell embryos although later stages exhibited increasing levels. ZO-1 was first detected immunocytochemically at the compacting eight-cell stage, coincident with or just after the expression of basolateral cell adhesion and apical microvillous polarity. Initially, ZO-1 was present as a series of spots along the boundary between free and apposed cell surfaces in intact embryos or cell couplets, but subsequently staining became more linear with blastocyst trophectoderm cells being bordered by a continuous ZO-1 belt. Inhibition of cell adhesion at the 8-cell stage delayed ZO-1 appearance and randomized its surface distribution in a reversible manner. Microfilament disruption, but not microtubule depolymerization, produced major disturbances in ZO-1 distribution. ZO- 1 assembly de novo appeared to be independent of proximate DNA and RNA synthesis but was inhibited substantially in the absence of protein synthesis during the eight-cell stage, a treatment that did not prevent intercellular adhesion and polarization. ZO-1 surface assembly, but not adhesion and polarization, was also perturbed when single eight-cells were combined with single four-cells. The results suggest that tight junction development in mouse embryos is a secondary event in epithelial biogenesis, being dependent upon cell adhesion and cytoskeletal activity for normal expression, and can be disrupted without disturbing the generation of a stably polarized phenotype.
CRSN Symposium : Focus on Schizophrenia Symposium du CRSN : le point sur la schizophrénie Postmortem investigations of the pathophysiology of schizophrenia : the role of susceptibility genes

287 Despite robust evidence for the heritability of schizophrenia, postmortem studies have not traditionally linked cellular and molecular neuropathology with underlying genetic mechanisms in this disorder. The completion of the first draft of the Human Genome Project and the use of novel strategies in studying complex genetic disorders including schizophrenia have led to the identification of a growing list of schizophrenia susceptibility genes. In this review, we describe the strategy used to incorporate 2 potential schizophrenia susceptibility genes in the postmortem investigation of the pathophysiology of schizophrenia driven by 2 well-established hypotheses, the dopamine hypothesis and the neurodevelopmental hypothesis. The first gene codes for catechol-O-methyltransferase, an enzyme involved in catecholamine degradation, and the second gene codes for brain-derived neurotrophic factor, a growth factor implicated in cell survival, synaptogenesis and the development of cortical pyramidal neurons.
Overtaking Vehicle Detection Techniques based on Optical Flow and Convolutional Neural Network

As the rise of the intelligent vehicle applications in recent years, the development of onboard vision systems for advanced driving assistance has become a popular research topic. This paper presents a real-time system using a monocular camera mounted on the rear of a vehicle to perform overtaking detection for safe lane change operations. In this work, the possible overtaking vehicle is first located based on motion cues. The candidate is then identified using Convolutional Neural Network (CNN) and tracked for behavior analysis in a short period of time. We also propose an algorithm to solve the issue of repetitive patterns which is commonly appeared in the highway driving. A series of experiments are carried out with real scene video sequences recorded by a dashcam. The performance evaluation has demonstrated the effectiveness of the
Digital to analog conversion performed in single electron technology

Investigates SET devices from the logic design perspective, using the SET tunnel junction's ability to control the transport of individual electrons. We introduce a modified turnstile circuit with clock and enable control signals, which has built-in memory and reset functionality. Using the modified turnstile circuit as a building block, we propose a scheme for an n-input digital to analog converter. We demonstrate the scheme by implementing and simulating a 3-bit digital to analog converter.
An overview of segmentation techniques for handwritten connected digits

Machine recognition of handwritten numerals has practical significance. Segmentation of connected digits is being recognized as a critical task in the field of document image analysis and recognition. Higher recognition rates for isolated digits Vs those obtained for connected numeral strings exemplify the vital role of connected digits segmentation. The present overview describes various segmentation techniques for connected digits in general numeral strings and mathematical expressions, worked out in this decade.
IS Researcher Issues

This paper reports on an international study of the key issues facing Information Systems researchers. A variant of the Delphi method was used to identify and rank the issues. The results identified a consistent and unified group of issues facing most researchers surveyed, regardless of location or research orientation. The results suggest that a reliable and valid instrument is available to measure key issues facing IS researchers. Further work to broaden the relevance to all regions is suggested.
Memory mechanisms in health and disease: Mechanistic basis of memory

Introduction (KP Giese) Long-Term Potentiation and Memory (KP Giese) Structural Synaptic and Dendritic Spine Plasticity in the Hippocampus (MG Stewart & VI Popov) Memory Beyond Synaptic Plasticity: The Role of Intrinsic Neuronal Excitability (A Papoutsi, K Sidiropoulou & P Poirazi) Adult Hippocampal Neurogenesis and Memory (SSD Stone & PW Frankland) Memory Consolidation and Its Underlying Mechanisms (CM Alberini, D Bambah-Mukku & DY Chen) The Role of CREB and CREB Co-Activators in Memory Formation (MJ Sekeres, D Sargin, PJ Ross & SA Josselyn) Memory Reconsolidation and Extinction (S Kida) Sex Differences in Memory Mechanisms (K Mizuno) Development of Memory Circuits in Drosophila (F Hirth) Experience-Dependent Plasticity in Barrel Cortex (G Albieri, GT Finnerty) Memory Circuits in the Hippocampus (TJ McHugh) Cerebellar Memory Circuits (CH Yeo) Impaired Memory Mechanisms in Models of Alzheimer's Disease (M Ohno) Restoring Memory Deficits in Cognitive Diseases (A Fischer) The Impact of Studies of Memory Mechanisms for Psychiatry (S Zaman).
The Role of bZIP Transcription Factors in Green Plant Evolution: Adaptive Features Emerging from Four Founder Genes

Background Transcription factors of the basic leucine zipper (bZIP) family control important processes in all eukaryotes. In plants, bZIPs are regulators of many central developmental and physiological processes including photomorphogenesis, leaf and seed formation, energy homeostasis, and abiotic and biotic stress responses. Here we performed a comprehensive phylogenetic analysis of bZIP genes from algae, mosses, ferns, gymnosperms and angiosperms. Methodology/Principal Findings We identified 13 groups of bZIP homologues in angiosperms, three more than known before, that represent 34 Possible Groups of Orthologues (PoGOs). The 34 PoGOs may correspond to the complete set of ancestral angiosperm bZIP genes that participated in the diversification of flowering plants. Homologous genes dedicated to seed-related processes and ABA-mediated stress responses originated in the common ancestor of seed plants, and three groups of homologues emerged in the angiosperm lineage, of which one group plays a role in optimizing the use of energy. Conclusions/Significance Our data suggest that the ancestor of green plants possessed four bZIP genes functionally involved in oxidative stress and unfolded protein responses that are bZIP-mediated processes in all eukaryotes, but also in light-dependent regulations. The four founder genes amplified and diverged significantly, generating traits that benefited the colonization of new environments.