Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of Candida albicans FAR1 in regulation of pheromone‐mediated mating, gene expression and cell cycle arrest

Candida albicans cells homozygous for the MTLa locus respond to α‐mating pheromone by activating gene expression and triggering morphological changes, and the cells undergo a pheromone‐mediated cell cycle arrest that can be influenced by nutritional conditions. Disruption of C. albicans ORF19.7105, which is the predicted homologue of the yeast FAR1 gene, blocks pheromone‐mediated morphological changes, prevents pheromone‐mediated cell cycle arrest, greatly compromises mating and also blocks pheromone‐triggered gene induction. C. albicans ORF19.7105 is not pheromone inducible, but overexpression of this gene from the actin promoter greatly enhances pheromone‐mediated cell cycle arrest, leading to clear halos in growth inhibition assays on a variety of solid media. Transcriptional profiling of the ORF19.7105‐overexpressing strain has established an enhanced responsiveness of pheromone‐induced genes. Thus the C. albicans FAR1 gene is a dosage‐dependent regulator of the pheromone response, and is critically required for all aspects of mating process of this pathogenic fungus.
Nanoelectromechanical Systems : Experiments and Modeling 1

Nanoelectromechanical systems (NEMSs) are systems with characteristic dimensions of a few nanometers. By exploiting nanoscale effects, NEMSs present interesting and unique characteristics, which deviate greatly from their predecessor microelectromechanical systems (MEMSs). For instance, NEMSbased devices can have fundamental frequencies in the microwave range (B100GH) (Rueckes et al. 2000); mechanical quality factors in the tens of thousands (ultralow energy dissipation); active mass in the femtogram range; force sensitivity at the attonewton level; mass sensitivity up to attogram (Ilic et al. 2000) and subattogram (Davis et al. 2000) levels; heat capacities far below a ‘‘yoctocalorie’’ (Roukes 1999); power consumption in the order of 10 aw (Roukes 2004); and extreme high integration level, approaching 10 elements per cm (Rueckes et al. 2000). All these distinguishing properties of NEMS devices pave the way to applications such as force sensors, chemical sensors, biological sensors, and ultrahigh frequency resonators. The interesting properties of the NEMS devices typically arise from the behavior of the active parts, which, in most cases, are in the forms of cantilevers or doubly clamped beams with dimensions at the nanometer scale. The materials for those active components include silicon, silicon carbide, carbon nanotubes, gold and platinum, to name a few. Silicon, the basic material employed in integrated circuit (IC) technology and MEMS, has been widely used to build NEMS. However, ultrasmall silicon-based NEMS nanoresonators failed to achieve the much anticipated high-quality factors due to the dominance of surface effects, such as surface oxidation and reconstruction, and thermoelastic damping. Limitations in strength and flexibility also compromise the performance of silicon-based NEMS actuators. Instead, carbon nanotubes appear to be ideal for NEMS given their nearly one-dimensional structures with high aspect ratio and nearly perfect-terminated surfaces and excellent electrical and mechanical properties. Due to significant advances in growth, manipulation, knowledge of electrical and mechanical properties, carbon nanotubes have become the most promising building blocks for the next generation of NEMS. Several carbon-nanotube-based functional NEMS devices have been reported so far (Kim and Lieber 1999, Rueckes et al. 2000, Akita et al. 2001, Fennimore et al. 2003, Kinaret et al. 2003, Ke and Espinosa 2004, Sazonova et al. 2004). Similarly to carbon nanotubes, nanowires are a type of onedimensional novel nanostructure well suited for building NEMS because of their size and controllable electrical properties. The purpose of this article is to provide a review of NEMS devices to date and to summarize the modeling currently being pursued to gain insight into their performance. This article is organized as follows: in the first part, we review carbon-nanotubeand -nanowire-based NEMS. In the second part, we present the multiphysics modeling of NEMS based on continuum theory.
Hybrid Chinese/English text detection in images and video frames

We propose a multiscale texture-based method using local energy analysis for hybrid Chinese/English text detection in images and video frames. Local energy analysis has been shown to work well in text detection, where remarkable local energy variations of pixels correspond to text region or boundary of other objects and lower local energy variations of pixels correspond to background or the interior of non-text objects. Local energy variation is calculated in a local region based on the wavelet transform coefficients of images. Hybrid Chinese/English text in images and video frames can be detected whether it is aligned horizontally or vertically. The font size of text to be detected may vary in a wide range of values. The proposed method has been tested on 321 frame images obtained from local TV programs and a tested dataset with low missed rate and false alarm rate.
Client-side mobile visual search

Visual search systems present a simple way to obtain information about our surroundings, our location or an object of interest. Typically, mobile applications of visual search remotely connect to large-scale systems capable of dealing with millions of images. Querying such systems may induce considerable delays, which can severeley harm usability or even lead to complete rejection by the user. In this paper, we investigate an interim solution and system design using a local visual search system for embedded devices. We optimized a traditional visual search system to decrease runtime and also storage space in order to scale to thousands of training images on current off-the-shelf smartphones. We demonstrate practical applicability in a prototype for mobile visual search on the same target platform. Compared with the unmodified version of the pipeline we achieve up to a two-fold speed-up in runtime, save 85% of storage space and provide substantially increased recognition performance. In addition, we integrate the pipeline with a popular Augmented Reality SDK on Android devices and use it as a pre-selector for tracking datasets. This allows to instantly use a large number of tracking targets without requiring user intervention or costly server-side recognition.
Comparison of Parametric Representation for Monosyllabic Word Recognition in Continuously Spoken Sentences

Haeb-Umbach et al (R. Haeb-Umbach, X. Aubert, P. Bey erlein, D. Klakow, M. Ullrich, A. Wendemuth, P. Wilcox, “Acoustic Modeling in the Philips Hub-4 Continu ous-Speech Recognition System,” DARPA Broadcast News, Transcription & Understanding Workshop, Feb. 1998.* Parrott (Parrott Systems, Inc., Internet web page “http:// www.Say-parrot.com/us/technology/algorithms/recogni tion/index.html.” Feb. 2000). * Padmanabhan et al (M. Padmanabhan, L.R. Bahl, D. Naha moo, M. Picheny, “Speaker Clustering and Transformation for Speaker Adaptation in Speech Recognition Systems', IEEE Transactions on Speech and Audio Processing, Jan. 1998). * Bahl et al., “Performance of the IBM Large Vocabulary Continuous Speech Recognition System on the ARPA Wall Street Journal Task, ICASSP-95, 1995. Davis et al., “Comparison of Parametric Representation for Monosyllabic Word Recognition in Continuously Spoken Sentences", IEEE Trans. on ASSP, vol. 28, pp. 357-366, 1980.
Spatial Reasoning About Robot Compliant Movements and Optimal Paths in Qualitatively Modeled Environments

This article describes a framework for qualitatively represent ing and reasoning about spatial configurations. The framework contains interval/region-based formalisms for describing dis tances, angles, and locations. These representations lead to the formulation of inference rules, as well as search-heuristics that enable the derivation of spatial relationships and paths among objects. The qualitative framework can be used in two principal ways: to formulate solutions to spatial configuration problems where exact geometric knowledge is not available, and to provide guidance for the application of quantitative configuration analysis and planning methods. The applications of the proposed framework are demonstrated through simula tion examples that involve inferring constrained instantaneous configurations as well as coupler curves for compliant mo tion planning and deriving collision-free paths for open-chain mechanisms moving among static obstacles.
Bearing Fault Diagnosis Based on Feature Weighted FCM Cluster Analysis

A new method of fault diagnosis based on feature weighted FCM is presented. Feature-weight assigned to a feature indicates the importance of the feature. This paper shows that an appropriate assignment of feature-weight can improve the performance of fuzzy c-means clustering. Feature evaluation based on class separability criterion is discussed in this paper. Experiment shows that the algorithm is able to reliably recognize not only different fault categories but also fault severities. Therefore, it is a promising approach to fault diagnosis of rotating machinery. KeywordsRolling bearing ; Fault diagnosis; Feature weighted fuzzy c-means ; Cluster analysis
Memory-based semantic role labeling: Optimizing features, algorithm, and output

In this paper we interpret the semantic role labeling problem as a classification task, and apply memory-based learning to it in an approach similar to Buchholz et al. (1999) and Buchholz (2002) for grammatical relation labeling. We apply feature selection and algorithm parameter optimization strategies to our learner. In addition, we investigate the effect of two innovations: (i) the use of sequences of classes as classification output, combined with a simple voting mechanism, and (ii) the use of iterative classifier stacking which takes as input the original features and a pattern of outputs of a first-stage classifier. Our claim is that both methods avoid errors in sequences of predictions typically made by simple classifiers that are unaware of their previous or subsequent decisions in a sequence.
A Clairvoyant Approach to Evaluating Software (In)Security

Nearly all modern software has security flaws---either known or unknown by the users. However, metrics for evaluating software security (or lack thereof) are noisy at best. Common evaluation methods include counting the past vulnerabilities of the program, or comparing the size of the Trusted Computing Base (TCB), measured in lines of code (LoC) or binary size. Other than deleting large swaths of code from project, it is difficult to assess whether a code change decreased the likelihood of a future security vulnerability. Developers need a practical, constructive way of evaluating security. This position paper argues that we actually have all the tools needed to design a better, empirical method of security evaluation. We discuss related work that estimates the severity and vulnerability of certain attack vectors based on code properties that can be determined via static analysis. This paper proposes a grand, unified model that can predict the risk and severity of vulnerabilities in a program. Our prediction model uses machine learning to correlate these code features of open-source applications with the history of vulnerabilities reported in the CVE (Common Vulnerabilities and Exposures) database. Based on this model, one can incorporate an analysis into the standard development cycle that predicts whether the code is becoming more or less prone to vulnerabilities.
Evaluation of Two Approaches for Breast Surface Measurement Applied to a Radar-Based Imaging System

Locating the surface of an object, in this case the breast, is an important first step in many imaging situations; this surface information may be a necessary part of the reconstruction, it may be needed for the cancellation of the surface reflection, or (as herein) it may be needed as a preparatory step before imaging. This paper presents two complementary approaches developed for the purpose of surface localization. The proposed approaches are evaluated using data from both phantom measurements and volunteer scans.
Prognostic factors in tongue cancer – relative importance of demographic, clinical and histopathological factors

The incidence of and mortality from squamous cell carcinoma (SCC) of the tongue have increased during the recent decades in the Western world. Much effort has been made to predict tumour behaviour, but we still lack specific prognostic indicators. The aim of our study was to evaluate the relative importance of the known demographic, clinical and histological factors in a homogeneous population-based group of patients with SCC of the mobile tongue. The demographic and clinical factors were reviewed retrospectively from primary and tertiary care patient files. Histological prognostic factors were determined from pre-treatment biopsies. The TNM stage was found to be the most important prognostic factor. In particular, local spread outside the tongue rather than spread to regional lymph nodes was related to poor prognosis. Several demographic and histopathological factors were closely related to TNM stage. When the cases were divided into stage I–II carcinomas and stage III–IV carcinomas, it appeared that the patient’s older age (> 65 years), a high malignancy score and an absence of overexpressed p53 protein were associated with a poorer prognosis in stage I–II carcinomas. Such cases may require more aggressive treatment. Among patients with stage III–IV carcinomas, heavy use of alcohol was significantly associated with a poor disease-specific survival time. © 2000 Cancer Research Campaign
Homogeneity Pursuit

This article explores the homogeneity of coefficients in high-dimensional regression, which extends the sparsity concept and is more general and suitable for many applications. Homogeneity arises when regression coefficients corresponding to neighboring geographical regions or a similar cluster of covariates are expected to be approximately the same. Sparsity corresponds to a special case of homogeneity with a large cluster of known atom zero. In this article, we propose a new method called clustering algorithm in regression via data-driven segmentation (CARDS) to explore homogeneity. New mathematics are provided on the gain that can be achieved by exploring homogeneity. Statistical properties of two versions of CARDS are analyzed. In particular, the asymptotic normality of our proposed CARDS estimator is established, which reveals better estimation accuracy for homogeneous parameters than that without homogeneity exploration. When our methods are combined with sparsity exploration, further efficiency can be achieved beyond the exploration of sparsity alone. This provides additional insights into the power of exploring low-dimensional structures in high-dimensional regression: homogeneity and sparsity. Our results also shed lights on the properties of the fused Lasso. The newly developed method is further illustrated by simulation studies and applications to real data. Supplementary materials for this article are available online.
Query Query Parser Query Optimization Query Rewriter Query Planner Logical Plan Space Physical Plan Space Cost Model Code Generator

Many applications require efficient management and querying of graph structured data. For example, Systems Biology studies metabolic pathways and gene regulation networks modeled as directed graphs. These graphs consist of tens of thousands of molecules and interactions between them. To get a better understanding of these networks biologists need to query the networks and extract information. In this paper we propose a framework for cost-based optimization of graph queries in relational database management systems. The result of graph queries is a set of subgraphs that is selected from the data graph based on conditions on nodes and paths posed in the query. We present the pathway query language as syntax to express graph queries. We show how to utilize techniques of classical cost-based query optimization to optimize graph queries. To handle graph specific predicates, such as the existence of paths, we propose new operators. In addition we describe two implementations of path operators in more detail and give an overview of future work.
DBSeer: Pain-free Database Administration through Workload Intelligence

The pressing need for achieving and maintaining high performance in database systems has made database administration one of the most stressful jobs in information technology. On the other hand, the increasing complexity of database systems has made qualified database administrators (DBAs) a scarce resource. DBAs are now responsible for an array of demanding tasks; they need to (i) provision and tune their database according to their application requirements, (ii) constantly monitor their database for any performance failures or slowdowns, (iii) diagnose the root cause of the performance problem in an accurate and timely fashion, and (iv) take prompt actions that can restore acceptable database performance.However, much of the research in the past years has focused on improving the raw performance of the database systems, rather than improving their manageability. Besides sophisticated consoles for monitoring performance and a few auto-tuning wizards, DBAs are not provided with any help other than their own many years of experience. Typically, their only resort is trial-and-error, which is a tedious, ad-hoc and often sub-optimal solution.In this demonstration, we present DBSeer, a workload intelligence framework that exploits advanced machine learning and causality techniques to aid DBAs in their various responsibilities. DBSeer analyzes large volumes of statistics and telemetry data collected from various log files to provide the DBA with a suite of rich functionalities including performance prediction, performance diagnosis, bottleneck explanation, workload insight, optimal admission control, and what-if analysis. In this demo, we showcase various features of DBSeer by predicting and analyzing the performance of a live database system. Will also reproduce a number of realistic performance problems in the system, and allow the audience to use DBSeer to quickly diagnose and resolve their root cause.
Trusted Measurement Model Based on Multitenant Behaviors

With a fast growing pervasive computing, especially cloud computing, the behaviour measurement is at the core and plays a vital role. A new behaviour measurement tailored for Multitenants in cloud computing is needed urgently to fundamentally establish trust relationship. Based on our previous research, we propose an improved trust relationship scheme which captures the world of cloud computing where multitenants share the same physical computing platform. Here, we first present the related work on multitenant behaviour; secondly, we give the scheme of behaviour measurement where decoupling of multitenants is taken into account; thirdly, we explicitly explain our decoupling algorithm for multitenants; fourthly, we introduce a new way of similarity calculation for deviation control, which fits the coupled multitenants under study well; lastly, we design the experiments to test our scheme.
Modeling and Predicting Emotion in Music

Human emotion responses to music are dynamic processes that evolve naturally over time in synchrony with the observed music signal. It is because of this dynamic nature that systems that seek to predict emotion in music must necessarily analyze such processes on short-time intervals, modeling not just the relationships between acoustic data and emotion parameters, but also how those relationships evolve over time. In this work, we discuss modeling such relationships using a conditional random field (CRF), a powerful graphical model that is trained to predict the conditional probability p(y|x) for a sequence of labels y given a sequence of features x. We train our graphical model on the emotional responses of individual annotators in an 11×11 quantized representation of the arousal-valence (A-V) space. Our model is fully connected and can produce estimates of the conditional probability for each A-V bin, allowing us to easily model complex emotion-space distributions (e.g. multimodal) as an A-V heatmap. In selecting acoustic features for music emotion recognition, we discuss the application of regressionbased deep belief networks (DBNs) to learn features directly from magnitude spectra. These features are specifically optimized for the prediction of emotion, and the trained models can potentially provide new insight into the relationships between music and emotion.
More Photos From Me to Thee: Factors Influencing the Intention to Continue Sharing Personal Photos on an Online Social Networking (OSN) Site among Young Adults in the Netherlands

ABSTRACT The current study investigated the factors influencing photo sharing continuance intention of Dutch Facebook users aged 18 to 25 years old. The focus was specifically on personal photos (those that include the person sharing), as their disclosure is expected to result in privacy-related issues for the individual sharing. An online survey with 473 respondents was implemented to test the various research hypotheses and to address the question on whether or not the impact of several disclosure predictors differs between men and women. Results of structural modeling analysis (multigroup analysis) reveal that for both male and female users, self-presentation-related and communication-related benefits and habit positively influence their photo sharing continuance intention. Furthermore, descriptive social norms and competence-based trust in Facebook significantly contribute to female users’ repeat photo sharing intention.
Effect on lightning electromagnetic fields from the return-stroke parameters

Choice of the return-stroke parameters has a great influence on the calculation results of lightning electromagnetic pulse(LEMP) fields. In this paper, LEMP fields under different return-stroke parameters are calculated based on the MTLL model in which the channel-base current is described by a pulse function. And effect of different return-stroke speeds and channel heights on LEMP fields of different field regions are also discussed. The results show that the lightning channel height between 1 kilometre and a few tens of kilometres has a little influence on the LEMP fields, but the return-stroke speed has a distinct influence on the LEMP fields.