Miracle-dz/UniFAR-FTUs huggingface dataset:
SO(32) spinors of type I and other solitons on brane-antibrane pair

We construct the SO(32) spinor state in weakly coupled type I string theory as a kink solution of the tachyon field on the D-string - anti-D-string pair and calculate its mass. We also give a description of this system in terms of an exact boundary conformal field theory and show that in this description this state can be regarded as a non-supersymmetric D0-brane in type I string theory. This construction can be generalised to represent the D0-brane in type IIA string theory as a vortex solution of the tachyon field on the membrane anti-membrane pair, and the D-string of type I string theory as a topological soliton of the tachyon field on the D5-brane anti- D5-brane pair.
Can Price Sway Children Preference? Preliminary Experiment among Malay Children.

This study deals with young Malay children’s behavior concerning money. The objectives are to determine the children knowledge of money value, source of their money knowledge and whether price can sway their preference. The literature on Children Socialization and Information Processing Theory in consumer behavior suggests that most young children under 8 are not cognitively skilled about pricing and to use price in decision making. An interview has been conducted to gain the children insight about their money value and the source of money knowledge. In order to test for the effects of price, an experiment has been conducted to determine their preference. The study has found that children didn’t have strong knowledge of money value and pricing of products not dependably influence their preference. In order to conclude for a better generalization, this preliminary finding could be used as a initial point for a larger scale of research in term of sample size, range of aged group and ethnicity.
Dietary assessment and estimation of intakedensitiesMichael

The U.S. government has conducted nationwide food consumption surveys since 1936. Information obtained from these surveys is used to design food assistance programs, guide food and nutrition policy, and monitor the dietary status of the population. The distribution of usual intakes of a nutrient in the population is of interest to policy makers. Here, usual intake is deened as the long-run average intake of a nutrient by an individual. Usual intakes are not observable in practice. Instead, we observe daily intakes for a sample of individuals and a small number of days, and assume that observed intakes measure usual intakes with error. The distributions of observed intakes, however, are typically very skewed, and the day-today variability in intakes tends to be large relative to the between-individual variance, and can be heterogeneous across individuals (Nusser, Carriquiry, Dodd, and Fuller, 1996). In this paper, we present a Bayesian approach to estimating the distribution of usual intakes of a nutrient in a population. Starting with a sample of dietary intakes, we model a function to map the intakes into the normal scale. This function combines a power transformation and a cubic spline (constrained to be monotonic) with unknown number and location of knots, and is estimated using reversible jump Markov chain Monte Carlo methods (Green, 1995). From each \draw" of the transformation function we obtain a transformed set of intakes which are approximately normally distributed. We then remove the day-today variability in daily intakes by tting a measurement error model to each set of transformed observations. Each set of estimated individual usual intakes is then mapped back to the original scale using the inverse of the transformation function. Posterior distributions of percentiles and other attributes of the density for each nutrient are estimated accounting for all major sources of uncertainty. We apply these methods to a subset of the 1994-1996 Continuing Survey of Food Intakes by Individuals (CSFII) collected by the USDA (USDA, 1997).
IBM Research Report A Simple Method for Sparse Signal Recovery from Noisy Observations Using Kalman Filtering. Embedding Approximate Quasi-Norms for Improved Accuracy

In a primary research report we have introduced a simple method for recovering sparse signals from a series of noisy observations using a Kalman filter (KF). The new method utilizes a so-called pseudomeasurement technique for optimizing the convex minimization problem following from the theory of compressed sensing (CS). The CS-embedded KF approach has shown promising results when applied for reconstruction of linear Gaussian sparse processes. This report presents an improved version of the KF algorithm for the recovery of sparse signals. In this work we substitute the l1 norm by an approximate quasi-norm lp, 0 ≤ p < 1. This modification, which better suits the original combinatorial problem, greatly improves the accuracy of the resulting KF algorithm. This, however, involves the implementation of an extended KF (EKF) stage for properly computing the state statistics. Sparse Signal Recovery Consider an R-valued random discrete-time process {xk}k=1 that is sparse in some known orthonormal sparsity basis ψ ∈ R, that is zk = ψ xk, |supp(zk)| << n (1) where supp(zk) denotes the support of zk. Assume that zk evolves according to zk+1 = Azk + wk (2) where A ∈ R and {wk}k=1 is a zero-mean white Gaussian sequence with covariance Qk. The process xk is measured using a sequence of noisy observations given by yk = Hxk + ζk = H zk + ζk (3) where {ζk} ∞ k=1 is a zero-mean white Gaussian sequence with covariance Rk, and H := H ψ ∈ R with m < n. Letting y := [y1, . , yk], our problem is defined as follows. We are interested in a y -measurable estimator x̂k such that the minimum mean square error (MMSE) E [
An improved model for image denoising

In this paper, we propose an improved model for image denoising, which combines the second-order ROF model and the fourth-order partial differential equations (PDE), and it is solved by the combination algorithm of the split Bregman method, an algebraic multigrid method and Krylov subspace acceleration. At the same time, we prove that the model is strictly convex and exists a unique global minimizer. We have also conducted a variety of numerical experiments to verify the efficiency of the model and combination algorithm. The results show that our model can reduce blocky effects and our algorithm is efficient and robust to solve the proposed model.
Improved word alignment in patent domain

This paper presents a new method for word alignment in patent domain which incorporates both generative and discriminative models. In this framework, the advantage of generative model that can learn large numbers of parameters from a sentence-aligned parallel corpus automatically in a unsupervised way can be kept, as well as get an improvement through discriminative models which can deploy various features in a supervised way. Even with only 300 word-aligned Chinese-English sentence pairs, incorporates with a 1M parallel Chinese-English patent sentences released by NTCIR9, experiments show that our method can get a promising performance.
Multi-niche analysis of dynamics and policies in Dutch renewable energy innovation journeys (1970–2006): hype-cycles, closed networks and technology-focused learning

This article analyses long-term innovation policies and development trajectories of four renewable energy technologies: wind energy, biomass, fuel cells and hydrogen, and photovoltaics. These trajectories and policies are characterised by many costly failures, setbacks, hype-disappointment cycles, tensions, and struggles. Although setbacks and non-linearities are a normal part of innovation journeys, a comparative analysis of four cases shows the recurrence of particular problems. Using Strategic Niche Management as analytical approach, we conclude that major problems exist with regard to learning processes (too much technology-push, focused on R&D), social networks (supply side oriented, narrow, closed) and expectations (hype-disappointment cycles, limited competence to assess promises).
Linking Task: Identifying Authors and Book Titles in Verbose Queries

In this paper, we present our contribution in INEX 2016 Social Book Search Track. This year, we participate in a new track called Mining track. This track focuses on detecting and linking book titles in online book discussion forums. We propose a supervised approach based on Support Vector Machine (SVM) classification process combined with Conditional Random Fields (CRF) to detect book titles. Then, we use a Levenshtein distance to link books to their unique book ID.
Mainstreaming disability in the United Nations treaty bodies

ABSTRACT As of the beginning of this century, the United Nations (UN) human rights system had comprehensively elided persons with disabilities from its purview. The Convention on the Rights of Persons with Disabilities (CRPD) responded to this lacuna in 2006. The CRPD obligates states parties to mainstream disability by protecting and promoting the human rights of persons with disabilities in all policies and programs and intersects disability with other discriminated-against populations. This article investigates the success of the UN in mainstreaming disability throughout its human rights treaty bodies over the period 2000–2014 by comparing the seven years before and the eight years after the CRPD's adoption for six core UN treaty bodies. In doing so, the article provides initial and unique insight into how well the UN implements human rights norms into treaty bodies and provides a template for future research on the inclusion of vulnerable group-based rights in the UN and beyond. Despite some significant variations between treaty bodies, we find an overall dramatic increase in the quantitative incidence of disability rights being referenced. Nevertheless, a closer look into the practices of two treaty bodies shows that the human rights of persons with disabilities, while noted by those bodies, are included fully only on occasion. For the UN to truly mainstream disability (or other) human rights, those rights must go beyond mere formal references and also be substantively integrated.
Long-chain bases, phosphatidic acid, MAPKs, and reactive oxygen species as nodal signal transducers in stress responses in Arabidopsis

Due to their sessile condition, plants have developed sensitive, fast, and effective ways to contend with environmental changes. These mechanisms operate as informational wires conforming extensive and intricate networks that are connected in several points. The responses are designed as pathways orchestrated by molecules that are transducers of protein and non-protein nature. Their chemical nature imposes selective features such as specificity, formation rate, and generation site to the informational routes. Enzymes such as mitogen-activated protein kinases and non-protein, smaller molecules, such as long-chain bases, phosphatidic acid, and reactive oxygen species are recurrent transducers in the pleiotropic responses to biotic and abiotic stresses in plants. In this review, we considered these four components as nodal points of converging signaling pathways that start from very diverse stimuli and evoke very different responses. These pleiotropic effects may be explained by the potentiality that every one of these four mediators can be expressed from different sources, cellular location, temporality, or magnitude. Here, we review recent advances in our understanding of the interplay of these four specific signaling components in Arabidopsis cells, with an emphasis on drought, cold and pathogen stresses.
An Empirical Study on Preference Distribution of Facebook Users

Online social networks connect users all around the world,, now is becoming a valuable crowdsourced data source for implementing intelligent people-centric services, e.g., personalized recommendations. Understanding users' characteristics, behaviors is critical for enabling such intelligent services. This paper studies users' preference distribution based on a huge Facebook dataset with 479,048 users, 11 preference types, 879,868 distinct preference items, 6,276,422 preference items. We observe that Music, TV, Movie are the most popular preference types on Facebook, users share more common items in TV, Movie, Sport, Music than the other preference types. We also find that the preference popularity distribution follows power law. In addition, we investigate the effects of users' location on the distribution of the popular preferences, conclude that the most popular preferences are very different among different countries. For instance, only one same Music ("Michael Jackson"), one same TV ("Family Guy"), no same Movie present as the top 10 preference items in all of US, Denmark, Germany.
Aggressive release consistency for software distributed shared memory

As a software-based distributed shared memory (DSM) system is especially sensitive to the traffic amount over the network, we propose a new software DSM model. The model postpones the enforcement of data coherence at the time of the first shared memory access after an acquire, instead of at the time of the acquire like the lazy release consistency (LRC) model. This leads to an aggressive implementation of release consistency and thus a reduced number of messages transferred over the network when compared with LRC. Our model is evaluated on the basis of the TreadMarks framework using three applications, where TreadMarks is a software DSM implementation following LRC. The experimental results on a network of workstations indicate that our model leads to fewer messages transmitted across the network than LRC, by over 16% for one application and over 12% for the other two.
Interweaving Objects, Gestures, and Talk in Context

In a large French hospital, a group of professional experts (including physicians and software engineers) are working on the computerization of a blood-transfusion traceability device. By focusing on a particular moment in this slow process of design, we analyze their collaborative practices during a work session. The analysis takes a praxeological and interactionist approach and is inspired by discussions on the role of artifacts in social practices currently developed within various research frameworks in this field: activity theory, distributed cognition, conversation analysis, and actor network theory. After a brief presentation of the place of objects and artifacts in these ways of approaching action and human cognition, we show how the collective activity analyzed here is generated by the interweaving of discursive, gestural, and artifactual resources.
Can P2P help the cloud go green?

The demand for cloud services is growing at a phenomenal rate, and so is the energy cost of the data centres powering those services. This is pressing cloud service providers to look for ways of reducing energy consumption. One approach is to utilize energy-efficient hardware and/or software in the data centres, the other approach is to relocate some services, e.g., personal files and data rendering, to end-host computers, a.k. a. peers. In the later approach, peers contribute their communication and computation resources to exchange data and provide services, while the data centre performs central administration and authentication, as well as backend processing. In this paper, we model the energy consumption for both approaches and then perform analytical studies. Our analysis shows that (1) making the data centre energy efficient can reduce the energy cost significantly; (2) the number of hops from the data centre to the peers and among peers directly influences the energy saving; (3) it is preferred to utilize peers that are already online for other purposes; (4) introducing content delivery network (CDN) servers and enabling proxy service on home modems are the keys to make a hybrid P2P-cloud network go green. We further verified these findings by simulation.
Evaluation of GeneXpert MTB/RIF for Diagnosis of Tuberculous Meningitis

ABSTRACT Tuberculous meningitis (TBM) is the most severe form of tuberculosis. Microbiological confirmation is rare, and treatment is often delayed, increasing mortality and morbidity. The GeneXpert MTB/RIF test was evaluated in a large cohort of patients with suspected tuberculous meningitis. Three hundred seventy-nine patients presenting with suspected tuberculous meningitis to the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam, between 17 April 2011 and 31 December 2012 were included in the study. Cerebrospinal fluid samples were tested by Ziehl-Neelsen smear, mycobacterial growth indicator tube (MGIT) culture, and Xpert MTB/RIF. Rifampin (RIF) resistance results by Xpert were confirmed by an MTBDR-Plus line probe assay and all positive cultures were tested by phenotypic MGIT drug susceptibility testing. Overall, 182/379 included patients (48.0%) were diagnosed with tuberculous meningitis. Sensitivities of Xpert, smear, and MGIT culture among patients diagnosed with TBM were 59.3% (108/182 [95% confidence interval {CI}, 51.8 to 66.5%]), 78.6% (143/182 [95% CI, 71.9 to 84.3%]) and 66.5% (121/182 [95% CI, 59.1 to 73.3%]), respectively. There was one false-positive Xpert MTB/RIF test (99.5% specificity). Four cases of RIF resistance (4/109; 3.7%) were identified by Xpert, of which 3 were confirmed to be multidrug-resistant (MDR) TBM and one was culture negative. Xpert MTB/RIF is a rapid and specific test for the diagnosis of tuberculous meningitis. The addition of a vortexing step to sample processing increased sensitivity for confirmed TBM by 20% (P = 0.04). Meticulous examination of a smear from a large volume of cerebrospinal fluid (CSF) remains the most sensitive technique but is not practical in most laboratories. The Xpert MTB/RIF represents a significant advance in the early diagnosis of this devastating condition.
IMPACT OF RAPID INCIDENT DETECTION ON FREEWAY ACCIDENT FATALITIES

Incident detection systems have the potential of reducing the time between the occurrence of accidents and the notification of emergency medical services, called the accident notification time. Reductions in this time, in turn, may affect the numbers of fatalities. A statistical analysis is conducted to determine the quantitative relationship between fatalities and the accident notification time on urban freeways. Using this relationship, the impact of freeway incident detection systems on fatalities is estimated. are also derived. The economic benefits of fatality reduction
Analysis of Sparse Representation and Blind Source Separation

In this letter, we analyze a two-stage cluster-then-l1-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l1 norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l1norm solution and the l0norm solution is also analyzed according to a probabilistic framework. If the obtained l1norm solution is sufficiently sparse, then it is equal to the l0norm solution with a high probability. Furthermore, the l1norm solution is robust to noise, but the l0norm solution is not, showing that the l1norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed
Correlation of End-Tidal Carbon Dioxide with Arterial Carbon Dioxide in Mechanically Ventilated Patients

Background: Patients undergone mechanical ventilation need rapid and reliable evaluation of their respiratory status. Monitoring of End-tidal carbon dioxide (ETCO2) as a surrogate, noninvasive measurement of arterial carbon dioxide (PaCO2) is one of the methods used for this purpose in intubated patients. Objectives: The aim of the present trial was to study the relationship between end-tidal CO2 tensions with PaCO2 measurements in mechanically ventilated patients. Materials and Methods: End-tidal carbon dioxide levels were recorded at the time of arterial blood gas sampling. Patients who were undergoing one of the mechanical ventilation methods such as: synchronized mandatory mechanical ventilation (SIMV), continuous positive airway pressure (CPAP) and T-Tube were enrolled in this study. The difference between ETCO2 and PaCO2 was tested with a paired t-test. The correlation of end-tidal carbon dioxide to (ETCO2) CO2 was obtained in all patients. Results: A total of 219 arterial blood gases were obtained from 87 patients (mean age, 71.7 ± 15.1 years). Statistical analysis demonstrated a good correlation between the mean of ETCO2 and PaCO2 in each of the modes of SIMV, CPAP and T-Tube; SIMV (42.5 ± 17.3 and 45.8 ± 17.1; r = 0.893, P < 0.0001), CPAP (37 ± 9.7 and 39.4 ± 10.1; r = 0.841, P < 0.0001) and T-Tube (36.1 ± 9.9 and 39.4 ± 11; r = 0.923, P < 0.0001), respectively. Conclusions: End-tidal CO2 measurement provides an accurate estimation of PaCO2 in mechanically ventilated patients. Its use may reduce the need for invasive monitoring and/or repeated arterial blood gas analyses.
A multiple electrode scheme for optimal non-invasive electrical stimulation

Transcranial electrical stimulation involves the delivery of weak electrical currents to the brain via scalp electrodes to elicit neuromodulatory effects. The current is conventionally passed through two large electrodes resulting in diffused electric fields. In this paper, we propose a novel paradigm in which multiple small electrodes with independent current controls are systematically optimized to yield targeted and effective stimulation under safety constraints. We employ the finite element method, in conjunction with a magnetic resonance imagery based model of the human head, to formulate a linear system relating the applied scalp current to the resulting electric field. Optimization techniques are then applied to derive stimulation parameters which maximize either intensity or focality at the target location. Results demonstrate that the optimal electrode configuration is strongly dependent on both the desired field orientation and the optimization criterion. The proposed scheme yields improvements of 98% in target intensity and 80% in focality compared to the conventional two-electrode montage. Additionally, the presented framework effectively optimizes electrode placement in the classical bipolar configuration, which is useful if only a single channel current source is available. Consequently, the proposed scheme promises to deliver increased efficacy and improved patient safety to clinical settings in which the target site is identified by a clinician.