Miracle-dz/UniFAR-FTUs huggingface dataset:
The University of Melbourne in the Million Query Track of TREC 2007

The University of Melbourne submitted runs from four system s to the TREC 2007 Million Query track, as summarized in Table 1. The umelbstd system used the Zettair information retrieval engine ( http://seg.rmit. edu.au/zettair), with the default similarity measure, which is based on a la nguage model with Dirichlet priors. Neither stemming nor stopping was employ ed. The Zettair engine uses a dynamic query pruning technique to reduce query time, as descr ib d in Lester et al. [2005]. For the umelbexp runs, each query was first posed to a commercial search engine , with the domain for the query restricted to the . gov domain. The query was then expanded with the non-stop words contained in the first five snippets, and the expanded query wa s submitted to the same system as umelbstd. The umelbimp system used the impact-based system described by Anh and Mof fat [2005]. This approach is based on the vector space model, where each d istinct term in the document collection is categorised as a dimension. An object (docume nt or query) is represented as an impact vector in this space, where the projection of the obje ct in each dimension is called the impact of the corresponding term in this object and is defined as an integral number valued between
Option Valuation in Multivariate SABR Models

We consider the joint dynamic of a basket of n-assets where each asset itself follows a SABR stochastic volatility model. Using the Markovian Projection methodology we approximate a univariate displaced diffusion SABR dynamic for the basket to price caps and floors in closed form. This enables us to consider not only the asset correlation but also the skew, the cross-skew and the decorrelation in our approximation. The latter is not possible in alternative approximations to price e.g. spread options. We illustrate the method by considering the example where the underlyings are two constant maturity swap (CMS) rates. Here we examine the influence of the swaption volatility cube on CMS spread options and compare our approximation formulae to results obtained by Monte Carlo simulation and a copula approach.
Smart home on smart phone

Mobile phone with high accessibility and usability is regarded as the ideal interface for the users to monitor and control the approaching smart home environment. Moreover, networking technologies and protocols have been advanced enough to support a universal monitoring and controlling interface on smart phones. This paper presents HouseGenie, an interactive, direct manipulation application on mobile, which supports a range of basic home monitoring and controlling functionalities as a replacement of individual remotes of smart home appliances. HouseGenie also addresses several common requirements that may be behind the vision, such as scenario, short-delay alarm, area restriction and so on. We demonstrate that HouseGenie not only provides intuitive presentations and interactions for smart home management, but also improves user experience comparing to present solutions.
Filtered-s Lyapunov algorithm for active control of nonlinear noise processes

This paper presents a filtered-s Lyapunov algorithm for nonlinear active noise control (NANC) using the filter blank implementation based on functional link artificial neural network (FLANN) filter. First, a Lyapunov function of the tracking error is defined, and adaptive FLANN filter coefficients are then adaptive adjusted based Lyapunov stability theory so that the error converges to zero asymptotically. Analysis of theory and simulation results both show that it has fast error convergence and lower stable state error properties. Moreover, its stability is guaranteed by Lyapunov stability theory.
Statistical Model Checking Approach for Stationary Dependability Verication

We propose to perform statistical probabilistic model checking by using perfect simulation in order to verify steady-state and unbounded until formulas over Markov chains. The model checking of probabilistic models by statistical methods has received increased attention in the last years since it provides an interesting alternative to numerical model checking which is poorly scalable with the increasing model size. In the previous statistical model checking works, the unbounded until formulas can not be efficiently verified and the steady-state formulas have not been considered, due to the burn-in time problem to detect the steady-state. Perfect simulation by coupling in the past is an extension of Markov chain Monte Carlo MCMC method that allows us to obtain the samples according to the steady-state distribution of the underlying Markov chain and thus it avoids the burn-in time problem to detect the steady-state. Therefore we propose to verify unbounded until and steady-state dependability properties for large Markov chains by combining perfect simulation and statistical hypothesis testing.
The Effect of Disease Modifying Therapies on Brain Atrophy in Patients with Relapsing-Remitting Multiple Sclerosis: A Systematic Review and Meta-Analysis

Background The aim of the present meta-analysis was to evaluate the effect of disease-modifying drugs (DMD) on brain atrophy in patients with relapsing-remitting multiple sclerosis (RRMS) using available randomized-controlled trial (RCT) data. Methods We conducted a systematic review and meta-analysis according to PRISMA guidelines of all available RCTs of patients with RRMS that reported data on brain volume measurements during the study period. Results We identified 4 eligible studies, including a total of 1819 RRMS patients (71% women, mean age 36.5 years, mean baseline EDSS-score: 2.4). The mean percentage change in brain volume was found to be significantly lower in DMD versus placebo subgroup (standardized mean difference: -0.19; 95%CI: -0.27–-0.11; p<0.001). We detected no evidence of heterogeneity between estimates (I2 = 30%, p = 0.19) nor publication bias in the Funnel plots. Sensitivity analyses stratifying studies according to brain atrophy neuroimaging protocol disclosed no evidence of heterogeneity (p = 0.16). In meta-regression analyses, the percentage change in brain volume was found to be inversely related with duration of observation period in both DMD (meta-regression slope = -0.03; 95% CI: -0.04–-0.02; p<0.001) and placebo subgroups (meta-regression slope = -0.05; 95% CI: -0.06–-0.04; p<0.001). However, the rate of percentage brain volume loss over time was greater in placebo than in DMD subgroup (p = 0.017, ANCOVA). Conclusions DMD appear to be effective in attenuating brain atrophy in comparison to placebo and their benefit in delaying the rate of brain volume loss increases linearly with longer treatment duration.
Reduction of Common-Mode Voltages for Five-Level Active NPC Inverters by the Space-Vector Modulation Technique

In this paper, a novel space-vector pulse-width modulation (PWM) scheme for reducing the common-mode voltage (CMV) in the three-phase five-level active neutral-point clamped (5L-ANPC) inverter is proposed, where only the 55 selected voltage vectors among the entire 125 that produce low values of the CMV are utilized. This PWM scheme can significantly reduce the CMV without the increase of switching losses and total harmonic distortion of the output voltages. With the 55 selected voltage vectors, the inverter can still operate up to the maximum modulation index. In addition, the capacitor voltages are controlled by redundant switches of the 5L-ANPC inverters. It has been shown that the peak value of the CMV is decreased to one-twelfth of the dc-link voltage from the simulation and experimental results.
Introducing a SAS® macro for doubly robust estimation

Estimation of the effect of a treatment or exposure with a causal interpretation from studies where exposure is not randomized may be biased if confounding and selection bias are not taken into appropriate account. Such adjustment for confounding is often carried out through regression modeling of the relationships among treatment, confounders, and outcome. Correct specification of the regression model is one of the most fundamental assumptions in statistical analysis. Even when all relevant confounders have been measured, an unbiased estimate of the treatment effect will be obtained only if the model itself reflects the true relationship among treatment, confounders, and the outcome. Outside of simulation studies, we can never know whether or not the model we have constructed includes all relevant confounders and accurately depicts those relationships. Doubly robust estimation of the effect of exposure on outcome combines inverse probability weighting by a propensity score with regression modeling in such a way that as long as either the propensity score model is correctly specified or the regression model is correctly specified the effect of the exposure on the outcome will be correctly estimated, assuming that there are no unmeasured confounders. While several authors have shown doubly-robust estimators to be powerful tools for modeling, they are not in common usage yet in part because they are difficult to implement. We have developed a simple SAS® macro for obtaining doubly robust estimates. We will present sample code and results from analyses of simulated data.
Ethical, legal, and social implications of genomic medicine.

It is commonly believed that the use of an individual patient's genetic information should be held to a slightly different legal standard than the use of general medical information. This article in the Genomic Medicine series provides examples of appropriate and inappropriate uses of genetic information. For physicians, the biggest dilemma is whether to inform the relatives of patients who have received a diagnosis of a heritable condition for which there is treatment and who are reluctant to share this information with their families.
Janus -- Analytics-Driven Transition Planner

In this paper, we address the problem of transition of IT operations from one service provider to another. We present analytics-driven solutions to generate a transition plan while addressing various aspects such as coverage, risk, time, and cost. We model the IT operations through graphs and use the well defined problems in graph theory to build solutions for transition planner. We demonstrate the proof-of-concept of proposed ideas using a real-world case-study.
Shaggy, the Homolog of Glycogen Synthase Kinase 3, Controls Neuromuscular Junction Growth in Drosophila

A protein-trap screen using the Drosophila neuromuscular junction (NMJ) as a model synapse was performed to identify genes that control synaptic structure or plasticity. We found that Shaggy (Sgg), the Drosophila homolog of the mammalian glycogen synthase kinases 3 α and β, two serine-threonine kinases, was concentrated at this synapse. Using various combinations of mutant alleles of shaggy, we found that Shaggy negatively controlled the NMJ growth. Moreover, tissue-specific expression of a dominant-negative Sgg indicated that this kinase is required in the motoneuron, but not in the muscle, to control NMJ growth. Finally, we show that Sgg controlled the microtubule cytoskeleton dynamics in the motoneuron and that Futsch, a microtubule-associated protein, was required for Shaggy function on synaptic growth.
Supply Response of Cocoa in Ghana: A Farm-Level Profit Function Analysis.

Available estimates of cocoa supply response in Ghana are derived from aggregate time series data. The present study presents results of a cross-section profit function analysis, based on farm-level data. The results are compared with those of previous time series studies, and the methodologies of both types of study are compared and evaluated. Copyright 1998 by Oxford University Press.
For Better or For Worse? Valenced Comparative Frames and Regulatory Focus

Three experiments examine how prevention-focused and promotion-focused consumers evaluate the comparison brand and what information they anchor on in direct comparative ads framed positively or negatively. Negative (vs. positive) frames lead prevention-focused respondents to exhibit higher evaluations for the advertised brand and lower evaluations for the comparison brand. Under promotion focus, positive (vs. negative) frames lead to more favorable attitudes toward the advertised brand with no difference in attitudes for the comparison brand. Preference for consistency is posited as a possible process explanation. We also find an evaluation order effect: prevention-focused (promotion-focused) individuals evaluate the comparison (advertised) brand first. (c) 2007 by JOURNAL OF CONSUMER RESEARCH, Inc.
Cloud Microphysical Properties Retrieved from Rapid-Sample AERI Data

The Atmospheric Emitted Radiance Interferometer (AERI) measures downwelling infrared radiance from 550-3000 cm (18.2 – 3.3 μm) at high-spectral-resolution (~ 1 cm) (Knuteson et al. 2004). The original temporal sampling strategy (a 3-min average of sky radiance every 8 min) was chosen to optimize the signal-to-noise in the observations for clear sky applications. Clear-sky AERI observations have led to marked improvements in both the foreign-broadened and self-broadened water vapor continuum absorption models (Tobin et al. 1999 and Turner et al. 2004a, respectively), in the Atmospheric Radiation Measurement Program’s (ARM) water vapor observations (Revercomb et al. 2003, Turner et al. 2003), and in the retrieval of water vapor and temperature profiles from groundbased radiance observations (Feltz et al. 2003).
Immunosuppression via adenosine receptor activation by adenosine monophosphate released from apoptotic cells

Apoptosis is coupled with recruitment of macrophages for engulfment of dead cells, and with compensatory proliferation of neighboring cells. Yet, this death process is silent, and it does not cause inflammation. The molecular mechanisms underlying anti-inflammatory nature of the apoptotic process remains poorly understood. In this study, we found that the culture supernatant of apoptotic cells activated the macrophages to express anti-inflammatory genes such as Nr4a and Thbs1. A high level of AMP accumulated in the apoptotic cell supernatant in a Pannexin1-dependent manner. A nucleotidase inhibitor and A2a adenosine receptor antagonist inhibited the apoptotic supernatant-induced gene expression, suggesting AMP was metabolized to adenosine by an ecto-5’-nucleotidase expressed on macrophages, to activate the macrophage A2a adenosine receptor. Intraperitoneal injection of zymosan into Adora2a- or Panx1-deficient mice produced high, sustained levels of inflammatory mediators in the peritoneal lavage. These results indicated that AMP from apoptotic cells suppresses inflammation as a ‘calm down’ signal. DOI: http://dx.doi. org/10.7554/eLife.02172. 001
Bounds of optimal learning

Learning is considered as a dynamic process described by a trajectory on a statistical manifold, and a topology is introduced defining trajectories continuous in information. The analysis generalises the application of Orlicz spaces in non-parametric information geometry to topological function spaces with asymmetric gauge functions (e.g. quasi-metric spaces defined in terms of KL divergence). Optimality conditions are formulated for dynamical constraints, and two main results are outlined: 1) Parametrisation of optimal learning trajectories from empirical constraints using generalised characteristic potentials; 2) A gradient theorem for the potentials defining optimal utility and information bounds of a learning system. These results not only generalise some known relations of statistical mechanics and variational methods in information theory, but also can be used for optimisation of the exploration-exploitation balance in online learning systems.
Poster : System thinking of the Software Vulnerability Market via Complex Network Theory

Uncovering the patterns of the software vulnerabilities can be helpful for the policy making to remove or reduce the effect of software vulnerabilities. In order to understand the emerging pattern in the software vulnerabilities, taking advantage of the complex network theory, this paper considers the software vulnerability market as a dynamic complex system and then proposes a heterogeneous network model to represent its evolution over time. The preliminary empirical result uncovers the universal scale-free property, which inspires the further complex network study for the software vulnerability market. Keywords— Software Vulnerability Market, Complex Network, System Thinking, Heterogeneous Network Model, Scale-free
Modulation of systemic and mucosal immunity against an inactivated vaccine of Newcastle disease virus by oral co-administration of live attenuated Salmonella enterica serovar Typhimurium expressing chicken interleukin-18 and interferon-α

Newcastle disease (ND) is a highly contagious disease of chickens causing significant economic losses worldwide. Due to limitations in the efficacy against currently circulating ND viruses, existing vaccination strategies require improvements, and incorporating immunomodulatory cytokines with existing vaccines might be a novel approach. Here, we investigated the systemic and mucosal immunomodulatory properties of oral co-administration of chicken interleukin-18 (chIL-18) and chicken interferon-α (chIFN-α) using attenuated Salmonella enterica serovar Typhimurium on an inactivated ND vaccine. Our results demonstrate that oral administration of S. enterica serovar Typhimurium expressing chIL-18 or chIFN-α provided enhanced systemic and mucosal immune responses, as determined by serum hemagglutination inhibition antibody and NDV Ag-specific IgG as well as NDV Ag-specific IgA in lung and duodenal lavages of chickens immunized with inactivated ND vaccine via the intramuscular or intranasal route. Notably, combined oral administration of S. enterica serovar Typhimurium expressing chIL-18 and chIFN-α significantly enhanced systemic and mucosal immunity in ND-vaccinated chickens, compared to single administration of S. enterica serovar Typhimurium expressing chIL-18 or chIFN-α. In addition, oral co-administration of S. enterica serovar Typhimurium expressing chIL-18 and chIFN-α provided enhanced NDV Ag-specific proliferation of peripheral blood mononuclear cells and Th1-biased cell-mediated immunity, compared to single administration of either construct. Therefore, our results provide valuable insight into the modulation of systemic and mucosal immunity by incorporation of immunomodulatory chIL-18 and chIFN-α using Salmonella vaccines into existing ND vaccines.
Retinogeniculate Axons Undergo Eye-Specific Segregation in the Absence of Eye-Specific Layers

Spontaneous retinal activity mediated by cholinergic transmission regulates the segregation of retinal ganglion cell axons in the lateral geniculate nucleus of the thalamus into eye-specific layers. The details of how the layers form are unknown. Mice lacking the β2 subunit of the neuronal nicotinic acetylcholine receptor lack ACh-mediated waves and as a result, do not form eye-specific layers at any stage of development. However, during the second postnatal week, β2−/− mice have glutamate-mediated waves. Here we show that after the first postnatal week, even in the absence of layers, retinothalamic axons segregate into an unlayered, patchy distribution of eye-specific regions. These results indicate that spontaneous neural activity may independently regulate eye-specific segregation and the formation of layers at the developing retinothalamic projection.
Logics for Emerging Applications of Databases Contents List of Contributors Query Answering in Inconsistent Databases

In this chapter, we summarize the research on querying inconsistent databases we have been conducting over the last five years. The formal framework we have used is based on two concepts: repair and consistent query answer. We describe different approaches to the issue of computing consistent query answers: query transformation, logic programming, inference in annotated logics, and specialized algorithms. We also characterize the computational complexity of this problem. Finally, we discuss related research in artificial intelligence, databases, and logic programming.
Scalable Hierarchical Multitask Learning in Sequence

Multitask learning methods investigate the challenge of combining information from several related problem domains. For a large family of multitask problems, relationships between tasks can be described by a hierarchical structure. This is particularly the case for many problems in Computational Biology, where different tasks correspond to different organisms, whose relationship to each other is defined by phylogeny. In this work we describe a set of algorithms that combine large-scale classification methods with ideas from Domain Adaptation and Multitask Learning. We introduce several methods of exploiting hierarchical task relations for Multitask Learning. These algorithms are designed for large-scale applications and scale to problems with a great number of training examples. The performance of the presented methods is demonstrated on synthetic data, as well as on splice-site data arising from a problem in genomic sequence analysis.
Heuristically Expanding Knowledge-Based Neural Networks

Knowledge-based neural networks are networks whose topology is determined by mapping the dependencies of a domain-speciic rulebase into a neural network. However, existing network training methods lack the ability to add new rules to the (reformulated) rulebases. Thus, on domain theories that are lacking rules, generalization is poor, and training can corrupt the original rules, even those that were initially correct. We present TopGen, an extension to the Kbann algorithm, that heuristically searches for possible expansions of a knowledge-based neural network, guided by the domain theory, the network, and the training data. It does this by dynamically adding hidden nodes to the neural representation of the domain theory, in a manner analogous to adding rules and con-juncts to the symbolic rulebase. Experiments indicate that our method is able to heuristi-cally nd eeective places to add nodes to the knowledge-base network and verify that new nodes must be added in an intelligent manner.
Investigation of a Pregnancy Lifestyle Intervention Using Mediation Analysis and a Power Analysis Simulation

In this report, we attempt to assess how a behavioral intervention on pregnant women achieved its success with regard to specific weight gain and weight loss outcomes. The study established the significance of the intervention in limiting gestational weight gain and promoting the return to pre-pregnancy weight, but the way in which the intervention was successful has yet to be explored. We explain how mediation analysis can be used to investigate certain variables, and introduce the methods necessary for conducting such analysis. We proceed to apply these techniques to various behavior variables as well as perform a power analysis simulation to better understand the relationship between true mediated effect size, sample size, and power to detect the mediated effect.