Miracle-dz/UniFAR-FTUs huggingface dataset:
sucABandsucCDaremutuallyessential genes inEscherichia coli

sucAB and sucCD of Escherichia coli encode enzymes that generate succinyl-CoA from 2-oxoglutarate and succinate, respectively. Their mutual essentiality was studied. sucAB and sucCD could be deleted individually, but not simultaneously. The mutual essentiality of sucAB and sucCD was further confirmed by the conditional expression of sucABCD, sucAB, and sucCD under the control of a PBAD in E. coli MG1655, E. coli MG1655 (DsucCD), and E. coli MG1655 (DsucAB), respectively. These strains grew well in Luria–Bertani medium containing 0.1% arabinose, but not in the absence of arabinose unless the medium was supplemented with succinyl-CoA. Our results indicate that either sucAB or sucCD is enough to produce succinyl-CoA that is essential for cell viability.
Does a switch of budget regimes constrain managerial discretion? : evidence for Italian public enterprises' investment

This study examines the effect of the hardening of the budget constraint on the investment behaviour of Italian state owned enterprises (SOEs). It carries out a natural experiment that exploits the 1987 shift of budget regimes due to the pressure of European Union economic policies on the Italian government. Drawing from the theory of capital market imperfections, we apply the empirical framework for the analysis of investment-cash flow sensitivity to a panel of state-owned manufacturing firms during the period 1977-93.
Structure-based Generation System for E 2 E NLG Challenge

This paper describes the structure-based generation system (SBG System) for Endto-End Natural Language Generation (E2E NLG) Challenge. The input of SBG System is each meaning representation (MR) in E2E data, which is a new dataset for training end-to-end, data-driven natural language generation systems in the restaurant domain. The output of SBG System is the corresponding Natural Language (NL) Reference for each MR. We follow the traditional approach when building SBG System including two main sub-tasks. The first sub-task is sentence planning, in which we create the overall sentence structures and determine the appropriate structure for each input MR. The second subtask is a surface realization, in which we find the exact word forms and linearize the structure into a string. The generated NL references from development and experiment sets by SBG System are compared to a baseline as well as other high-score systems with both automatic and human evaluation. The evaluation results show that our method generates high-quality NL references and has a meaningful contribution to the NLG state-
Supernova neutrinos and nucleosynthesis

Observations of metal-poor stars indicate that at least two different nucleosynthesis sites contribute to the production of r-process elements. One site is responsible for the production of light r-process elements Z ? 50, while the other produces the heavy r-process elements. We have analyzed recent observations of metal-poor stars selecting only stars that are enriched in light r-process elements and poor in heavy r-process elements. We find a strong correlation between the observed abundances of the N = 50 elements (Sr, Y and Zr) and Fe. It suggest that neutrino-driven winds from core-collapse supernova are the main site for the production of these elements. We explore this possibility by performing nucleosynthesis calculations based on long-term Boltzmann neutrino transport simulations. We use an equation of state that reproduces recent constrains on the nuclear symmetry energy. We predict that the early ejecta is neutron rich with Ye ? 0.48, it becomes proton rich around 4? s and reaches Ye = 0.586 at 9? s when our simulation stops. The nucleosynthesis in this model produces elements between Zn and Mo, including 92Mo. The elemental abundances are consistent with the observations of the metal-poor star HD 12263. For the elements between Ge and Mo, we produce mainly the neutron-deficient isotopes. This prediction can be confirmed by observations of isotopic abundances in metal-poor stars. No elements heavier than Mo (Z = 42) and no heavy r-process elements are produced in our calculations.
Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview

Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed.
Amino Functionalisation of Carbon Nanotube Webs for Achieving High Structural Performance of Multifunctional Nano-enhanced Hierarchical Composites

Carbon nanotube (CNT) assemblies such as webs (CNTw) are functionalised in gas-phase reaction in order to chemically modify the CNT surface without disturbing its overall structure. It is proposed for the first time to amino functionalise CNT assemblies in gas-phase to promote bonding with epoxy matrices. Two different diamines are selected, aliphatic (ethylenediamine, EDA) and aromatic (1,5-diaminonaphtalene, DAN). The presence of the diamine molecules is confirmed by infrared spectroscopy and the amount incorporated is estimated by thermogravimetric analysis. Its influence on fracture toughness is studied using a Mode I interlaminar fracture toughness. The functionalisation of the CNTw with EDA results in an increase in GICof 20 % while the same procedure with DAN gives no improvement. This different observed behaviour is due to the different temperature required to activate the diamines to react with the epoxy rings of the epoxy system, as seen by differential scanning calorimetry.
Quantum Chemistry for Surface Segregation in Metal Alloys

Metal alloys are vital materials for the fabrication of high-flux, high-selectivity hydrogen separation membranes. A phenomenon that occurs in alloys that does not arise in pure metals is surface segregation, where the composition of the surface differs from the bulk composition. Little is known about the strength of surface segregation in the alloys usually considered for hydrogen membranes. Despite this lack of knowledge, surface segregation may play a decisive role in the ability of appropriately chosen alloys to be resistant to chemical poisoning, since membrane poisoning is controlled by surface chemistry. The aim of this Phase I project is to develop quantum chemistry approaches to assess surface segregation in a prototypical hydrogen membrane alloy, fcc Pd{sub 75}Cu{sub 25}. This alloy is known experimentally to have favorable surface properties as a poison resistant H{sub 2} purification membrane (Kamakoti et al., Science 307 (2005) 569-573), but previous efforts at modeling surfaces of this alloy have ignored the possible role of surface segregation (Alfonso et al., Surf. Sci. 546 (2003) 12-26).
Drugs and Violence in Afghanistan: A Panel Var With Unobserved Common Factor Analysis

This paper addresses the relationship between the level of violence and the opium market in Afghanistan’s provinces. We first provide an overview of the nature and extent of the Afghan drug trafficking. This is followed by a vector autoregressive analysis of the nexus opium-insurgency activities using monthly time-series data on opium prices and the number of security incidents for 15 Afghan provinces over the period 2004–2009. We use a multifactor error structure, the common correlated effect, to include unobservable common factors; Impulse Response functions to describe the time path of the dependent variables in response to shocks; and the mean group estimator to summarize our results across the provinces. Results suggest a conflict-induced reduction in opium prices, while the reverse opium-violence mechanism is mostly negligible. Moreover, unobservable common factors are the main drivers of opium prices and violence.
A Biosorption of Heavy Metal Ions from Aqueous Solutions Using Fish Scale (Catla catla)

In recent years, heavy metals pollution has become one of the most serious environmental problems. With rapid development of many industries, wastes containing heavy metals are directly or indirectly being discharged into the environment. Biosorption is a cost effective excellent tool for removing heavy metals from the wastewater aqueous solutions. In this study, freshwater fish scale, inexpensive biowaste offers potential for the removal of metal ions by biosorption.
An Australian/UK comparison of contemporary teaching and learning technologies

The last decade has delivered substantial changes in construction and property education in Australia and the UK. There has been an increase in the number of courses offered in built environment education and the profile of a typical student has changed. In both countries students are under pressure to balance study and work due to the higher costs of living and education. This has placed demands on providers to deliver teaching and learning which meets student, industry and professional needs. Simultaneously there has been an increase in the application of technology in the business and corporate world which has resulted in increased efficiencies and new challenges. This paper evaluates changes in construction and property education courses to embrace new technology. The focus is on the delivery of innovative teaching and learning materials and the interaction between students, staff and the community. Results from questionnaires from new and existing students at Deakin University and Nottingham Trent University were used alongside examples of teaching and learning as illustrative case studies, the emphasis is placed on pushing the boundaries of the conventional built environment education process. The findings show that by embracing technology there can be a „win-win‟ scenario for students, staff and industry stakeholders. Whilst courses adopt varying levels of technology, it seems inevitable that educators must evolve the delivery of education to become efficient and effective as the century progresses.
SECURITY CONSERVATION BASED COMMON EVALUATION OF MUTUAL INFORMATION IN CLOUD COMPUTING

Cloud technology is not only storage medium to store data but resources can also be shared across multiple users. But the integrity of information in cloud is subjected to doubtfulness due to the presence of hardware or software malfunctions and human faults. A number of mechanisms permit both data owners and public verifiers to effectively inspect cloud data correctness without downloading the whole information from the cloud server. The proposed paper introduces novel Token Based Ring System (TBRS) based Privacy-Preserving Technique which raises the security level of the information and supports hiding the identity privacy for group members and shared data stored in the cloud. We exploit enhanced ring signatures to preserve the identity of the signer. With this the identity of the signer on each block in shared data is kept private by utilizing tokens. We further extend the concept of ring signatures to support dynamic groups. Enhanced security and performance analysis prove that the proposed schemes are highly securable and efficient.
Asymptotics for penalized splines in generalized additive models

This paper discusses asymptotic theory for penalized spline estimators in generalized additive models. The purpose of this paper is to establish the asymptotic bias and variance as well as the asymptotic normality of the penalized spline estimators proposed by Marx and Eilers (1998). Furthermore, the asymptotics for the penalized quasi likelihood fit in mixed models are also discussed.
Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach

Multi-parameter evidence synthesis (MPES) is receiving growing attention from the epidemiological community as a coherent and flexible analytical framework to accommodate a disparate body of evidence available to inform disease incidence and prevalence estimation. MPES is the statistical methodology adopted by the Health Protection Agency in the UK for its annual national assessment of the HIV epidemic, and is acknowledged by the World Health Organization and UNAIDS as a valuable technique for the estimation of adult HIV prevalence from surveillance data. This paper describes the results of utilizing a Bayesian MPES approach to model HIV prevalence in the Netherlands at the end of 2007, using an array of field data from different study designs on various population risk subgroups and with a varying degree of regional coverage. Auxiliary data and expert opinion were additionally incorporated to resolve issues arising from biased, insufficient or inconsistent evidence. This case study offers a demonstration of the ability of MPES to naturally integrate and critically reconcile disparate and heterogeneous sources of evidence, while producing reliable estimates of HIV prevalence used to support public health decision-making.
QUANTILE ESTIMATION WITH ADAPTIVE IMPORTANCE SAMPLING

We introduce new quantile estimators with adaptive importance sampling. The adaptive estimators are based on weighted samples that are neither independent nor identically distributed. Using the law of iterated logarithm for martingales, we prove the convergence of the adaptive quantile estimators for general distributions with non-unique quantiles, thereby extending the work of Feldman and Tucker (1966). We illustrate the algorithm with an example from credit portfolio risk analysis.
Weakly decomposable regularization penalties and structured sparsity

It has been shown in literature that the Lasso estimator, or ℓ1‐penalized least squares estimator, enjoys good oracle properties. This paper examines which special properties of the ℓ1‐penalty allow for sharp oracle results, and then extends the situation to general norm‐based penalties that satisfy a weak decomposability condition.
On the modes of the Poisson distribution of order k

Sharp upper and lower bounds are established for the modes of the Poisson distribution of order k. The lower bound established in this paper is better than the previously established lower bound. In addition, for k = 2, 3, 4, 5, a recent conjecture is presently proved solving partially an open problem since 1983.
Optimal properties of centroid-based classifiers for very high-dimensional data

We show that scale-adjusted versions of the centroid-based classifier enjoys optimal properties when used to discriminate between two very high-dimensional populations where the principal differences are in location. The scale adjustment removes the tendency of scale differences to confound differences in means. Certain other distance-based methods, for example, those founded on nearest-neighbor distance, do not have optimal performance in the sense that we propose. Our results permit varying degrees of sparsity and signal strength to be treated, and require only mild conditions on dependence of vector components. Additionally, we permit the marginal distributions of vector components to vary extensively. In addition to providing theory we explore numerical properties of a centroid-based classifier, and show that these features reflect theoretical accounts of performance.
Exact sampling for intractable probability distributions via a Bernoulli factory

Many applications in the field of statistics require Markov chain Monte Carlo methods. Determining appropriate starting values and run lengths can be both analytically and empirically challenging. A desire to overcome these problems has led to the development of exact, or perfect, sampling algorithms which convert a Markov chain into an algorithm that produces i.i. d. samples from the stationary distribution. Unfortunately, very few of these algorithms have been developed for the distributions that arise in statistical applications, which typically have uncountable support. Here we study an exact sampling algorithm using a geometrically ergodic Markov chain on a general state space. Our work provides a significant reduction to the number of input draws necessary for the Bernoulli factory, which enables exact sampling via a rejection sampling approach. We illustrate the algorithm on a univariate Metropolis-Hastings sampler and a bivariate Gibbs sampler, which provide a proof of concept and insight into hyper-parameter selection. Finally, we illustrate the algorithm on a Bayesian version of the one-way random effects model with data from a styrene exposure study.
An updated comparison of model ensemble and observed temperature trends in the tropical troposphere

A debate exists over whether tropical troposphere temperature trends in climate models are inconsistent with observations (Karl et al. 2006, IPCC (2007), Douglass et al 2007, Santer et al 2008). Most recently, Santer et al (2008, herein S08) asserted that the Douglass et al statistical methodology was flawed and that a correct methodology showed there is no statistically significant difference between the model ensemble mean trend and either RSS or UAH satellite observations. However this result was based on data ending in 1999. Using data up to the end of 2007 (as available to S08) or to the end of 2008 and applying exactly the same methodology as S08 results in a statistically significant difference between the ensemble mean trend and UAH observations and approaching statistical significance for the RSS T2 data. The claim by S08 to have achieved a "partial resolution" of the discrepancy between observations and the model ensemble mean trend is unwarranted.
Percentile rank scores are congruous indicators of relative performance, or aren't they?

Percentile ranks and the I3 indicator were introduced by Bornmann, Leydesdorff, Mutz and Opthof. These two notions are based on the concept of percentiles (or quantiles) for discrete data. As several definitions for these notions exist we propose one that we think is suitable in this context. Next we show that if the notion of relative congruous indicators is carefully defined then percentile rank scores are congruous indicators of relative performance. The I3 indicator is a strictly congruous indicator of absolute performance.
ROBUST EMPIRICAL MEAN ESTIMATORS

We study robust estimators of the mean of a probability measure P, called robust empirical mean estimators. This elementary construction is then used to revisit a problem of aggregation and a problem of estimator selection, extending these methods to not necessarily bounded collections of previous estimators. We consider then the problem of robust M-estimation. We propose a slightly more complicated construction to handle this problem and, as examples of applications, we apply our general approach to least- squares density estimation, to density estimation with Kullback loss and to a non-Gaussian, unbounded, random design and heteroscedas- tic regression problem. Finally, we show that our strategy can be used when the data are only assumed to be mixing.
Stochastic partial differential equation based modelling of large space–time data sets

Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that the solution of a stochastic advection–diffusion partial differential equation provides a flexible model class for spatiotemporal processes which is computationally feasible also for large data sets. The Gaussian process defined through the stochastic partial differential equation has, in general, a non‐separable covariance structure. Its parameters can be physically interpreted as explicitly modelling phenomena such as transport and diffusion that occur in many natural processes in diverse fields ranging from environmental sciences to ecology. To obtain computationally efficient statistical algorithms, we use spectral methods to solve the stochastic partial differential equation. This has the advantage that approximation errors do not accumulate over time, and that in the spectral space the computational cost grows linearly with the dimension, the total computational cost of Bayesian or frequentist inference being dominated by the fast Fourier transform. The model proposed is applied to post‐processing of precipitation forecasts from a numerical weather prediction model for northern Switzerland. In contrast with the raw forecasts from the numerical model, the post‐processed forecasts are calibrated and quantify prediction uncertainty. Moreover, they outperform the raw forecasts, in the sense that they have a lower mean absolute error.
Functional learning through kernels

This paper reviews the functional aspects of statistical learning theory. The main point under consideration is the nature of the hypothesis set when no prior information is available but data. Within this framework we first discuss about the hypothesis set: it is a vectorial space, it is a set of pointwise defined functions, and the evaluation functional on this set is a continuous mapping. Based on these principles an original theory is developed generalizing the notion of reproduction kernel Hilbert space to non hilbertian sets. Then it is shown that the hypothesis set of any learning machine has to be a generalized reproducing set. Therefore, thanks to a general “representer theorem”, the solution of the learning problem is still a linear combination of a kernel. Furthermore, a way to design these kernels is given. To illustrate this framework some examples of such reproducing sets and kernels are given.
Estimation and inference for high-dimensional non-sparse models

To successfully work on variable selection, sparse model structure has become a basic assumption for all existing methods. However, this assumption is questionable as it is hard to hold in most of cases and none of existing methods may provide consistent estimation and accurate model prediction in nons-parse scenarios. In this paper, we propose semiparametric re-modeling and inference when the linear regression model under study is possibly non-sparse. After an initial working model is selected by a method such as the Dantzig selector adopted in this paper, we re-construct a globally unbiased semiparametric model by use of suitable instrumental variables and nonparametric adjustment. The newly defined model is identifiable, and the estimator of parameter vector is asymptotically normal. The consistency, together with the re-built model, promotes model prediction. This method naturally works when the model is indeed sparse and thus is of robustness against non-sparseness in certain sense. Simulation studies show that the new approach has, particularly when $p$ is much larger than $n$, significant improvement of estimation and prediction accuracies over the Gaussian Dantzig selector and other classical methods. Even when the model under study is sparse, our method is also comparable to the existing methods designed for sparse models.