Miracle-dz/UniFAR-FTUs huggingface dataset:
Interval simulation: Raising the level of abstraction in architectural simulation

Detailed architectural simulators suffer from a long development cycle and extremely long evaluation times. This longstanding problem is further exacerbated in the multi-core processor era. Existing solutions address the simulation problem by either sampling the simulated instruction stream or by mapping the simulation models on FPGAs; these approaches achieve substantial simulation speedups while simulating performance in a cycle-accurate manner. This paper proposes interval simulation which takes a completely different approach: interval simulation raises the level of abstraction and replaces the core-level cycle-accurate simulation model by a mechanistic analytical model. The analytical model estimates core-level performance by analyzing intervals, or the timing between two miss events (branch mispredictions and TLB/cache misses); the miss events are determined through simulation of the memory hierarchy, cache coherence protocol, interconnection network and branch predictor. By raising the level of abstraction, interval simulation reduces both development time and evaluation time. Our experimental results using the SPEC CPU2000 and PARSEC benchmark suites and the M5 multi-core simulator, show good accuracy up to eight cores (average error of 4.6% and max error of 11% for the multi-threaded full-system workloads), while achieving a one order of magnitude simulation speedup compared to cycle-accurate simulation. Moreover, interval simulation is easy to implement: our implementation of the mechanistic analytical model incurs only one thousand lines of code. Its high accuracy, fast simulation speed and ease-of-use make interval simulation a useful complement to the architect's toolbox for exploring system-level and high-level micro-architecture trade-offs.
Scalable analysis of variable software

The advent of variability management and generator technology enables users to derive individual variants from a variable code base based on a selection of desired configuration options. This approach gives rise to the generation of possibly billions of variants that, however, cannot be efficiently analyzed for errors with classic analysis techniques. To address this issue, researchers and practitioners usually apply sampling heuristics. While sampling reduces the analysis effort significantly, the information obtained is necessarily incomplete and it is unknown whether sampling heuristics scale to billions of variants. Recently, researchers have begun to develop variability-aware analyses that analyze the variable code base directly exploiting the similarities among individual variants to reduce analysis effort. However, while being promising, so far, variability-aware analyses have been applied mostly only to small academic systems. To learn about the mutual strengths and weaknesses of variability-aware and sampling-based analyses of software systems, we compared the two strategies by means of two concrete analysis implementations (type checking and liveness analysis), applied them to three subject systems: Busybox, the x86 Linux kernel, and OpenSSL. Our key finding is that variability-aware analysis outperforms most sampling heuristics with respect to analysis time while preserving completeness.
Applying soft computing approaches to river level forecasting

Abstract This paper assesses one of many potential enhancements to conventional flood forecasting that can be achieved through the use of soft computing technologies. A methodology is outlined in which the forecasting data set is split into subsets before training with a series of neural networks. These networks are then recombined via a rule-based fuzzy logic model that has been optimized using a genetic algorithm. The methodology is demonstrated using historical time series data from the Ouse River catchment in northern England. The model forecasts are assessed on global performance statistics and on a more specific flood-related evaluation measure, and they are compared to benchmarks from a statistical model and naive predictions. The overall results indicate that this methodology may provide a well performing, low-cost solution, which may be readily integrated into existing operational flood forecasting and warning systems.
A Chandra X-ray survey of nearby dwarf starburst galaxies: II. Starburst properties and outflows

We present a comprehensive comparison of the X-ray properties of a sample of eight dwarf starburst galaxies observed with Chandra (IZw18, VIIZw403, NGC1569, NGC3077, NGC4214, NGC4449, NGC5253, He2–10). In PaperI we presented in detail the data reduction and analysis of the individual galaxies. For the unresolved X-ray sources we find the following: point sources are in general located close to bright H ii regions, rims of superbubbles, or young stellar clusters. The number of X-ray point sources appears to be a function of the current star formation rate and the blue luminosity of the hosts. Ultraluminous X-ray sources are only found in those dwarf galaxies which are currently interacting. The power law index of the combined cumulative X-ray point source luminosity function is α = 0.24 ± 0.06, shallower than that of more massive starburst galaxies (α = 0.4 − 0.8) and of non-starburst galaxies (α ∼ 1.2). For those galaxies showing extended X-ray emission (6 out of the 8 galaxies), we derive the following: Superwinds develop along the steepest gradient of the H i distribution with volume densities of 0.02 −0.06cm 3 , pressures of 1 −3 ×10 5 Kcm 3 , thermal energies of 2 − 30 × 10 54 erg, and hot gas masses of 2 − 20 × 10 6 M⊙ (∼ 1 per cent of the H i masses. On global scales, the distribution of the X-ray emission looks remarkably similar to that seen in Hα (comparing azimuthal averages); locally however their distribution is clearly distinct in many cases – this can be explained by the different emission mechanisms (forward vs. reverse shocks). Mass-loading of order 1 to 5 is required to explain the differences between the amount of hot gas and and the modelled mass-loss from massive stars. The metallicity of the dwarf galaxies correlates with the diffuse X-ray luminosity and anti-correlates with the cooling time of the hot gas. The diffuse X-ray luminosity is also a function of the current star formation rate. The mechanical luminosities of the developing superwinds are energetic enough to overcome the gravitational potentials of their host galaxies. This scenario is supported by the overpressures of the hot gas compared to the ambient ISM. Extended H i envelopes such as tidal tails, however, may delay outflows on timescales exceeding those of the cooling time of the hot gas.
A comprehensive set of plasmids for vanillate- and xylose-inducible gene expression in Caulobacter crescentus

Caulobacter crescentus is widely used as a powerful model system for the study of prokaryotic cell biology and development. Analysis of this organism is complicated by a limited selection of tools for genetic manipulation and inducible gene expression. This study reports the identification and functional characterization of a vanillate-regulated promoter (Pvan) which meets all requirements for application as a multi-purpose expression system in Caulobacter, thus complementing the established xylose-inducible system (Pxyl). Furthermore, we introduce a newly constructed set of integrating and replicating shuttle vectors that considerably facilitate cell biological and physiological studies in Caulobacter. Based on different narrow and broad-host range replicons, they offer a wide choice of promoters, resistance genes, and fusion partners for the construction of fluorescently or affinity-tagged proteins. Since many of these constructs are also suitable for use in other bacteria, this work provides a comprehensive collection of tools that will enrich many areas of microbiological research.
IMPACT OF CORPORATE GOVERNANCE ON BANKING SECTOR PERFORMANCE IN NIGERIA

The research study considered the impact of corporate governance on the performance of banks in Nigeria. The increased incidence of bank failure in the recent period generated the current debate on transparency and disclosure of financial information to the various users, as a means of appraising good governance in banks. This study made use of both primary and secondary data in ensuring that data obtained are sufficient for a reasonable conclusion. The secondary data obtained from the annual financial statement of the banks for a period of five accounting year was used in analyzing the financial ratios for the study. 158 questionnaires were retrieved from respondents out of the 200 questionnaires distributed. The primary data was analyzed through the chi-square analysis method. The study concludes that corporate governance significantly contributes to positive performance in the banking sector. It therefore recommends that corporate governance codes should be adapted to meet the need of Nigerian business environment.
Contextually appropriate reference generation

We describe a system for contextually appropriate anaphor and pronoun generation for Turkish. It uses binding theory and centering theory to model local and nonlocal references. We describe the rules for Turkish, and their computational treatment. A cascaded method for anaphor and pronoun generation is proposed for handling pro-drop and discourse constraints on pronominalization. The system has been tested as a stand-alone nominal expression generator, and also as a reference planning component of a transfer-based MT system.
From Boom 'til Bust: How Loss Aversion Affects Asset Prices

In 1996 Alan Greenspan warned that stock prices were "unduly escalated" and reflected "irrational exuberance". In this paper we describe an economy that can support a prolonged surge of asset prices, accompanied by a sharp increase of volatility. We study an equilibrium model where some agents are risk averse while others have loss averse preferences over wealth, according to prospect theory. We derive closed-form solutions for the equilibrium prices. In good states of the world, the loss averse investors with wealth above the threshold are momentum traders, thereby pushing prices far above the level in the benchmark economy. In moderately bad states of the world, the loss averse investors are contrarian, and equilibrium prices are kept relatively high and stable. Finally in extremely bad states, the loss averse investors are forced to retreat from the stock market in order to avoid bankruptcy, resulting in a sharp price drop.
The effect of sequence context on spontaneous Polζ-dependent mutagenesis in Saccharomyces cerevisiae

The Polζ translesion synthesis (TLS) DNA polymerase is responsible for over 50% of spontaneous mutagenesis and virtually all damage-induced mutagenesis in yeast. We previously demonstrated that reversion of the lys2ΔA746 −1 frameshift allele detects a novel type of +1 frameshift that is accompanied by one or more base substitutions and depends completely on the activity of Polζ. These ‘complex’ frameshifts accumulate at two discrete hotspots (HS1 and HS2) in the absence of nucleotide excision repair, and accumulate at a third location (HS3) in the additional absence of the translesion polymerase Polη. The current study investigates the sequence requirements for accumulation of Polζ-dependent complex frameshifts at these hotspots. We observed that transposing 13 bp of identity from HS1 or HS3 to a new location within LYS2 was sufficient to recapitulate these hotspots. In addition, altering the sequence immediately upstream of HS2 had no effect on the activity of the hotspot. These data support a model in which misincorporation opposite a lesion precedes and facilitates the selected slippage event. Finally, analysis of nonsense mutation revertants indicates that Polζ can simultaneously introduce multiple base substitutions in the absence of an accompanying frameshift event.
Dark nuclei. I. Cosmology and indirect detection

In a companion paper Detmold et al. [Phys. Rev. D 90, 114506 (2014)]PRVDAQ1550-799810.1103/PhysRevD.90. 114506, lattice field theory methods are used to show that in two-color, two-flavor QCD there are stable nuclear states in the spectrum. As a commonly studied theory of composite dark matter, this motivates the consideration of possible nuclear physics in this and other composite dark sectors. In this work, early Universe cosmology and indirect detection signatures are explored for both symmetric and asymmetric dark matter, highlighting the unique features that arise from considerations of dark nuclei and associated dark nuclear processes. The present day dark matter abundance may be composed of dark nucleons and/or dark nuclei, where the latter are generated through dark nucleosynthesis. For symmetric dark matter, indirect detection signatures are possible from annihilation, dark nucleosynthesis, and dark nuclear capture and we present a novel explanation of the Galactic center gamma ray excess based on the latter. For asymmetric dark matter, dark nucleosynthesis may alter the capture of dark matter in stars, allowing for captured particles to be processed into nuclei and ejected from the star through dark nucleosynthesis in the core. Notably, dark nucleosynthesis realizes a novel mechanism for indirect detection signals of asymmetric dark matter from regions such as the Galactic center, without having to rely on a symmetric dark matter component.
Operational Calculus for Differentiable Programming

In this work we present a theoretical model for differentiable programming. We construct an algebraic language that encapsulates formal semantics of differentiable programs by way of Operational Calculus. The algebraic nature of Operational Calculus can alter the properties of the programs that are expressed within the language and transform them into their solutions. In our model programs are elements of programming spaces and viewed as maps from the virtual memory space to itself. Virtual memory space is an algebra of programs, an algebraic data structure one can calculate with. We define the operator of differentiation ($\partial$) on programming spaces and, using its powers, implement the general shift operator and the operator of program composition. We provide the formula for the expansion of a differentiable program into an infinite tensor series in terms of the powers of $\partial$. We express the operator of program composition in terms of the generalized shift operator and $\partial$, which implements a differentiable composition in the language. Such operators serve as abstractions over the tensor series algebra, as main actors in our language. We demonstrate our models usefulness in differentiable programming by using it to analyse iterators, deriving fractional iterations and their iterating velocities, and explicitly solve the special case of ReduceSum.
Adaptive Learning in Financial Markets

We investigate adaptive or evolutionary learning in a repeated version of the Grossman and Stiglitz (1980) model. We demonstrate that any process that is a monotonic selection dynamic will converge to the rational expectations asset demands if the proportion of informed traders is fixed. We also show that these learning processes have a unique asymptotically stable fixed point at the Grossman-Stiglitz (GS) equilibrium. The robustness of learning to noisy experimentation is studied using Binmore and Samuelson's (1999) deterministic drift approximation. Conditions on economic and learning process parameters for adaptive learning to lead to the GS rational expectations equilibrium are presented. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.
Predictors of multiple concurrent and multiple sexual partnerships among male and female youth aged 18–24 in South Africa

This study aimed to determine predictors of multiple concurrent and multiple sexual partnerships among South African male and female youth. Data were from a cross-sectional population-based household survey of 3 123 youths aged 18 to 24 years from four South African provinces (males = 53.6%, blacks = 97.8%). Results from a sub-sample of being sexually active in the past 12 months (n = 1787) indicate that 5.3% (7.1% in males and 3.1% in females) youth participants had multiple concurrent sexual partners, and 38.8% (50.1% in males and 25.8% in females) had had two or more sexual partners in the past 12 months. Among females, multiple concurrent partnerships were only predicted by lower social network resources. Predictors of multiple sexual partnerships (two or more sexual partners in the past 12 months) among males included high HIV risk perception, never been tested for HIV and hazardous or harmful alcohol use. By contrast, among females, high peer pressure, lower education and ever having taken drugs predicted involvement with multiple sexual partners. Multi-level health promotion interventions are indicated for sexual behaviour change among South African youth.
Sociomathematical norms and the teacher's mathematical belief: A case study from a Korean in-service elementary teacher

Background:The purpose of the present study is to analyze how the sociomathematical norm, or the process of promoting the understanding of mathematical concepts and principles through the interaction between teacher and students, is established in relation to the teacher’s mathematical beliefs in a fourth-grade mathematics class.Material and methods:13 sessions of Teacher K’s elementary mathematics class for a period of one year were observed and monitored by recording, and the students’ lesson activities sheets were collected after each class. Both the students and K were interviewed. The sociomathematical norm was modified and complemented, and coded into five components.Results:Teacher K highlighted both the view of ‘Mathematics as formal,’ which argues that mathematics requires logical thinking, and the view of ‘Mathematics as procedural,’ which claims that a mathematics problem allows for multiple solutions.Conclusions:A teacher’s mathematical belief is reflected in his or her decision making at every moment of a math class, and greatly influences teaching and learning, specifically, the instructions, objectives, content and methods involved in their classroom environment.
No . 630 Firms , Destinations , and Aggregate Fluctuations

This paper uses a database covering the universe of French firms for the period 1990– 2007 to provide a forensic account of the role of individual firms in generating aggregate fluctuations. We set up a simple multi-sector model of heterogeneous firms selling to multiple markets to motivate a theoretically-founded set of estimating equations that decompose firms’ annual sales growth rate into different components. We find that the firm-specific component contributes substantially to aggregate sales volatility, mattering about as much as the components capturing shocks that are common across firms within a sector or country. We then decompose the firm-specific component to provide evidence on two mechanisms that generate aggregate fluctuations from microeconomic shocks highlighted in the recent literature: (i) when the firm size distribution is fat-tailed, idiosyncratic shocks to large firms contribute to aggregate fluctuations (the “granularity” hypothesis of Gabaix, 2011), and (ii) sizable aggregate volatility can arise from idiosyncratic shocks due to input-output linkages across the economy (Acemoglu et al., 2012). We find that firm linkages are approximately twice as important as granularity in driving aggregate fluctuations. JEL Classifications: E32, F12, F41
An evolutionary argument for unconscious personal death unawareness

Abstract There is strong experimental evidence that reminders of personal death influence individuals’ attitudes and behaviours. However, there is less support for the typically underlying hypothesis of ‘death denial’, which holds that terror resulting from the awareness of personal mortality is addressed through a variety of psychological and cultural defences. This paper suggests it is evolutionarily more plausible that humans are unconsciously unaware of their own deaths, which would mitigate death-related anxiety without the need for costly and complex defences. While some aspects of the argument are necessarily speculative, problems with the death denial model, as well as relevant aspects of literature on consciousness and self-awareness, positive illusions and self-deception, support the possibility of unconscious death unawareness. The cognitive requirements of human calculated reciprocity also suggest a potentially adaptive function for personal death unawareness, as well as a maladaptive effect related to human tendencies to enter into reciprocal contracts involving afterlife rewards.
Multi-objective clustering ensemble for high-dimensional data based on Strength Pareto Evolutionary Algorithm (SPEA-II)

Clustering is one of the fundamental data analysis techniques, which aims to find distinct groups of similar objects and discovers hidden structures in data. A recent clustering approach, clustering ensembles tries to derive an improved clustering solution based on previously generated different candidate clustering solutions. Clustering ensembles have two steps: generating multiple candidate clustering solutions from the data and forming a final clustering solution from previously generated candidate clustering solutions. A problem of the first step is the text representation, where word frequencies are often used as features. Other semantic information of the text such as topics, hypertext, etc are ignored. The problem for the second step is that the current popular median partition approach selects one clustering solution from previously generated candidate clustering solutions. A common clustering ensemble approach uses word frequencies as features to represent text data (documents). However, documents usually contain semantically rich information i.e. words, hypertext, titles, topics etc. The cluster ensemble approach ignores the semantic information of the documents and hence is prone to produce futile groupings of the documents. In this research work, we present a new multi-objective clustering ensemble method based on Strength Pareto Evolutionary Algorithm (SPEA-II). Our method utilizes the semantic information (rich features) to address the first problem of clustering ensembles. The cluster oriented evolutionary approach which derives the final clustering solution by selecting better quality clusters is in the second step of our method to address the second problem. The results show that our new method provides better results than other clustering ensemble methods.