Miracle-dz/UniFAR-FTUs huggingface dataset:
Incongruence between Verbal and Non-Verbal Information Enhances the Late Positive Potential

Smooth social communication consists of both verbal and non-verbal information. However, when presented with incongruence between verbal information and nonverbal information, the relationship between an individual judging trustworthiness in those who present the verbal-nonverbal incongruence and the brain activities observed during judgment for trustworthiness are not clear. In the present study, we attempted to identify the impact of incongruencies between verbal information and facial expression on the value of trustworthiness and brain activity using event-related potentials (ERP). Combinations of verbal information [positive/negative] and facial expressions [smile/angry] expressions were presented randomly on a computer screen to 17 healthy volunteers. The value of trustworthiness of the presented facial expression was evaluated by the amount of donation offered by the observer to the person depicted on the computer screen. In addition, the time required to judge the value of trustworthiness was recorded for each trial. Using electroencephalography, ERP were obtained by averaging the wave patterns recorded while the participants judged the value of trustworthiness. The amount of donation offered was significantly lower when the verbal information and facial expression were incongruent, particularly for [negative × smile]. The amplitude of the early posterior negativity (EPN) at the temporal lobe showed no significant difference between all conditions. However, the amplitude of the late positive potential (LPP) at the parietal electrodes for the incongruent condition [negative × smile] was higher than that for the congruent condition [positive × smile]. These results suggest that the LPP amplitude observed from the parietal cortex is involved in the processing of incongruence between verbal information and facial expression.
Specialization and extrapolation of software cost models

Despite the widespread availability of software effort estimation models (e.g. COCOMO [2], Price-S [12], SEER-SEM [13], SLIM [14]), most managers still estimate new projects by extrapolating from old projects [3, 5, 7]. In this delta method, the cost of the next project is the cost of the last project multiplied by some factors modeling the difference between old and new projects [2]. Delta estimation is simple, fast, and best of all, can take full advantage of local costing information. However delta estimation fails when the experience base (the old projects) can not be extrapolated to the new projects. Previously [10], we have shown that for a set of NASA projects, delta estimation would usually fail since most of the features and coefficients of the learned model vary wildly across sub-samples of the training data. In that prior work, no solution was offered for this problem.Here, we offer a solution and report the results of experiment with feature subset selection (FSS) and extrapolation. FSS methods are usually assessed via the mean change in model performance. However, as shown below, FSS can significantly reduce the variance as well. Hence, FSS should be routinely used in cost estimation.Our results should stop the trend in the effort modeling community of continually adding to the number of features in a model in order to improve estimation performance. Here we show that there are benefits in intelligently subtracting model features.
Introducing deeper nulls for time modulated linear symmetric antenna array using real coded genetic algorithm

A novel approach based on the real coded genetic algorithm (RGA) is proposed to improve nulling performance of time modulated linear antenna arrays. The RGA adjusts the excitation amplitude of each radiating element to place deep nulls in the desired direction. Various results are presented to show the advantages of this approach considering one and multiple desired nulls.
Bringing the School to the Children : Shortening the Path to EFA

Recent education planning initiatives in West and Central Africa show that the path to EFA may be shortened considerably by reconsidering the way basic education is delivered in isolated rural communities. Since independence, education systems have been expanding rapidly and are now serving most of the easy-to-reach population. For progress to continue, the focus must be shifted toward the sparsely populated areas, which means adjusting the type of schools used, and building them close to where children live. Most out-of-school children live in rural areas. Unfortunately, few rural schools offer the complete primary cycle. A number of factors contribute to the incomplete-cycle phenomenon. The most significant is that the potential student population is insufficient for a three- or six-teacher school. Having children walk to school from neighboring villages also contributes to low enrollment and low student-teacher ratios. Since teachers generally do not teach more than 1 or 2 grades at a time in a classroom, rural communities usually have low student-teacher ratios, and education system administrators cannot justify sending additional teachers to the school. In addition, schools with incomplete cycles tend to have extremely low survival rates.
A composite complexity measure for component-based systems

The increasing importance of software measurement has led to design and development of new software measures. Controlling and minimizing software complexity is the most important objective of each software development paradigm because it affects all other software quality attributes like reusability, reliability, testability, maintainability etc. For this purpose, a number of software complexity measures have been reported to quantify different aspects of complexity. Component-based software development (CBSD) is an evolving paradigm where emphasis is laid on reuse of existing components and effective designing of new components. As the development of component-based software is rising, more and more complexity metrics are being developed for the same. In this paper, we have attempted to design a composite complexity measure to quantify important aspects of complexity of a component-based system. The proposed measure takes into account two major complexities of a component-based system : one due to individual component and the other due to its interaction with other components. Individual component complexity may arise due to size of a component, type and nesting level of control structures present in code component. Component's interaction complexity may be due to its interface with other components. Graph theoretic notions and concept of weights have been used to illustrate interaction among software components and to compute complexity. The proposed measure has been applied to four cases chosen for present study and yields quiet encouraging results which may further help in controlling the complexity of component-based systems so as to minimize both integration and maintenance efforts.
Heritable variation in colour patterns mediating individual recognition

Understanding the developmental and evolutionary processes that generate and maintain variation in natural populations remains a major challenge for modern biology. Populations of Polistes fuscatus paper wasps have highly variable colour patterns that mediate individual recognition. Previous experimental and comparative studies have provided evidence that colour pattern diversity is the result of selection for individuals to advertise their identity. Distinctive identity-signalling phenotypes facilitate recognition, which reduces aggression between familiar individuals in P. fuscatus wasps. Selection for identity signals may increase phenotypic diversity via two distinct modes of selection that have different effects on genetic diversity. Directional selection for increased plasticity would greatly increase phenotypic diversity but decrease genetic diversity at associated loci. Alternatively, heritable identity signals under balancing selection would maintain genetic diversity at associated loci. Here, we assess whether there is heritable variation underlying colour pattern diversity used for facial recognition in a wild population of P. fuscatus wasps. We find that colour patterns are heritable and not Mendelian, suggesting that multiple loci are involved. Additionally, patterns of genetic correlations among traits indicated that many of the loci underlying colour pattern variation are unlinked and independently segregating. Our results support a model where the benefits of being recognizable maintain genetic variation at multiple unlinked loci that code for phenotypic diversity used for recognition.
Energy Output Prediction Model on Time Series Analysis and Neural Network

There are a large number of time series problems. In order to research structure and law of system we need to structure time series model which is used to predict and analysis this system. Now time series analysis methods adopt usually AR (Autoregressive) or ARMA (Autoregressive-Moving Average) model, but the actual problems are too complex to attain result and apply to in practice. At first this article analyzes the basis principle of production system time series and sets up time series-artificial neural network model by BP neural network, then predicts energy output by this model. This model has advantages of convenience, excellent dynamic capability, high prediction veracity, etc., which has practical value in reality.
Quantum automata, braid group and link polynomials

The spin-network quantum simulator model, which essentially encodes the (quantum deformed) SU(2) Racah-Wigner tensor algebra, is particularly suitable to address problems arising in low dimensional topology and group theory. In this combinatorial framework we implement families of finite-states and discrete-time quantum automata capable of accepting the language generated by the braid group, and whose transition amplitudes are colored Jones polynomials. The automaton calculation of the polynomial of (the plat closure of) a link L on 2N strands at any fixed root of unity is shown to be bounded from above by a linear function of the number of crossings of the link, on the one hand, and polynomially bounded in terms of the braid index 2N, on the other. The growth rate of the time complexity function in terms of the integer k appearing in the root of unity q can be estimated to be (polynomially) bounded by resorting to the field theoretical background given by the Chern-Simons theory.
Economic Dispatch Problem Based on Improved Particle Swarm Optimization

An improved particle swarm optimization (IPSO) is used to solve economic dispatch problem (EDP). The IPSO has two position updating strategies. In the early stage of iteration, the individual in the population updates the position according to its own best experience with a large probability. In the later stage of iteration, the individual updates the position according to the best experience in the population with a large probability. In addition, the IPSO introduces a mutation operator after position updating, which can increase the diversity of population and prevent the premature convergence. IPSO has been used to solve EDP with valve point effect. The experimental results show that IPSO is an effective algorithm to solve EDP.
The Harey Tortoise: Managing Heterogeneous Write Performance in SSDs

Recent years have witnessed significant gains in the adoption of flash technology due to increases in bit density, enabling higher capacities and lower prices. Unfortunately, these improvements come at a significant cost to performance with trends pointing toward worst-case flash program latencies on par with disk writes. We extend a conventional flash translation layer to schedule flash program operations to flash pages based on the operations' performance needs and the pages' performance characteristics. We then develop policies to improve performance in two scenarios: First, we improve peak performance for latency-critical operations of short bursts of intensive activity by 36%. Second, we realize steady-state bandwidth improvements of up to 95% by rate-matching garbage collection performance and external access performance.
Definition and evaluation of mutation operators for GUI-level mutation analysis

Automated testing has become essential in software industry to meet market demands for faster delivery and higher quality software. Testing is performed on many levels of system abstraction, from tests on source code to Graphical User Interface (GUI) tests. New testing techniques and frameworks are also continuously released to the market. Mutation analysis has been proposed as a way of assessing the quality of these new test techniques/frameworks as well as existing test suites in practice. The analysis is performed by seeding defects, referred to as mutants, into the system under test with the assumption that a technique/test suite of high quality will “kill” the mutants. However, whilst support for mutation analysis exists for test techniques that operate on on lower levels of system abstraction, i.e. method-level mutation operators, the support for GUI-level mutation analysis is currently lacking. In this paper we perform an empirical analysis of 18 GUI-level mutation operators defined in our previous work and compare their efficiency and comprehensiveness to state-of-practice lower level mutation operators. The main findings of our analysis are (1) that traditional method-level mutation operators are not precise enough for GUI-level mutation; (2) the defined GUI-based mutation operators provide comprehensive support for GUI-level mutation; and (3) GUI-based mutation operators can be automated but are challenged by the dependencies between GUI widgets.
Visual code annotations for cyberphysical programming

User interfaces for source code editing are a crucial component in any software development environment, and in many editors visual annotations (overlaid on the textual source code) are used to provide important contextual information to the programmer. This paper focuses on the real-time programming activity of `cyberphysical' programming, and considers the type of visual annotations which may be helpful in this programming context.
Genetics of Lafora progressive myoclonic epilepsy: current perspectives

Lafora disease (LD) is a fatal neurodegenerative disorder caused by loss-of-function mutations in either laforin glycogen phosphatase gene (EPM2A) or malin E3 ubiquitin ligase gene (NHLRC1). LD is associated with gradual accumulation of Lafora bodies (LBs). LBs are aggregates of polyglucosan, a long, linear, poorly branched, hyperphosphorylated, insoluble form of glycogen. Loss-of-function mutations either in the EPM2A or in the NHLRC1 gene lead to polyglucosan formation. One hypothesis on LB formation is based on findings that laforin–malin complex downregulates glycogen synthase (GS) through malin-mediated ubiquitination, and the other one is based on findings that laforin dephosphorylates glycogen. According to the first hypothesis, polyglucosan formation is a result of increased GS activity, and according to the second, an increased glycogen phosphate leads to glycogen conformational change, unfolding, precipitation, and conversion to polyglucosan, while GS remains bound to the precipitating glycogen. In this review, we summarize all the recent findings that have important implications for the treatment of LD, all of them showing that partial inhibition of GS activity may be sufficient to prevent the progression of the disease. The current perspective in LD is high-throughput screening for small molecules that act on the disease pathway, that is, partial inhibitors of GS, which opens a therapeutic window for potential treatment of this fatal disease.
Multiple view 3-D reconstruction in water

This paper presents a camera model to deal with underwater scene reconstruction from multiple images. Effects due to change in path of light ray at the medium interface is modelled for a general medium with unknown refractive index. Our model calculates the refractive index of the medium and simultaneously removes the geometric refraction effects in images using point correspondences in image pairs. With known internal parameters of the camera we find the external parameters of the camera and then 3-D reconstruction is obtained.
Coverage planning for outdoor wireless LAN systems

Wireless LANs (WLANs) are becoming increasingly popular for providing high data rate network access to mobile computers. Most of the currently deployed systems operate in the 2.4 GHz unlicensed frequency band. However, increasing demand for higher data rates and network capacities has led to new system standards for the 5 GHz band. We examine methods for obtaining a close-to-optimal positioning of WLAN access points (APs) and evaluate their performance in a typical downtown or campus environment. The system performance is evaluated using an objective function which aims to maximize both the coverage area and the overall signal quality. The optimization algorithms evaluate this objective function over a discrete search space, thereby considerably reducing the inherent complexity of the problem, while at the same time providing a reasonable approximation to the continuous optimization problem. Numerical results show that random search algorithms, such as simulated annealing, can yield very good solutions. However, the convergence speed of simulated annealing strongly depends on the fine-tuning of simulation parameters and a good choice of the initial set of transmitter positions. Successive removal algorithms, such as pruning, though usually producing sub-optimal solutions, converge in polynomial time. We therefore propose a combination of the two approaches - using pruning for obtaining an initial set of transmitter positions and refining these by using either neighborhood search or simulated annealing.
Title : Visualization of Mouse Barrel Cortex using Ex-vivo Track Density Imaging Author names :

Affiliations: 1 Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia, 2 Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia, 3 ITEE, University of Queensland, Brisbane, Queensland, Australia, 4 Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia, 5 Queensland Brain Institute, Brisbane, Queensland, Australia * indicates corresponding author: n.kurniawan@uq.edu. au, § indicates equal first authors, # indicates equal last authors.
Hybrid system design for singularityless task level robot controllers

This paper presents a hybrid system approach in the design of a singularityless task level controller. To achieve a singularityless motion control in the neighborhood of singularity, the hybrid system approach is used to integrate the task level controller and joint level controller. First, a hybrid system model is developed for the singularityless task level controller. A max-plus dynamic model is used to integrate the discrete switching control and continuous motion control in the controller. Based on this model, a smooth trajectory and control command for the hybrid system can be obtained. The Lyapunov theory was used to prove the stability of the singularityless controller. The new singularityless task level controller has been experimentally implemented and tested on a PUMA 560 robot manipulator. Experimental results have been employed to verify the theoretical conclusions, thus clearly demonstrating the advantages of the new task level control method.
Systematic design and functional analysis of artificial microRNAs

Unlike short interfering RNAs (siRNAs), which are commonly designed to repress a single messenger RNA (mRNA) target through perfect base pairing, microRNAs (miRNAs) are endogenous small RNAs that have evolved to concurrently repress multiple mRNA targets through imperfect complementarity. MicroRNA target recognition is primarily determined by pairing of the miRNA seed sequence (nucleotides 2–8) to complementary match sites in each mRNA target. Whereas siRNA technology is well established for single target knockdown, the design of artificial miRNAs for multi-target repression is largely unexplored. We designed and functionally analysed over 200 artificial miRNAs for simultaneous repression of pyruvate carboxylase and glutaminase by selecting all seed matches shared by their 3′ untranslated regions. Although we identified multiple miRNAs that repressed endogenous protein expression of both genes, seed-based artificial miRNA design was highly inefficient, as the majority of miRNAs with even perfect seed matches did not repress either target. Moreover, commonly used target prediction programs did not substantially discriminate effective artificial miRNAs from ineffective ones, indicating that current algorithms do not fully capture the features important for artificial miRNA targeting and are not yet sufficient for designing artificial miRNAs. Our analysis suggests that additional factors are strong determinants of the efficacy of miRNA-mediated target repression and remain to be discovered.
Momentum Strategies

We relate the predictability of future returns from past returns to the market's underreaction to information, focusing on past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Market risk, size and book-to- market effects do not explain the drifts. Security analysts' earnings forecasts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information.
‘A Special Kind of City Knowledge’: Innovative Clusters, Tacit Knowledge and the ‘Creative City’

That the cultural industries are highly networked and operate in clusters is now well established. The notion of cluster is linked to the idea of place-based advantage, with cultural industries gaining competitive advantage from mobilising the resources of places to compete in global markets. ‘Place’ in the cultural industries is frequently taken to be the city, where city is seen as the key resource for cultural industry clusters and a primary point of intervention for cultural industry policy in creative city policy-making. In this article, I want to look at some of the implications of these moves for both academic research and policy discussion. The reasons for this emphasis on policy relates to some large questions of urban governance and cultural politics surrounding the proactive government of clusters which are raised by recent work on the cultural industries, notably by Alan Scott.
The Unhelpfulness of Treaty Law in Solving the Sino-Japan Sovereign Dispute over the Diaoyu Islands

The sovereignty dispute over the Diaoyu Islands between China and Japan is a sensitive issue touching upon various aspects of international law. One of the major claims of both countries is whether the Islands have been ceded to Japan, and if so, have they been reverted to China. Since cession and reversion were completed through a series of treaties, this paper explores the dispute by evaluating treaty law. The paper first outlines three sovereignty claims over the Islands and then provides a chronological review of the pertinent treaties. It then discusses the nonapplicability contention and the treaty interpretation contention, two interpretations popular among mainland Chinese scholars. The article concludes that treaty law cannot provide a satisfactory solution to the dispute and suggests that both countries should resort to other international laws of territorial acquisition and strive for more innovative political solutions.