Miracle-dz/UniFAR-FTUs huggingface dataset:
When a Nudge Isn&Apos;T Enough: Defaults and Saving Among Low-Income Tax Filers

Recent evidence suggests that the default options implicit in economic choices (e.g., 401(k) savings by white-collar workers) have extraordinarily large effects on decision-making. This study presents a field experiment that evaluates the effect of defaults on savings among a highly policy-relevant population: low-income tax filers. In the control condition, tax filers could choose (i.e., opt in) to receive some of their federal tax refund in the form of U.S. Savings Bonds. In the treatment condition, a fraction of the tax refund was automatically directed to U.S. Savings Bonds unless tax filers actively chose another allocation. We find that the opt-out default had no impact on savings behavior. Furthermore, our treatment estimate is sufficiently precise to reject effects as small as one-fifth of the participation effects found in the 401(k) literature. Ancillary evidence suggests that this "nudge" was ineffective in part because the low-income tax filers in our study had targeted plans to spend their refunds. These results suggest that choice architecture based on defaults may be less effective in certain policy-relevant settings, particularly where intentions are strong.
A Hybrid Particle Swarm Optimization Neural Network Approach for Short Term Load Forecasting

Short term load forecasting (STLF) plays a significant role in national/regional power planning and operation with insufficient electric energy increased need. The accuracy of the operation system, which is derived from the accuracy of the forecasting approach used, will determine the economics of the operation of the power system. Conventional methods including time series, regression analysis or ARMA model entail exogenous input together with a number of assumptions. The use of neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with some drawbacks such as very slow convergence and easy entrapment in a local minimum. This paper presents a hybrid approach of neural network with particle swarm optimization training algorithm for developing the accuracy of predictions. The approach is applied to forecast daily peak loads (maximum of load during the day) of the Beibei, Chongqing electricity system based on previous data available for electricity demand. Traditional ARMA model and BP neural network are investigated as comparison basis. The experimental results show that the proposed approach can achieve better prediction performance.
Parallel Breadth-First Heuristic Search on a Shared-Memory Architecture

We consider a breadth-first approach to memory-efficient graph search and discuss how to parallelize it on a sharedmemory architecture that uses multithreading to achieve parallelism. The approach we develop for parallelizing breadthfirst search uses layer synchronization , in which threads expand all nodes in one layer of the breadth-first search graph before considering any nodes in the next layer. We show that this allows simple and effective techniques for termination detection, dynamic load balancing, and concurrent access to shared data structures. The Fifteen Puzzle is used as an initial
Decision-making, cultural transmission and adaptation in economic anthropology

This paper argues that economic anthropologists need to reduce their reliance on cost-benefit decisionmaking, and incorporate a cognitively informed understanding of social learning, cultural transmission and information processing. Human behavioral patterns are unlikely to be primarily a product of cost-benefit decision-making because: 1) Laboratory data show that human information processing is so fraught with errors, biases and mis-calibrations that, if this is our primary mode of adaptation or of achieving individual goals, we should observe systematic patterns of maladaptation or of goal-averting behavior; and 2) Ethnographic data show that individuals often lack the kinds of information required by typical cost-benefit models. As a theoretical alternative, I argue that humans rely heavily on biased cultural transmission. By selectively copying certain individuals or ideas, biased cultural transmission can (over time) generate the well-integrated, adaptive, behavioral patterns that we observe ethnographically.
Thinning of planar circular array antennas using Firefly Algorithm

In this paper, a new optimization technique known as Firefly Algorithm (FA) is used for antenna array synthesis. FA is an evolutionary global optimization technique inspired from flashing light of fireflies. FA is used for thinning of multiple concentric circular ring arrays. The aim is to achieve a uniformly excited antenna array that will produce a narrow beamwidth and minimum side lobe level (SLL). The low SLL is achieved by thinning the ring array. The ring array taken in this work is a nine ring concentric circular antenna array. Results demonstrate that the elements in array can be brought down to 129 from 279 in number with concurrent reduction in SLL of 20.30 dB with a fixed first null beam-width.
Effort-Aware Defect Prediction Models

Defect Prediction Models aim at identifying error-prone modules of a software system to guide quality assurance activities such as tests or code reviews. Such models have been actively researched for more than a decade, with more than 100 published research papers. However, most of the models proposed so far have assumed that the cost of applying quality assurance activities is the same for each module. In a recent paper, we have shown that this fact can be exploited by a trivial classifier ordering files just by their size: such a classifier performs surprisingly good, at least when effort is ignored during the evaluation. When effort is considered, many classifiers perform not significantly better than a random selection of modules. In this paper, we compare two different strategies to include treatment effort into the prediction process, and evaluate the predictive power of such models. Both models perform significantly better when the evaluation measure takes the effort into account.
Global climate change and ENSO: a theoretical framework

To better understand what drives El Niño, an analytical model of the coupled ocean-atmosphere system over the equatorial Pacific is constructed. The equatorial atmosphere is approximated as a linear feedback system whose surface winds are driven by sea surface temperature (SST) gradients and whose thermal effect is to restore the entire equatorial SST to its maximum value-the SST of the warm-pool. The upper ocean is represented by a shallow water model capped by a mixed layer with a constant depth. The zonal mean stratification of the thermocline is maintained by upwelling from the deep ocean. The model captures the oscillatory behavior of the present tropical Pacific climate—the El Niño–Southern Oscillation. The main features of the oscillation in the model agree well with the observed El Niño including the period of the oscillation and the phase relationship between the variations of SST and the variations in the depth of the thermocline. Moreover, the model predicts that the climate of the eastern tropical Pacific has two regimes: one is warm and steady, and the other is cold and oscillating, consistent with the inference from geoarcheological data that El Niño did not exist during the early-to-mid-Holocene when the global and regional climate was warmer than today. The transition from the steady climate to the oscillating climate takes place when the temperature contrast between the surface warm-pool and the deep ocean exceeds a critical value. A stability analysis reveals that the zonal SST contrast and the accompanying wind-driven currents have to be sufficiently strong to become oscillatory and that requires a sufficiently large difference between the temperature of the warm-pool and the temperature of the deep ocean. On the timescale of millennia, a sufficiently cold equatorial deep ocean implies a sufficiently cold high latitudes, a condition which is met by the present climate, but possibly not by the climate of the early-to-mid Holocene. In the oscillating regime, the magnitude of El Niño is found to increase monotonically with increases in the difference between the temperature of the warm pool and the temperature of the deep ocean. The increase in the magnitude of El Niño is accompanied with an increase in the zonal SST contrast in the equatorial region. The implication of these results for the response of El Niño to an increase in the greenhouse effect is discussed.
Balancing Selection at a Frog Antimicrobial Peptide Locus: Fluctuating Immune Effector Alleles?

Balancing selection is common on many defense genes, but it has rarely been reported for immune effector proteins such as antimicrobial peptides (AMPs). We describe genetic diversity at a brevinin-1 AMP locus in three species of leopard frogs (Rana pipiens, Rana blairi, and Rana palustris). Several highly divergent allelic lineages are segregating at this locus. That this unusual pattern results from balancing selection is demonstrated by multiple lines of evidence, including a ratio of nonsynonymous/synonymous polymorphism significantly higher than 1, the ZnS test, incongruence between the number of segregating sites and haplotype diversity, and significant Tajima's D values. Our data are more consistent with a model of fluctuating selection in which alleles change frequencies over time than with a model of stable balancing selection such as overdominance. Evidence for fluctuating selection includes skewed allele frequencies, low levels of synonymous variation, nonneutral values of Tajima's D within allelic lineages, an inverse relationship between the frequency of an allelic lineage and its degree of polymorphism, and divergent allele frequencies among populations. AMP loci could be important sites of adaptive genetic diversity, with consequences for host–pathogen coevolution and the ability of species to resist disease epidemics.
A Corpus-based Study of the German Recipient Passive

In this paper, we investigate the usage of a non-canonical German passive alternation for ditransitive verbs, the recipient passive, in naturally occuring corpus data. We propose a classifier that predicts the voice of a ditransitive verb based on the contextually determined properties its arguments. As the recipient passive is a low frequent phenomenon, we first create a special data set focussing on German ditransitive verbs which are frequently used in the recipient passive. We use a broad-coverage grammar-based parser, the German LFG parser, to automatically annotate our data set for the morpho-syntactic properties of the involved predicate arguments. We train a Maximum Entropy classifier on the automatically annotated sentences and achieve an accuracy of 98.05%, clearly outperforming the baseline that always predicts active voice baseline (94.6%).
Context-Aware Semantic Association Ranking

Discovering complex and meaningful relationships, which we call Semantic Associations, is an important challenge. Just as ranking of documents is a critical component of today's search engines, ranking of relationships will be essential in tomorrow's semantic search engines that would support discovery and mining of the Semantic Web. Building upon our recent work on specifying types of Semantic Associations in RDF graphs, which are possible to create through semantic metadata extraction and annotation, we discuss a framework where ranking techniques can be used to identify more interesting and more relevant Semantic Associations. Our techniques utilize alternative ways of specifying the context using ontology. This enables capturing users' interests more precisely and better quality results in relevance ranking.
Chinese Subcategorization Annotation Based on Machine Learning

There have been a lot of researches focusing on large-scaled automatic acquisition of subcategorization frames, and many achievements have been made for lexicon building in quite a few languages, but subcategorization annotation for individual sentences still remains in a rarely touched field. This paper proposed to annotate Chinese subcategorization as a classification task by means of sequence kernel methods, which exploited the potential relations among the respective sentential constituents. Our final classification with word sequence kernel congregation and POS sequence kernel C-SVM achieved a very promising accuracy ratio of 92.36% on the testing set, which is 13.51% higher than the baseline performance of the existing Chinese SCF hypothesis generator.
An empirical analysis of the leader–member exchange and employee turnover intentions mediated by mobbing: evidence from sport organisations

Abstract Despite the existence of numerous important factors that affect organisations’ efficiency and productivity, including leader–member exchange, mobbing, and employee turnover intentions, studies analysing the relationships among these variables are scarce in the sports literature. Hence, the purpose of this study was to investigate the relationships between leader–member exchange and turnover intentions, focusing primarily on the mediating role of mobbing. The sample for the study was collected from participants who worked in a public organisation providing sports and physical activity services. The results of the study showed statistically significant relationships among leader–member exchange, mobbing, and employee turnover intentions. More specifically, leader–member exchange was found to have a significant negative relationship with mobbing and employee turnover intentions. Mobbing, on the other hand, had a significant positive relationship with employee turnover intentions and mediated the relationship between leader–member exchange and employee turnover intentions. Managerial and research implications and contributions of the study were discussed.
Data-centric Consistency Policies: A Programming Model for Distributed Applications with Tunable Consistency

The consistency level of operations over replicated data is an important parameter in distributed applications. It impacts correctness, performance, and availability. It is now common to find single applications using many different consistency levels at the same time; however, current commercial frameworks do not provide high-level abstractions for specifying or reasoning about different consistency properties of an application. Research frameworks that do tend to require a substantial effort from developers to specify operation dependencies, orderings and invariants to be preserved. We propose an approach for specifying consistency properties based on the observation that correctness criteria and invariants are a property of data, not operations. Hence, it is reasonable to define the consistency properties required to enforce various data invariants on the data itself rather than on the operations. The result is a system that is simpler to describe and reason about. In this paper, we outline an abstract model of programming language constructs and a static checker for data-centric consistency control, and demonstrate this model through a simple prototype programming language implementation.
A different type of dirty work: Hidden taint, intersectionality, and emotion management in bureaucratic organizations

ABSTRACT This study explores the intersectionality among identity markers, such as gender, race, and class, and emotion management in two bureaucratic organizations – municipal courtrooms and airport security checkpoints. We name and explore a new type of dirty work called “hidden taint,” which we describe as a larger, encompassing category of dirty work that involves the experience and dynamic co-construction of taint. Utilizing qualitative methods including participant observation and interviews, we compare how hidden taint is experienced in each bureaucratic context. The results focus on the relationship between (1) hidden taint and the co-construction of emotion norms and (2) hidden taint and power dynamics. This study extends communication theory by naming and describing hidden taint as a new type of dirty work, extending literature on emotion management and intersectionality by exploring their connection to dirty work in occupations with varying levels of prestige, and detailing how emotion management is co-constructed between employees and patrons in bureaucratic contexts.
So-Grid: A self-organizing Grid featuring bio-inspired algorithms

This article presents So-Grid, a set of bio-inspired algorithms tailored to the decentralized construction of a Grid information system that features adaptive and self-organization characteristics. Such algorithms exploit the properties of swarm systems, in which a number of entities/agents perform simple operations at the local level, but together engender an advanced form of swarm intelligence at the global level. In particular, So-Grid provides two main functionalities: logical reorganization of resources, inspired by the behavior of some species of ants and termites that move and collect items within their environment, and resource discovery, inspired by the mechanisms through which ants searching for food sources are able to follow the pheromone traces left by other ants. These functionalities are correlated, since an intelligent dissemination can facilitate discovery. In the Grid environment, a number of ant-like agents autonomously travel the Grid through P2P interconnections and use biased probability functions to: (i) replicate resource descriptors in order to favor resource discovery; (ii) collect resource descriptors with similar characteristics in nearby Grid hosts; (iii) foster the dissemination of descriptors corresponding to fresh (recently updated) resources and to resources having high quality of service (QoS) characteristics. Simulation analysis shows that the So-Grid replication algorithm is capable of reducing the entropy of the system and efficiently disseminating content. Moreover, as descriptors are progressively reorganized and replicated, the So-Grid discovery algorithm allows users to reach Grid hosts that store information about a larger number of useful resources in a shorter amount of time. The proposed approach features characteristics, including self-organization, scalability and adaptivity, which make it useful for a dynamic and partially unreliable distributed system.
Peak oxygen uptake differentiates competitive from recreational male surfboard riders

The purpose of this study was to verify if competitive performance status was associated to different levels of specific aerobic fitness in Portuguese surfboard riders. Six recreational surfers (age: 32.3±3.1 years; body mass: 73.2±7.8kg; height: 1.75±0.05m) and six competitive international level surfers (age: 25±8.4years; body mass: 68.5±3.8kg; height: 1.74±0.05m) performed a maximal continuous incremental paddling test consisting of two-minute steps starting at 20W, with increments of 10W, for determination of peak oxygen uptake and maximal aerobic power. Ventilatory threshold and respiratory compensation point with corresponding heart rate and power output were also determined. Elite surfers presented higher values for peak oxygen uptake (43.6±7.9 ml.kg-1.min-1) than recreational surfers (31.1±7.4 ml.kg-1.min-1, p=0.01) however, there were no differences (p> 0.05) between groups for maximal aerobic power (elite: 76.6±18.6W; recreational: 76.6±16.6W). No significant differences between the power output at which ventilatory threshold or respiratory compensation point occurred but elite surfers reached ventilation threshold with 22.5±5.8 ml.kg-1.min-1 which was significantly higher than recreational surfers (16±3.7 ml.kg-1.min-1). These findings show that elite surfers have higher values of peak oxygen uptake than recreational surfers which suggest a higher aerobic adaptation that may be related to the different amount of exercise volume undertaken.
Video Quality Prediction over LTE Using Random Neural Networks

Long Term Evolution (LTE) is a fourth generation (4G) mobile communication system that supports wireless multimedia applications. The success of video applications over LTE networks very much depends on meeting the Quality of Experience (QoE) requirements of users. The aim of this paper is to present a new QoE prediction model for video over LTE networks. The model uses a combination of parameters in the application and network layer. The application layer parameters considered are the Content Type (CT), Sender Bit Rate (SBR) and Frame Rate (FR) and the network parameters are delay, packet loss rate (PLR). The video quality was predicted in terms of the Mean Opinion Score (MOS). Our method for calculating video MoS is based on statistic learning using Random Neural Network (RNN).
Listening with Your Eyes: Towards a Practical Visual Speech Recognition System Using Deep Boltzmann Machines

This paper presents a novel feature learning method for visual speech recognition using Deep Boltzmann Machines (DBM). Unlike all existing visual feature extraction techniques which solely extracts features from video sequences, our method is able to explore both acoustic information and visual information to learn a better visual feature representation in the training stage. During the test stage, instead of using both audio and visual signals, only the videos are used for generating the missing audio feature, and both the given visual and given audio features are used to obtain a joint representation. We carried out our experiments on a large scale audio-visual data corpus, and experimental results show that our proposed techniques outperforms the performance of the hadncrafted features and features learned by other commonly used deep learning techniques.
Texture-Based Analysis of COPD: A Data-Driven Approach

This study presents a fully automatic, data-driven approach for texture-based quantitative analysis of chronic obstructive pulmonary disease (COPD) in pulmonary computed tomography (CT) images. The approach uses supervised learning where the class labels are, in contrast to previous work, based on measured lung function instead of on manually annotated regions of interest (ROIs). A quantitative measure of COPD is obtained by fusing COPD probabilities computed in ROIs within the lung fields where the individual ROI probabilities are computed using a k nearest neighbor (kNN ) classifier. The distance between two ROIs in the kNN classifier is computed as the textural dissimilarity between the ROIs, where the ROI texture is described by histograms of filter responses from a multi-scale, rotation invariant Gaussian filter bank. The method was trained on 400 images from a lung cancer screening trial and subsequently applied to classify 200 independent images from the same screening trial. The texture-based measure was significantly better at discriminating between subjects with and without COPD than were the two most common quantitative measures of COPD in the literature, which are based on density. The proposed measure achieved an area under the receiver operating characteristic curve (AUC) of 0.713 whereas the best performing density measure achieved an AUC of 0.598. Further, the proposed measure is as reproducible as the density measures, and there were indications that it correlates better with lung function and is less influenced by inspiration level.