Miracle-dz/UniFAR-FTUs huggingface dataset:
Which Vary More in Repertory Grid Data: Constructs or Elements?

Personal construct theory has a focus on constructs rather than elements, as can be clearly seen in the corollaries proposed by George Kelly. Yet, in the operationalization of his fundamental postulate, namely the repertory grid technique, there is an equal focus on both constructs and elements. Here we examine the relative contributions to variation in the grid data of both elements and constructs and use the intraclass correlation to examine this in several data sets. It is shown that in many instances, elements contribute more to the variation in grid data, irrespective of whether elements and constructs are supplied or elicited, or whether the ratings are made construct by construct or element by element. Implications of this for both the theory and practice are discussed.
Large-scale Validation of Counterfactual Learning Methods: A Test-Bed

The ability to perform effective off-policy learning would revolutionize the process of building better interactive systems, such as search engines and recommendation systems for e-commerce, computational advertising and news. Recent approaches for off-policy evaluation and learning in these settings appear promising. With this paper, we provide real-world data and a standardized test-bed to systematically investigate these algorithms using data from display advertising. In particular, we consider the problem of filling a banner ad with an aggregate of multiple products the user may want to purchase. This paper presents our test-bed, the sanity checks we ran to ensure its validity, and shows results comparing state-of-the-art off-policy learning methods like doubly robust optimization, POEM, and reductions to supervised learning using regression baselines. Our results show experimental evidence that recent off-policy learning methods can improve upon state-of-the-art supervised learning techniques on a large-scale real-world data set.
Fine-Grained Voltage Boosting for Improving Yield in Near-Threshold Many-Core Processors

Process variation is a major impediment in optimizing yield, energy, and performance in near-threshold many-core processors. In this paper, we present a comprehensive analysis on yield losses in near-threshold many-core processors. Based on our analysis, we propose energy-efficient yield improvement techniques for near-threshold many-core processors: SRAM cell arrays and Wordline driver voltage Boosting (SWBoost) and Cache voltage Boosting (CBoost). Results reveal that SWBoost and CBoost improve a chip yield by up to 66% and 83%, respectively. Furthermore, runtime energy overheads of SWBoost and CBoost are only 0.46% and 0.54%, respectively, which are much lower than conventional voltage boosting techniques.
Variations Across the Menstrual Cycle in EEG Activity During Thinking and Mental Relaxation

Abstract Previous studies have indicated: (1) peak performance on tests of divergent creative thinking during the ovulatory phase of the menstrual cycle; (2) compared to convergent analytical thinking, divergent thinking was found to be associated with a distinctly increased dimensional complexity of ongoing EEG activity. Based on these findings, we hypothesized that cortical information processing during the ovulatory phase is characterized by an increased EEG dimensionality. Each of 16 women was tested on 3 occasions: during the ovulatory phase, the luteal phase, and menses. Presence of the phases was confirmed by determination of plasma concentrations of estradiol, progesterone, and luteinizing hormone. The EEG was recorded while the women performed: (1) tasks of divergent thinking; (2) tasks of convergent thinking; and (3) during mental relaxation. In addition to EEG dimensional complexity, conventional spectral power analysis was performed. Behavioral data confirmed enhanced creative performance duri.
The impact of partnership network stability on new product development cost in the pharmaceutical industry

conducted a multilevel and longitudinal regression analysis of the data. Over the last two years, we also interviewed more than 20 experts in the pharmaceutical industry, who were actively involved in testing new drugs. We used the accumulated knowledge to both validate our arguments leading to the hypotheses and to substantiate our discussion based on our results. This work contributes to the NPD theory as it identifies the network related factors affecting the NPD testing costs and reveals the role of the partnership stability in decreasing these costs. Moreover, the findings are of value to practitioners involved in cost control of new product testing and any practitioner dealing with partnership management.
Factors related to elevated vaginal pH in the first trimester of pregnancy

Objective. To assess different bacterial and epidemiological factors associations with increased vaginal pH in the pregnant women population during the first trimester. Design. A cross‐sectional, observational study. Setting. Three outpatient clinics in Riga. Population. From July 2009 until January 2010, 139 unselected consecutive pregnant women at the first prenatal visit. Methods. Pregnant women were submitted to an interview, vaginal examination and vaginal specimen collection for pH measurement and native microscopy. Main outcome measures. Vaginal pH ≥4.5 was considered as elevated. Abnormal bacterial microflora was classified according to Donders. Results. Elevated vaginal pH was significantly associated with bacterial vaginosis (p < 0.001), aerobic vaginitis (p < 0.001) and mixed aerobic vaginitis and bacterial vaginosis flora (p < 0.001) and presence of sperm cells in the smears (p= 0.024). Most cases with sperm were associated with abnormal vaginal flora. Normal lactobacillary morphotypes were more often found in the pH ≤4.4 group (p < 0.001), while leptosomic and short types were found more frequently with increased pH. Conclusions. Elevated vaginal pH is associated with different types of abnormal vaginal flora and the presence of sperm cells.
A Preliminary Evaluation of the Undergraduate Research Participation Program of the National Science Foundation

AbstractThe possible effects of participating in the Undergraduate Research Participation Program of the National Science Foundation was examined in a longitudinal study of undergraduate college students attending a representative national sample of colleges and universities. Participants in the program were shown to be significantly superior to nonparticipants in terms of their academic and extracurricular achievements during high school and also in terms of their average academic ability as measured by standardized tests. Participation in the program appears to increase the student’s chances of being author or co-author of a scientific article; it also increases his interest in becoming a college professor and in obtaining the PhD degree. Although a large number of potentially biasing student input variables were statistically controlled, the possibility exists that these apparent effects of the program may in fact be attributable to additional uncontrolled variables that bias the selection of the URP p.
Measuring Low Magnetic Field in Electromagnetic Flow Meter

Space distribution of low magnetic field amounting to 1 mT can be measured in detail using a commercial 3D probe with a modified control circuit. The probe was calibrated carefully using a known field from Helmholtz coils. The 3D positioning system enables the measurement of the field distribution. The apparatus was used for measuring the magnetic field of the electromagnetic flow meter. Additional experiments confirmed high reproducibility of results for repeated probe movement. The magnetic field was calculated numerically by the application of Biot-Savart Law for simplified model of magnetic excitation. The agreement between experiment and theory is good, provided that production inaccuracies and limitations of the model geometry are taken into account.
Guaranteed Robust Nonlinear State Estimator with Application to Global Vehicle Tracking

This paper deals with guaranteed recursive state estimation in a bounded-error context with application to global dynamical vehicle tracking. As in Kalman or approximate Bayesian filtering, prediction and correction phases alternate. A distinctive feature of the method advocated here is that its results are guaranteed, in the sense that the statements made about the possible values of the state vector are mathmatically proved, although all calculations are performed approximately on a computer. Sets will thus be provided that are guaranteed to contain all values of the state that are consistent with the information available and the bounds assumed on the state perturbations and measurement errors. Complexity issues are addressed and some tools are provided to facilitate real-time implementation. Results obtained with an actual vehicle are reported.
Special Interests and Technological Change

We study an OLG economy where productivity growth comes from two alternative sources: process innovation and learning-by-doing. There is a trade-off between the two in so far as frequent technological updates reduce the scope for learning on existing technologies. A conflict is shown to arise between the young and the old, because the former favor innovation while the latter prefer learning. We model the interaction between overlapping generations and policy makers as a dynamic common agency problem, where competing generations invest a certain amount of resources to lobby either for the maintenance of the current technology or the adoption of a new one. By focusing on truthful Markov perfect equilibria, we characterize the political equilibrium and show its dependence on the underlying demographic, technological and preference parameters.
Involvement of Reactive Oxygen Species, Glutathione Metabolism, and Lipid Peroxidation in the Cf-Gene-Dependent Defense Response of Tomato Cotyledons Induced by Race-Specific Elicitors of Cladosporium fulvum

The chronological order of responses to Cladosporium fulvum (Cooke) (Cf) race-specific elicitors was assessed in cotyledons of three near-isogenic tomato (Lycopersicon esculentum Mill.) lines carrying either Cf-9 or Cf-2 or no Cf gene. The responses observed were dependent on the presence of a Cf gene, Avr-gene product dose injected, and the relative humidity (RH) of the growth chamber. At ambient RH, superoxide formation and lipid peroxidation occurred after 2 h (Cf9) and 4 h (Cf2). At elevated RH (98%) and at lower avirulence elicitor dose, Cf-Avr-dependent lipid peroxidation was considerably attenuated. Significant electrolyte leakage occurred by 18 h but only at the lower RH. Total glutathione levels began to increase 2 to 4 h and 4 to 8 h after challenge of Cf9 and Cf2 cells, respectively, and by 48 h reached 665 and 570% of initial levels. A large proportion of this accumulation (87%) was as oxidized glutathione. When the RH was increased to 98%, increases in glutathione levels were strongly attenuated. Increased lipoxygenase enzyme activity was detected 8 h postchallenge in either incompatible interaction. These results indicate that the activation of the Cf-Avr-mediated defense response results in severe oxidative stress.
Molecular and phenotypic divergence in the bluethroat (Luscinia svecica) subspecies complex

Subspecies complexes may provide valuable insights into the early stages of the speciation process. The bluethroat (Luscinia svecica) consists of many morphologically distinct subspecies that differ most strikingly in the ornamental colour pattern of the male throat. We investigated the genetic and phenotypic differentiation in this subspecies complex, using (i) microsatellite genotyping (11 loci) of a sample of 364 individuals from bluethroat populations in Europe and Asia, and (ii) spectrometric and morphological measurements of a sample of 131 museum skin specimens. Population genetic analyses, based on microsatellite allele frequency variation, revealed a slight but significant overall population differentiation (FST = 0.042). There was a well‐differentiated southern group of subspecies with white or no throat spots and a less‐differentiated northern group of chestnut‐spotted populations. Phylogenetic analyses indicated that the southern all‐blue and white‐spotted forms are ancestral to the chestnut‐spotted subspecies. In addition to the qualitative variation in throat plumage pattern already described in the literature, we found significant quantitative variation among subspecies in hue, chroma and brightness of the ultraviolet (UV)/blue throat coloration, and this variation seemed to be unrelated to the phylogenetic distance between subspecies.
Analysis of Audit Fees for Nonprofits

In contrast to the extensive research on audit fees of for-profit companies, literature on nonprofit audit fees is limited. In this article, audit fee determinants are tested using a Belgian sample of nonprofits. We find that Big4 auditors charge fee premiums, and that nonprofit expert auditors charge lower fees when expertise is measured at audit partner level (as opposed to audit firm level). These findings hold after controlling for self-selection by using propensity score matching. We also address the resource dependence and agency characteristics of nonprofit audit clients and find that both are relevant in explaining audit pricing.
Phase of Spontaneous Slow Oscillations during Sleep Influences Memory-Related Processing of Auditory Cues

Slow oscillations during slow-wave sleep (SWS) may facilitate memory consolidation by regulating interactions between hippocampal and cortical networks. Slow oscillations appear as high-amplitude, synchronized EEG activity, corresponding to upstates of neuronal depolarization and downstates of hyperpolarization. Memory reactivations occur spontaneously during SWS, and can also be induced by presenting learning-related cues associated with a prior learning episode during sleep. This technique, targeted memory reactivation (TMR), selectively enhances memory consolidation. Given that memory reactivation is thought to occur preferentially during the slow-oscillation upstate, we hypothesized that TMR stimulation effects would depend on the phase of the slow oscillation. Participants learned arbitrary spatial locations for objects that were each paired with a characteristic sound (eg, cat–meow). Then, during SWS periods of an afternoon nap, one-half of the sounds were presented at low intensity. When object location memory was subsequently tested, recall accuracy was significantly better for those objects cued during sleep. We report here for the first time that this memory benefit was predicted by slow-wave phase at the time of stimulation. For cued objects, location memories were categorized according to amount of forgetting from pre- to post-nap. Conditions of high versus low forgetting corresponded to stimulation timing at different slow-oscillation phases, suggesting that learning-related stimuli were more likely to be processed and trigger memory reactivation when they occurred at the optimal phase of a slow oscillation. These findings provide insight into mechanisms of memory reactivation during sleep, supporting the idea that reactivation is most likely during cortical upstates. SIGNIFICANCE STATEMENT Slow-wave sleep (SWS) is characterized by synchronized neural activity alternating between active upstates and quiet downstates. The slow-oscillation upstates are thought to provide a window of opportunity for memory consolidation, particularly conducive to cortical plasticity. Recent evidence shows that sensory cues associated with previous learning can be delivered subtly during SWS to selectively enhance memory consolidation. Our results demonstrate that this behavioral benefit is predicted by slow-oscillation phase at stimulus presentation time. Cues associated with high versus low forgetting based on analysis of subsequent recall performance were delivered at opposite slow-oscillation phases. These results provide evidence of an optimal slow-oscillation phase for memory consolidation during sleep, supporting the idea that memory processing occurs preferentially during cortical upstates.
Overview of the Pathology of Three Widely Used Animal Models of Acute Lung Injury

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are syndromes of acute diffuse damage to the pulmonary parenchyma by a variety of local or systemic insults. Increased alveolar capillary membrane permeability was recognized as the common end organ injury and a central feature in all forms of ALI/ARDS. Although great strides have been made in understanding the pathogenesis of ALI/ARDS and in intensive care medicine, the treatment approach to ARDS is still relying on ventilatory and cardiovascular support based on the recognition of the clinical picture. In the course of evaluating novel treatment approaches to ARDS, 3 models of ALI induced in different species, i.e. the surfactant washout lavage model, the oleic acid intravenous injection model and the endotoxin injection model, were widely used. This review gives an overview of the pathological characteristics of these models from studies in pigs, dogs or sheep. We believe that a good morphological description of these models, both spatially and temporally, will help us gain a better understanding of the real pathophysiological picture and apply these models more accurately and liberally in evaluating novel treatment approaches to ARDS.
University of Glasgow at TREC 2015: Experiments with Terrier in Contextual Suggestion, Temporal Summarisation and Dynamic Domain Tracks

In TREC 2015, we focus on tackling the challenges posed by the Contextual Suggestion, Temporal Summarisation and Dynamic Domain tracks. For Contextual Suggestion, we in- vestigate the use of user-generated data in location-based social networks (LBSN) to suggest venues. For Temporal Summarisation, we examine features for event summarisa- tion that explicitly model the entities involved in the events. Meanwhile, for the Dynamic Domain track, we explore re- source selection techniques for identifying the domain of in- terest and diversifying sub-topic intents.
Aplicação da teoria dos jogos na análise de alianças estratégicas

Este estudo tem o objetivo de discutir e compreender a aplicação da Teoria dos Jogos e suas contribuições e limitações no processo de formação de Alianças Estratégicas. O tema central é a formação de alianças estratégicas e a relação entre estas decisões e a aplicação da lógica da Teoria dos Jogos, principalmente jogos cooperativos, neste processo de formação de relações de cooperação. Este artigo é apresentado como um ensaio teórico e foca na funcionalidade e possível aplicabilidade da Teoria dos Jogos, sendo usada no processo de análise e formação de Alianças Estratégicas e estruturas de cooperação. Verificou-se que a Teoria dos Jogos é considerada uma ferramenta matemática e econômica, com potencial a agregar no processo decisório de formação de Alianças Estratégicas. Entretanto, observa-se que existem algumas limitações metodológicas para a sua aplicação plena, dentre elas o equilíbrio de Nash. Existem condições que devem ser satisfeitas, para que se possa aplicar a Teoria dos Jogos de modo satisfatório. Este ensaio conclui com sugestões para estudos futuros. Palavras-chave: Alianças Estratégicas; Teoria dos Jogos; Cooperação; Estratégia
A Comparison of Different Classification Techniques for Bank Direct Marketing

Classification is a data mining techniques used to predict group membership for data instance. In this paper, we present the comparison of different classification techniques in open source data mining software which consists of a decision tree methods and machine learning for a set of bank direct marketing dataset. All decision tree methods tested are J48-graft and LAD tree while machine learning tested are radial basis function network and support vector machine. The experiment results show are a bout classification sensitivity, specificity, accuracy, mean absolute error and root mean squared error. The results on bank direct marketing data also the efficiency of machine learning methods by using support vector machine is better than that of all algorithms.
SR-Code: Smart Relay Network Coding for Data Collection for Wireless Sensor Networks

Reliab ility in data collect ion for wireless sensor networks is one of the major problems in IoT applications . Sensor nodes are usually placed in harsh conditions where data communication is at risk of losing packets. Retransmissions are considered costly in terms of delay and power consumptions, especially that wireless sensor nodes are battery operated. In this context we introduce SR-Code, a novel network coding algorithm that achieves reliability in harsh conditions. SR-Code utilizes the XOR operator to code overheard packets. The targeted network topology is a 2-tier network where data loss can occur in all t iers. SR-Code utilizes bit addresses where each node is identified by a single bit in an address bit vector. Identifying packets and computing the cardinality of coded messages can be easily done using address bit vectors. SR-Code realizes redundancy as a function of overheard packet. SR-Code achieved a reliability factor of 75% when the number of packets lost was 100% of the orig inal (un-coded) packet sent.
Empowering Software Defined Network controller with packet-level information

Packet level information, such as packet content and inter-arrival time, are necessary for some network monitoring and control applications. However, current Software Defined Networks (SDN) such as OpenFlow provide limited access to packet-level information in the controller. In this paper, we propose an extension that enables the controller to access packet-level information through per-flow sampling. Our extension is flexible and powerful, yet it can be implemented entirely in the data plane at line rate. We present a set of possible applications that can take advantage of this new packet-level information, including examples that are extremely difficult, if not impossible in current SDN.
Parent Training in Groups: Pilot Study with Parents of Infants with Developmental Delay

This paper describes an innovative 8-week parent-child interaction skills training group for parents of infants with developmental delay. We developed the "Skill Building Group" within an attachment theory framework. Parents actively learned skills in sensitive responding to infant cues through small and large group discussion of video segments of common parenting challenges, homework assignments, and peer support. We conducted a pilot study comparing 12 participants and 5 controls on several self-report pre-test/post-test measures. Results for the intervention group indicated decreases in dysfunctional parent-child interaction, parental distress, sadnessldepression, and follow-up service utilization, and high levels of consumer satisfaction. For the comparison group, pre-post differences were not significant.