Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of Assessor Behavior in Crowdsourced Preference Judgments

We describe a pilot study using Amazon’s Mechanical Turk to collect preference judgments between pairs of full-page layouts including both search results and image results. Specifically, we analyze the behavior of assessors that participated in our study to identify some patterns that may be broadly indicative of unreliable assessments. We believe this analysis can inform future experimental design and analysis when using crowdsourced human judgments.
Everything’s negative about Nigeria: A study of US media reporting on Nigeria

Previous research on international communication cites under-reporting and negative coverage as major problems of Western media reporting of the African continent. These problems are present specifically in US television coverage of African countries. Utilising agenda-setting and media framing theory, this study content analyses US television media coverage of Nigeria, Africa’s largest economy and most populous nation, in two 5-year time periods, 2005–2009 and 2010–2014. Reports broadcast by the big three networks (ABC, CBS, and NBC) were coded for issues, sources, valence, and frames (N = 643). Results corroborate existing research regarding the predominance of episodic frames and negative coverage across time periods. New findings concerning coverage of Nigeria by Western media organisations are discussed.
Syde: a tool for collaborative software development

Team collaboration is essential for the success of multi-developer projects. When team members are spread across different locations, individual awareness of the activity of others drops due to communication barriers. We built Syde, a tool infrastructure to reestablish team awareness by sharing change and conflict information across developer's workspaces. Our main challenge is to balance the tradeoff between offering relevant information about the activity of the team and avoiding information overload. The novelty of our approach is that we model source code changes as first-class entities to record the detailed evolution of a multi-developer project. Hence, Syde delivers precise change information to interested developers.
Pricing, product diversity, and search costs: a Bertrand-Chamberlin-Diamond model

We study price competition in the presence of search costs and product differentiation. The limit cases of the model are the ‘‘Bertrand Paradox,’’ the ‘‘Diamond Paradox,’’ and Chamberlinian monopolistic competition. Market prices rise with search costs and decrease with the number of firms. Prices may initially fall with the degree of product differentiation because more diversity leads to more search and hence more competition. Equilibrium diversity rises with search costs, while the optimum level falls, so entry is excessive. The market failure is most pronounced for low preference for variety and high search costs.
Measuring the Impact of Beliefs on Luxury Buying Behavior in an Emerging Market

Purpose Demand for luxury brands is increasing at a very fast pace in emerging markets like India. But very few quantitative studies have been conducted to explore the reasons behind this sudden surge in demand. Thus, the purpose of this paper is to understand the impact of beliefs on consumer buying behavior for luxury fashion brands in the Indian context employing theory of planned behavior and to develop a comprehensive understanding regarding motivating factors behind luxury goods consumption. Design/methodology/approach Researcher-controlled sampling techniques (judgment and snowball sampling) were employed to collect data from actual users of luxury fashion brands in New Delhi (India). Statistical tests including confirmatory factor analysis and structural equation modeling were applied for data analysis. Findings The findings show that all three beliefs – attitudinal belief, normative belief (NB) and control belief – were positively and significantly related to attitude, subjective norm and perceived behavioral control (PBC). NB was found to have a positive impact on PBC as well as actual consumer purchasing behavior for luxury fashion brands. Originality/value This research contributes to the extant literature by bringing to light new findings that could help provide meaningful insights to the academicians and marketing practitioners.
Lapse rate modeling: a rational expectation approach

Lapse Rate Modeling: A Rational Expectation Approach Abstract The surrender option embedded in many life insurance products is a clause that allows policyholders to terminate the contract early. Pricing techniques based on the American Contingent Claim (ACC) theory are often used, though the actual policyholders' behavior is far from optimal. Inspired by many prepayment models for mort- gage backed securities, this paper builds a Rational Expectation (RE) model describing the policyholders' behavior in lapsing the contract. A market model with stochastic interest rates is considered, and the pricing is carried out through numerical approximation of the corre- sponding two{space{dimensional parabolic partial di®erential equa- tion. Extensive numerical experiments show the di®erences in terms of pricing and interest rate elasticity between the ACC and RE ap- proaches as well as the sensitivity of the contract price with respect to changes in the policyholders' behavior.
Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes

Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under different weather conditions. In this letter, we investigate the problem of efficiently coping with seasonal appearance changes in online localization. We propose a lazy data association approach for matching streams of incoming images to a reference image sequence in an online fashion. We present a search heuristic to quickly find matches between the current image sequence and a database using a data association graph. Our experiments conducted under substantial seasonal changes suggest that our approach can efficiently match image sequences while requiring a comparably small number of image to image comparisons.
Lexicalized structural priming in second language online sentence comprehension

Prior work has shown intriguing differences between first language (L1) and second language (L2) comprehension priming of relative clauses. We investigated English reduced relative clause priming in Chinese adult learners of English. Participants of different education levels read sentences in a self-paced, moving window paradigm. Critical sentences had a temporarily ambiguous reduced relative clause. Across lists, critical sentences were rotated, so that they occurred either as prime or as target, and had either the same or different verb as the critical sentence with which they were paired. Prime/target pairs were separated by several filler sentences, which never contained a relative clause. Mean reading times for the disambiguating region in the target sentences were faster than in the prime sentences, but only in the same-verb condition, not in the different-verb condition. This pattern of results is consistent with L1 comprehension priming research, suggesting that similar lexically specific mechanisms are involved in L1 and L2 comprehension priming of reduced relative clauses. These findings are in line with lexicalist accounts of sentence comprehension (e.g. MacDonald et al., 1994), according to which syntactic information is bound to specific words. In addition, these findings argue against theories that postulate fundamental differences in processing of L1 and L2 (e.g. Clahsen and Felser, 2006a, 2006b).
Very deep convolutional neural networks for robust speech recognition

This paper describes the extension and optimisation of our previous work on very deep convolutional neural networks (CNNs) for effective recognition of noisy speech in the Aurora 4 task. The appropriate number of convolutional layers, the sizes of the filters, pooling operations and input feature maps are all modified: the filter and pooling sizes are reduced and dimensions of input feature maps are extended to allow adding more convolutional layers. Furthermore appropriate input padding and input feature map selection strategies are developed. In addition, an adaptation framework using joint training of very deep CNN with auxiliary features i-vector and fMLLR features is developed. These modifications give substantial word error rate reductions over the standard CNN used as baseline. Finally the very deep CNN is combined with an LSTM-RNN acoustic model and it is shown that state-level weighted log likelihood score combination in a joint acoustic model decoding scheme is very effective. On the Aurora 4 task, the very deep CNN achieves a WER of 8.81%, further 7.99% with auxiliary feature joint training, and 7.09% with LSTM-RNN joint decoding.
Sporadic cerebral amyloid angiopathy revisited: recent insights into pathophysiology and clinical spectrum

Sporadic cerebral amyloid angiopathy (CAA) is a common age related cerebral small vessel disease, characterised by progressive deposition of amyloid-β (Aβ) in the wall of small to medium sized arteries, arterioles and capillaries of the cerebral cortex and overlying leptomeninges. Previously considered to be a rare neurological curiosity, CAA is now recognised as an important cause of spontaneous intracerebral haemorrhage and cognitive impairment in the elderly, two fundamental challenges in the field of cerebrovascular disease. Our understanding of the pathophysiology and clinical manifestations of CAA continues to evolve rapidly, with the use of transgenic mouse models and advanced structural and/or molecular neuroimaging techniques. Yet, despite remarkable recent interest, CAA remains under-recognised by neurologists and stroke physicians. In this review, a fresh look at key developments in understanding the complex pathophysiology, important clinical and radiological features, diagnostic approaches and prospects for rational therapies for this enigmatic small vessel disorder is provided.
A selective follow-up study on a public health survey

Background: The non-response rates in surveys are increasing which is problematic as it means that a progressively smaller proportion of the population represents the majority, and it is uncertain how health survey results are affected. This follow-up was performed on the non-responders to the postal questionnaire in the public health survey Life and Health, conducted in Örebro County Council, Sweden, where large differences in response rates had been found between different socio-demographic groups and geographical areas. The main objective was to analyse non-response bias regarding self-rated health. Methods: This follow-up study was conducted as a census to all non-responders in the area that had the lowest response rate and, in one other geographical area used as a control. It was carried out by telephone interviews, 49.3% (580 individuals) answered the follow-up. The outcome variable was self-rated health, a main variable in public health surveys. Differences in response patterns between responders and initial non-responders were approximated by prevalences with confidence intervals and adjusted odds ratios. Results: Poor health was more common in the initial non-response group than among the responders, even with consideration given to sex, age, country of birth and education. However, good health was equally common among responders and initial non-responders. Conclusions: Public health surveys can be biased due to certain groups being under-represented or not represented at all. For this reason, in repeated public health surveys, we recommend selective follow-ups of such groups at regular intervals.
The Uncovered Set and the Limits of Legislative Action

We present a simulation technique for sorting out the size, shape, and location of the uncovered set to estimate the set of enactable outcomes in “real-world” social choice situations, such as the contemporary Congress. The uncovered set is a well-known but underexploited solution concept in the literature on spatial voting games and collective choice mechanisms. We explain this solution concept in nontechnical terms, submit some theoretical observations to improve our theoretical grasp of it, and provide a simulation technique that makes it possible to estimate this set and thus enable a series of tests of its empirical relevance.
Comments on Conceptualizing and Measuring the Exchange of Beauty and Status

In this comment, I identify two methodological issues in McClintock’s (2014) article on beauty exchange. First, McClintock’s difference models, which find no evidence of exchange, are poor measures of exchange that fail to account for important confounders and rely on an overly narrow conceptualization of exchange. Second, McClintock codes her log-linear models to find a difference in the effect of men’s and women’s beauty in exchange rather than the total effect of women’s beauty, which is both statistically significant and substantively large.
Consumption with Habit Formation

In a representative-agent model of intertemporal consumption-saving with stochastic income and habit formation, we have shown that precautionary savings observed in the data cannot be attributed only to income uncertainty, but also to the time-non-separability of preferences. We have found that, with habit-forming preferences, consumption depends not only on permanent income but also on past consumption and the stronger the habits the lower the effect of income uncertainty on consumption. For a given constant coefficient of risk aversion, habit-forming consumer will have smaller precautionary savings per unit of income risk faced than the one with time-separable preferences. By allowing habit forming preferences in consumption, a closed form solution, explaining excess-sensitivity, excess-smoothness and excess-growth puzzles of consumption, and thus, providing a better framework for empirically testing the behavior of consumption over the business cycle, is found with only i.i. d. income innovations. Dans un modele intertemporel de consommation et d'epargne avec revenu stochastique et formation d'habitudes, nous avons demontre que l'epargne precautionnelle observee dans les donnees peut etre attribuee non seulement a l'incertitude de revenu0501s aussi a l'inseparabilite des preferences. Nous avons trouve que, avec les preferences qui forment des habitudes, la consommation depend non seulement du revenu permanent0501s aussi de niveaux de consommations passes. De plus, plus les habitudes sont resistantes, moins grand sera l'effet de l'incertitude de revenu sur la consommation. Pour un coefficient constant fixe de l'aversion au risque, le consommateur avec habitudes va avoir une epargne precautionnelle plus basse par unite de risque de revenu par rapport a celle avec des preferences temps-separables. En introduisant la formation d'habitudes dans la consommation, et en supposant seulement des innovations i.i. d. , nous avons trouve une solution fermee qui explique les trois enigmes de consommation, l'exces de sensitivite, l'exces de lissete et l'exces de croissance anticipee, et qui propose un meilleur modele pour tester le trajet de la consommation pendant le cycle economique.
A Realistic Neural Mass Model of the Cortex with Laminar-Specific Connections and Synaptic Plasticity – Evaluation with Auditory Habituation

In this work we propose a biologically realistic local cortical circuit model (LCCM), based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1) activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2) realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1) besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6), there exists a parallel “short-cut” pathway (layer 4 to layer 5/6), (2) the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3) the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3) are more strongly habituated than backward connections (from Layer 5/6 to layer 4). Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG), which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.
Safety and tolerability of atomoxetine in treatment of attention deficit hyperactivity disorder in adult patients: An integrated analysis of 15 clinical trials

The safety profile of atomoxetine in the treatment of attention deficit hyperactivity disorder has been studied in many clinical trials. We performed an integrated safety analysis of 15 clinical trials in adults with attention deficit hyperactivity disorder. The analysis pooled patient data into three groups: acute placebo-controlled trials; long-term placebo-controlled trials; all trials. In total, 4829 adults (18–77 years, median: 36 years) were exposed to atomoxetine. Statistically significantly more atomoxetine-treated than placebo-treated patients experienced treatment-emergent adverse events (81.3% vs. 68.3% acute; 90.6% vs. 76.8% long term) and discontinued due to adverse events (8.9% vs. 4.0% acute; 17.9% vs. 6.3% long term). No statistically significant differences were observed in the proportion of patients experiencing serious adverse events. No previously unknown adverse events were identified. The most common adverse events included nausea, dry mouth, decreased appetite, insomnia and erectile dysfunction. Mean increases in heart rate (+5.2 beats per min) and blood pressure (systolic +2 mmHg, diastolic +1.9 mmHg) were modest. The proportion of patients experiencing clinically significant increases in blood pressure and heart rate at any time was statistically significantly higher with atomoxetine (systolic blood pressure 13–17%, diastolic blood pressure 37–40%, heart rate 42–43%) compared to placebo (systolic blood pressure 8–13%, diastolic blood pressure 29–34%, heart rate 21–26%). There was no increased risk of suicidal ideation or behaviour. Our findings confirm atomoxetine’s known safety profile. From a safety perspective, atomoxetine is a useful treatment option for adults with attention deficit hyperactivity disorder.
Neural-Symbolic Learning and Reasoning: Contributions and Challenges

The goal of neural-symbolic computation is to integrate robust connectionist learning and sound symbolic reasoning. With the recent advances in connectionist learning, in particular deep neural networks, forms of representation learning have emerged. However, such representations have not become useful for reasoning. Results from neural-symbolic computation have shown to offer powerful alternatives for knowledge representation, learning and reasoning in neural computation. This paper recalls the main contributions and discusses key challenges for neural-symbolic integration which have been identified at a recent Dagstuhl seminar.
Spectral Probabilistic Modeling and Applications to Natural Language Processing

Probabilistic modeling with latent variables is a powerful paradigm that has led to key advances in many applications such natural language processing, text mining, and computational biology. Unfortunately, while introducing latent variables substantially increases modeling power, the key algorithmic problems of learning and inference for these models can become substantially more complicated. Most existing solutions formulate the task as a nonconvex optimization problem, where convergence to the optimal solution is not guaranteed due to local minima. In this thesis, we propose to tackle these problems through the lens of linear/multi-linear algebra. Viewing latent variable models from this perspective allows us to approach key problems such as structure learning, parameter learning, and inference using tools such as matrix/tensor decompositions, inversion, and ergodicity coefficients. These new tools enable us to develop novel solutions to learning and inference in latent variable models with theoretical and practical advantages. In addition, we focus on applications in Natural Language Processing, using our insights to not only devise new algorithms, but also to propose new models for language modeling and unsupervised parsing.
Tablet-Based Application for Objective Measurement of Motor Fluctuations in Parkinson Disease

Background: The motor subscale of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS-III) has limited applicability for the assessment of motor fluctuations in the home setting. Methods: To assess whether a self-administered, tablet-based application can reliably quantify differences in motor performance using two-target finger tapping and forearm pronation-supination tasks in the ON (maximal dopaminergic medication efficacy) and OFF (reemergence of parkinsonian deficits) medication states, we recruited 11 Parkinson disease (PD) patients (age, 60.6 ± 9.0 years; disease duration, 12.8 ± 4.1 years) and 11 healthy age-matched controls (age, 62.5 ± 10.5 years). The total number of taps, tap interval, tap duration, and tap accuracy were algorithmically calculated by the application, using the more affected side in patients and the dominant hand in healthy controls. Results: Compared to the OFF state, PD patients showed a higher number of taps (84.2 ± 20.3 vs. 54.9 ± 26.9 taps; p = 0.0036) and a shorter tap interval (375.3 ± 97.2 vs. 708.2 ± 412.8 ms; p = 0.0146) but poorer tap accuracy (2,008.4 ± 995.7 vs. 1,111.8 ± 901.3 pixels; p = 0.0055) for the two-target task in the ON state, unaffected by the magnitude of coexistent dyskinesia. Overall, test-retest reliability was high (r >0.75) and the discriminatory ability between OFF and ON states was good (0.60 ≤ AUC ≤ 0.82). The correlations between tapping data and MDS-UPDRS-III scores were only moderate (–0.55 to 0.55). Conclusions: A self-administered, tablet-based application can reliably distinguish between OFF and ON states in fluctuating PD patients and may be sensitive to additional motor phenomena, such as accuracy, not captured by the MDS-UPDRS-III.