Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of head thrust test with head autorotation test reveals that the vestibulo-ocular reflex is enhanced during voluntary head movements.

OBJECTIVES To compare 2 clinical tests of vestibular function, the head autorotation test (HART) and the head thrust test (HTT), and to determine why they give disparate results in patients with known unilateral vestibular deficiency (UVD) due to labyrinthectomy. METHODS We used scleral coils to measure the horizontal (yaw) vestibulo-ocular reflex (VOR) in 5 healthy human subjects and in 11 patients who underwent labyrinthectomy. We used 2 paradigms. Using HART, subjects visually fixated a target during self-generated, swept-frequency, sinusoidal, horizontal head rotations. Using HTT, patients fixated the target during horizontal head thrusts delivered randomly in direction and time. RESULTS In subjects without UVD, eye movements were almost perfectly compensatory for both paradigms. In subjects with UVD, VOR gain for ipsilesional head thrusts was low for both paradigms, but significantly (P<.001) higher (less abnormal) for HART (0.60 +/- 0.13) than for HTT (0.14 +/- 0.13). Contralesional gain was reduced for both, to 0.64 +/- 0.20 for HART and to 0.57 +/- 0.17 for HTT. Because ipsilesional and contralesional gains were not statistically different for HART (P =.69), comparison of VOR gains for half-cycle responses to the HART stimulus could not reliably identify the side of the known lesion. In contrast, HTT consistently identified the side of the lesion for all subjects with UVD. To investigate whether preprogramming contributes to the boost in VOR as measured by HART, we compared the gain and response delay of eye movements during actively self-generated and passively received head thrusts. For subjects without UVD, response delays were shorter for active (6 +/- 1 milliseconds) than for passive (12 +/- 1 milliseconds) HTT. For ipsilesional rotations of subjects with UVD, active HTT yielded a significantly higher gain (0.44 +/- 0.20) (P<.001) and a shorter delay (15 +/- 6 milliseconds) (P<.001) than did passive HTT (0.14 +/- 0.13 and 37 +/- 15 milliseconds, respectively). Contralesional test results revealed a similar performance boost for active head movements. Data are given as mean +/- SD. CONCLUSION When comparison of half-cycle gains is used to identify the lesion side, self-generated predictable head movement paradigms, such as HART and active HTT, are less accurate than passive HTT in the characterization of UVD, in part because preprogramming can augment the VOR during voluntary head movements.
Students as Surrogates for Managers: Evidence from a Replicated Experiment

Using students as surrogates for managers in experiments is commonplace, yet the validity of this practice has not been fully established. To explore the appropriateness of using student samples, we replicate an experiment previously conducted employing a sample of senior managers involved in financial reporting. The result is that although student and manager responses are significantly different from a statistical perspective, both samples lead to the same conclusion for this experiment. The findings suggest that having some disassociation between students and the target population they are meant to represent does not necessarily make them inappropriate surrogates. To examine when inferences are best supported, we explore the comparability for student subgroups and managers along dimensions of experience, knowledge, culture, and gender. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd. Rsum L'utilisation des etudiants en lieu et place des gestionnaires pendant les experiences est une pratique courante, mais dont la validite n'est pas pleinement etablie. Pour en examiner la validite, nous reprenons, dans cet article, une experience anterieure menee sur un echantillon compose de cadres superieurs impliques dans la communication d'information financiere. L'etude montre que meme si les resultats des etudiants et ceux des gestionnaires different considerablement du point de vue statistique, dans le cadre de notre experience, les deux echantillons permettent de deboucher sur les memes conclusions. Elle revele aussi que le fait de dissocier les etudiants de la population cible qu'ils sont censes remplacer ne remet pas forcement en cause leur statut de substituts appropries. Pour examiner les meilleurs arguments qui appuient les inferences formulees, nous explorons la comparabilite entre les sous-groupes d'etudiants et les gestionnaires a partir des quatre dimensions suivantes: l'experience, la connaissance, la culture et l'appartenance sexuelle.
Computational Approaches for Stochastic Shortest Path on Succinct MDPs

We consider the stochastic shortest path (SSP) problem for succinct Markov decision processes (MDPs), where the MDP consists of a set of variables, and a set of nondeterministic rules that update the variables. First, we show that several examples from the AI literature can be modeled as succinct MDPs. Then we present computational approaches for upper and lower bounds for the SSP problem: (a) for computing upper bounds, our method is polynomial-time in the implicit description of the MDP; (b) for lower bounds, we present a polynomial-time (in the size of the implicit description) reduction to quadratic programming. Our approach is applicable even to infinite-state MDPs. Finally, we present experimental results to demonstrate the effectiveness of our approach on several classical examples from the AI literature.
T Cell Co-Stimulation: Inhibition of Immunosuppression?

Effective T cell-dependent immune responses require an antigen-specific signal through the T cell receptor (TCR) and simultaneous antigen non-specific signaling through a co-stimulatory receptor, whereas antigen signals alone induce anergy. Besides the prototype co-stimulatory receptor CD28, T cells also express a variety of other co-stimulatory receptors (1). However, although co-stimulation is an established crucial mechanism, there are a several generally neglected causes of uncertainty about its role. It is thus difficult to reconcile the fact that co-stimulatory signals are completely independent of TCR signaling (2–9) with a simple coupled enhancing effect on TCR-induced activation and this is reinforced by the abundance of co-stimulatory receptors (1). The uncertainty about the role of co-stimulation as simply stimulatory is further reinforced by the fact that T cell activation in its absence does not generally cause anergy but development of regulatory T (Treg) cells (10–12), and that co-stimulation is not obligatory for T cell activation per se. The immense power and lethality of T cell responses induced by certain superagonistic anti-CD28 antibodies and superantigens may also seem inconsistent with the concept that co-stimulation simply delivers an immunostimulatory signal (13, 14). A different interpretation is suggested by recent findings that some forms of co-stimulation, rather than being stimulatory, inhibits a mechanism preventing TCR-induced activation (15). The studies unveiling this role of co-stimulation were initiated by evidence that T cell motility, adhesion, and activation depend on the large transmembrane cell surface receptor low-density lipoprotein receptor-related protein 1 (LRP1) and its ligand thrombospondin-1 (TSP-1) (16–22). LRP1 consists of an α-chain (515 kDa) containing ligand-binding domains, a β-chain (85 kDa) containing the transmembrane domain and the cytoplasmic tail (23, 24), and has binding sites for more than 40 ligands. TSP1 is a 450-kDa glycoprotein composed of three identical disulfide-linked polypeptide chains that display binding sites for various cell surface receptors (25). Co-stimulation through CD28, integrins, and CXCR4 inhibits a protease mechanism that removes LRP1 from the cell surface and antagonizes TCR-induced activation (15).
Method to assess the functional role of noisy brain signals by mining envelope dynamics

Data-driven spatial filtering approaches are commonly used to assess rhythmic brain activity from multichannel recordings such as electroencephalography (EEG). As spatial filter estimation is prone to noise, non-stationarity effects and limited data, a high model variability induced by slight changes of, e.g., involved hyperparameters is generally encountered. These aspects challenge the assessment of functionally relevant features which are of special importance in closed-loop applications as, e.g., in the field of rehabilitation. We propose a data-driven method to identify groups of reliable and functionally relevant oscillatory components computed by a spatial filtering approach. Therefore, we initially embrace the variability of decoding models in a large configuration space before condensing information by density-based clustering of components' functional signatures. Exemplified for a hand force task with rich within-trial structure, the approach was evaluated on EEG data of 18 healthy subjects. We found that functional characteristics of single components are revealed by distinct temporal dynamics of their event-related power changes. Based on a within-subject analysis, our clustering revealed seven groups of homogeneous envelope dynamics on average. To support introspection by practitioners, we provide a set of metrics to characterize and validate single clusterings. We show that identified clusters contain components of strictly confined frequency ranges, dominated by the alpha and beta band. Our method is applicable to any spatial filtering algorithm. Despite high model variability, it allows capturing and monitoring relevant oscillatory features. We foresee its application in closed-loop applications such as brain-computer interface based protocols in stroke rehabilitation.
Sex-specific offspring growth according to maternal testosterone, corticosterone, and glucose levels

Sex allocation theory in vertebrates has greatly benefited from the recent advances in studies on the physiological mechanisms of birth sex ratio variation (e.g., maternal glucose, stress, and testosterone levels). The same physiological mechanisms may, however, also mediate permanent sex-specific effects on individuals after birth. Together with biased maternal postpartum investment on sex allocation, they can have long-term fitness consequences for the offspring. We studied whether maternal preconception levels of serum glucose and testosterone, and fecal corticosterone metabolites differently influenced male and female pup weight until weaning in field voles (Microtus agrestis). In this species, high maternal preconception serum glucose and testosterone levels have previously been associated with the excess of male pups at birth. Our results suggest that male, but not female, pup weight increases with higher maternal preconception testosterone level whereas high maternal serum glucose level promoted female pup weight only. The level of maternal fecal corticosterone metabolites was not related to pup weight in either sex. These findings suggest that in field voles the same physiological mechanisms influencing sex ratio at birth may also influence offspring postnatal weight; however, such influences can act in conflict, as seen in the case of maternal glucose level.
Deep learning improved by biological activation functions

`Biologically inspired' activation functions, such as the logistic sigmoid, have been instrumental in the historical development of machine learning techniques. However in the field of deep learning, they have been largely displaced by rectified (ReLU) or exponential (ELU) linear units to mitigate the effects of vanishing gradients associated with error back-propagation. The logistic sigmoid however does not represent the true input-output relation in real neurones under physiological conditions. Here, radical root unit (RRU) activation functions are introduced, exhibiting input-output non-linearities that are substantially more biologically plausible since their functional form is based on known current-frequency relationships. In order to evaluate whether RRU activations improve deep learning performance, networks are constructed with identical architectures except differing in their transfer functions (ReLU, ELU, and RRUs). Multilayer perceptrons, stacked auto-encoders, and convolutional networks are used to test supervised and unsupervised learning based on the MNIST dataset. Results of learning performance, quantified using loss and error measurements, demonstrate that the RRU networks not only train faster than their ReLU and ELU counterparts, but also lead to improved generalised models in the absence of formal regularisation. These results therefore confirm that revisiting the properties of biological neurones and their circuitry might prove invaluable in the field of deep learning.
Does Education Matter for Economic Growth?

Empirical growth regressions typically include mean years of schooling as a proxy for human capital. However, empirical research often finds that the sign and significance of schooling depends on the sample of observations or the specification of the model. We use a nonparametric local-linear regression estimator and a nonparametric variable relevance test to conduct a rigorous and systematic search for significance of mean years of schooling by examining five of the most comprehensive schooling databases. Contrary to a few recent papers that have identified significant nonlinearities between education and growth, our results suggest that mean years of schooling is not a statistically relevant variable in growth regressions. However, we do find evidence (within a cross-sectional framework), that educational achievement, measured by mean test scores, may provide a more reliable measure of human capital than mean years of schooling.
Faceshop

We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features tools to express a desired image manipulation by providing both geometry and color constraints as user-drawn strokes. As an alternative to the direct user input, our proposed system naturally supports a copy-paste mode, which allows users to edit a given image region by using parts of another exemplar image without the need of hand-drawn sketching at all. The proposed interface runs in real-time and facilitates an interactive and iterative workflow to quickly express the intended edits. Our system is based on a novel sketch domain and a convolutional neural network trained end-to-end to automatically learn to render image regions corresponding to the input strokes. To achieve high quality and semantically consistent results we train our neural network on two simultaneous tasks, namely image completion and image translation. To the best of our knowledge, we are the first to combine these two tasks in a unified framework for interactive image editing. Our results show that the proposed sketch domain, network architecture, and training procedure generalize well to real user input and enable high quality synthesis results without additional post-processing.
Weighted model counting beyond two-variable logic

It was recently shown by van den Broeck at al. that the symmetric weighted first-order model counting problem (WFOMC) for sentences of two-variable logic FO2 is in polynomial time, while it is #P1-complete for some FO3-sentences. We extend the result for FO2 in two independent directions: to sentences of the form φ∧∀x∃=1 y ψ (x, y) with φ and ψ formulated in FO2 and to sentences of the uniform one-dimensional fragment U1 of FO, a recently introduced extension of two-variable logic with the capacity to deal with relation symbols of all arities. We note that the former generalizes the extension of FO2 with a functional relation symbol. We also identify a complete classification of first-order prefix classes according to whether WFOMC is in polynomial time or #P1-complete.
Critical review of health effects of soyabean phyto-oestrogens in post-menopausal women

A consensus view of soyabean phyto-oestrogens in clinical interventions in post-menopausal women is presented that is based on data from the EU-funded project Phytohealth. The phyto-oestrogens, primarily genistein and daidzein, were given as soyabean-protein isolates, whole-soyabean foods or extracts, supplements or pure compounds. A comprehensive literature search was conducted with well-defined inclusion or exclusion criteria. For areas for which substantial research exists only placebo-controlled double-blind randomised controlled trials (RCT) conducted on healthy post-menopausal women were included. For emerging areas all available human studies in post-menopausal women were reviewed. In order to make cross comparisons between studies the doses of isoflavones were calculated as aglycone equivalents. There is a suggestion, but no conclusive evidence, that isoflavones from the sources studied so far have a beneficial effect on bone health. The consumption of whole-soyabean foods and soyabean-protein isolates has some beneficial effects on lipid markers of cardiovascular risk. The consumption of isolated isoflavones does not affect blood lipid levels or blood pressure, although it may improve endothelial function. For menopausal symptoms there is currently limited evidence that soyabean-protein isolates, soyabean foods or red-clover (Trifolium pratense L.) extract are effective but soyabean isoflavone extracts may be effective in reducing hot flushes. There are too few RCT studies to reach conclusions on the effects of isoflavones on breast cancer, colon cancer, diabetes or cognitive function. The health benefits of soyabean phyto-oestrogens in healthy post-menopausal women are subtle and even some well-designed studies do not show protective effects. Future studies should focus on high-risk post-menopausal women, especially in the areas of diabetes, CVD, breast cancer and bone health.
Local modal regression

A local modal estimation procedure is proposed for the regression function in a nonparametric regression model. A distinguishing characteristic of the proposed procedure is that it introduces an additional tuning parameter that is automatically selected using the observed data in order to achieve both robustness and efficiency of the resulting estimate. We demonstrate both theoretically and empirically that the resulting estimator is more efficient than the ordinary local polynomial regression (LPR) estimator in the presence of outliers or heavy-tail error distribution (such as t-distribution). Furthermore, we show that the proposed procedure is as asymptotically efficient as the LPR estimator when there are no outliers and the error distribution is a Gaussian distribution. We propose an expectation–maximisation-type algorithm for the proposed estimation procedure. A Monte Carlo simulation study is conducted to examine the finite sample performance of the proposed method. The simulation results confirm the theoretical findings. The proposed methodology is further illustrated via an analysis of a real data example.
Survival Contradiction Between Stage IIA and Stage IIIA Rectal Cancer: A Retrospective Study

Background: When compared with patients harboring stage IIB and stage IIC disease, those with stage IIIA colorectal cancer have a better prognosis. We aimed to compare the cause-specific survival (CSS) of the patients with stage IIA rectal cancer with that of the patients with stage IIIA rectal cancer. Methods: Data analyzed about patients with stage IIA and stage IIIA rectal cancer was from the US Surveillance, Epidemiology, and End Results (SEER) database. We then validated the results using data derived from Fudan University Shanghai Cancer Center (FUSCC). Results: A total of 16,788 patients (13,551 staged IIA and 3,237 staged IIIA) were identified in SEER database. A multivariate analysis manifested that patients with stage IIIA disease were more likely to have a better CSS (HR 0.894, 95% CI 0.816-0.979, p=0.016) compared with patients with stage IIA rectal cancer. In the subgroup of patients whose number of lymph nodes harvested (LNH) <12, multivariate analysis signified that patients with stage IIIA disease were more prone to have favorable CSS (HR 0.805, 95% CI 0.719-0.901, p<0.001) compared with patients with stage IIA rectal cancer. In LNH≥12 subgroup, the Kaplan-Meier analysis revealed no significant difference between patients experiencing stage IIA and IIIA rectal cancer (p=0.618). Validation of data from FUSCC proved that patients with stage IIIA rectal cancer were more inclined to have better CSS (HR 0.407, 95% CI 0.187-0.885, p=0.019) in comparison to those with stage IIA rectal cancer. Specifically, in LNH<12 subgroup, the survival outcomes of stage IIIA patients were significantly better than that of the stage IIA patients (p=0.019), while there was no statistical significance between these two stages in the subgroup of patients with LNH≥12 (p=0.180). Conclusions: Patients with stage IIA rectal cancer have poorer CSS than patients with stage IIIA rectal cancer, particularly when inadequate lymph nodes are harvested.
Approximating Nash Equilibria for Black-Box Games: A Bayesian Optimization Approach

Game theory has emerged as a powerful framework for modeling a large range of multi-agent scenarios. Many algorithmic solutions require discrete, finite games with payoffs that have a closed-form specification. In contrast, many real-world applications require modeling with continuous action spaces and black-box utility functions where payoff information is available only in the form of empirical (often expensive and/or noisy) observations of strategy profiles. To the best of our knowledge, few tools exist for solving the class of expensive, black-box continuous games. In this paper, we develop a method to find equilibria for such games in a sequential decision-making framework using Bayesian Optimization. The proposed approach is validated on a collection of synthetic game problems with varying degree of noise and action space dimensions. The results indicate that it is capable of improving the maximum regret in noisy and high dimensions to a greater extent than hierarchical or discretized methods.
Scheduled Multi-Task Learning: From Syntax to Translation

Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner. We propose a framework in which our model begins learning syntax and translation interleaved, gradually putting more focus on translation. Using this approach, we achieve considerable improvements in terms of BLEU score on relatively large parallel corpus (WMT14 English to German) and a low-resource (WIT German to English) setup.