Miracle-dz/UniFAR-FTUs huggingface dataset:
Modular organization across changing task demands in healthy and poststroke gait

Our goal was to link impaired module patterns to mobility task performance in persons poststroke. Kinematic, kinetic, and electromyography (EMG) data were collected from 27 poststroke subjects and from 17 healthy control subjects. Each subject walked on a treadmill at their self‐selected walking speed in addition to a randomized block design of four steady‐state mobility capability tasks: walking at maximum speed, and walking at self‐selected speed with maximum cadence, maximum step length, and maximum step height. The number of modules required to account for >90% of the variability accounted for the EMG patterns of each muscle was found using nonnegative matrix factorization. Module compositions of each module during each task were compared to the average module in self‐selected walking using Pearson's correlations. Additionally, to compare module timing, the percentage of integrated module activation timing within six regions of the gait cycle was calculated. Statistical analyses were used to compare the correlations and integrated timing across tasks. Mobility performance measures of task capability were speed change, cadence change, step length change, and step height change. We found that although some poststroke subjects had a smaller number of modules than healthy subjects, the same underlying modules (number and composition) in each subject (both healthy and poststroke) that contribute to steady‐state walking also contribute to specific mobility capability tasks. In healthy subjects, we found that module timing, but not composition, changes when functional task demands are altered during walking. However, this adaptability in module timing, in addition to mobility capability, is limited in poststroke subjects.
Analysing and predicting recurrent interactions among learners during online discussions in a MOOC

High attrition rates are one of the biggest concerns in MOOCs. One of the possible causes may be learners’ lack of interactions and low levels of participations in MOOCs online discussions. Research to measure and predict recurrent interactions of learners in MOOCs online discussions has the potential to gain inside into the likely impact on the attrition rate. It is argued that personalisation in MOOCs has the potential to increase learners’ interactions and associated factors to continuous friendships. In this paper, a detailed analysis has been carried out of learners’ interactions within a MOOC. This paper investigates learners’ interaction habits and their recurrent interactions throughout the entire duration of a MOOC’s course, and consequently proposes a method to measure the interactions and predict possible interactions between peers. The findings denote that when a learner interacted with their peer, they most probably interact again in the following weeks. Moreover, our proposed prediction method also demonstrate promising results towards predicting future interactions between learners based on their previous relationships
Continuous-Time Trajectory Estimation for Event-based Vision Sensors

Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), do not output a sequence of video frames like standard cameras, but a stream of asynchronous events. An event is triggered when a pixel detects a change of brightness in the scene. An event contains the location, sign, and precise timestamp of the change. The high dynamic range and temporal resolution of the DVS, which is in the order of micro-seconds, make this a very promising sensor for high-speed applications, such as robotics and wearable computing. However, due to the fundamentally different structure of the sensor's output, new algorithms that exploit the high temporal resolution and the asynchronous nature of the sensor are required. In this paper, we address ego-motion estimation for an event-based vision sensor using a continuous-time framework to directly integrate the information conveyed by the sensor. The DVS pose trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines and it is optimized according to the observed events. We evaluate our method using datasets acquired from sensor-in-the-loop simulations and onboard a quadrotor performing flips. The results are compared to the ground truth, showing the good performance of the proposed technique.
CandidaDB: a genome database for Candida albicans pathogenomics

CandidaDB is a database dedicated to the genome of the most prevalent systemic fungal pathogen of humans, Candida albicans. CandidaDB is based on an annotation of the Stanford Genome Technology Center C.albicans genome sequence data by the European Galar Fungail Consortium. CandidaDB Release 2.0 (June 2004) contains information pertaining to Assembly 19 of the genome of C.albicans strain SC5314. The current release contains 6244 annotated entries corresponding to 130 tRNA genes and 5917 protein-coding genes. For these, it provides tentative functional assignments along with numerous pre-run analyses that can assist the researcher in the evaluation of gene function for the purpose of specific or large-scale analysis. CandidaDB is based on GenoList, a generic relational data schema and a World Wide Web interface that has been adapted to the handling of eukaryotic genomes. The interface allows users to browse easily through genome data and retrieve information. CandidaDB also provides more elaborate tools, such as pattern searching, that are tightly connected to the overall browsing system. As the C.albicans genome is diploid and still incompletely assembled, CandidaDB provides tools to browse the genome by individual supercontigs and to examine information about allelic sequences obtained from complementary contigs. CandidaDB is accessible at http://genolist.pasteur. fr/CandidaDB.
Reduction of apoptosis by proanthocyanidin-induced autophagy in the human gastric cancer cell line MGC-803

Proanthocyanidins are flavonoids that are widely present in the skin and seeds of various plants, with the highest content in grape seeds. Many experiments have shown that proanthocyanidins have antitumor activity both in vivo and in vitro. Autophagy and apoptosis of tumor cells induced by drugs are two of the major causes of tumor cell death. However, reports on the effect of autophagy induced by drugs in tumor cells are not consistent and suggest that autophagy can have synergistic or antagonistic effects with apoptosis. This research was aimed at investigating whether proanthocyanidins induced autophagy and apoptosis in human gastric cancer cell line MGC-803 cells and to identify the mechanism of proanthocyanidins action to further determine the effect of proanthocyanidins-induced autophagy on apoptosis. MTT assay was used to examine the proanthocyanidin cytotoxicity against human gastric cancer cell line MGC-803. Transmission electron microscopy and monodansylcadaverine (MDC) staining were used to detect autophagy. Annexin V APC/7-AAD double staining and Hoechst 33342/propidium iodide (PI) double staining were used to explore apoptosis. Western blotting was used to determine expression of proteins related to autophagy and apoptosis. Real-time quantitative PCR technology was used to determine the mRNA level of Beclin1 and BCL-2. The results showed that proanthocyanidins exhibit a significant inhibitory effect on the human gastric cancer cell line MGC-803 proliferation in vitro and simultaneously activate autophagy and apoptosis to promote cell death. Furthermore, when proanthocyanidin-induced autophagy is inhibited, apoptosis increases significantly, proanthocyanidins can be used together with autophagy inhibitors to enhance cytotoxicity.
Stochastic Online AUC Maximization

Area under ROC (AUC) is a metric which is widely used for measuring the classification performance for imbalanced data. It is of theoretical and practical interest to develop online learning algorithms that maximizes AUC for large-scale data. A specific challenge in developing online AUC maximization algorithm is that the learning objective function is usually defined over a pair of training examples of opposite classes, and existing methods achieves on-line processing with higher space and time complexity. In this work, we propose a new stochastic online algorithm for AUC maximization. In particular, we show that AUC optimization can be equivalently formulated as a convex-concave saddle point problem. From this saddle representation, a stochastic online algorithm (SOLAM) is proposed which has time and space complexity of one datum. We establish theoretical convergence of SOLAM with high probability and demonstrate its effectiveness on standard benchmark datasets.
Turning inward: Tocqueville and the structuring of reflexivity

ABSTRACT In this paper, I argue that the dominant view of reflexivity in contemporary social science is overly decontextualized, despite the value that reflexivity scholars have placed on the dynamic and active nature of individual thought and action. While this problem has been highlighted before, in terms of how habitual actions shape the way that individuals engage in reflexive thought, little attention has been given to the ways in which non-internalized elements of the environment condition this process. I illustrate my argument with examples from Tocqueville’s classic text Democracy in America. I demonstrate how Tocqueville provided a framework for analyzing the way that societal-level norms and opportunity structures impact personal reflexive capacities. In this respect, his work is unparalleled and as such remains relevant to contemporary debates. I therefore argue for the application of Tocqueville’s work into arenas it has seldom entered.
Predicting Scientific Grid Data Transfer Characteristics

Big data scientists routinely transfer massive amounts of data. By understanding and modelling different aspects of these data transfers, we can make using big data more efficient and user-friendly. In this paper, we first develop a set of data storage location prediction heuristics. These heuristics help big data scientists manage and discover locations to transfer their data from and to. We show, via analysis of historical Globus operations, that our approaches can predict the storage locations accessed by users with 78.2% and 95.5% accuracy for top-1 and top-3 recommendations, respectively. Predicting transfer bandwidth allows for more optimal selections of data replicas to download from and for more optimal scheduling and routing of data transfers. We show that existing bandwidth prediction techniques perform poorly on real-world data and develop heuristics that (performance statistics).
3D active appearance model for aligning faces in 2D images

Perceiving human faces is one of the most important functions for human robot interaction. The active appearance model (AAM) is a statistical approach that models the shape and texture of a target object. According to a number of the existing works, AAM has a great success in modeling human faces. Unfortunately, the traditional AAM framework could fail when the face pose changes as only 2D information is used to model a 3D object. To overcome this limitation, we propose a 3D AAM framework in which a 3D shape model and an appearance model are used to model human faces. Instead of choosing a proper weighting constant to balance the contributions from appearance similarity and the constraint on consistent 2D shape with 3D shape in the existing work, our approach directly matches 2D visual faces with the 3D shape model. No balancing weighting between 2D shape and 3D shape is needed. In addition, only frontal faces are needed for training and non-frontal faces can be aligned successfully. The experimental results with 20 subjects demonstrate the effectiveness of the proposed approach.
The Contact Hypothesis Re-evaluated

This paper evaluates the state of contact hypothesis research from a policy perspective. Building on Pettigrew and Tropp's (2006) influential meta-analysis, we assemble all intergroup contact studies that feature random assignment and delayed outcome measures, of which there are 27 in total, nearly two-thirds of which were published following the original review. We find the evidence from this updated dataset to be consistent with Pettigrew and Tropp's (2006) conclusion that contact “typically reduces prejudice. ” At the same time, our meta-analysis suggests that contact's effects vary, with interventions directed at ethnic or racial prejudice generating substantially weaker effects. Moreover, our inventory of relevant studies reveals important gaps, most notably the absence of studies addressing adults' racial or ethnic prejudices, an important limitation for both theory and policy. We also call attention to the lack of research that systematically investigates the scope conditions suggested by Allport (1954) under which contact is most influential. We conclude that these gaps in contact research must be addressed empirically before this hypothesis can reliably guide policy.
Social Trust in Opportunistic Networks

Opportunistic networks enable mobile users to participate in various social interactions with applications such as content distribution and micro-blogs. Because of their distributed nature, securing user interactions relies rather on trust than hard cryptography. Trust is often based on past user interactions such as in reputation systems relying on ratings. Yet, a more fundamental trust, social trust - assessing a user is genuine with honest intentions - must be established beforehand as many identities can be created easily (i.e., sybils). By leveraging the social network structure and its dynamics (conscious secure pairing and wireless contacts), we propose two complementary approaches for social trust establishment: explicit social trust and implicit social trust. Complexity, trust propagation and security issues are evaluated using real world complex graphs, synthetic mobility models and mobility traces. We show how our approach limits the maximum number of sybils independently of the network size and is more robust against manipulation attacks compared to state-of-the-art approaches such as PGP- like certification chains and distributed community detection algorithms.
The use of visual information in natural scenes

Despite the complexity and diversity of natural scenes, humans are very fast and accurate at identifying basic-level scene categories. In this paper we develop a new technique (based on Bubbles, Gosselin & Schyns, 2001a; Schyns, Bonnar, & Gosselin, 2002) to determine some of the information requirements of basic-level scene categorizations. Using 2400 scenes from an established scene database (Oliva & Torralba, 2001), the algorithm randomly samples the Fourier coefficients of the phase spectrum. Sampled Fourier coefficients retain their original phase while the phase of nonsampled coefficients is replaced with that of white noise. Observers categorized the stimuli into 8 basic-level categories. The location of the sampled Fourier coefficients leading to correct categorizations was recorded per trial. Statistical analyses revealed the major scales and orientations of the phase spectrum that observers used to distinguish scene categories.
Focusing the Corporate Product: Securities Analysts and De-diversification

The issue of corporate control is examined through an analysis of the de-diversification activity of publicly held American firms from 1985 to 1994. Prominent accounts of such behavior depict newly powerful shareholders as having demanded a dismantling of the inefficient, highly diversified corporate strategies that arose in the late 1950s and the 1960s. This paper highlights an additional factor that spurred such divestiture: the need to present a coherent product identity in the stock market. It is argued that because they straddle the industry categories that investors—and securities analysts, who specialize by industry—use to compare like assets, diversified firms hinder efforts at valuing their shares. As a result, managers of such firms face pressure from analysts to dediversify so that their stock is more easily understood. Results indicate that, in addition to such factors as weak economic performance, de-diversification is more likely when a firm's stock price is low and there is a significant mismatch between its corporate strategy and the identity attributed to the firm by analysts.
Financial pressure, monetary policy effects and inventory adjustment by UK and Spanish firms

This paper examines the adjustment of inventories by firms in the UK and Spain. A widely-held view—but one which has not found much support in previous studies of inventories— is that a key channel for monetary policy is through influencing inventory accumulation. Using a large company-level panel dataset for both countries, significant effects associated with borrowing costs on inventories are estimated. Financial effects associated with liquidity and the borrowing ratio are estimated to be weaker in Spain than in the UK. Since the Spanish financial system is more clearly bank-based, this is interpreted as contrary to the ‘bank-dependence hypothesis’.
Global Gaussian approach for scene categorization using information geometry

Local features provide powerful cues for generic image recognition. An image is represented by a “bag” of local features, which form a probabilistic distribution in the feature space. The problem is how to exploit the distributions efficiently. One of the most successful approaches is the bag-of-keypoints scheme, which can be interpreted as sparse sampling of high-level statistics, in the sense that it describes a complex structure of a local feature distribution using a relatively small number of parameters. In this paper, we propose the opposite approach, dense sampling of low-level statistics. A distribution is represented by a Gaussian in the entire feature space. We define some similarity measures of the distributions based on an information geometry framework and show how this conceptually simple approach can provide a satisfactory performance, comparable to the bag-of-keypoints for scene classification tasks. Furthermore, because our method and bag-of-keypoints illustrate different statistical points, we can further improve classification performance by using both of them in kernels.
Serum anti-Mullerian hormone as a surrogate for antral follicle count for definition of the polycystic ovary syndrome.

CONTEXT Despite its frequency, the polycystic ovary syndrome (PCOS) is still a difficult diagnosis in endocrinology, gynecology, and reproductive medicine. To help solve this issue, the Rotterdam consensus conference proposed to include the ultrasonographic follicle count as a new diagnostic criterion, in addition to hyperandrogenism and oligo-anovulation. Unfortunately, its assessment does not offer sufficient reliability worldwide. OBJECTIVE The aim of our study was to check whether anti-Müllerian hormone (AMH) measurement in the serum could be a surrogate for antral follicle count in the diagnostic criteria of PCOS. DESIGN, SETTING, AND PATIENTS Serum AMH was measured with a second-generation immunoassay in a cohort of 73 PCOS patients and 96 controls, and its diagnostic power was evaluated by receiver operating characteristic curves. PCOS was diagnosed according to the Rotterdam definition. RESULTS Serum AMH levels were 3-fold higher in PCOS patients than in controls (81.6 vs. 33.5 pmol/liter; P < 0.001) and were significantly related to the follicle number in the two groups. The area under the receiver operating characteristic curve for the AMH assay was 0.851, indicating a good diagnostic potency. Setting the threshold at 60 pmol/liter offered the best compromise between specificity (92%) and sensitivity (67%). CONCLUSIONS The serum AMH level is an accurate marker of the ovarian early antral follicle number and offers a good diagnostic potency. In situations where accurate ultrasonographic data are not available, AMH could thus be used instead of the follicle count as a diagnostic criterion and incorporated as such in the Rotterdam definition of PCOS.
Optical spectroscopy of X-Mega targets in the Carina nebula – VI. FO 15: a new O-type double-lined eclipsing binary

We report the discovery of a new O-type double-lined spectroscopic binary with a short orbital period of 1.4 d. We find the primary component of this binary, FO 15, to have an approximate spectral type 05.5Vz, i.e. a zero-age main-sequence (ZAMS) star. The secondary appears to be of spectral type 09.5V. We have performed a numerical model fit to the public All Sky Automated Survey photometry, which shows that FO 15 is also an eclipsing binary. We find an orbital inclination of ∼80°. From a simultaneous light curve and radial velocity solution, we find the masses and radii of the two components to be 30 ± 1 and 16 ± 1M ⊙ and 7.5 ± 0.5 and 5.3 ± 0.5 R ⊙ . These radii, and hence also the luminosities, are smaller than those of normal O-type stars, but similar to recently bom ZAMS O-type stars. The absolute magnitudes derived from our analysis locate FO 15 at the same distance as η Carinae. From Chandra and XMM X-ray images, we also find that there are two close X-ray sources: one coincident with FO 15 and the other one without optical counterpart. The latter seems to be a highly variable source, presumably due to a pre-main-sequence stellar neighbour of FO 15.
Testing a dynamic model of the impact of psychological capital on work engagement and job performance

Purpose Psychological Capital (PsyCap), consisting of hope, efficacy, resilience, and optimism, is a positive state associated with attitudes, behaviors and performance. The purpose of this paper is to investigate a dynamic mediational model posing work engagement as the mediator of the longitudinal relation between PsyCap and job performance. Design/methodology/approach Data came from all white collar employees who responded to this study’s variables (n=420) from a comprehensive data set drawn from a large communications service company over two consecutive years. Job performance was rated at the end of each year by the direct supervisor as part of the organizational appraisal system. Findings Structural equation modeling analysis found that both absolute levels and increases in PsyCap predicted subsequent work engagement increases which in turn predicted job performance increases. Moreover, the mediating role of the changes in work engagement between previous PsyCap and performance change was confirmed over time. Research limitations/implications There is much to gain in conceptualizing the relations among PsyCap, work engagement and job performance as dynamic, rather than static. The results support the conservation of resources theory, in which employees are motivated to acquire, protect and foster their valued (psychological) resources to attain successful performance outcomes, in order to create a gain cycle of resources (Salanova et al., 2010). Moreover, it provide further empirical validation for the idea that processes, like work engagement, are sustained by personal resources, and that these latter exerts mostly an indirect effect on organizational behavior outcomes (Xanthopoulou et al., 2009b). Practical implications These results are important from a practical point of view, because they point to the importance of training interventions aimed at developing and sustaining PsyCap as an important determinant of workers’ motivation and behavior within the organization. Considerable literature offers practical insights and guidelines for developing PsyCap (Luthans et al., 2006, 2015; Luthans and Youssef-Morgan, 2017). Originality/value Despite the demonstrated state-like, dynamic nature of PsyCap, its relationship with performance has mainly been statically analyzed and the role of possible mediating mechanisms largely ignored. This study begins to fill this research gap by investigating the dynamic nature of PsyCap in relation to work engagement and job performance and whether over time engagement mediates the relationship between PsyCap and job performance.