Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of sensitivity to reward on reactivity to alcohol-related cues.

AIMS To investigate the role of sensitivity to reward in mediating social drinkers' reactivity to alcohol cues. DESIGN A standard cue-reactivity paradigm was employed. Two groups of social drinkers (heavy and light) were assessed after exposure to the sight, smell and taste of a neutral cue (water) and then an alcohol cue (glass of beer). SETTING Sessions were conducted in a laboratory based environment. PARTICIPANTS Twenty heavy (12 males, eight females) and 18 light social drinkers (seven males, 11 females) were recruited; mean age was 23.6 years. MEASUREMENTS The Card Arranging Reward Responsivity Objective Test (CARROT), assessing behavioural responsiveness to a monetary incentive; urge to drink; positive affect; and the BAS scales, assessing sensitivity to reward. FINDINGS Heavy drinkers displayed a significant increase in responsivity to rewards (i.e. CARROT) and self-reported urge to drink, but not positive affect, after exposure to alcohol. For the heavy drinkers, heightened sensitivity to reward (i.e. BAS scales) was significantly related to cue-elicited urge to drink and positive affect. CONCLUSION The results are consistent with a conditioned appetitive motivational model of alcohol use and suggest that Gray's theory of personality may be of some benefit in explaining variation in reactivity responses.
A Data Transfer Fusion Method for Discriminating Similar Spectral Classes

Hyperspectral data provide new capabilities for discriminating spectrally similar classes, but such class signatures sometimes will be difficult to analyze. To incorporate reliable useful information could help, but at the same time, may also lead increased dimensionality of the feature vector making the hyperspectral data larger than expected. It is challenging to apply discriminative information from these training data to testing data that are not in the same feature space and with different data distributions. A data fusion method based on transfer learning is proposed, in which transfer learning is introduced into boosting algorithm, and other out-date data are used to instruct hyperspectral image classification. In order to validate the method, experiments are conducted on EO-1 Hyperion hyperspectral data and ROSIS hyperspectral data. Significant improvements have been achieved in terms of accuracy compared to the results generated by conventional classification approaches.
Austrian Economics and the Study of Entrepreneurship: Concepts and Contributions

Entrepreneurship researcher’s growing interest in Austrian economics has not been matched by systematic efforts to tease out and incorporate this tradition's basic ideas. To remedy this situation, this paper introduces the Austrian market-process tradition and also compares it to the equilibrium-focused neoclassical tradition. Taking this contrast as point of departure, we reexamine three central questions in the field of entrepreneurship studies: Who is an entrepreneur? , What is an opportunity? , and What is the role of planning? Based on the answers to these specific questions, the paper concludes with a broader discussion of how Austrian concepts can contribute to the theoretical and methodological development of entrepreneurship studies writ large.
Reduction of misclassification rates of obesity by body mass index using dual‐energy X‐ray absorptiometry scans to improve subsequent prediction of per cent fat mass in a Caucasian population

• Body mass index (BMI) is not accurate in the classification of excess body fat, failing to identify as many as half of individuals with excess per cent fat mass. • Normal‐weight obesity, which goes undiagnosed when BMI is the only measure of adiposity utilized, has been shown to be associated with an increased risk of cardiovascular comorbidities and mortality. • Dual‐energy X‐ray absorptiometry (DXA) is an accurate and relatively inexpensive method for indirect assessment of body composition.
Performance Analysis of FSO Communications With Diversity Methods: Add More Relays or More Apertures?

This paper targets the performance analysis of multiple-input-multiple-output (MIMO) point-to-point single-aperture relay-assisted and MIMO relay-assisted free-space optical (FSO) communications. The objective of the paper is to compare the different FSO diversity methods via an outage probability analysis over gamma-gamma turbulence channels in the case of independent fading among the different apertures of the communicating nodes. Through a well-tailored calculation method, we approximate the gamma-gamma distribution and the gamma-gamma sum-distribution by appropriate gamma distributions. This method resulted in simple closed-form outage probability expressions that are accurate for average-to-large values of the signal-to-noise ratio (SNR) and in asymptotic expressions that are useful for evaluating the diversity gains and coding gains. In particular, we derived the diversity gains of MIMO repetition coding (RC), MIMO transmit laser selection (TLS), MIMO all-active relaying (AR) and MIMO selective-relaying (SR). We prove that MIMO-RC and MIMO-TLS, on one hand, and MIMO-AR and MIMO-SR, on the second hand, achieve the same diversity gain and we evaluate the coding gain of TLS with respect to RC and of SR with respect to AR. By comparing the MIMO and cooperative methods with point receivers, we reach the central result that it is always better, from a diversity gain point of view, to add more apertures to the source and destination rather than adding more relays in their vicinity despite the fact that the fading variance along FSO links decreases with the distance. For receivers that implement aperture averaging, we derive conditions under which MIMO techniques are unconditionally better than cooperative techniques with any network topology.
Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization

The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration.
A Self-Stabilizing Algorithm for Finding a Minimal Distance-2 Dominating Set in Distributed Systems

The study of various dominating set problems is an important area within graph theory. In applications, a dominating set in a system can be considered as an ideal place for allocating resources. And, a minimal dominating set allows allocating a smaller number of resources. Distance-versions of the concept of minimal dominating sets are more applicable to modeling real-world problems, such as placing a smaller number of objects within acceptable distances of a given population. However, due to the main restriction that any processor in a distributed system can only access the data of its direct neighbors, a self-stabilizing algorithm for finding a minimal distance-k (with k≥2) dominating set is hard to get, and its correctness is hard to verify. In this paper, a self-stabilizing algorithm for finding a minimal distance-2 dominating set is proposed. The algorithm can be applied to any distributed system that operates under the central demon model. The correctness of the algorithm is verified.
Recurrent neural networks towards detection of SQL attacks

In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker we divide SQL statements into tokens and pass them to our detection system, which predicts the next token, taking into account previously seen tokens. In the learning phase tokens are passed to recurrent neural network (RNN) trained by back-propagation through time (BPTT) algorithm. Teaching data are shifted by one token forward in time with relation to input. The purpose of the testing phase is to predict the next token in the sequence. All experiments were conducted on Jordan and Elman networks using data gathered from PHP Nuke portal. Experimental results show that the Jordan network outperforms the Elman network predicting correctly queries of the length up to ten.
TUNING THE CATALYTIC PROPERTY OF Pt/GRAPHENE USING SMALL ORGANIC MOLECULE

We attempt to detect the possibility to modify the catalytic property of Pt/graphene using small organic molecule. Results of density functional theory calculations show that ssDNA segment and L-leucine modify the electron transfer at the interface, changing the electronic property and redox property of Pt/graphene catalyst system. L-leucine promotes electron transfer from Pt and increases the HOMO and LUMO of L-Pt/G system, while ssDNA segment lessens electron transfer from Pt and increases the HOMO and LUMO. With such electronic properties, L-leucine is beneficial to enhance the activity of Pt/graphene for catalytic a-dehydrogenation of ethanol while ssDNA fragment has adverse effects on the catalyzed a-dehydrogenation of ethanol. Reults will favour the design of active next-generation catalyst based on metal/graphene composite for novel applications.
Incremental Updates for Rapid Glossy Global Illumination

We present an integrated global illumination algorithm including non‐diffuse light transport which can handle complex scenes and enables rapid incremental updates. We build on a unified algorithm which uses hierarchical radiosity with clustering and particle tracing for diffuse and non‐diffuse transport respectively. We present a new algorithm which chooses between reconstructing specular effects such as caustics on the diffuse radiosity mesh, or special purpose caustic textures, when high frequencies are present. Algorithms are presented to choose the resolution of these textures and to reconstruct the high‐frequency non‐diffuse lighting effects. We use a dynamic spatial data structure to restrict the number of particles re‐emitted during the local modifications of the scene. By combining this incremental particle trace with a line‐space hierarchy for incremental update of diffuse illumination, we can locally modify complex scenes rapidly. We also develop an algorithm which, by permitting slight quality degradation during motion, achieves quasi‐interactive updates. We present an implementation of our new method and its application to indoors and outdoors scenes.
Early visual experience and the receptive-field organization of optic flow processing interneurons in the fly motion pathway

The distribution of local preferred directions and motion sensitivities within the receptive fields of so-called tangential neurons in the fly visual system was previously found to match optic flow fields as induced by certain self-motions. The complex receptive-field organization of the tangential neurons and the recent evidence showing that the orderly development of the fly's peripheral visual system depends on visual experience led us to investigate whether or not early visual input is required to establish the functional receptive-field properties of such tangential neurons. In electrophysiological investigations of two identified tangential neurons, it turned out that dark-hatched flies which were kept in complete darkness for 2 days develop basically the same receptive-field organization as flies which were raised under seasonal light/dark conditions and were free to move in their cages. We did not find any evidence that the development of the sophisticated receptive-field organization of tangential neurons depends on sensory experience. Instead, the input to the tangential neurons seems to be “hardwired” and the specificity of these cells to optic flow induced during self-motions of the animal may have evolved on a phylogenetical time scale.
Effect of taekwondo training on sensori-motor performance and postural control in children with and without developmental coordination disorder

Developmental coordination disorder (DCD) is a common motor dysfunction, affecting approximately 6% of children at primary school age. A reported 73% to 87% of children with DCD exhibit balance problems. Suboptimal balance ability demonstrated in a child with DCD requires attention because any impairment in postural control may limit the child’s participation in daily activities, increase the risk of falls and affect development of motor skills. Training in dynamic sports such as dancing is reported to improve postural control in young people. Taekwondo (TKD) is a popular sport famous for its fast kicking techniques. Practitioners are frequently required to stand and pivot on one foot. This manoeuvre is of interest as a potential therapeutic activity to improve balance control in children with DCD. However, the effects of TKD training on postural control have not been fully explored, particularly in children with DCD. Therefore, this thesis explores the effects of TKD training on postural control and sensori-motor performance in children with DCD. Two cross-sectional studies (studies 1 and 2) were conducted to investigate and identify deficits in postural control status, i.e. (1) sensory organization, (2) motor strategy, and (3) standing postural control, in children with DCD. Three additional cross-sectional studies (studies 3 to 5) were then conducted to investigate the potential effects of TKD training in terms of (1) sensory organization, (2) knee joint proprioception, (3) knee muscle strength, and (4) unipedal stance stability, in typically developing adolescents. Finally, a
Improving Performance of Rural Supply Chains Using Mobile Phones: Reducing Information Asymmetry to Improve Stock Availability in Low-resource Environments

Ensuring availability of essential goods, such as medicines or vaccines, at the point of care in the villages is a significant challenge in rural supply chains. The crux of the problem lies in information asymmetry of supply and demand, as well as ad hoc distribution practices. These supply chains are not managed efficiently, and often information flows upstream in the chain in an incomplete, incorrect and untimely manner. This ultimately affects procurement and distribution decisions, consequently leading to stock outs of important goods at the point of care. To address the problem of stock availability, we have implemented a "Bulletin Board" that digitally captures needs (demand) and availability (supply) of goods in real-time from any location using low-end mobile phones, and broadcasts this information to vendors and managers, upstream in the supply chain. We demonstrate that by reducing information asymmetry between supply and demand, the overall system "self-organizes" in a manner that stocks are appropriately re-distributed, thereby improving availability at the point of care. We demonstrate these concepts in the context of vaccine and medicine availability in a public health supply chain. In a study in Karnataka, India, we have observed vaccine stock availability increase to 99% and replenishment responsiveness improve by 64%. However, such an approach relies on obtaining high quality of data from the last mile, which is a big challenge in low-resource environments, where mobile networks are unreliable and human capacity is low. We describe the design of a mobile phone based service that leverages insights from information asymmetry theory and effective service design to achieve sustainable supply chain performance in low-resource environments.
L'hypofrontalité et la schizophrénie: changement de perspective.

Many attempts to isolate a dysfunctional site in the brain of schizophrenic patients have converged in a neuropathological conception of the disease based on frontal lobe hypoactivity. Recent data from studies using cytoarchitectony, computerized tomographic scans, cerebral regional blood flow, smooth pursuit eye movements and neuropsychological assessments are discussed. Current data on the hypofrontality hypothesis in schizophrenia seem to suggest an associationist rather than a localisationist perspective of the disorder.
Eurographics/siggraph Symposium on Computer Animation (2003) Adaptive Wisp Tree -a Multiresolution Control Structure for Simulating Dynamic Clustering in Hair Motion

Realistic animation of long human hair is difficult due to the number of hair strands and to the complexity of their interactions. Existing methods remain limited to smooth, uniform, and relatively simple hair motion. We present a powerful adaptive approach to modeling dynamic clustering behavior that characterizes complex long-hair motion. The Adaptive Wisp Tree (AWT) is a novel control structure that approximates the large-scale coherent motion of hair clusters as well as small-scaled variation of individual hair strands. The AWT also aids computation efficiency by identifying regions where visible hair motions are likely to occur. The AWT is coupled with a mul-tiresolution geometry used to define the initial hair model. This combined system produces stable animations that exhibit the natural effects of clustering and mutual hair interaction. Our results show that the method is applicable to a wide variety of hair styles. Figure 1: Comparisons between real hair and simulation results.
A Part‐aware Surface Metric for Shape Analysis

The notion of parts in a shape plays an important role in many geometry problems, including segmentation, correspondence, recognition, editing, and animation. As the fundamental geometric representation of 3D objects in computer graphics is surface‐based, solutions of many such problems utilize a surface metric, a distance function defined over pairs of points on the surface, to assist shape analysis and understanding. The main contribution of our work is to bring together these two fundamental concepts: shape parts and surface metric. Specifically, we develop a surface metric that is part‐aware. To encode part information at a point on a shape, we model its volumetric context – called the volumetric shape image (VSI) – inside the shape's enclosed volume, to capture relevant visibility information. We then define the part‐aware metric by combining an appropriate VSI distance with geodesic distance and normal variation. We show how the volumetric view on part separation addresses certain limitations of the surface view, which relies on concavity measures over a surface as implied by the well‐known minima rule. We demonstrate how the new metric can be effectively utilized in various applications including mesh segmentation, shape registration, part‐aware sampling and shape retrieval.
Stochastic patrolling in adversarial settings

In this paper, we consider a patrolling problem in adversarial environments where intruders use the information about a patrolling path to increase chances of successful attacks. We use Markov chains to design randomized patrolling paths on graphs. We present four different intruder models, each of which use information about the patrolling path in a different manner. We characterize the expected rewards for those intruder models as a function of the Markov chain that is being used for patrolling. We show that minimizing the reward functions is a non-convex optimization problem. We propose a pattern search based algorithm to determine a locally optimal patrolling strategy. We also show that for a certain type of intruder, a deterministic patrolling policy given by an orienteering tour of the graph is the optimal patrolling strategy.
Macroeconomic Fluctuations and Corporate Financial Fragility

Using a large sample of accounting data for non-financial companies in France, this paper studies the interactions between macroeconomic shocks and companies' financial fragility. We consider links in both directions, namely whether firms' bankruptcies are affected by macroeconomic variables, and whether bankruptcies determine the business cycle. We estimate forecasting equations for firms' bankruptcy using Shumway's (2001) approach and study the joint dynamics of bankruptcies and macroeconomic variables within an exogenous VAR type model estimated at the sector level. We find evidence of reciprocal links between the bankruptcy rate and the output gap and highlight significant "second round effects" of shocks to the output gap on bankruptcies. We show how taking into account the dynamic transmission of macroeconomic shocks matters in stress testing exercises.
Dynamic Quantum Clustering: A Tool for Unsupervised Exploration of Structures in Data

A given set of data-points in some feature space may be associated with a Schroedinger equation whose potential is determined by the data. This is known to lead to good clustering solutions. Here we extend this approach into a full-fledged dynamical scheme using a time-dependent Schroedinger equation with a small diffusion component. Moreover, we approximate this Hamiltonian formalism by a truncated calculation within a set of Gaussian wave functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition or feature filtering.
NOBLE: non-blocking programming support via lock-free shared abstract data types

An essential part of programming for multi-core and multi-processor includes ef cient and reliable means for sharing data. Lock-free data structures are known as very suitable for this purpose, although experienced to be very complex to design. In this paper, we present a software library of non-blocking abstract data types that have been designed to facilitate lock-free programming for non-experts. The system provides: i) ef cient implementations of the most commonly used data types in concurrent and sequential software design, ii) a lock-free memory management system, and iii) a run time-system. The library provides clear semantics that are at least as strong as those of corresponding lock-based implementations of the respective data types. Our software library can be used for facilitating lockfree programming; its design enables the programmer to: i) replace lock-based components of sequential or parallel code easily and ef ciently , ii) use well-tuned concurrent algorithms inside a software or hardware transactional system. In the paper we describe the design and functionality of the system. We also provide experimental results that show that the library can considerably improve the performance of software systems.