Miracle-dz/UniFAR-FTUs huggingface dataset:
Building a Framework for Implementing Total Responsibility Management

While numerous writers have argued in general terms about the principles of Corporate Social Responsibility (CSR) and sustainability management (e.g. Dunphy, Griffiths and Benn 2003, 2007) there has been little empirical exploration of these highly generalised prescriptions. We explore a framework for the management and implementation of CSR and sustainability developed by leading scholars Waddock and Bodwell (2007). Waddock and Bodwell’s (2007) Total Responsibility Management (TRM) model implies that TRM begins with inspiration, gains strength with integration and stays relevant with constant innovation. Using data from two corporations with strong performances according to CSR and sustainability assessment mechanisms such as the Dow Jones Sustainability Index, we aim to identify examples of the management practices and processes associated with inspiration, integration and innovation. We conclude that there is clear evidence that inspiration and integration systems of TRM are associated with the successful implementation of CSR and sustainability. Innovation may be another factor in the implementation of CSR but this may be more important in some industry sectors than others.
The Demon of Writing: Powers and Failures of Paperwork

Since the middle of the eighteenth century, political thinkers of all kinds--radical and reactionary, professional and amateur--have been complaining about "bureaucracy. " But what, exactly, are they complaining about? In The Demon of Writing, Ben Kafka offers a critical history and theory of one of the most ubiquitous, least understood forms of media: paperwork. States rely on records to tax and spend, protect and serve, discipline and punish. But time and again, this paperwork proves to be unreliable. Examining episodes that range from the story of a clerk who lost his job and then his mind in the French Revolution to an account of Roland Barthes's brief stint as a university administrator, Kafka reveals the powers, the failures, and even the pleasures of paperwork. Many of its complexities, he argues, have been obscured by the comic-paranoid style that characterizes much of our criticism of bureaucracy. Kafka proposes a new theory of what Karl Marx called the "bureaucratic medium. " Moving from Marx to Freud, he argues that this theory of paperwork must include both a theory of praxis and of parapraxis.
CONTEXT-BASED GLOBAL MULTI-CLASS SEMANTIC SEGMENTATION OF IMAGES INSPIRED BY THE HUMAN VISUAL SYSTEM

Semantic scene understanding is one of the several significant goals of robotics. In this paper, we propose a framework that is able to simultaneously detect and segment objects of different classes using a simple pairwise interactive context term, for the sake of achieving a preliminary milestone of Semantic scene understanding. The context is incorporated as pairwise interactions between pixels, imposing a prior on the labeling. Our model formulates the multi-class image segmentation task as an energy minimization problem and finds a globally optimal solution using belief propagation and neural network. We experimentally evaluate the proposed method on three publicly available datasets: the MSRC-1, the CorelB datasets, and the PASCAL VOC database. Results show the applicability and efficacy of the proposed method to the multi-class segmentation problem. Keywords— Context-based, global, semantic, image segmentation, human visual system
Start date : 03 / 01 / 2010 End date : 20 / 12 / 2011 Lead Author : Nicolas Baghdadi ( Irstea ) Contributors : Irstea ( Rémi Cresson ) Irstea (

In this deliverable, we present the potential use for the C-band polarimetric SAR (Radar Synthetic Aperture) parameters to characterize the soil surface over bare agriculture fields. RADARSAT-2 data were analyzed to evaluate the polarimetric SAR parameters’ sensitivities to the soil moisture and surface roughness. Moreover, an approach was developed to estimate soil surface parameters from Cband polarimetric SAR data. An inversion technique based on Multi-Layer Perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases in using or not a-priori knowledge on soil parameters. The inversion approach was then validated against a real dataset composed of RADARSAT-2 images in polarimetric mode.
EcosimPro and its EL Object-Oriented Modeling Language

This paper introduce the modeling and simulation tool EcosimPro and the possibility in the new version 4 of using object orientation in its modeling language called EL (Ecosimpro Language ) which is the official language of The European Space Agency (ESA). Also shows the power that the use of classes gives to EcosimPro regarding other simulation environments.
Control approaches for robotic knee exoskeleton and their effects on human motion

In this paper we compare three noninvasive control methods for a robotic knee exoskeleton and asses their kinematic influences on the repetitive squatting motions of able-bodied human subjects. The motion of the subjects wearing the knee exoskeleton was also compared to the motion of the subjects performing the same task without using the assistance of the knee exoskeleton. We chose the squatting motion because it approximates common movements with high metabolic cost, such as standing up from a chair and ascending or descending the stairs. Beside the two classical robotic control approaches, i.e. the position control and the gravity compensation, we propose a method that is based on a single adaptive frequency oscillator combined with an adaptive Fourier series in a feedback loop. The method can extract frequency and phase of an arbitrary periodic signal in real-time. This method is particularly appropriate for controlling novel robotic assisting devices since it does not require complex signal sensing or user calibration. The results show that the total knee torque was increased while using the exoskeleton device compared to the squatting without the assistance of the exoskeleton device. In effect, there were significant kinematic adaptations observed when the exoskleton device assisted the motion of the subjects. However, no significant kinematic differences were found between different control methods. We conclude that an assistive device can augment the abilities of the able-bodied humans in the targeted joints (i.e. the joints provided with additional mechanical power) but, on the other hand, significantly alters whole body kinematics.
Macrophage Exosomes Induce Placental Inflammatory Cytokines: A Novel Mode of Maternal–Placental Messaging

During pregnancy, the placenta forms the interface between mother and fetus. Highly controlled regulation of trans‐placental trafficking is therefore essential for the healthy development of the growing fetus. Extracellular vesicle‐mediated transfer of protein and nucleic acids from the human placenta into the maternal circulation is well documented; the possibility that this trafficking is bi‐directional has not yet been explored but could affect placental function and impact on the fetus. We hypothesized that the ability of the placenta to respond to maternal inflammatory signals is mediated by the interaction of maternal immune cell exosomes with placental trophoblast. Utilizing the BeWo cell line and whole placental explants, we demonstrated that the human placenta internalizes macrophage‐derived exosomes in a time‐ and dose‐dependent manner. This uptake was via clathrin‐dependent endocytosis. Furthermore, macrophage exosomes induced release of proinflammatory cytokines by the placenta. Taken together, our data demonstrates that exosomes are actively transported into the human placenta and that exosomes from activated immune cells modulate placental cytokine production. This represents a novel mechanism by which immune cells can signal to the placental unit, potentially facilitating responses to maternal inflammation and infection, and thereby preventing harm to the fetus.
Speaking up in ad hoc multiteam systems: Individual-level effects of psychological safety, status, and leadership within and across teams

Speaking up in high-risk organizations plays a pivotal role in the mitigation of errors and can make the difference between life and death. To date, speaking up has been studied mostly within teams. However, many high-risk organizations rely on the effective collaboration across teams in ad hoc multiteam systems (MTS). This study widens the scope of research from teams to MTS and empirically compares the mechanisms involved in speaking up within versus across teams in ad hoc MTS-aircrews. In a sample of 1490 aircrew members of a European airline, we found that crewmembers’ individual level perceptions of psychological safety mediated both the relationship between status and speaking up and between perceived leader inclusiveness and speaking up within teams. Across teams, however, psychological safety and leader inclusiveness—as perceived by pursers in their role as boundary spanners—showed no effect. Instead, it was pursers’ within-team perceptions of psychological safety that mediated between status and speaking up across teams. Teams in ad hoc MTS may thus serve as safe harbours supporting their members also in interactions across team boundaries. Implications of findings for research on MTS and practical recommendations to improve speaking up within and across teams in ad hoc MTS are discussed.
Distributed hydrological modelling of a heavily glaciated Alpine river basin

Abstract A glacier submodel was successfully integrated into the distributed hydrological model WaSiM-ETH to simulate the discharge of a heavily glaciated drainage basin. The glacier submodel comprises a distributed temperature index model including solar radiation to simulate the melt rate of glaciated areas. Meltwater and rainfall are transformed into glacier discharge by using a linear reservoir approach. The model was tested on a high-alpine sub-basin of the Rhone basin (central Switzerland) of which 48% is glaciated. Continuous discharge simulations were performed for the period 1990–1996 and compared with hourly discharge observations. The pronounced daily and annual fluctuations in discharge were simulated well. The obtained efficiency criterion, R2, exceeds 0.89 for all years. The good performance of the glacier submodel is also demonstrated by integrating it into the hydrological model PREVAH.
Context dependent "long units" for speech recognition

It is expected that longer-than-phoneme units such as syllables or multi-phone units can deal with sources of performance degradation, such as pronunciation variation or coarticulation, better than phoneme-sized units like triphones. The possible number of contextual realizations of those “long units” (LU) is very high, causing an explosion of the number of parameters to be estimated. As the training data are limited, the usual solution is to share parameters between different units to improve parameter estimation. Another problem is how to provide a model for a unit (syllable/multiphones) that was not present during training (unseen unit). In this paper we evaluate and compare syllable and automatically derived multi-phone units. We introduce a method called “contextual factorization” to share parameters between different models and we propose a figure of merit to decide which decomposition of an unseen syllable is the most appropriate. Performance is improved comparing to a triphone based system.
A Survey of Load Balancers in Modern Multi-Threading Systems

Multithreaded architectures are a feasible approach to exploit parallelism in both regular and irregular applications. Today a large collection of multi-threading architectures with different threaded models, and implementation platforms are available. These architectures provide support for multithreading either at hardware level, with customized functional units, or at the software level, as emulators written in some high-level language. The later approach is usually prefered because of its favorable price tag, speed of development, and portability. In this article we review some of these architectures focusing in their capabilities to provide load balancing for irregular, data-parallel and recursive applications. The paper is anchored on a description of our own implementation of load balancers for EARTH - Efficient Architecture for Running Threads. Most of the multithreading architectures that we review are software emulations based on off-the-shelf hardware and compiler technologies.
Measuring the mode volume of plasmonic nanocavities using coupled optical emitters

Metallic optical systems can confine light to deep sub-wavelength dimensions, but verifying the level of confinement at these length scales typically requires specialized techniques and equipment for probing the near-field of the structure. We experimentally measured the confinement of a metal-based optical cavity by using the cavity modes themselves as a sensitive probe of the cavity characteristics. By perturbing the cavity modes with conformal dielectric layers of sub-nm thickness using atomic layer deposition, we find the exponential decay length of the modes to be less than 5% of the free-space wavelength (\lambda) and the mode volume to be of order \lambda^3/1000. These results provide experimental confirmation of the deep sub-wavelength confinement capabilities of metal-based optical cavities.
Leveraging Social and Content-based Recommendation in P2P Systems

We focus on peer-to-peer (P2P) content recommendation for on-line communities, where social relationships between users can be exploited as a parameter to increase the trust of recommendation. Most of the existing solutions establish friendship relationships based on users behavior or declared trust. In this paper, we propose a novel P2P recommendation approach (called F2Frec) that leverages content and social-based recommendation by maintaining a P2P and friend-to-friend network. This network is used as a basis to provide useful and high quality recommendations. Based on F2Frec, we propose new metrics, such as usefulness and similarity (among users and their respective friend network), necessary to enable friendship establishment and to select recommendations. We define our proposed metrics based on users' topic of interest and relevant topics that are automatically extracted from the contents stored by each user. Our experimental evaluation, using the TREC09 dataset and Wiki vote social network, shows the benefits of our approach compared to anonymous recommendation. In addition, we show that F2Frec increases recall by a factor of 8.8 compared with centralized collaborative filtering.
Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization

This paper presents Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization algorithm (DECMOSA-SQP), which uses the self-adaptation mechanism from DEMOwSA algorithm presented at CEC 2007 and a SQP local search. The constrained handling mechanism is also incorporated in the new algorithm. Assessment of the algorithm using CEC 2009 special session and competition on constrained multiobjective optimization test functions is presented. The functions are composed of unconstrained and constrained problems. Their results are assessed using the IGD metric. Based on this metric, algorithm strengths and weaknesses are discussed.
Simulation Models for Socioeconomic Inequalities in Health: A Systematic Review

Background: The emergence and evolution of socioeconomic inequalities in health involves multiple factors interacting with each other at different levels. Simulation models are suitable for studying such complex and dynamic systems and have the ability to test the impact of policy interventions in silico. Objective: To explore how simulation models were used in the field of socioeconomic inequalities in health. Methods: An electronic search of studies assessing socioeconomic inequalities in health using a simulation model was conducted. Characteristics of the simulation models were extracted and distinct simulation approaches were identified. As an illustration, a simple agent-based model of the emergence of socioeconomic differences in alcohol abuse was developed. Results: We found 61 studies published between 1989 and 2013. Ten different simulation approaches were identified. The agent-based model illustration showed that multilevel, reciprocal and indirect effects of social determinants on health can be modeled flexibly. Discussion and Conclusions: Based on the review, we discuss the utility of using simulation models for studying health inequalities, and refer to good modeling practices for developing such models. The review and the simulation model example suggest that the use of simulation models may enhance the understanding and debate about existing and new socioeconomic inequalities of health frameworks.
Does Inflammation Determine Whether Obesity Is Metabolically Healthy or Unhealthy? The Aging Perspective

Obesity is a major health issue in developed as well as developing countries. While obesity is associated with relatively good health status in some individuals, it may become a health issue for others. Obesity in the context of inflammation has been studied extensively. However, whether obesity in its various forms has the same adverse effects is a matter of debate and requires further research. During its natural history, metabolically healthy obesity (MHO) converts into metabolically unhealthy obesity (MUHO). What causes this transition to occur and what is the role of obesity-related mediators of inflammation during this transition is discussed in this paper.
A study on the impacts of verbal interaction in proximate GSS sessions

Although distributed group support systems (GSS) are getting popular, there are still some limitations. One of the limitations is few substitutions for face-to-face (FTF) interaction (whether it is related to meeting content or not), so we may say that FTF interaction is one of the advantages of proximate types of GSS. However, there are few studies that focus only on the role of FTF interaction in proximate GSS sessions, so it is unclear what the real advantages may be. If we understand the role of FTF interaction better, we can understand how proximate GSS is important perhaps understand better the role of face to face interaction when compared to distributed GSS. When data regarding face to face interaction was studies, many experimental studies separated FTF interaction from computer based interaction in GSS sessions. But, if people are gathered at same place under actual meeting conditions, it is assumed that they automatically talk about something. Therefore FTF interaction, especially verbal interaction in proximate GSS sessions, is an important issue to consider in actual GSS conditions. The purpose of this study is to clarify impacts of verbal interaction in actual proximate GSS sessions. Eight proximate GSS sessions were observed and data were gathered using questionnaires from 59 participants. The relationship between meeting satisfaction, group cohesion and verbal interaction was analyzed. Results show that, "oral participation in a discussion" and "informal chat during a session" tends to enhance meeting satisfaction and group cohesion more than "contribution using GSS". Therefore, we may say formal or informal verbal interactions in proximate GSS sessions play an important role and this role is one of the advantages of proximate GSS.
Social Psychology and Neuroscience: Strange Bedfellows or a Healthy Marriage?

In this paper, we assess what neuroscience theory and method have contributed to the study of group processes and intergroup relations and what we see as potential future contributions to the discipline. We briefly review the historical relation between neuroscience and social psychology, identify issues that may limit the value of neuroscience to the study of group processes and relations, and then argue that social neuroscience indeed holds significant promise for understanding many key elements of group processes and intergroup relations. Both the potential problems and the potential benefits of bridging neuroscience and social psychology are considered in terms of theoretical considerations, empirical issues, and practical implications. We conclude that, although not all group phenomena may be reducible to neural activity and pathways, there are significant benefits to social psychology by having an even broader multidisciplinary orientation within social psychology, one that incorporates the complementary perspectives, techniques, and knowledge of neuroscience.
Determining the Important Ideas in Sub-Groups of Interest in Social Software Networks

Social networking websites allow users to connect, organize and share information and ideas online. To date, the social software on these sites offer only basic functionality, which do not take advantage of the robust and stable network dynamics that emerge from typical tagging system usage. I propose that the field of social network analysis contains well defined and explored measures for analyzing data, such as that found in social software. To investigate how social networking analysis may be applied and what functionalities may be offered, I address a single problem: How can the most important elements (nodes) be found for a given sub-community of interest, from a social software website? To answer this question, this paper acts as a brief survey in which we describe the algorithms used in a three-step process for a particular type of social network found in collaborative tagging datasets. The process involves graph reduction by folding the tripartite graphs, discovering sub-communities using the notion of λ-sets (lambda sets), and identifying the most important nodes using formulations of centrality. I discuss the algorithms, timings and predicted outcomes of each step in the process of finding the important nodes, which I believe can be applied more generally in other social software data networks.
Probiotic Beverage with Soy Isoflavone Consumption for Breast Cancer Prevention: A Case-control Study

The purpose of this study is to evaluate how beverages containing Lactobacillus casei Shirota (BLS) and soy isoflavone consumption since adolescence affected the incidence of breast cancer. In a population-based case-control study, three hundred and six cases with breast cancer and 662 controls aged 40 to 55 were matched for age and residential area and included in the analyses. Diet, lifestyle and other breast cancer risk factors were investigated using the self-administered questionnaire and interview. Odds ratios (ORs) of BLS and soy isoflavone consumption for breast cancer incidence were independently and jointly estimated using a conditional logistic regression. The ORs of BLS consumption (≥ four times a week against < four times a week) was 0.65 and statistically significant (p = 0.048). The analysis of association between soy consumption and breast cancer incidence showed the more the isoflavone consumption is, the lower the odds of breast cancer becomes. Adjusted ORs for breast cancer in the second, the third and the fourth quartiles of soy consumption against the first quartile were 0.76, 0.53 and 0.48, respectively (trend test, p = 0.0002). The BLS-isoflavone interaction was not statistically significant; however, a biological interaction was suggested. Regular consumption of BLS and isoflavones since adolescence was inversely associated with the incidence of breast cancer in Japanese women.