For each of these evaluation settings, I also compared the performance of the linear and the gaussian kernels, and I evaluated how well the background class performed against a single lumped socialbehavior class.
The average per class performance on the left-out training data captures the per- formance on curated video clips known to have good tracks. Performance is computed by evaluating the classifier trained on 9 out of the 10 video training sets and testing this classifier on the left-out training set. Since the tracks are known to be good, the confusion matrix for the linear kernel (Figure 5-3) and gaussian kernel (Figure 5-4) approximate the system performance in the case of consistently good tracks. A few important patterns emerge from this data. The upright behavior is mostly classified as nose-to-nose sniffing. In both these behaviors, the mice often exhibit mutual nose contact, but during the upright behavior, the mice must be reared. When viewed from the top, the main signal to detect rearing is a mouse’s ellipse profile shrinking to a small size. Although the feature representation does capture ellipse size, the occlusion tracker described in Section 4.2.5 makes the ellipse size signal unreliable. The occlusion tracker assumes fixed mouse dimensions, so the ellipse size will never shrink during close interactions.
To conclude this overview, we would like to quote these appealing sentences at the end of a recent and excellent review about genetics of human socialbehavior : “The past two decades have seen remarkable progress in unraveling the
complexities of the neurogenetic architecture of the human social brain. Nevertheless, much remains to be learned, especially about how our species has created a global society composed of billions of interacting individuals whose basic brain structure has remained mostly unchanged for the past 50,000 years. This global society is indeed a remarkable achievement for an organ weighing only 1350 g, and attests to its remarkable plasticity in processing a continuous stream of environmental information using neuroanatomical and neurogenetic mechanisms laid down over millions of years of hominid evolution.” We wish also to remind that Gerald Edelman,
nutritional needs (e.g., young and adults, queens and workers) permanently co-occur, differential social interactions and continuous homogenization of gut microbiota through social contacts favor
Figure 3. Integrating microbiota, host nutrition and socialbehavior. Populations of holobionts are represented in a nutrient space defined by two nutrients. Foods (grey lines) are defined by their ratio in nutrients X and Y. Animal hosts (yellow dots), gut microbes (blue and red dots), and holobionts (grey surfaces) are characterized by their nutritional states (animal: NS A ; microbe: NS M ; holobiont: NS H ) and intake targets (animal: IT A ; microbe: IT M ; holobiont: IT H ). Black arrows illustrate the feeding decisions of each holobiont or sub-groups of interacting holobionts. Three hypothetical scenarios depict the expected nutritional interactions between gut microbes and hosts in groups of increasing social complexity. (A) In solitary species, where individuals feed independently and do not interact, the diversity of environmental microbes may generate a high variability in the NS H and the IT H of the different holobionts. Each holobiont can reach its IT H by alternating its intake of the two complementary foods. (B) In gregarious species, where individuals feed together and regularly interact, horizontal transmission of microbes may reduce inter-holobionts variability of NS Hs and IT Hs . In this example where individuals do not interact evenly, horizontal transmission favors the emergence of sub-groups (black circles) of highly interacting individuals with similar gut microbiota that can collectively reach their IT H . (C) In advanced societies, where individuals with different nutritional needs (e.g., young and adults, queens and workers) permanently co-occur, differential social interactions and continuous homogenization of gut microbiota through social contacts favor the emergence of classes of highly interacting individuals with distinct NS Hs and IT Hs (e.g., castes). Holobionts can cooperate with other holobionts from the same class to reach their common IT H .
al., 2010) and to ameliorate the symptoms of obsessive– compul- sive disorder (Ansseau et al., 1987) in humans.
Despite these findings, there has been contention about whether the vHPC has a critical role in social interaction. Specif- ically, although ibotenic acid lesions in neonatal rats produced dramatic changes in social interaction when the rats were tested in adulthood in the absence of changes in general anxiety-re- lated behaviors, no such changes in social interaction were observed when the lesions were performed in adult rats (Sams- Dodd et al., 1997; Becker et al., 1999). This result suggests that the vHPC does not play an active role in social interaction in adult- hood. However, these lesions were nonspecific and may have targeted multiple circuits in the vHPC with opposing functions in social interaction, thereby producing a zero sum effect on behav- ior. Furthermore, in these studies, the lesions were performed 2 weeks before testing, and compensatory mechanisms in the adult brain may have contributed to the lack of change in social inter- action observed. In contrast to this nonspecific ablation of the vHPC region, in this study we transiently modulated the activity across a specific subpopulation of synapses in the vHPC without causing any permanent damage. Whereas in vivo electrophysio- logical studies have been performed in the BLA (Wang et al., 2011) and vHPC during anxiety (Adhikari et al., 2010), and re- cordings have also been performed in the BLA during social be- haviors (Katayama et al., 2009), our findings provide the first evidence that BLA inputs to the vHPC have a causal relationship with socialbehavior. Furthermore, the neural encoding dynam- ics of vHPC-projecting BLA neurons have yet to be revealed dur- ing social interaction.
opposition and aggression and lower levels of shyness and social withdrawal; b) these initial differences in kindergarten should decrease as children with no child care experience undergo their own social group adaptation during elementary school.
The purpose of this study was to test this hypothesis with longitudinal data on five social behaviors: aggression, prosociality, opposition, shyness, social withdrawal, from kindergarten to the end of primary school (grade 6). We compared behavioral changes during the elementary school years in children with and without preschool child care experience to document the developmental course of effects that are being attributed to child care services. We specifically measured the pace at which differences in socialbehavior during kindergarten decrease from 6 to 12 years of age. . These differences may decrease either linearly (i.e. regular decrease) or in a non-linear fashion (e.g. quick decrease in the first few years). We examined the associations between the development of socialbehavior and four features of preschool child care experience: 1) receiving any preschool child care services versus remaining in parental care; 2) the intensity of the child care services (number of hours per week); 3) the type of child care services (e.g. center-based versus family-based), and 4) the age at which the child first received child care services (Averdijk et al., 2011; Belsky, 2006; Côté et al., 2013; Jacob, 2009). Finally, we controlled for a host of confounders including children’s personal characteristics, family socioeconomic characteristics, structure and context, family functioning and parenting.
et al., 2005) have characterized face-to-face conversations using wearable sensors. They have built a computational model based on Coupled Hidden Markov Models (CHMMs) to describe interactions between two people and characterize their dynamics in order to estimate the success of the intended goals. Otsuka et al. (Otsuka et al., 2007) proposed a Dynamic Bayesian Network (DBN) to estimate addressing and turn taking ("who responds to whom and when?"). The DBN framework is composed of three layers. The first one perceives speech and head gestures, the second layer estimates gaze patterns while the third one estimates conversation regimes. The objective of Otsuka and colleagues is to evaluate the interaction between regimes and behaviors during multi-party conversations. For social affect detection, Petridis and Pantic (Petridis and Pantic, 2008) presented an audiovisual approach to distinguish laughter from speech and showed that this approach outperforms the unimodal ones. The model uses a combination of AdaBoost and Neural Networks, where AdaBoost is used as a feature selector rather than a classifier. The model achieved a 86.9% recall rate with 76.7% precision. A Decision Tree is used in (Banerjee and Rudnicky, 2004) for automatic role detection in multiparty conversations. Based mostly on acoustic features, the classifier assigns roles to each participant including effective participator, presenter, current information provider, and information consumer. In (Jayagopi et al., 2009), Support Vectors Machines (SVM) have been used to rate each person’s dominance in multiparty interactions. The results showed that, while audio modality remains the most relevant, visual cues contribute in improving the discriminative power of the classifier. More complete reviews on models and issues related to nonverbal analysis of social interaction can be found in (Gatica- Perez, 2009) (Vinciarelli et al., 2012).
Another argument in favor of a specific CLCM comes from examining mechanisms that pertain to environmental threats broadly defined and carrying the potential for lethal outcomes. Theories pertaining to hazard precautions (e.g., Boyer & Liénard, 2006 ) describe the way indi- viduals engage in behaviors aimed at avoiding or palliating hazards such as crop productivity and natural disasters (which explains ritualistic behaviors for instance) and thus pertain to en- gagement in preventive action, especially in the case of latent (i.e., inferred) threats. However, once a threat has manifested itself and conse- quences appear on one’s social network, a spe- cific subset of behaviors should be present to cope with social losses. Other mechanisms are frequently invoked to explain the “coalitional” outcomes observed among humans under threat, especially when threat pertains to pathogens and infectious diseases. As such, pathogen and disease-related threats are susceptible to trigger increased outgroup derogation and ingroup identification as part of a “behavioral-immune system” designed to protect individuals from getting sick ( Schaller, 2011 ). Still, this mecha- nism can trigger responses in absence of threats that aren’t necessarily deadly and is thus not tailored to account for similar behavioral pat- terns in the face threats of higher lethality (e.g., war, death of one’s relatives).
1995 ). To isolate the infl uence of one mouse on a social interaction, assays testing social approach use a three-chambered box in which a mouse chooses between interacting with a restrained mouse or an inanimate object ( Landauer and Balster, 1982; Pomerantz et al., 1983; Carter et al., 1995; Winslow, 2003 ). Social approach data is collected by noting how much time the mouse spends in each of the three chambers, and comparing the amount of time the mouse spends in the chamber with the stimulus mouse to the time spent with the inanimate object. These data can be collected in a number of ways: by a trained observer viewing chamber crossings ( Brodkin et al., 2004; Brigman et al., 2009 ), by counting infrared beam breaks between chambers of the apparatus ( Nadler et al., 2004 ), or by using video recording and computer software to track the mouse’s movement in the apparatus ( Kwon et al., 2006; Tabuchi et al., 2007; Blundell et al., 2009 ). The ability to track a mouse’s movements allows a more sensitive output, as behaviors may be recorded in addition to chamber crossings. Various tools exist to study mouse behavior which can track mouse movement during social approach. Software packages such as Ethovision (Noldus), SMART Triwise Video Tracking (Harvard Apparatus), or VideoMot2 (TSE), have features which allow the user to track movement and specifi c aspects of mouse behavior such as motion, rearing, head orien- tation, measure interactions between multiple mice, and some (Ethovision) are able to detect behaviors such as grooming, fi ght- ing, or tail rattling. These tools offer many options for analyzing video recorded animal behavior, however, commercially available tools may be prohibitively expensive for individual researchers or small laboratories. A number of free programs exist that may be used for video analysis of socialbehavior, such as ImageJ 1 ( Nakajima INTRODUCTION
Cela dit, les deux caractéristiques les plus notables de ce forum auront été la présence massive des femmes, et des femmes africaines surtout, et celle des organisations religieuses. La première est un acquis très important de ce FSM VII par comparaison avec ses prédécesseurs, tandis que la seconde reflète une réalité très importante sur le terrain des luttes sociales en Afrique. Contrairement à ce qui a été avancé par plusieurs analystes, ce n’était ni la première fois que le FSM se tenait en Afrique, ni non plus le premier forum social en sol africain, loin de là. En effet, en 2006, la ville de Bamako, au Mali, avait été l’hôte d’un des trois forums sociaux polycentriques avec Caracas et Karachi, tandis que des forums
This is why the traditional (commercial) entrepreneurship literature proves useful to identify theoretical arguments related to the role of social networks in explaining the emergence of social entrepreneurship. First, distributional issues and exclusion from the economy are considered to foster ethnic entrepreneurship through a stronger distinct network. Using this argument, it has been suggested here that social entrepreneurship may result from the cooperation efforts of individuals who are discriminated and set aside from the society and the economy. Then, the better and faster diffusion of information achieved through greater network density increases awareness of opportunities. In the same way, our research proposal suggests social entrepreneurship to emerge from higher awareness to social needs and social opportunities developed, thanks to greater connectivity. Third, the structural-hole argument shows how entrepreneurs may be understood as brokers between unconnected parts of the network. It was suggested to consider social entrepreneurship as emerging from bridge-building between several logics, borne by previously unconnected networks. Finally, Larson and Starr’s (1993) network model of organization formation has been suggested of particular interest to social entrepreneurship research as it entails the participation of multiple stakeholders in entrepreneurship emergence. In particular, it may be concluded that as social networks focused on addressing social needs grow and formalize, they may crystallize to form a socially oriented venture.
Social network approaches have become increasingly popular in behavioral and ecological research, enabling extensive analyses of simultaneous interactions among multiple individuals and across long periods of time ( Krause et al., 2007; Croft et al., 2008; Sih et al., 2009; Sueur et al., 2011; Pinter-Wollman et al., 2013 ). Our study now illustrates how this approach can benefit research on nutritional behavior, ultimately helping to elucidate complex interactions between the environment, the nutritional strategies of individual animals and the consequences thereof for social interactions and collective phenomena. Beyond the example of nutritionally mediated dominance hierarchies, the broader integration of social network analyses into nutrition research has potential for investigating the nutritional ecology of species exhibiting a great diversity of social forms, from temporary aggregations of feeding animals to permanent and fully eusocial colonies of cooperatively foraging nestmates ( Krause and Ruxton, 2002 ). These interactions may include several types of nutrient driven social networks, including social and competitive interactions among foragers (as in this study), transfer of social information about food resources, and exchange of foods (or specific nutrients) or microorganisms (symbionts or pathogens) between individuals. Predominantly, behavioral research utilizing network analyses has focused on descriptive approaches that identify the structure of animal interactions.
D’autre part, se sont progressivement mis en place des dispositifs nouveaux, dont le fonctionnement différait des CMS principalement du fait de leur spécialisation. Ces dispositifs, souvent d’inspiration nationale, comme le Revenu Minimum d’Insertion ou le Fond de Solidarité au Logement, occupent les mêmes types de profession que les CMS, suivant le public auquel ils sont destinés et les objectifs qui leur sont alloués. Leur mise en place a créé un ensemble de changements et de remises en question au sein de l’institution sans pour autant avoir été réellement source d’innovation à un niveau organisationnel. Aux mutations du travail social n’ont pas correspondu celles de l’institution territoriale. De fait, les dispositifs territorialisés et les nouvelles procédures qui les ont accompagnés n’ont pas pu offrir de relations stables avec la conception plus classique du travail social (cf. Simbille, 1993) 1 . Sans s’être substitués à cette
Specifically, we start with a single periodically curved beam of radius r and periodicty ε. With the help of the decomposition of didsplacements, which yields elementary and residual displacements for the beam, some basic results are shown. For the general definition and properties of this decomposition see [7, 13, 14, 15]. The resulting elementary displacements are further modified to account for the curved behavior and to simplify estimates on the full structure. These general results for one beam are transferred on the whole textile structure and global fields are introduced, which are defined on the 2D mid-plane of the limit plate. The definition of the global and local displacement fields is similar to the method of the scale-splitting operators in , where the Q 1 -interpolation is used to obtain global fields. For all fields, local and global, Korn-like estimates are
Une première acception éloignée de la sociologie de la littérature : Putman
Ces vingt dernières années ont vu les travaux sur cette forme de capital proliférer de manière exponentielle. Ces travaux s’inscrivent pour beaucoup dans la lignée de ceux produits par Robert Putman, professeur de sciences politiques à Harvard, qui a contribué à lancer l’intérêt pour le capital social, avec son article « Bowling Alone. America’s Declining Social Capital » (Putman, 1995, trad. 2006) et son ouvrage Bowling Alone. The Collapse and Revival of American Community (Putman, 2000). Mais la définition de Putman, si elle se réclame des travaux de James Coleman, promoteur en sociologie d’une théorie des capitaux (capital humain et capital social), induit un autre usage du capital social que ceux de la théorie des champs et de l’analyse des réseaux : Putman fait de ce capital un indice de la cohésion d’un groupe, d’une société, etc., donc d’un collectif, là où il était conçu généralement comme la propriété d’un individu. Chez Putman, le capital social devient ainsi une « forme de bien public » (Houard & Jacquemain), mesuré à partir d’indicateurs très généraux, comme le taux de participation à des élections, le nombre d’adhérents à de grandes associations nationales, etc. Cette extension de la définition a lancé des travaux dans de nombreuses directions et dans de nombreux domaines (notamment, d’après le repérage de Bevort et Lallement , dans ceux de la santé, du développement économique et de la socio-économie).