Speed-independent gait identification for mobile devices

Due to the intensive use of mobile phones for diferent purposes, these devices usually contain condential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner s gait. Nowadays, gait identication in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identication problem, they show di±culties when dealing with gaits at diferent speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at diferent velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at diferent motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

A supervised cooperative learning system for early detection of language disorders

The Quality of Life of a person may depend on early attention to his neurodevel-opment disorders in childhood. Identification of language disorders under the age of six years old can speed up required diagnosis and/or treatment processes. This paper details the enhancement of a Clinical Decision Support System (CDSS) aimed to assist pediatricians and language therapists at early identification and re-ferral of language disorders. The system helps to fine tune the Knowledge Base of Language Delays (KBLD) that was already developed and validated in clinical routine with 146 children. Medical experts supported the construction of Gades CDSS by getting scientific consensus from literature and fifteen years of regis-tered use cases of children with language disorders. The current research focuses on an innovative cooperative model that allows the evolution of the KBLD of Gades through the supervised evaluation of the CDSS learnings with experts¿ feedback. The deployment of the resulting system is being assessed under a mul-tidisciplinary team of seven experts from the fields of speech therapist, neonatol-ogy, pediatrics, and neurology.

Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

Interleukin 3 enhances cytotoxic T lymphocyte development and class I major histocompatibility complex "re-presentation" of exogenous antigen by tumor-infiltrating antigen-presenting cells.

We show that interleukin 3 (IL-3) enhances the generation of tumor-specific cytotoxic T lymphocytes (CTLs) through the stimulation of host antigen-presenting cells (APCs). The BALB/c (H-2d) spontaneous lung carcinoma line 1 was modified by gene transfection to express ovalbumin as a nominal "tumor antigen" and to secrete IL-3, a cytokine enhancing myeloid development. IL-3-transfected tumor cells are less tumorigenic than the parental cell line, and tumor-infiltrating lymphocytes isolated from these tumors contain increased numbers of tumor-specific CTLs. By using B3Z86/90.14 (B3Z), a unique T-cell hybridoma system restricted to ovalbumin/H-2b and implanting the tumors in (BALB/c x C57BL/6)F1 (H-2d/b) mice, we demonstrate that the IL-3-transfected tumors contain an increased number of a rare population of host cells that can process and "re-present" tumor antigen to CTLs. Electron microscopy allowed direct visualization of these host APCs, and these studies, along with surface marker phenotyping, indicate that these APCs are macrophage-like. The identification of these cells and their enhancement by IL-3 offers a new opportunity for tumor immunotherapy.

Identification of the local productive systems in Spain: a new approach

Paper submitted to the 44th European Congress of the European Regional Science Association, Porto, 25-29 August 2004.

Social Changes Reflected in Specialized Languages: Lexical Re-/deconstruction in Lesbian Studies

One of the most important factors of recognition, belonging and identification in scientific communities is their specialized language: doctors, mathematicians and anthropologists feel they are part of a group with which they can interact because they share a common “language”. While ideology is present in all academic registers, it is in human sciences where its presence (or absence) leads to more visible linguistic phenomena. An interesting example is that of lesbian studies: as non-heterosexual members of society have become less stigmatized, lesbian studies have developed a language of their own. In our paper, we shall explore the mechanisms used in the creation of specific vocabulary in this academic area, paying special attention to the refashioning or deconstruction of meaning of established terms as a result of changes in social perception or the challenging of pre-determined meanings.

Modifications post-traductionnelles des canaux calciques cardiaques de type L : identification des résidus asparagine qui participent à la glycosylation de la sous-unité auxiliaire CaVα2δ1

Les canaux calciques de type L CaV1.2 sont principalement responsables de l’entrée des ions calcium pendant la phase plateau du potentiel d’action des cardiomyocytes ventriculaires. Cet influx calcique est requis pour initier la contraction du muscle cardiaque. Le canal CaV1.2 est un complexe oligomérique qui est composé de la sous-unité principale CaVα1 et des sous-unités auxiliaires CaVβ et CaVα2δ1. CaVβ joue un rôle déterminant dans l’adressage membranaire de la sous-unité CaVα1. CaVα2δ1 stabilise l’état ouvert du canal mais le mécanisme moléculaire responsable de cette modulation n’a pas été encore identifié. Nous avons récemment montré que cette modulation requiert une expression membranaire significative de CaVα2δ1 (Bourdin et al. 2015). CaVα2δ1 est une glycoprotéine qui possède 16 sites potentiels de glycosylation de type N. Nous avons donc évalué le rôle de la glycosylation de type-N dans l’adressage membranaire et la stabilité de CaVα2δ1. Nous avons d’abord confirmé que la protéine CaVα2δ1 recombinante, telle la protéine endogène, est significativement glycosylée puisque le traitement à la PNGase F se traduit par une diminution de 50 kDa de sa masse moléculaire, ce qui est compatible avec la présence de 16 sites Asn. Il s’est avéré par ailleurs que la mutation simultanée de 6/16 sites (6xNQ) est suffisante pour 1) réduire significativement la densité de surface de! CaVα2δ1 telle que mesurée par cytométrie en flux et par imagerie confocale 2) accélérer les cinétiques de dégradation telle qu’estimée après arrêt de la synthèse protéique et 3) diminuer la modulation fonctionnelle des courants générés par CaV1.2 telle qu’évaluée par la méthode du « patch-clamp ». Les effets les plus importants ont toutefois été obtenus avec les mutants N663Q, et les doubles mutants N348Q/N468Q, N348Q/N812Q, N468Q/N812Q. Ensemble, ces résultats montrent que Asn663 et à un moindre degré Asn348, Asn468 et Asn812 contribuent à la biogenèse et la stabilité de CaVα2δ1 et confirment que la glycosylation de type N de CaVα2δ1 est nécessaire à la fonction du canal calcique cardiaque de type L.

La puissance technique de la médecine : des pistes de réflexion

Compte-rendu / Review

Identification of toxic substances in the upper Illinois River : final report /

"Principal investigators: Richard E. Sparks (and) Philippe E. Ross; project manager, Frank S. Dillon.

Centromere identification and inactivation on a neo-Y chromosome fusion in threespine stickleback fish (Gasterosteus aculeatus)

Thesis (Ph.D.)--University of Washington, 2016-05

Working under a black cloud:how to sustain organizational identification after a merger

This article argues against the merger folklore that maintains that a merger negatively affects well-being and work attitudes primarily through the threat of job insecurity. We hold that the workplace is not only a resource for fulfilling a person's financial needs, but that it is an important component of the self-concept in terms of identification with the organization, as explained by social identity theory. We unravel the key concepts of the social identity approach relevant to the analysis of mergers and review evidence from previous studies. Then, we present a study conducted during a merger to substantiate our ideas about the effects of post-merger organizational identification above and beyond the effects of perceived job insecurity. We recommend that managers should account for these psychological effects through the provision of continuity and specific types of communication. © 2006 British Academy of Management.

Pre-entry organisational identification:considering the theoretical and managerial implications of future members’ identification with an organisation

From a Social Identity Theory perspective, organisational identification arises through a cognitive process of self-categorisation. As a consequence a person need not have a formal relationship with an organisation in order to identify with it. In this conceptual paper, the authors draw on this proposal to argue that future members are capable of identifying with an organisation prior to entry, and that this initial pre-entry identification could contribute to a person’s subsequent post-entry organisational identification. The paper further suggests that because no distinction need be drawn between organisational identification in current and future members, we might expect to find the same antecedents of identification in both instances. The group engagement model (Tyler and Blader 2003) is called on to propose that when a future member experiences pride in, and respect from, an organisation before they join, this should positively influence their pre-entry organisational identification. The authors explore the managerial implications of these propositions, and argue that an organisation’s actions and practices that have been shown to influence a post-entry organisational identification should have an equivalent impact on future members’ organisational identification when observed during the pre-entry period. Two examples of such practices, organisational support and organisational communication, are used to illustrate this suggestion and a number of ways are discussed through which these practices may be experienced by a person before they join an organisation.

The definition, theoretical modelling and empirical research of front-line service employee corporate identity within a UK national grocery retailer:determining the FLE corporate brand identification construct

Over the past forty years the corporate identity literature has developed to a point of maturity where it currently contains many definitions and models of the corporate identity construct at the organisational level. The literature has evolved by developing models of corporate identity or in considering corporate identity in relation to new and developing themes, e.g. corporate social responsibility. It has evolved into a multidisciplinary domain recently incorporating constructs from other literature to further its development. However, the literature has a number of limitations. It remains that an overarching and universally accepted definition of corporate identity is elusive, potentially leaving the construct with a lack of clear definition. Only a few corporate identity definitions and models, at the corporate level, have been empirically tested. The corporate identity construct is overwhelmingly defined and theoretically constructed at the corporate level, leaving the literature without a detailed understanding of its influence at an individual stakeholder level. Front-line service employees (FLEs), form a component in a number of corporate identity models developed at the organisational level. FLEs deliver the services of an organisation to its customers, as well as represent the organisation by communicating and transporting its core defining characteristics to customers through continual customer contact and interaction. This person-to-person contact between an FLE and the customer is termed a service encounter, where service encounters influence a customer’s perception of both the service delivered and the associated level of service quality. Therefore this study for the first time defines, theoretically models and empirically tests corporate identity at the individual FLE level, termed FLE corporate identity. The study uses the services marketing literature to characterise an FLE’s operating environment, arriving at five potential dimensions to the FLE corporate identity construct. These are scrutinised against existing corporate identity definitions and models to arrive at a definition for the construct. In reviewing the corporate identity, services marketing, branding and organisational psychology literature, a theoretical model is developed for FLE corporate identity, which is empirically and quantitatively tested, with FLEs in seven stores of a major national retailer. Following rigorous construct reliability and validity testing, the 601 usable responses are used to estimate a confirmatory factor analysis and structural equation model for the study. The results for the individual hypotheses and the structural model are very encouraging, as they fit the data well and support a definition of FLE corporate identity. This study makes contributions to the branding, services marketing and organisational psychology literature, but its principal contribution is to extend the corporate identity literature into a new area of discourse and research, that of FLE corporate identity

Can an episodic approach to social identification account for identification as interaction between individual and collective identity work in an increasingly flexible and transient identity formation context?

A review of available literature suggests that social identification exists at the interface between individual and collective identity work. This poster proposes that it is the interaction between these two processes that leads a person to define themselves in terms of their membership of a particular social group. The poster suggests that identity work undertaken by the group (or ‘the creation of identities as widely understood signs with a set of rules and conventions for their use’, Schwalbe & Mason-Schrock, 1996, p.115), can be used by a person to inform their own individual identity work and, from this, the extent of alignment between their identity and the perceived identity of the group. In stable or internally-structured groups collective identity work may simply take the form of communication and preservation of dominant collective identities. However, in unstable, new or transitional groups, interaction between individual and collective identity work may be more dynamic, as both collective and individual identities are simultaneously codified, enacted and refined. To develop an understanding of social identification that is applicable in both stable and transitional social groups, it is useful to consider recent proposals that identification may occur cyclically as a series of discrete episodes (Ashforth, Harrison & Corley, 2008). This poster draws on the literature to present these suggestions in greater detail, outlining propositions for social identification that are relevant to transient as well as stable identity formation, supported by suggestion of how episodes of social identification may lead to a person identifying with a group.