100 research outputs found
An exploration of pharmacological and psychological interventions in patients with treatment-resistant affective disorders
No full textPreface:The Mood and Anxiety Disorder Service in Southampton is a regional specialistservice for patients with complex and usually treatment-resistant affective disorders, which accepts referrals mainly from secondary care mental health services. Professor David Baldwin is the lead consultant for this service. He has clinical and research expertise in the identification, assessment and treatment of depressive illness and anxiety disorders, and is the lead author of evidence-based treatment guidelines for anxiety disorders.I have worked with Professor David Baldwin since 2008. Through his encouragement and support, I have undertaken a series of pharmacological and psychological interventions in patients with treatment-resistant affective disorders. My dual aims were to examine treatment recommendations within the Service, and to investigate the potential feasibility, effectiveness, and acceptability of non-pharmacological treatment options for groups of patients with severe treatment-resistant depression or generalised anxiety disorder (GAD): partly in the hope that this might inform the development of additional treatment interventions within the local services.Inspection of the medical records of patients referred to the tertiary services inSouthampton allowed an examination of currently offered pharmacological and psychological treatments. I realized that for most patients, and often over long periods of treatment in secondary care mental health services, there had been a lack of non-pharmacological treatments options apart from cognitive behaviour therapy (CBT). This thesis includes a description of patients referred to the Service, including their demographic and clinical characteristics, and current and recommended treatments: and an account of the effectiveness and acceptability of two non-pharmacological group interventions, namely mindfulness-based CBT in patients with highly recurrent unipolar depressive disorder, and Yogic breathing in patients with treatment resistant GA
Cyber-d Subjects: DeLillo’s Cosmopolis and Shteyngart’s Super Sad True Love Story
Get PDFU posljednje vrijeme sve je prisutnija zabrinutost pisaca i drugih koji se bave književnošću zbog činjenice da je roman umjetnost koja umire i da je navika čitanja u opadanju među širim stanovništvom. Jedan od ključnih razloga koji se navodi je pojava cyber prostora, koji nudi brojne mogućnosti za slobodno vrijeme natječući se s čitanjem za pozornost širokog sloja stanovništva. Osim toga, često se raspravlja o tome kako je cyber prostor utjecao na promjenu psihe ljudi preopterećujući ih informacijama i pružajući im neometan pristup različitim načinima zabave, što je uzrokovalo da ljudi gube sposobnost da na duže vrijeme zadrže pozornost na nekom određenom zadatku. To također, navodno, uzrokuje da se ljudi sve više i više oslanjaju na strojeve da izvrše njihove svakodnevne zadatke i misle umjesto njih. Takvi su problemi istaknuti u romanu Dona DeLilla Cosmopolis (2003) te romanu Garyja Shteyngarta Super tužna istinita ljubavna priča (2010), koja su oba smještena u distopijskim društvima gdje ljudi rabe svoje uređaje kao štake koje im omogućuju kretanje svijetom. Te tjeskobe, prema tvrdnjama iznesenima u radu, uzrokovane su promjenama u književnim poljima, koje su nastale povećanim mogućnostima stvaranja, diseminacije i čitanja tekstova u doba interneta. Te bojazni, dakle, bit će uokvirene i „raspakirane“ kako bi se razumjela i istaknula njihova ideološka podloga.In recent times, concerns have been raised by writers, and others associated with the literary field, about the fact that the novel may be a dying art and that the habit of reading among the general populace is on the decline. One of the key reasons cited for the same is the emergence of the cyberspace, which offers a number of options for leisure time activities that compete with reading for the attention of the masses. Furthermore, it is commonly argued that the cyberspace has impacted and altered people’s psyches as the information overload and ready access to various modes of entertainment cause people to lose the capacity to maintain focus for an extended period of time on any one particular task. This also, purportedly, causes people to become more and more reliant on machines to do their everyday tasks, their thinking for them. Such concerns are highlighted in Don DeLillo’s Cosmopolis (2003) and Gary Shteyngart’s A Super Sad True Love Story (2010), both of which are set in dystopic societies where people use their devices as crutches to enable them to navigate the world. These anxieties, this paper will argue, are caused by shifts in the literary fields which have been brought on by increasing possibilities in terms of creation, dissemination, and reading of texts in the internet age. These misgivings, therefore, will be framed and unpacked to understand and highlight their ideological underpinnings
Données, apprentissage et respect de la vie privée dans les systèmes de recommandation
No full textRecommendation systems have gained tremendous popularity, both in academia and industry. They have evolved into many different varieties depending mostly on the techniques and ideas used in their implementation. This categorization also marks the boundary of their application domain. Regardless of the types of recommendation systems, they are complex and multi-disciplinary in nature, involving subjects like information retrieval, data cleansing and preprocessing, data mining etc. In our work, we identify three different challenges (among many possible) involved in the process of making recommendations and provide their solutions. We elaborate the challenges involved in obtaining user-demographic data, and processing it, to render it useful for making recommendations. The focus here is to make use of Online Social Networks to access publicly available user data, to help the recommendation systems. Using user-demographic data for the purpose of improving the personalized recommendations, has many other advantages, like dealing with the famous cold-start problem. It is also one of the founding pillars of hybrid recommendation systems. With the help of this work, we underline the importance of user’s publicly available information like tweets, posts, votes etc. to infer more private details about her. As the second challenge, we aim at improving the learning process of recommendation systems. Our goal is to provide a k-nearest neighbor method that deals with very large amount of datasets, surpassing billions of users. We propose a generic, fast and scalable k-NN graph construction algorithm that improves significantly the performance as compared to the state-of-the art approaches. Our idea is based on leveraging the bipartite nature of the underlying dataset, and use a preprocessing phase to reduce the number of similarity computations in later iterations. As a result, we gain a speed-up of 14 compared to other significant approaches from literature. Finally, we also consider the issue of privacy. Instead of directly viewing it under trivial recommendation systems, we analyze it on Online Social Networks. First, we reason how OSNs can be seen as a form of recommendation systems and how information dissemination is similar to broadcasting opinion/reviews in trivial recommendation systems. Following this parallelism, we identify privacy threat in information diffusion in OSNs and provide a privacy preserving algorithm for the same. Our algorithm Riposte quantifies the privacy in terms of differential privacy and with the help of experimental datasets, we demonstrate how Riposte maintains the desirable information diffusion properties of a network.Les systèmes de recommandation sont devenus une partie indispensable des services et des applications d’internet, en particulier dû à la surcharge de données provenant de nombreuses sources. Quel que soit le type, chaque système de recommandation a des défis fondamentaux à traiter. Dans ce travail, nous identifions trois défis communs, rencontrés par tous les types de systèmes de recommandation: les données, les modèles d'apprentissage et la protection de la vie privée. Nous élaborons différents problèmes qui peuvent être créés par des données inappropriées en mettant l'accent sur sa qualité et sa quantité. De plus, nous mettons en évidence l'importance des réseaux sociaux dans la mise à disposition publique de systèmes de recommandation contenant des données sur ses utilisateurs, afin d'améliorer la qualité des recommandations. Nous fournissons également les capacités d'inférence de données publiques liées à des données relatives aux utilisateurs. Dans notre travail, nous exploitons cette capacité à améliorer la qualité des recommandations, mais nous soutenons également qu'il en résulte des menaces d'atteinte à la vie privée des utilisateurs sur la base de leurs informations. Pour notre second défi, nous proposons une nouvelle version de la méthode des k plus proches voisins (knn, de l'anglais k-nearest neighbors), qui est une des méthodes d'apprentissage parmi les plus populaires pour les systèmes de recommandation. Notre solution, conçue pour exploiter la nature bipartie des ensembles de données utilisateur-élément, est évolutive, rapide et efficace pour la construction d'un graphe knn et tire sa motivation de la grande quantité de ressources utilisées par des calculs de similarité dans les calculs de knn. Notre algorithme KIFF utilise des expériences sur des jeux de données réelles provenant de divers domaines, pour démontrer sa rapidité et son efficacité lorsqu'il est comparé à des approches issues de l'état de l'art. Pour notre dernière contribution, nous fournissons un mécanisme permettant aux utilisateurs de dissimuler leur opinion sur des réseaux sociaux sans pour autant dissimuler leur identité
J.M. Coetzee: Construction and Representation of Historical Reality (Effect)
No full textSouth African novelist J.M. Coetzee has often been accused of refusing to engage with socio-political conflicts that mark his society. This paper will frame and analyse representation and conceptualization of history in Coetzee’s post-apartheid novels—Disgrace (2000) and Elizabeth Costello (2003). The central argument will be that, far from ignoring historical struggles and developments, Coetzee’s work engages with and encodes the same by using the grammar of novelistic discourse, which it positions as a rival to normative modern historical discourse.</jats:p
Cyber-d Subjects: DeLillo’s Cosmopolis and Shteyngart’s Super Sad True Love Story
No full textIn recent times, concerns have been raised by writers, and others associated with the literary field, about the fact that the novel may be a dying art and that the habit of reading among the general populace is on the decline. One of the key reasons cited for the same is the emergence of the cyberspace, which offers a number of options for leisure time activities that compete with reading for the attention of the masses. Furthermore, it is commonly argued that the cyberspace has impacted and altered people’s psyches as the information overload and ready access to various modes of entertainment cause people to lose the capacity to maintain focus for an extended period of time on any one particular task. This also, purportedly, causes people to become more and more reliant on machines to do their everyday tasks, their thinking for them. Such concerns are highlighted in Don DeLillo’s Cosmopolis (2003) and Gary Shteyngart’s A Super Sad True Love Story (2010), both of which are set in dystopic societies where people use their devices as crutches to enable them to navigate the world. These anxieties, this paper will argue, are caused by shifts in the literary fields which have been brought on by increasing possibilities in terms of creation, dissemination, and reading of texts in the internet age. These misgivings, therefore, will be framed and unpacked to understand and highlight their ideological underpinnings
Of Pallus and Pants: Fabricating the New Woman of the New Nation in Andaz (1949), Mr. and Mrs. 55 (1955), Shri 420 (1955)
No full textDonnées, apprentissage et respect de la vie privée dans les systèmes de recommandation
No full textRecommendation systems have gained tremendous popularity, both in academia and industry. They have evolved into many different varieties depending mostly on the techniques and ideas used in their implementation. This categorization also marks the boundary of their application domain. Regardless of the types of recommendation systems, they are complex and multi-disciplinary in nature, involving subjects like information retrieval, data cleansing and preprocessing, data mining etc. In our work, we identify three different challenges (among many possible) involved in the process of making recommendations and provide their solutions. We elaborate the challenges involved in obtaining user-demographic data, and processing it, to render it useful for making recommendations. The focus here is to make use of Online Social Networks to access publicly available user data, to help the recommendation systems. Using user-demographic data for the purpose of improving the personalized recommendations, has many other advantages, like dealing with the famous cold-start problem. It is also one of the founding pillars of hybrid recommendation systems. With the help of this work, we underline the importance of user’s publicly available information like tweets, posts, votes etc. to infer more private details about her. As the second challenge, we aim at improving the learning process of recommendation systems. Our goal is to provide a k-nearest neighbor method that deals with very large amount of datasets, surpassing billions of users. We propose a generic, fast and scalable k-NN graph construction algorithm that improves significantly the performance as compared to the state-of-the art approaches. Our idea is based on leveraging the bipartite nature of the underlying dataset, and use a preprocessing phase to reduce the number of similarity computations in later iterations. As a result, we gain a speed-up of 14 compared to other significant approaches from literature. Finally, we also consider the issue of privacy. Instead of directly viewing it under trivial recommendation systems, we analyze it on Online Social Networks. First, we reason how OSNs can be seen as a form of recommendation systems and how information dissemination is similar to broadcasting opinion/reviews in trivial recommendation systems. Following this parallelism, we identify privacy threat in information diffusion in OSNs and provide a privacy preserving algorithm for the same. Our algorithm Riposte quantifies the privacy in terms of differential privacy and with the help of experimental datasets, we demonstrate how Riposte maintains the desirable information diffusion properties of a network.Les systèmes de recommandation sont devenus une partie indispensable des services et des applications d’internet, en particulier dû à la surcharge de données provenant de nombreuses sources. Quel que soit le type, chaque système de recommandation a des défis fondamentaux à traiter. Dans ce travail, nous identifions trois défis communs, rencontrés par tous les types de systèmes de recommandation: les données, les modèles d'apprentissage et la protection de la vie privée. Nous élaborons différents problèmes qui peuvent être créés par des données inappropriées en mettant l'accent sur sa qualité et sa quantité. De plus, nous mettons en évidence l'importance des réseaux sociaux dans la mise à disposition publique de systèmes de recommandation contenant des données sur ses utilisateurs, afin d'améliorer la qualité des recommandations. Nous fournissons également les capacités d'inférence de données publiques liées à des données relatives aux utilisateurs. Dans notre travail, nous exploitons cette capacité à améliorer la qualité des recommandations, mais nous soutenons également qu'il en résulte des menaces d'atteinte à la vie privée des utilisateurs sur la base de leurs informations. Pour notre second défi, nous proposons une nouvelle version de la méthode des k plus proches voisins (knn, de l'anglais k-nearest neighbors), qui est une des méthodes d'apprentissage parmi les plus populaires pour les systèmes de recommandation. Notre solution, conçue pour exploiter la nature bipartie des ensembles de données utilisateur-élément, est évolutive, rapide et efficace pour la construction d'un graphe knn et tire sa motivation de la grande quantité de ressources utilisées par des calculs de similarité dans les calculs de knn. Notre algorithme KIFF utilise des expériences sur des jeux de données réelles provenant de divers domaines, pour démontrer sa rapidité et son efficacité lorsqu'il est comparé à des approches issues de l'état de l'art. Pour notre dernière contribution, nous fournissons un mécanisme permettant aux utilisateurs de dissimuler leur opinion sur des réseaux sociaux sans pour autant dissimuler leur identité
Clearing Misconceptions—Pregnancy Complications and Miscarriage Due to Maternal Infections
No full textClearing Misconceptions: Pregnancy Complications and Miscarriage Due to Maternal TORCH Infections
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