582 research outputs found
Gillian Dooley interviews Joris Luyendijk, author of 'Fit to Print: Misrepresenting the Middle East'.
No full textInterview with Joris Luyendijk, author of 'Fit to Print: Misrepresenting the Middle East', a book about the problems of foreign journalism in the Middle East
La symbolique maternelle dans quatre romans de Françoise Mallet-Joris /
No full textSo far, Francoise Mallet-Joris has been categorized either as a Catholic novelist or as a moderate feminist. Accused of conservatism by some, perceived by others as immoral, she has been considerably underrated by a critical audience anxious to maintain traditional literary categories. This thesis attempts to demonstrate that faith and feminism, far from conflicting with each other, are linked in Mallet-Joris' work with the process of writing, thus forming a triple entity where the common denominator is the theme of maternity. This theme will be analysed in four of Mallet-Joris' novels, Les Mensonges, Les Signes et les Prodiges, Allegra, and La Tristesse du Cerf-volant, using a symbolic approach whose usefulness lies in the twofold definition of a symbol as, on the one hand, a materialisation of the inexpressible and on the other, a split unity. For the temporal modality and the concept of identity inherent in the maternal experience place it outside the narrative system, thus putting any author who wishes to tackle this area in the position of either inventing a new narrative form or attempting a compromise between already existing forms and the specific content of the maternal experience. It is this latter alternative that Francoise Mallet-Joris adopts. Although as far as form is concerned, Mallet-Joris can hardly be termed innovative, she demonstrates on an ideological plane an originality which is largely the product of using the symbol of the Virgin Mary as an intermediary between the maternal experience and the symbolic order
Joris Janzen Van Horne and his Descendants
No full textDetailed listing of marriages and children descending from the 1666 marriage of Joris Jansen (of Hoorn) and Maria Rutgers (of Amersfoort). Introduction and early entries describe communities in Netherlands. Family settled in areas now part of Jersey City: Bergen, Communipaw, Paulus Hook, and became prominent citizens. Listing for early generations include anecdotes of community life, and references to slaves. Details of descendants through early 1900s. Full indexes of names and places. Also includes articles on disputed land claims of family, a description and history of the "house of four chimneys" (the Van Horne homestead), and a Washington Irving story describing colonial history of Communipaw as a holdout of Dutch language and culture resisting English rule (written under pen name Hermanus Vanderdonk). With many plates of portraits of family, and household scenes. Family name known variously as Van Horn, Van Hoorn, Van Horne
Framing metamemory judgments: judgments of retention intervals (JORIs)
No full text2010 Summer.Includes bibliographic references (pages 57-60).Covers not scanned.Print version deaccessioned 2022.Prior research has shown that participants’ predictions of memory performance are not sensitive to the time between study and test. However, this work has largely relied in one metacognitive measure, Judgments of Learning (JOLs), to assess such awareness. Thus, in three experiments I explored a new metacognitive measure. Judgments of Retention Interval (JORIs), in which participants determine how long (in minutes) information will be remembered. Results demonstrated that the metacognitive measure itself influences assessments of monitoring and control. For example participants chose to restudy more items when JORIs were made, compared with fewer restudy choices from participants who made JOLs (Experiment 2). However, participants demonstrated difficulty incorporating information about a retention interval into their judgments regardless of the type of judgment made (i.e., JOLs or JORIs). Results are considered within existing theoretical frameworks. I suggest that the metacognitive measure needs to be considered in order to accurately assess metacognitive awareness, and additional work is needed to assess metacognitive awareness of RI
Do we really want AI answering on our behalf? A study of smart replies usage
No full textThis research focuses on the acceptability of smart replies in emails and text messages. Our goal is to find criteria influencing the use of smart replies in AI-mediated communications and define the acceptability of such practices from the sender's perception. We conducted qualitative and quantitative research using surveys and interviews with a population of French native speakers. During our experiment, we shared various communication scenarios (including both professional and personal contexts) with fifty college students. We offered them the choice between smart replies or their own handwritten reply. We then followed up with interviews with a subset of students to better understand their replies to the survey and their relationship to AI-mediated communication. Analysis of the collected data points toward a broader acceptance of smart replies when the author only intends to acknowledge that the message was received and understood. On the contrary, reply suggestions are often dismissed by the sender as too casual for professional communications and too formal for family or friends
Structuring multidimensional data : exploring medical data with an instance-based approach
No full textL'exploitation, a posteriori, des données médicales accumulées par les praticiens représente un enjeu majeur pour la recherche clinique comme pour le suivi personnalisé du patient. Toutefois les professionnels de santé manquent d'outils adaptés leur permettant d'explorer, comprendre et manipuler aisément leur données. Dans ce but, nous proposons un algorithme de structuration d'éléments par similarité et représentativité. Cette méthode permet de regrouper les individus d'un jeu de données autour de membres représentatifs et génériques aptes à subsumer les éléments et résumer les données. Cette méthode, procédant dimension par dimension avant d'agréger les résultats, est adaptée aux données en haute dimension et propose de plus des résultats transparents, interprétables et explicables. Les résultats obtenus favorisent l'analyse exploratoire et le raisonnement par analogie via une navigation de proche en proche : la structure obtenue est en effet similaire à l'organisation des connaissances utilisée par les experts lors du processus décisionnel qu'ils emploient. Nous proposons ensuite un algorithme de détection d'anomalies qui permet de détecter des anomalies complexes et en haute dimensionnalité en analysant des projections sur deux dimensions. Cette approche propose elle aussi des résultats interprétables. Nous évaluons ensuite ces deux algorithmes sur des données réelles et simulées dont les éléments sont décrits par de nombreuses variables : de quelques dizaines à plusieurs milliers. Nous analysant particulièrement les propriétés du graphe résultant de la structuration des éléments. Nous décrivons par la suite un outil de prétraitement de données médicales ainsi qu'une plateforme web destinée aux médecins. Via cet outil à l'utilisation intuitif nous proposons de structurer de manière visuelle les éléments pour faciliter leur exploration. Ce prototype fournit une aide à la décision et au diagnostique médical en permettant au médecin de naviguer au sein des données et d'explorer des patients similaires. Cela peut aussi permettre de vérifier des hypothèses cliniques sur une cohorte de patients.A posteriori use of medical data accumulated by practitioners represents a major challenge for clinical research as well as for personalized patient follow-up. However, health professionals lack the appropriate tools to easily explore, understand and manipulate their data. To solve this, we propose an algorithm to structure elements by similarity and representativeness. This method allows individuals in a dataset to be grouped around representative and generic members who are able to subsume the elements and summarize the data. This approach processes each dimension individually before aggregating the results and is adapted to high-dimensional data and also offers transparent, interpretable and explainable results. The results we obtain are suitable for exploratory analysis and reasoning by analogy: the structure is similar to the organization of knowledge and decision-making process used by experts. We then propose an anomaly detection algorithm that allows complex and high-dimensional anomalies to be detected by analyzing two-dimensional projections. This approach also provides interpretable results. We evaluate these two algorithms on real and simulated high-dimensional data with up to thousands of dimensions. We analyze the properties of graphs resulting from the structuring of elements. We then describe a medical data pre-processing tool and a web application for physicians. Through this intuitive tool, we propose a visual structure of the elements to ease the exploration. This decision support prototype assists medical diagnosis by allowing the physician to navigate through the data and explore similar patients. It can also be used to test clinical hypotheses on a cohort of patients
Structuration de données multidimensionnelles : une approche basée instance pour l'exploration de données médicales
No full textA posteriori use of medical data accumulated by practitioners represents a major challenge for clinical research as well as for personalized patient follow-up. However, health professionals lack the appropriate tools to easily explore, understand and manipulate their data. To solve this, we propose an algorithm to structure elements by similarity and representativeness. This method allows individuals in a dataset to be grouped around representative and generic members who are able to subsume the elements and summarize the data. This approach processes each dimension individually before aggregating the results and is adapted to high-dimensional data and also offers transparent, interpretable and explainable results. The results we obtain are suitable for exploratory analysis and reasoning by analogy: the structure is similar to the organization of knowledge and decision-making process used by experts. We then propose an anomaly detection algorithm that allows complex and high-dimensional anomalies to be detected by analyzing two-dimensional projections. This approach also provides interpretable results. We evaluate these two algorithms on real and simulated high-dimensional data with up to thousands of dimensions. We analyze the properties of graphs resulting from the structuring of elements. We then describe a medical data pre-processing tool and a web application for physicians. Through this intuitive tool, we propose a visual structure of the elements to ease the exploration. This decision support prototype assists medical diagnosis by allowing the physician to navigate through the data and explore similar patients. It can also be used to test clinical hypotheses on a cohort of patients.L'exploitation, a posteriori, des données médicales accumulées par les praticiens représente un enjeu majeur pour la recherche clinique comme pour le suivi personnalisé du patient. Toutefois les professionnels de santé manquent d'outils adaptés leur permettant d'explorer, comprendre et manipuler aisément leur données. Dans ce but, nous proposons un algorithme de structuration d'éléments par similarité et représentativité. Cette méthode permet de regrouper les individus d'un jeu de données autour de membres représentatifs et génériques aptes à subsumer les éléments et résumer les données. Cette méthode, procédant dimension par dimension avant d'agréger les résultats, est adaptée aux données en haute dimension et propose de plus des résultats transparents, interprétables et explicables. Les résultats obtenus favorisent l'analyse exploratoire et le raisonnement par analogie via une navigation de proche en proche : la structure obtenue est en effet similaire à l'organisation des connaissances utilisée par les experts lors du processus décisionnel qu'ils emploient. Nous proposons ensuite un algorithme de détection d'anomalies qui permet de détecter des anomalies complexes et en haute dimensionnalité en analysant des projections sur deux dimensions. Cette approche propose elle aussi des résultats interprétables. Nous évaluons ensuite ces deux algorithmes sur des données réelles et simulées dont les éléments sont décrits par de nombreuses variables : de quelques dizaines à plusieurs milliers. Nous analysant particulièrement les propriétés du graphe résultant de la structuration des éléments. Nous décrivons par la suite un outil de prétraitement de données médicales ainsi qu'une plateforme web destinée aux médecins. Via cet outil à l'utilisation intuitif nous proposons de structurer de manière visuelle les éléments pour faciliter leur exploration. Ce prototype fournit une aide à la décision et au diagnostique médical en permettant au médecin de naviguer au sein des données et d'explorer des patients similaires. Cela peut aussi permettre de vérifier des hypothèses cliniques sur une cohorte de patients
Structuration de données multidimensionnelles : une approche basée instance pour l'exploration de données médicales
No full textA posteriori use of medical data accumulated by practitioners represents a major challenge for clinical research as well as for personalized patient follow-up. However, health professionals lack the appropriate tools to easily explore, understand and manipulate their data. To solve this, we propose an algorithm to structure elements by similarity and representativeness. This method allows individuals in a dataset to be grouped around representative and generic members who are able to subsume the elements and summarize the data. This approach processes each dimension individually before aggregating the results and is adapted to high-dimensional data and also offers transparent, interpretable and explainable results. The results we obtain are suitable for exploratory analysis and reasoning by analogy: the structure is similar to the organization of knowledge and decision-making process used by experts. We then propose an anomaly detection algorithm that allows complex and high-dimensional anomalies to be detected by analyzing two-dimensional projections. This approach also provides interpretable results. We evaluate these two algorithms on real and simulated high-dimensional data with up to thousands of dimensions. We analyze the properties of graphs resulting from the structuring of elements. We then describe a medical data pre-processing tool and a web application for physicians. Through this intuitive tool, we propose a visual structure of the elements to ease the exploration. This decision support prototype assists medical diagnosis by allowing the physician to navigate through the data and explore similar patients. It can also be used to test clinical hypotheses on a cohort of patients.L'exploitation, a posteriori, des données médicales accumulées par les praticiens représente un enjeu majeur pour la recherche clinique comme pour le suivi personnalisé du patient. Toutefois les professionnels de santé manquent d'outils adaptés leur permettant d'explorer, comprendre et manipuler aisément leur données. Dans ce but, nous proposons un algorithme de structuration d'éléments par similarité et représentativité. Cette méthode permet de regrouper les individus d'un jeu de données autour de membres représentatifs et génériques aptes à subsumer les éléments et résumer les données. Cette méthode, procédant dimension par dimension avant d'agréger les résultats, est adaptée aux données en haute dimension et propose de plus des résultats transparents, interprétables et explicables. Les résultats obtenus favorisent l'analyse exploratoire et le raisonnement par analogie via une navigation de proche en proche : la structure obtenue est en effet similaire à l'organisation des connaissances utilisée par les experts lors du processus décisionnel qu'ils emploient. Nous proposons ensuite un algorithme de détection d'anomalies qui permet de détecter des anomalies complexes et en haute dimensionnalité en analysant des projections sur deux dimensions. Cette approche propose elle aussi des résultats interprétables. Nous évaluons ensuite ces deux algorithmes sur des données réelles et simulées dont les éléments sont décrits par de nombreuses variables : de quelques dizaines à plusieurs milliers. Nous analysant particulièrement les propriétés du graphe résultant de la structuration des éléments. Nous décrivons par la suite un outil de prétraitement de données médicales ainsi qu'une plateforme web destinée aux médecins. Via cet outil à l'utilisation intuitif nous proposons de structurer de manière visuelle les éléments pour faciliter leur exploration. Ce prototype fournit une aide à la décision et au diagnostique médical en permettant au médecin de naviguer au sein des données et d'explorer des patients similaires. Cela peut aussi permettre de vérifier des hypothèses cliniques sur une cohorte de patients
THE MONOLITH DRAWING
No full textRobin Evans describes the way in which architecture always exceeds its representations for every architecture meanders through different stages of being image [*]. Stan Allen defines the practice of producing images of architecture to be a combination between representation and expression [*]. The practice of notational drawing, driven by professional codes, indeed allows us to represent spaces as empirical objects. The diagram, as he explains, is a moment where one takes a distance from such a professional vocabulary to allow the image or drawing to become expressive of something perhaps less tangible [*]. This distinction between notation and diagramming has been termed by the American Philosopher Nelson Goodman (1976) as the difference between allographic and autographic art forms.
Allographic art is “capable of being reproduced at a distance from the author by means of notation” [*]. An example of this would be music scores or indeed an architectural drawing. Autographic art its authenticity on the other hand is clearly dependent on direct contact with the author. Its value therefore lays in the original, such as in a painting or diagram.
This paper aims at describing a drawing protocol through which the dialectics between representation and expression are under perpetual review. The protocol, termed The Monolith Drawing, acts as a performative discourse syncopating between representation and expression like Rubin’s Vase allowing both models to simultaneously exist on for and background.
A significant attribute of The Monolith Drawing is its investment in the enormity of absence in order to avoid, as much as possible, ready made vocabularies and pre-determined routes of reflection. As such overthrowing the Architect’s personal formations of truth to allow him/her to interact with the vastness of what one could address as collective memory. This allows for a particular kind of embryonic architecture, born out of the organizational framework in which individual consciousness can coincide with shared understanding (or indeed collective memory). This is important to prevent the act of drawing to be reduced to subjective idealism and allow it to enter the dimensions of an architectural history.
The Monolith Drawing invests in the entirety of history actively referencing historical archetypical elements to explore aspects of duration freed from historical classification and taxonomy. This in turn seems to create a practice of paradox with the appearing congruence between intuition and tradition. It is this kind of practice which is enabled a search for architecture escaping a historical periphery in order to (re) enter history in pursuit of productive points of intersection and overlap. In doing so, any boundary between historicized and present day architecture is carefully erased. Any safe distance between historical and contemporary information is eliminated to allow the drawing to engage with a process of intimate reflection. Such production of architecture demands a distancing between the architect as author and the authored product. The Monolith Drawing, as this paper will explain, is partly responsible for its own becoming with the architect standing at a (critical) distance. Such positioning of the architect, as maker of drawing constructs or choreographer of (historical) information is the actuation of a practice in which any linear or secular understanding of time is rejected and the idea of space or more specifically ‘distance’ is allowed to change.sponsorship: KU Leuvenstatus: Accepte
From the narrative about other to the narrative about yourself. "La double confidence" by Françoise Mallet-Joris
No full textThis article is devoted to the book The Double Confidence (2000) by the Belgian novelist Françoise Mallet‐Joris, which is the fusion of the biography of Marceline Debordes‐Valmore and the autobiography of the author. The writing of the biography triggers the author's memory and awakens her memories (mainly her difficult relationship with her mother, Suzanne Lilar): she allows her to observe herself, to analyze herself, to ask questions she avoid, to re‐emerge repressed. I study the elements that link the work of Mallet‐Joris to "narrations of filiation", I examine the binary structure, the singularity of discursive strategies, as well as the therapeutic function of this self‐biographic project
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