14,712 research outputs found
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
Outcomes and Quality of Life After Ross Reintervention: Would You Make the Same Choice Again?
No full textBackground. The Ross procedure was introduced as a long-term if not definitive solution for aortic pathology. However, the rate of reoperation is not negligible. Methods. This single-center prospective study assessed the general outcome of Ross reoperation and patients' perceived quality of life compared with 2 control groups (Ross non-reoperation and mechanical aortic valve replacement). Patient's preference regarding the choice between mechanical aortic valve and Ross procedure was investigated in a subgroup that could theoretically have been directed to either of the 2 procedures. Results. Between 2005 and 2017, 64 consecutive patients underwent reoperation after Ross. Median age was 31 years. Median freedom from reoperation after the Ross procedure was 136 months. An autograft reoperation was required in 49, and 25 had homograft failure. No inhospital death was recorded. Mean follow-up was 77 months (range, 6-164 months). Quality of life was assessed with the 36-Item Short Form Health Survey questionnaire. The Ross reoperation group showed a lower score involving psychological concerns compared with the other groups. In the reoperated-on patients group, 52 had adequate aortic annulus dimensions to receive a prosthetic valve instead of a Ross procedure. When asked whether they would make the same choice, only 31% confirmed the preference. Conclusions. Reoperations after Ross procedure have low mortality and morbidity. Long-term follow-up showed a high quality of life, even after reoperations. However, owing to psychological concerns after the redo operation, when choosing a Ross procedure, it is our duty to thoroughly explain to patients that a high level of disillusion is predictable in case of reoperations. (C) 2020 by The Society of Thoracic Surgeon
A Manifesto of Nodalism
No full textThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
Optimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
Influence of hosts on the ecology of arboviral transmission: Potential mechanisms influencing dengue, Murray Valley encephalitis, and Ross River virus in Australia
No full textEcological interactions are fundamental to the transmission of infectious disease. Arboviruses are particularly elegant examples, where rich arrays of mechanisms influence transmission between vectors and hosts. Research on host contributions to the ecology of arboviral diseases has been undertaken within multiple subdisciplines, but significant gaps in knowledge remain and multidisciplinary approaches are needed. Through our multidisciplinary review of the literature we have identified five broad areas where hosts may influence the ecology of arboviral transmission: host immunity; cross-protective immunity and antibody-dependent enhancement; host abundance; host diversity; and pathogen spillover and dispersal. Herein we discuss the known and theoretical roles of hosts within these topics and then apply this knowledge to three epidemiologically important mosquito-borne arboviruses that occur in Australia: dengue virus (DENV), Murray Valley encephalitis virus (MVEV), and Ross River virus (RRV). We argue that the underlying mechanisms by which hosts influence arboviral activity are numerous and attempts to delineate these mechanisms further are needed. Investigations that focus on hosts of vector-borne diseases are likely to be rewarding, particularly where the ecology of vectors is relatively well understood. From an applied perspective, enhanced knowledge of host influences upon vector-borne disease transmission is likely to enable better management of disease burden. Finally, we suggest a framework that may be useful to identify and determine host contributions to the ecology of arboviruses
Technical Report: A Framework for Confusion Mitigation in Task-Oriented Interactions
No full textConfusion is a mental state that can be triggered in task-oriented interactions and which can if left unattended lead to boredom, frustration, or disengagement from the task at hand. Since previous work has demonstrated that confusion can be detected in embodied situated interactions from visual and auditory cues, in this technique report, we propose appropriate interaction structures which should be used to mitigate confusion. We motivate and describe this dialogue mechanism through an information state-style policy with examples, and also outline the approach we are taking to integrate such a meta-conversational goal alongside core task-oriented considerations in modern data driven conversational techniques. While the current policy design is a starting point, we believe it raises some interesting challenges for the integration of a reusable meta-conversational policy with highly data-driven approaches which have been enabled by large language models
A Framework for Confusion Mitigation in Task-Oriented Interactions
No full textConfusion is a mental state that can be triggered in task-oriented interactions and which can if left unattended lead to boredom, frustration, or disengagement from the task at hand. Previous work has demonstrated that confusion can be detected in situated human-robot interactions from visual and auditory cues. Therefore, in the next step, we propose appropriate interaction structures in this study, which should be used to mitigate confusion. We motivate and describe this dialogue mechanism through an information state-style dialogue framework and policies, and also outline the approach we are taking to integrate such a meta-conversational goal alongside core task-oriented considerations in modern data-driven conversational techniques
Dialogue Policies for Confusion Mitigation in Situated HRI
No full textConfusion is a mental state triggered by cognitive disequilibrium that can occur in many types of task-oriented interaction, including Human-Robot Interaction (HRI). People may become confused while interacting with robots due to communicative or even task-centred challenges. To build a smooth and engaging HRI, it is insufficient for an agent to simply detect confusion; instead, the system should aim to mitigate the situation. In light of this, in this paper, we present our approach to a linguistic design of dialogue policies to build a dialogue framework to alleviate interlocutor confusion. We also outline our sketch and discuss challenges with respect to its operationalisation
Hmm, You Seem Confused! Tracking Interlocutor Confusion for Situated Task-Oriented HRI
No full textOur research seeks to develop a long-lasting and high-quality en- gagement between the user and the social robot, which in turn requires a more sophisticated alignment of the user and the system than is currently commonly available. Close monitoring of inter- locutors’ states, and we argue their confusion state in particular, and adjusting dialogue policies based on this state of confusion is needed for successful joint activity. In this paper, we present an ini- tial study of a human-robot conversation scenarios using a Pepper robot to investigate the confusion states of users. A Wizard-of-Oz (WoZ) HRI experiment is illustrated in detail with stimuli strategies to trigger confused states from interlocutors. For the collected data, we estimated emotions, head pose, and eye gaze, and these features were analysed against the silence duration time of the speech data and the post-study self-reported confusion states that are reported by participants. Our analysis found a significant relationship be- tween confusion states and most of these features. We see these results as being particularly significant for multimodal situated dialogues for human-robot interaction and beyond
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