Tractable Space Research Articles

The virtue of ignorance: How epistemic agency needs cognitive limitations

AbstractThe thesis defended in this article is that epistemology should treat some of our cognitive limitations not as unfortunate defects or external perturbations to be idealized away in theories of epistemic agency, but as necessary underpinnings of good reasoning. We begin with a problem regarding deliberation that calls epistemic agency into question: our reasons in support of belief are never conclusive and never rule out all doubt. Yet we must rule out all doubt to close deliberation; we must close deliberation to form a full belief; and we must form a full belief to have knowledge. The problem with the first step calls the whole into question. The solution (if we seek an alternative to rejecting traditional epistemic agency, including the existence of beliefs) is our limitations: they prevent us from considering all possible doubt, leaving a tractable space of possibilities. When these limitations are virtuous, they contribute to an effective cognitive system. Once we understand the role of our limitations, it will lead us to a deeper understanding of deliberation, belief, epistemic virtue, and epistemic agency. Limitations are as much a part of agency as, for example, logical relations are. Idealizing them away means idealizing away actual agency.

Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories

Classification and analysis of driving behaviors offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising of two layers: feature representation layer and clustering layer. In the feature representation layer, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space, which quantifies the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to cluster homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results show that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit the decision-making and design of autonomous vehicles.

Developmental adaptations of trypanosome motility to the tsetse fly host environments unravel a multifaceted in vivo microswimmer system.

The highly motile and versatile protozoan pathogen Trypanosoma brucei undergoes a complex life cycle in the tsetse fly. Here we introduce the host insect as an expedient model environment for microswimmer research, as it allows examination of microbial motion within a diversified, secluded and yet microscopically tractable space. During their week-long journey through the different microenvironments of the fly´s interior organs, the incessantly swimming trypanosomes cross various barriers and confined surroundings, with concurrently occurring major changes of parasite cell architecture. Multicolour light sheet fluorescence microscopy provided information about tsetse tissue topology with unprecedented resolution and allowed the first 3D analysis of the infection process. High-speed fluorescence microscopy illuminated the versatile behaviour of trypanosome developmental stages, ranging from solitary motion and near-wall swimming to collective motility in synchronised swarms and in confinement. We correlate the microenvironments and trypanosome morphologies to high-speed motility data, which paves the way for cross-disciplinary microswimmer research in a naturally evolved environment.

Emotion recognition in never-seen languages using a novel ensemble method with emotion profiles

Over the last years, researchers have addressed emotional state identification because it is an important issue to achieve more natural speech interactive systems. There are several theories that explain emotional expressiveness as a result of natural evolution, as a social construction, or a combination of both. In this work, we propose a novel system to model each language independently, preserving the cultural properties. In a second stage, we use the concept of universality of emotions to map and predict emotions in never-seen languages. Features and classifiers widely tested for similar tasks were used to set the baselines. We developed a novel ensemble classifier to deal with multiple languages and tested it on never-seen languages. Furthermore, this ensemble uses the Emotion Profiles technique in order to map features from diverse languages in a more tractable space. The experiments were performed in a language-independent scheme. Results show that the proposed model improves the baseline accuracy, whereas its modular design allows the incorporation of a new language without having to train the whole system.

From Theory to Practice: Model-Based Diagnosis in Industrial Applications


 
 
 Due to the increasing complexity of technical systems, accurate fault identification is crucial in order to reduce maintenance costs and system downtime. Model-based diagnosis has been proposed as an approach to improve fault localization. By utilizing a system model, possible causes, i.e. defects, for observable anomalies can be computed. Even though model-based diagnosis rests on solid theoretical background, it has not been widely adopted in practice. The reasons are twofold: on the one hand it requires an initial modeling effort and on the other hand a high computational complexity is associated with the diagnosis task in general. In this paper we address these issues by proposing a process for abductive model-based diagnosis in an industrial setting. Suitable models are created automatically from failure assessments available. Further, the compiled system descriptions reside within a tractable space of abductive diagnosis. In or- der to convey the feasibility of the approach we present results of an empirical evaluation based on several failure assessments.
 
 

Social informatics: Beyond emergence

Social informatics: Beyond emergence

On the proper spinor genus of a quadratic form

The object of this paper is to exhibit a procedure and a setting in which lattices on a given quadratic space Vover a global field can be studied via lattices on another more tractable space. This will enable us to prescribe conditions under which the proper spinor genus of a lattice can be characterized without reference to the spinor norm. The method involves modification of Vby orthogonal adjunction and cancellation, and by suitable local changes. In effect this allows us to reap some of the benefits of isotropy in a situation that may be anisotropic. Other descriptions of the proper spinor genus can be found in Cassels [2],Kneser[7],O’Meara [8], and Watson [12]; and a related concept has been defined by Jones [6]. See O'Meara's survey article [9] for historical details. Our notation and terminology are those of [8].