In this paper, we propose a new kind of nonprioritized operator which we call two level credibility-limited revision. When revising through a two level credibility-limited revision there are two levels of credibility and one of incredibility. When revising by a sentence at the highest level of credibility, the operator behaves as a standard revision, if the sentence is at the second level of credibility, then the outcome of the revision process coincides with a standard contraction by the negation of that sentence. If the sentence is not credible, then the original belief set remains unchanged. In this article, we axiomatically characterize several classes of two level credibility-limited revision operators.

This paper contributes to a recent research program that extends arguments supporting elementary conditionalization to arguments supporting conditionalization with general, measure-theoretic conditional probabilities. I begin by suggesting an amendment to the framework that Rescorla (2018) has used to characterize regular conditional probabilities in terms of avoiding Dutch book. If we wish to model learning scenarios in which an agent gains complete membership knowledge about some subcollection of the events of interest to her, then we should focus on updating policies that are what I shall call proper. I go on to characterize regular conditional probabilities in proper learning scenarios using what van Fraassen (1999) calls The General Reflection Principle.

We investigate a recent proposal for modal hypersequent calculi. The interpretation of relational hypersequents incorporates an accessibility relation along the hypersequent. These systems give the same interpretation of hypersequents as Lellman’s linear nested sequents, but were developed independently by Restall for S5 and extended to other normal modal logics by Parisi. The resulting systems obey Došen’s principle: the modal rules are the same across different modal logics. Different modal systems only differ in the presence or absence of external structural rules. With the exception of S5, the systems are modular in the sense that different structural rules capture different properties of the accessibility relation. We provide the first direct semantical cut-free completeness proofs for K, T, and D, and show how this method fails in the case of B and S4.

This paper relaxes assumptions defining multivariate Brownian motion (BM) to construct processes with dependent increments as tractable models for problems in engineering and management science. We show that any Gaussian Markov process starting at zero and possessing stationary increments and a symmetric smooth kernel has a parametric kernel of a particular form, and we derive the unique unbiased, jointly sufficient, maximum-likelihood estimators of those parameters. As an application, we model a single-server queue driven by such a process and derive its transient distribution conditional on its history.

We present epistemic multilateral logic, a general logical framework for reasoning involving epistemic modality. Standard bilateral systems use propositional formulae marked with signs for assertion and rejection. Epistemic multilateral logic extends standard bilateral systems with a sign for the speech act of weak assertion (Incurvati & Schlöder, 2019) and an operator for epistemic modality. We prove that epistemic multilateral logic is sound and complete with respect to the modal logic $\mathbf {S5}$ modulo an appropriate translation. The logical framework developed provides the basis for a novel, proof-theoretic approach to the study of epistemic modality. To demonstrate the fruitfulness of the approach, we show how the framework allows us to reconcile classical logic with the contradictoriness of so-called Yalcin sentences and to distinguish between various inference patterns on the basis of the epistemic properties they preserve.

We present four classical theories of counterpossibles that combine modalities and counterfactuals. Two theories are anti-vacuist and forbid vacuously true counterfactuals, two are quasi-vacuist and allow counterfactuals to be vacuously true when their antecedent is not only impossible, but also inconceivable. The theories vary on how they restrict the interaction of modalities and counterfactuals. We provide a logical cartography with precise acceptable boundaries, illustrating to what extent nonvacuism about counterpossibles can be reconciled with classical logic.

We propose a dynamic hyperintensional logic of belief revision for non-omniscient agents, reducing the logical omniscience phenomena affecting standard doxastic/epistemic logic as well as AGM belief revision theory. Our agents don’t know all a priori truths; their belief states are not closed under classical logical consequence; and their belief update policies are such that logically or necessarily equivalent contents can lead to different revisions. We model both plain and conditional belief, then focus on dynamic belief revision. The key idea we exploit to achieve non-omniscience focuses on topic- or subject matter-sensitivity: a feature of belief states which is gaining growing attention in the recent literature.

Social jetlag (SJ) occurs when sleep-timing irregularities from social or occupational demands conflict with endogenous sleep–wake rhythms. SJ is associated with evening chronotype and poor mental health, but mechanisms supporting this link remain unknown. Impaired ability to retrieve extinction memory is an emotion regulatory deficit observed in some psychiatric illnesses. Thus, SJ-dependent extinction memory deficits may provide a mechanism for poor mental health. To test this, healthy male college students completed 7–9 nights of actigraphy, sleep questionnaires, and a fear conditioning and extinction protocol. As expected, greater SJ, but not total sleep time discrepancy, was associated with poorer extinction memory. Unexpectedly, greater SJ was associated with a tendency toward morning rather than evening chronotype. These findings suggest that deficient extinction memory represents a potential mechanism linking SJ to psychopathology and that SJ is particularly problematic for college students with a greater tendency toward a morning chronotype.

A data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.

This paper presents a new VR interaction environment for the evaluation of digital prototypes, specifically in designer–client review sessions, and documents its implementation via experience mapping. Usability of VR controllers and basic manipulation remains a barrier for lay users, and a range of typical implementations are reviewed, highlighting the need for an easily accessible interface for this setting. The resulting interface configuration – the Control Carousel – demonstrates how the appropriate use of familiar mechanisms can increase VR accessibility. Three case studies using the Carousel in commercial design projects are described, and the subsequent interface refinements outlined. Finally, the development of an experience map describing the logistical, interactive, and emotive factors affecting the Carousel's implementation is documented. This provides insights on how experience mapping can be used as part of a human-centred design process to ensure VR environments are attuned to the requirements of users, in this instance delivering improved collaborative reviews.

Optimal grasping points for a robotic gripper were derived, based on object and hand geometry, using deep neural networks (DNNs). The optimal grasping cost functions were derived using probability density functions for each local cost function of the normal distribution. Using the DNN, the optimum height and width were set for the robot hand to grasp objects, whose geometric and mass centre points were also considered in obtaining the optimum grasping positions for the robot fingers and the object. The proposed algorithm was tested on 10 differently shaped objects and showed improved grip performance compared to conventional methods.

This article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable.

This paper reports on laboratory and field experimental results for controlled robotic manipulators operating on moving platforms with unmodeled dynamics. The aim is to validate theoretical predictions for the dependence on control parameters of an adaptive control strategy. In addition, the results provide insight into different discretizations of the continuous-time formulation, suggesting the most suitable discretization scheme for hardware implementation. The second set of experimental results, obtained from an implementation of the control framework for synchronization and consensus in networks of robotic manipulators, similarly validate theoretical predictions on the sensitivity to network communication delays.