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Our goal is to model and experimentally assess trust evolution to predict future beliefs and behaviors of human-robot teams in dynamic environments. Research suggests that maintaining trust among team members in a human-robot team is vital for successful team performance. Research suggests that trust is a multi-dimensional and latent entity that relates to past experiences and future actions in a complex manner. Employing a human-robot collaborative task, we design an optimal assistance-seeking strategy for the robot using a POMDP framework. In the task, the human supervises an autonomous mobile manipulator collecting objects in an environment. The supervisor's task is to ensure that the robot safely executes its task. The robot can either choose to attempt to collect the object or seek human assistance. The human supervisor actively monitors the robot's activities, offering assistance upon request, and intervening if they perceive the robot may fail. In this setting, human trust is the hidden state, and the primary objective is to optimize team performance. We execute two sets of human-robot interaction experiments. The data from the first experiment are used to estimate POMDP parameters, which are used to compute an optimal assistance-seeking policy evaluated in the second experiment. The estimated POMDP parameters reveal that, for most participants, human intervention is more probable when trust is low, particularly in high-complexity tasks. Our estimates suggest that the robot's action of asking for assistance in high-complexity tasks can positively impact human trust. Our experimental results show that the proposed trust-aware policy is better than an optimal trust-agnostic policy. By comparing model estimates of human trust, obtained using only behavioral data, with the collected self-reported trust values, we show that model estimates are isomorphic to self-reported responses.

In this paper, we consider a Stochastic Delay Differential Equation with constant delay $r>0$ and, under the same conditions on the coefficients needed to ensure the smoothness of the density plus an ellipticity condition on the diffusion term, we prove that the density function of the solution is strictly positive in its support. In order to prove it, we give a Gaussian-type lower bound for the density of the solution combining the Nourdin and Viens' density bounding method together with Kohatsu-Higa's method., Comment: 23 pages

In strongly coupled field theories, perturbation theory cannot be employed to study the low-energy spectrum. Thus, non-perturbative techniques are required. We employ the variational method, a rigorous, non-perturbative approach which provides variational upper bounds on the energy eigenstates of the theory. An essential step in the variational method is the choice of trial wave function. In this work, we study the viability of employing a neural network as our variational ansatz. As a first step towards phenomenologically interesting strongly coupled theories like quantum chromodynamics, we study scalar field theories with quartic couplings., Comment: 5 pages, 6 figures, contribution to the 10th International Quark and Nuclear Physics Conference (QNP2024) held in Barcelona on July 2024

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Occlusal contacts are the locations at which the occluding surfaces of the maxilla and the mandible posterior teeth meet. Occlusal contact detection is a vital tool for restoring the loss of masticatory function and is a mandatory assessment in the field of dentistry, with particular importance in prosthodontics and restorative dentistry. The most common method for occlusal contact detection is articulating paper. However, this method can indicate significant medically false positive and medically false negative contact areas, leaving the identification of true occlusal indications to clinicians. To address this, we propose a multiclass Vision Transformer and Fully Convolutional Network ensemble semantic segmentation model with a combination hierarchical loss function, which we name as Hierarchical Fully Convolutional Branch Transformer (H-FCBFormer). We also propose a method of generating medically true positive semantic segmentation masks derived from expert annotated articulating paper masks and gold standard masks. The proposed model outperforms other machine learning methods evaluated at detecting medically true positive contacts and performs better than dentists in terms of accurately identifying object-wise occlusal contact areas while taking significantly less time to identify them. Code is available at https://github.com/Banksylel/H-FCBFormer., Comment: 15 pages, 5 figures, 2 tables, 5 equations, peer reviewed and accepted to Medical Imaging Understanding and Analysis (MIUA 2024)

Measurements of circular polarization of $\gamma$-ray emitted from neutron capture reactions provide valuable information for nuclear physics studies. The spin and parity of excited states can be determined by measuring the circular polarization from polarized neutron capture reactions. Furthermore, the $\gamma$-ray circular polarization in a neutron capture resonance is crucial for studying the enhancement effect of parity nonconservation in compound nuclei. The $\gamma$-ray circular polarization can be measured using a polarimeter based on magnetic Compton scattering. A polarimeter was constructed, and its performance indicators were evaluated using a circularly polarized $\gamma$-ray beam. Furthermore, as a demonstration, the $\gamma$-ray circular polarization was measured in $^{32}$S($\vec{\textrm{n}}$,$\gamma$)$^{33}$S reactions with polarized neutrons., Comment: 10pages, 13 figures

We present an expanded and improved deep-learning (DL) methodology for determining centers of star images on HST/WFPC2 exposures. Previously, we demonstrated that our DL model can eliminate the pixel-phase bias otherwise present in these undersampled images; however that analysis was limited to the central portion of each detector. In the current work we introduce the inclusion of global positions to account for the PSF variation across the entire chip and instrumental magnitudes to account for nonlinear effects such as charge transfer efficiency. The DL model is trained using a unique series of WFPC2 observations of globular cluster 47 Tuc, data sets comprising over 600 dithered exposures taken in each of two filters, F555W and F814W. It is found that the PSF variations across each chip correspond to corrections of the order of 100 mpix, while magnitude effects are at a level of about 10 mpix. Importantly, pixel-phase bias is eliminated with the DL model; whereas, with a classic centering algorithm, the amplitude of this bias can be up to 40 mpix. Our improved DL model yields star-image centers with uncertainties of 8-10 mpix across the full field of view of WFPC2., Comment: accepted for publication by PASP