Electronic Properties of Lithiated SnO-based Anode Materials

In this paper we use an ab-initio quantum transport approach to study the electron current flowing through lithiated SnO anodes for potential applications in Li-ion batteries. By investigating a set of lithiated structures with varying lithium concentrations, it is revealed that LixSnO can be a good conductor, with values comparable to bulk $\beta$-Sn and Li. A deeper insight into the current distribution indicates that electrons preferably follow specific trajectories, which offer superior conducting properties than others. These channels have been identified and it is shown here how they can enhance or deteriorate the current flow in lithiated anode materials

Dental Cooperative Competencies in the Production of Removable Dental Remedies

Kompetence zubního kooperativu při výrobě snímatelných stomatologických náhrad Cílem práce bylo zvážit rozdělení kompetencí stomatologického kooperativu při protetickém řešení defektů chrupu zejména starší populace za pomoci snímatelných stomatologických náhrad. Pro zjištění důsledků různých kombinací kompetencí byl sestaven model vytvářející tři scénáře rozložení kompetencí ve třech budoucích letech. Výsledek byl vyjádřen v klinických hodinách jednotlivých dentálních profesí, byla též posuzována změna nároku těchto hodin mezi jednotlivými profesemi. Výsledkem provedeného je konstatování, že vyšší zapojení axilárních stomatologických pracovníků může znamenat úsporu klinických hodin zubního lékaře minimálně o 25 % a v čase se tato úspora bude zvyšovat. Samotné zvýšení kompetencí zubních techniků při výrobě snímatelných náhrad uspoří zubním lékařům minimálně 9 % klinických hodin, jenž mohou znovu nabídnout na trhu stomatologické péče. Výsledkem je, že zvýšení kompetencí pracovníků ve stomatologii vede k úspoře klinických hodin zubního lékaře a tím může hrát významnou roli při poskytování stomatologické péče.Dental cooperative competencies in the production of removable dental remedies The aim of the work is consideration of distribution of dental co-operative competences in prosthetic solution of dentition defects especially with the removable dental restorations for older population. A model of three scenarios for the distribution of competencies in the three future years was developed to determine the implications of different combinations of competencies. The result was expressed in the clinical hours of individual dental professions including the change in the entitlement of these hours between different professions. As a result is stated that a higher involvement of axillary dental staff can mean for dentist at least 25% reduction of dental clinic hours and the reduction will increase over time. Increasing the competence of dental technicians in the process of the production of removable restorations will save dentists at least 9% of the clinical hours they can offer again in the dental care market. As a result increasing the competence of dental staff leads to the saving of dental clinic hours for dentist and that could be an important factor in providing dental care

TrackAgent: 6D Object Tracking via Reinforcement Learning

Tracking an object's 6D pose, while either the object itself or the observing camera is moving, is important for many robotics and augmented reality applications. While exploiting temporal priors eases this problem, object-specific knowledge is required to recover when tracking is lost. Under the tight time constraints of the tracking task, RGB(D)-based methods are often conceptionally complex or rely on heuristic motion models. In comparison, we propose to simplify object tracking to a reinforced point cloud (depth only) alignment task. This allows us to train a streamlined approach from scratch with limited amounts of sparse 3D point clouds, compared to the large datasets of diverse RGBD sequences required in previous works. We incorporate temporal frame-to-frame registration with object-based recovery by frame-to-model refinement using a reinforcement learning (RL) agent that jointly solves for both objectives. We also show that the RL agent's uncertainty and a rendering-based mask propagation are effective reinitialization triggers.Comment: International Conference on Computer Vision Systems (ICVS) 202

Support the Underground: Characteristics of Beyond-Mainstream Music Listeners

Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup's openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.Comment: Accepted for publication in EPJ Data Science - link to published version will be adde

A Framework for Designing Anthropomorphic Soft Hands through Interaction

Modeling and simulating soft robot hands can aid in design iteration for complex and high degree-of-freedom (DoF) morphologies. This can be further supplemented by iterating on the design based on its performance in real world manipulation tasks. However, this requires a framework that allows us to iterate quickly at low costs. In this paper, we present a framework that leverages rapid prototyping of the hand using 3D-printing, and utilizes teleoperation to evaluate the hand in real world manipulation tasks. Using this framework, we design a 3D-printed 16-DoF dexterous anthropomorphic soft hand (DASH) and iteratively improve its design over three iterations. Rapid prototyping techniques such as 3D-printing allow us to directly evaluate the fabricated hand without modeling it in simulation. We show that the design is improved at each iteration through the hand's performance in 30 real-world teleoperated manipulation tasks. Testing over 600 demonstrations shows that our final version of DASH can solve 16 of the 30 tasks compared to Allegro, a popular rigid hand in the market, which can only solve 7 tasks. We open-source our CAD models as well as the teleoperated dataset for further study and are available on our website (https://dash-through-interaction.github.io.