Search for: All records

In today’s world, augmented reality and virtual reality (AR/VR) technologies have become more accessible to the public than ever. This brings the possibility of immersive learning to the forefront of education for future generations. However, there is still much to discover and improve in using these technologies to analyze and understand learning. This paper explores the utilization of data captured through AR/VR headsets during an immersive training program for industrial robotics. This includes data on time spent, eye gaze, and hand movement during a range of activities to track a learner’s understanding of the content and intelligently estimate learner confidence within these environments using deep learning. Leveraging a dataset that comprises responses and confidence levels from 10 individuals across 35 questions, we aim to improve the uses and applicability of confidence estimation. We explore the possibility of training a model using learners’ data to dynamically fine-tune lessons and activities for each individual, thereby improving performance. We demonstrate that a pre-trained compact LSTM classification model can be fine-tuned with relatively small data, for enhanced performance on an individual basis for better personalized learning. 

The spongiolactones are marine natural products with an unusual rearranged spongiane skeleton and a fused β-lactone ring. These compounds have potential anticancer properties but their mode of action has yet to be explored. Here we employ activity-based protein profiling to identify the targets of a more potent spongiolactone derivative in live cancer cells, and compare these to the targets of a simpler β-lactone. These hits provide the first insights into the covalent mechanism of action of this natural product class. 

A bstract Charged lepton flavor violation is forbidden in the Standard Model but possible in several new physics scenarios. In many of these models, the radiative decays τ ± → ℓ ± γ ( ℓ = e, μ ) are predicted to have a sizeable probability, making them particularly interesting channels to search at various experiments. An updated search via τ ± → ℓ ± γ using full data of the Belle experiment, corresponding to an integrated luminosity of 988 fb − 1 , is reported for charged lepton flavor violation. No significant excess over background predictions from the Standard Model is observed, and the upper limits on the branching fractions, $$ \mathcal{B} $$ B ( τ ± → μ ± γ ) ≤ 4 . 2 × 10 − 8 and $$ \mathcal{B} $$ B ( τ ± → e ± γ ) ≤ 5 . 6 × 10 − 8 , are set at 90% confidence level.