The preparation of optically active thiols via iso-thiuronium salts: Addition and other reactions of asymmetric thiols.

(-)-Octane-2-thiol has been prepared by alkaline decomposition of the thiuronium salt derived from thiourea and (+)-2-bromo-octane. Fractional crystallisation of (+)-2-octyl (+)-camphor-1C-sulphonate failed to effect resolution into the diastereoisomeric salts. (+)- and (-)-1-Methyl-2-phenylethyl toluene-p-sulphonate have been converted into the thiuronium toluene-p-sulphonates, which on decomposition gave (-)- and (+)-1-phenylpropano-2-thiol. A mechanism is proposed for the reaction of thiourea with 2-bromo-octane and with 1-methyl-2-phenylethyl toluene-p-sulphonate. Addition (peroxide- and base-catalysed) of (+)-, (+)-, and (-)-1-phenylpropane-2-thiol to w-nitrostyrene has been investigated, two diastereoisomeric sulphides (or sulphones) being obtained in each case; configurational relationships are proposed for these products. With neither catalyst is the reaction sterically unilateral. It is tentatively concluded that free radical addition proceeds in a symmetrical manner, whereas the base-catalysed addition proceeds disymmetrically. (-)-1-Methyl-2-phenylethyl 2:4-dinitrophenyl sulphone has been shown to be optically stable under acidic conditions employed for oxidation. Addition of (+)-1-phenylpropane-2-thiol to trans-1:2-dibenzoylethylene gave a solid and an oily sulphide. Oxidation of these sulphides, in glacial acetic acid with hydrogen peroxide gave a common sulphone; enolisation of the keto-sulphide, or sulphone, is thought to occur. Oxidation with the hot reagent causes elimination; a tentative mechanism is proposed. Oxidation of the benzyl thiol addition product also proceeds with (partial) elimination in hot solution. From the addition of (+)-1-phenylpropane-2-thiol to 4'-nitrochalkone a single isomer of (+)-2-(p-nitrobenzoyl)-1-phenyl-1'-benzyldiethyl sulphide has been isolated; the benzyl thiol addition product of 4'-nitrochalkone has also been prepared. Alkylation reactions of (+)-1-phenylpropane-2-thiol have been carried out with two symmetrical carbinols (yielding solid sulphides) and with four disymmetric carbinols, of which two yielded solid sulphides; a single isomer of (+)-1-methyl-2-phenylethyl p-diphenylphenylmethyl sulphide and two isomers of (+)-1-methyl-2-phenyl ethyl p-dimethylamino-diphenylmethyl sulphide have been isolated

Stay on Track: A Frenet Wrapper to Overcome Off-road Trajectories in Vehicle Motion Prediction

Predicting the future motion of observed vehicles is a crucial enabler for safe autonomous driving. The field of motion prediction has seen large progress recently with State-of-the-Art (SotA) models achieving impressive results on large-scale public benchmarks. However, recent work revealed that learning-based methods are prone to predict off-road trajectories in challenging scenarios. These can be created by perturbing existing scenarios with additional turns in front of the target vehicle while the motion history is left unchanged. We argue that this indicates that SotA models do not consider the map information sufficiently and demonstrate how this can be solved, by representing model inputs and outputs in a Frenet frame defined by lane centreline sequences. To this end, we present a general wrapper that leverages a Frenet representation of the scene and that can be applied to SotA models without changing their architecture. We demonstrate the effectiveness of this approach in a comprehensive benchmark using two SotA motion prediction models. Our experiments show that this reduces the off-road rate on challenging scenarios by more than 90\%, without sacrificing average performance

From Prediction to Planning With Goal Conditioned Lane Graph Traversals

The field of motion prediction for automated driving has seen tremendous progress recently, bearing ever-more mighty neural network architectures. Leveraging these powerful models bears great potential for the closely related planning task. In this letter we propose a novel goal-conditioning method and show its potential to transform a state-of-the-art prediction model into a goal-directed planner. Our key insight is that conditioning prediction on a navigation goal at the behaviour level outperforms other widely adopted methods, with the additional benefit of increased model interpretability. We train our model on a large open-source dataset and show promising performance in a comprehensive benchmark

What and how: doing good research with young people, digital intimacies, and relationships and sex education

© 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. As part of a project funded by the Wellcome Trust, we held a one-day symposium, bringing together researchers, practitioners, and policymakers, to discuss priorities for research on relationships and sex education (RSE) in a world where young people increasingly live, experience, and augment their relationships (whether sexual or not) within digital spaces. The introduction of statutory RSE in schools in England highlights the need to focus on improving understandings of young people and digital intimacies for its own sake, and to inform the development of learning resources. We call for more research that puts young people at its centre; foregrounds inclusivity; and allows a nuanced discussion of pleasures, harms, risks, and rewards, which can be used by those working with young people and those developing policy. Generating such research is likely to be facilitated by participation, collaboration, and communication with beneficiaries, between disciplines and across sectors. Taking such an approach, academic researchers, practitioners, and policymakers agree that we need a better understanding of RSE’s place in lifelong learning, which seeks to understand the needs of particular groups, is concerned with non-sexual relationships, and does not see digital intimacies as disconnected from offline everyday ‘reality’

Parting with Misconceptions about Learning-based Vehicle Motion Planning

The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting. Existing systems struggle to simultaneously meet both requirements. Indeed, we find that these tasks are fundamentally misaligned and should be addressed independently. We further assess the current state of closed-loop planning in the field, revealing the limitations of learning-based methods in complex real-world scenarios and the value of simple rule-based priors such as centerline selection through lane graph search algorithms. More surprisingly, for the open-loop sub-task, we observe that the best results are achieved when using only this centerline as scene context (i.e., ignoring all information regarding the map and other agents). Combining these insights, we propose an extremely simple and efficient planner which outperforms an extensive set of competitors, winning the nuPlan planning challenge 2023.Comment: CoRL 202

Conditional Unscented Autoencoders for Trajectory Prediction

The \ac{CVAE} is one of the most widely-used models in trajectory prediction for \ac{AD}. It captures the interplay between a driving context and its ground-truth future into a probabilistic latent space and uses it to produce predictions. In this paper, we challenge key components of the CVAE. We leverage recent advances in the space of the VAE, the foundation of the CVAE, which show that a simple change in the sampling procedure can greatly benefit performance. We find that unscented sampling, which draws samples from any learned distribution in a deterministic manner, can naturally be better suited to trajectory prediction than potentially dangerous random sampling. We go further and offer additional improvements, including a more structured mixture latent space, as well as a novel, potentially more expressive way to do inference with CVAEs. We show wide applicability of our models by evaluating them on the INTERACTION prediction dataset, outperforming the state of the art, as well as at the task of image modeling on the CelebA dataset, outperforming the baseline vanilla CVAE. Code is available at https://github.com/boschresearch/cuae-prediction