PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud samples from scratch or conditioned on semantic contexts. This model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points, allowing inter-point correlations to be well-exploited and 3D shape generative processes to be better interpreted. Since point cloud object shapes are typically encoded by long-range dependencies, we augment our model with dedicated self-attention modules to capture such relations. Extensive evaluations show that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to realism and diversity. Several important applications, such as unsupervised feature learning and shape arithmetic operations, are also demonstrated

Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)

Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability. Researchers synthesize randomized edge-case scenarios to assist in the training process, though simulation introduces potential for overfit to latent rules and features. Automating worst-case scenario generation could yield informative data for improving self driving. To this end, we introduce a "Physically Adversarial Intelligent Network" (PAIN), wherein self-driving vehicles interact aggressively in the CARLA simulation environment. We train two agents, a protagonist and an adversary, using dueling double deep Q networks (DDDQNs) with prioritized experience replay. The coupled networks alternately seek-to-collide and to avoid collisions such that the "defensive" avoidance algorithm increases the mean-time-to-failure and distance traveled under non-hostile operating conditions. The trained protagonist becomes more resilient to environmental uncertainty and less prone to corner case failures resulting in collisions than the agent trained without an adversary

Developing a Taxonomy of Elements Adversarial to Autonomous Vehicles

As highly automated vehicles reach higher deployment rates, they find themselves in increasingly dangerous situations. Knowing that the consequence of a crash is significant for the health of occupants, bystanders, and properties, as well as to the viability of autonomy and adjacent businesses, we must search for more efficacious ways to comprehensively and reliably train autonomous vehicles to better navigate the complex scenarios with which they struggle. We therefore introduce a taxonomy of potentially adversarial elements that may contribute to poor performance or system failures as a means of identifying and elucidating lesser-seen risks. This taxonomy may be used to characterize failures of automation, as well as to support simulation and real-world training efforts by providing a more comprehensive classification system for events resulting in disengagement, collision, or other negative consequences. This taxonomy is created from and tested against real collision events to ensure comprehensive coverage with minimal class overlap and few omissions. It is intended to be used both for the identification of harm-contributing adversarial events and in the generation thereof (to create extreme edge- and corner-case scenarios) in training procedures.Comment: 18 pages total, 4 pages of references, initial page left blank for IEEE submission statement. Includes 4 figures and 2 tables. Written using IEEEtran document clas

Local Viscosity Control Printing for High-Throughput Additive Manufacturing of Polymers

Fused deposition modeling's (FDM) throughput is limited by process physics as well as practical considerations favoring single-head polymer extrusion. To expedite the thermoplastic additive manufacturing process, we propose a parallelized material deposition process called local viscosity control (LVC) additive manufacturing. LVC prints an entire layer in one step by selectively modulating the viscosity of polymer feedstock in contact with a heated wire mesh. Layers of molten polymer are contact printed, with the relative motion between the wire mechanism and a build plate allowing wetting and surface tension to transfer selectively heated, lower viscosity regions of polymer to a fixed substrate. Experiments demonstrate the viability of this process using a single cell depositing layered polycarbonate pixels. Theoretical analysis shows this process may offer similar capabilities in resolution to conventional FDM with a significantly higher production rate for commonly available input power

Light-Element Abundance Variations at Low Metallicity: the Globular Cluster NGC 5466

We present low-resolution (R~850) spectra for 67 asymptotic giant branch (AGB), horizontal branch and red giant branch (RGB) stars in the low-metallicity globular cluster NGC 5466, taken with the VIRUS-P integral-field spectrograph at the 2.7-m Harlan J. Smith telescope at McDonald Observatory. Sixty-six stars are confirmed, and one rejected, as cluster members based on radial velocity, which we measure to an accuracy of 16 km s-1 via template-matching techniques. CN and CH band strengths have been measured for 29 RGB and AGB stars in NGC 5466, and the band strength indices measured from VIRUS-P data show close agreement with those measured from Keck/LRIS spectra previously taken of five of our target stars. We also determine carbon abundances from comparisons with synthetic spectra. The RGB stars in our data set cover a range in absolute V magnitude from +2 to -3, which permits us to study the rate of carbon depletion on the giant branch as well as the point of its onset. The data show a clear decline in carbon abundance with rising luminosity above the luminosity function "bump" on the giant branch, and also a subdued range in CN band strength, suggesting ongoing internal mixing in individual stars but minor or no primordial star-to-star variation in light-element abundances.Comment: 10 pages, emulateapj format, AJ accepte

Changing minds: Children's inferences about third party belief revision

By the age of 5, children explicitly represent that agents can have both true and false beliefs based on epistemic access to information (e.g., Wellman, Cross, & Watson, 2001). Children also begin to understand that agents can view identical evidence and draw different inferences from it (e.g., Carpendale & Chandler, 1996). However, much less is known about when, and under what conditions, children expect other agents to change their minds. Here, inspired by formal ideal observer models of learning, we investigate children's expectations of the dynamics that underlie third parties' belief revision. We introduce an agent who has prior beliefs about the location of a population of toys and then observes evidence that, from an ideal observer perspective, either does, or does not justify revising those beliefs. We show that children's inferences on behalf of third parties are consistent with the ideal observer perspective, but not with a number of alternative possibilities, including that children expect other agents to be influenced only by their prior beliefs, only by the sampling process, or only by the observed data. Rather, children integrate all three factors in determining how and when agents will update their beliefs from evidence.National Science Foundation (U.S.). Division of Computing and Communication Foundations (1231216)National Science Foundation (U.S.). Division of Research on Learning in Formal and Informal Settings (0744213)National Science Foundation (U.S.) (STC Center for Brains, Minds and Machines Award CCF-1231216)National Science Foundation (U.S.) (0744213

Changing minds: Children’s inferences about third party belief revision

By the age of 5, children explicitly represent that agents can have both true and false beliefs based on epistemic access to information (e.g., Wellman, Cross, & Watson, 2001). Children also begin to understand that agents can view identical evidence and draw different inferences from it (e.g., Carpendale & Chandler, 1996). However, much less is known about when, and under what conditions, children expect other agents to change their minds. Here, inspired by formal ideal observer models of learning, we investigate children’s expectations of the dynamics that underlie third parties’ belief revision. We introduce an agent who has prior beliefs about the location of a population of toys and then observes evidence that, from an ideal observer perspective, either does, or does not justify revising those beliefs. We show that children’s inferences on behalf of third parties are consistent with the ideal observer perspective, but not with a number of alternative possibilities, including that children expect other agents to be influenced only by their prior beliefs, only by the sampling process, or only by the observed data. Rather, children integrate all three factors in determining how and when agents will update their beliefs from evidence.Young children use others’ prior beliefs and data to predict when third parties will retain their beliefs and when they will change their minds.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142970/1/desc12553_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142970/2/desc12553.pd