When, Why, and How Controversy Causes Conversation

How does controversy affect conversation? Five studies using both field and laboratory data address this question. Contrary to popular belief, controversial things are not necessarily more likely to be discussed. Controversy increases likelihood of discussion at low levels, but beyond a moderate level of controversy, additional controversy actually decreases likelihood of discussion. The controversy-conversation relationship is driven by two countervailing processes. Controversy increases interest (which increases likelihood of discussion) but simultaneously increases discomfort (which decreases likelihood of discussion). Contextual factors such as anonymity and whether people are talking to friends or strangers moderate the controversy-conversation relationship by impacting these component processes. Our framework sheds light on how, when, and why controversy affects whether or not things are discussed

TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation

Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime. Despite success in structured, on-road settings, current end-to-end approaches for scene prediction have yet to be successfully adapted for complex outdoor terrain. To this end, we present TerrainNet, a vision-based terrain perception system for semantic and geometric terrain prediction for aggressive, off-road navigation. The approach relies on several key insights and practical considerations for achieving reliable terrain modeling. The network includes a multi-headed output representation to capture fine- and coarse-grained terrain features necessary for estimating traversability. Accurate depth estimation is achieved using self-supervised depth completion with multi-view RGB and stereo inputs. Requirements for real-time performance and fast inference speeds are met using efficient, learned image feature projections. Furthermore, the model is trained on a large-scale, real-world off-road dataset collected across a variety of diverse outdoor environments. We show how TerrainNet can also be used for costmap prediction and provide a detailed framework for integration into a planning module. We demonstrate the performance of TerrainNet through extensive comparison to current state-of-the-art baselines for camera-only scene prediction. Finally, we showcase the effectiveness of integrating TerrainNet within a complete autonomous-driving stack by conducting a real-world vehicle test in a challenging off-road scenario

WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting

Widely adopted motion forecasting datasets substitute the observed sensory inputs with higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred through annotating the original scenes with perception systems' predictions. Such intermediate representations tie the quality of the motion forecasting models to the performance of computer vision models. Moreover, the human-designed explicit interfaces between perception and motion forecasting typically pass only a subset of the semantic information present in the original sensory input. To study the effect of these modular approaches, design new paradigms that mitigate these limitations, and accelerate the development of end-to-end motion forecasting models, we augment the Waymo Open Motion Dataset (WOMD) with large-scale, high-quality, diverse LiDAR data for the motion forecasting task. The new augmented dataset WOMD-LiDAR consists of over 100,000 scenes that each spans 20 seconds, consisting of well-synchronized and calibrated high quality LiDAR point clouds captured across a range of urban and suburban geographies (https://waymo.com/open/data/motion/). Compared to Waymo Open Dataset (WOD), WOMD-LiDAR dataset contains 100x more scenes. Furthermore, we integrate the LiDAR data into the motion forecasting model training and provide a strong baseline. Experiments show that the LiDAR data brings improvement in the motion forecasting task. We hope that WOMD-LiDAR will provide new opportunities for boosting end-to-end motion forecasting models.Comment: Dataset website: https://waymo.com/open/data/motion

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research

Simulation is an essential tool to develop and benchmark autonomous vehicle planning software in a safe and cost-effective manner. However, realistic simulation requires accurate modeling of nuanced and complex multi-agent interactive behaviors. To address these challenges, we introduce Waymax, a new data-driven simulator for autonomous driving in multi-agent scenes, designed for large-scale simulation and testing. Waymax uses publicly-released, real-world driving data (e.g., the Waymo Open Motion Dataset) to initialize or play back a diverse set of multi-agent simulated scenarios. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training, making it suitable for modern large-scale, distributed machine learning workflows. To support online training and evaluation, Waymax includes several learned and hard-coded behavior models that allow for realistic interaction within simulation. To supplement Waymax, we benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions, where we highlight the effectiveness of routes as guidance for planning agents and the ability of RL to overfit against simulated agents

Cellular Robustness Conferred by Genetic Crosstalk Underlies Resistance against Chemotherapeutic Drug Doxorubicin in Fission Yeast

10.1371/journal.pone.0055041PLoS ONE81

Social Acceptance and Word of Mouth: How the Motive to Belong Leads to Divergent WOM with Strangers and Friends

Abstract Consumers are increasingly sharing product experiences with strangers and friends online. Despite the prevalence of word of mouth (WOM), little is known about how and why WOM differs based on whether people are talking to strangers or friends. The current article theorizes that one important motivation for WOM is social acceptance. To fulfill this motivation, people form and maintain existing relationships with others. Building on research in interpersonal relationships, we theorize that when communicating with strangers, people attempt to self-enhance to attract strangers into forming relationships with the self; when sharing with friends, on the other hand, people attempt to connect emotionally in order to maintain existing ties. A series of seven studies provide backing for this simple yet encompassing framework. Results of the current article provide insights into the motivations behind WOM, synthesize prior findings, and show that people systematically share different content with strangers versus friends. The current work makes theoretical contributions to research in interpersonal communication, social influence, and WOM, and holds practical implications for marketers interested in understanding consumer word of mouth

How Content Acquisition Method Affects Word of Mouth

People often share word of mouth with others, and such social sharing is an integral part of everyday life. But the content (e.g., stories, news, information) that people transmit can be acquired in different ways. Sometimes people find content themselves, and other times people receive content from others (e.g., via email or conversation). Might these different acquisition methods impact subsequent sharing, and if so, how? Six studies demonstrate that acquisition method can impact transmission through changing how content is processed. Compared to received content, people are more likely to associate found content with themselves, which decreases processing. Reduced processing, in turn, lowers sensitivity to diagnostic content characteristics (e.g., whether content is interesting or well written), which reduces these characteristics’ impact on sharing. Thus while receivers are more likely to share interesting (than boring) content, the difference is attenuated (and in some cases, disappeared) among finders. These findings deepen insights into the psychological drivers of word of mouth and shed light on how contextual factors, content characteristics, and the self interact to drive social transmission

Transmitting Well-Reasoned Word of Mouth Impairs Memory For Product Experiences

We propose that writing reviews can impair reviewers' product memories. Across five studies, we find that writing reviewsespecially logical-rather than imagery-based reviews-can cause memory errors. This is because attempts to write a logical review involves a search for well-reasoned arguments rather than rehearsing the original product experience. [to cite]

Psychology of word of mouth marketing

Given the importance of online word of mouth (WOM), there has been an increasing need to understand the psychological mechanisms that underlie WOM transmission (i.e. sharing of opinions) and reception (i.e. processing of received messages). The goal of the current paper is to review some of the most recent research in online WOM (focusing on the past two to four years) as well as make suggestions regarding future research. [For earlier syntheses on WOM senders and social media marketing, see King et al., 2014, Stephen, 2016, Whitler, 2014] [6-8]