Normality: Part Descriptive, part prescriptive

People’s beliefs about normality play an important role in many aspects of cognition and life (e.g., causal cognition, linguistic semantics, cooperative behavior). But how do people determine what sorts of things are normal in the first place? Past research has studied both people’s representations of statistical norms (e.g., the average) and their representations of prescriptive norms (e.g., the ideal). Four studies suggest that people’s notion of normality incorporates both of these types of norms. In particular, people’s representations of what is normal were found to be influenced both by what they believed to be descriptively average and by what they believed to be prescriptively ideal. This is shown across three domains: people’s use of the word ‘‘normal” (Study 1), their use of gradable adjectives (Study 2), and their judgments of concept prototypicality (Study 3). A final study investigated the learning of normality for a novel category, showing that people actively combine statistical and prescriptive information they have learned into an undifferentiated notion of what is normal (Study 4). Taken together, these findings may help to explain how moral norms impact the acquisition of normality and, conversely, how normality impacts the acquisition of moral norms

Unsupervised Segmentation in Real-World Images via Spelke Object Inference

Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision. Here, we show how to learn static grouping priors from motion self-supervision by building on the cognitive science concept of a Spelke Object: a set of physical stuff that moves together. We introduce the Excitatory-Inhibitory Segment Extraction Network (EISEN), which learns to extract pairwise affinity graphs for static scenes from motion-based training signals. EISEN then produces segments from affinities using a novel graph propagation and competition network. During training, objects that undergo correlated motion (such as robot arms and the objects they move) are decoupled by a bootstrapping process: EISEN explains away the motion of objects it has already learned to segment. We show that EISEN achieves a substantial improvement in the state of the art for self-supervised image segmentation on challenging synthetic and real-world robotics datasets.Comment: 25 pages, 10 figure

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

General physical scene understanding requires more than simply localizing and recognizing objects -- it requires knowledge that objects can have different latent properties (e.g., mass or elasticity), and that those properties affect the outcome of physical events. While there has been great progress in physical and video prediction models in recent years, benchmarks to test their performance typically do not require an understanding that objects have individual physical properties, or at best test only those properties that are directly observable (e.g., size or color). This work proposes a novel dataset and benchmark, termed Physion++, that rigorously evaluates visual physical prediction in artificial systems under circumstances where those predictions rely on accurate estimates of the latent physical properties of objects in the scene. Specifically, we test scenarios where accurate prediction relies on estimates of properties such as mass, friction, elasticity, and deformability, and where the values of those properties can only be inferred by observing how objects move and interact with other objects or fluids. We evaluate the performance of a number of state-of-the-art prediction models that span a variety of levels of learning vs. built-in knowledge, and compare that performance to a set of human predictions. We find that models that have been trained using standard regimes and datasets do not spontaneously learn to make inferences about latent properties, but also that models that encode objectness and physical states tend to make better predictions. However, there is still a huge gap between all models and human performance, and all models' predictions correlate poorly with those made by humans, suggesting that no state-of-the-art model is learning to make physical predictions in a human-like way. Project page: https://dingmyu.github.io/physion_v2

The UK needs a sustainable strategy for COVID-19

The UK is well into the second wave of COVID-19, with 60 051 lives lost to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection to date, according to provisional data from the&nbsp;Office for National Statistics. Official UK Government&nbsp;data&nbsp;show that cases have been rising exponentially since late August, 2020, with increases across all regions in England in recent weeks. &nbsp;As of Nov 4, 2020, the UK had 25 177 confirmed daily cases. These are almost certainly underestimates as between Oct 17 and Oct 23, 2020, England alone had 52 000 estimated daily cases. &nbsp;Estimates of the effective reproduction number in England vary between 1·1 and 1·6.</p

Gravitational Lorentz Violations and Adjustment of the Cosmological Constant in Asymmetrically Warped Spacetimes

We investigate spacetimes in which the speed of light along flat 4D sections varies over the extra dimensions due to different warp factors for the space and the time coordinates (``asymmetrically warped'' spacetimes). The main property of such spaces is that while the induced metric is flat, implying Lorentz invariant particle physics on a brane, bulk gravitational effects will cause apparent violations of Lorentz invariance and of causality from the brane observer's point of view. An important experimentally verifiable consequence of this is that gravitational waves may travel with a speed different from the speed of light on the brane, and possibly even faster. We find the most general spacetimes of this sort, which are given by AdS-Schwarzschild or AdS-Reissner-Nordstrom black holes, assuming the simplest possible sources in the bulk. Due to the gravitational Lorentz violations these models do not have an ordinary Lorentz invariant effective description, and thus provide a possible way around Weinberg's no-go theorem for the adjustment of the cosmological constant. Indeed we show that the cosmological constant may relax in such theories by the adjustment of the mass and the charge of the black hole. The black hole singularity in these solutions can be protected by a horizon, but the existence of a horizon requires some exotic energy densities on the brane. We investigate the cosmological expansion of these models and speculate that it may provide an explanation for the accelerating Universe, provided that the timescale for the adjustment is shorter than the Hubble time. In this case the accelerating Universe would be a manifestation of gravitational Lorentz violations in extra dimensions.Comment: 28 pages, LaTeX, 4 figures included. v2: references added, added comment on experimental observations, and clarified comment on Lorentz violations in non-commutative theories. v3: typos fixed, eqs. 2.30 and 2.31 correcte

Human Mena Associates with Rac1 Small GTPase in Glioblastoma Cell Lines

Mammarian enabled (Mena), a member of the Enabled (Ena)/Vasodilator-stimulated phosphoprotein (VASP) family of proteins, has been implicated in cell motility through regulation of the actin cytoskeleton assembly, including lamellipodial protrusion. Rac1, a member of the Rho family GTPases, also plays a pivotal role in the formation of lamellipodia. Here we report that human Mena (hMena) colocalizes with Rac1 in lamellipodia, and using an unmixing assisted acceptor depletion fluorescence resonance energy transfer (u-adFRET) analysis that hMena associates with Rac1 in vivo in the glioblastoma cell line U251MG. Depletion of hMena by siRNA causes cells to be highly spread with the formation of lamellipodia. This cellular phenotype is canceled by introduction of a dominant negative form of Rac1. A Rac activity assay and FRET analysis showed that hMena knock-down cells increased the activation of Rac1 at the lamellipodia. These results suggest that hMena possesses properties which help to regulate the formation of lamellipodia through the modulation of the activity of Rac1

International Nonregimes: A Research Agenda1

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146934/1/j.1468-2486.2007.00672.x.pd