Alternatives to austerity

We are in the age of austerity. Across the globe, there have recently been calls from both the left and the right to rethink policies of austerity and to rein in the forces of globalization. Over the past two years, anti-austerity sentiment has been a major factor in public votes in Europe and the US. Anti-globalization, anti-debt and anti-PPP movements are gaining broad support. Claiming to speak for ordinary families hit by the effects of austerity, parties across the political spectrum are scrambling to improvise new policies. Some alternatives to austerity are simply old ideas repackaged or reappropriated and help to legitimize the current status quo, yet others seem to offer genuine respite from the established order, claiming new forms of social relations and redistribution. The authors argue that only through an analysis of the longer-term origins and multiple guises of austerity can we move towards proposals for social change. They challenge established understandings of austerity and ask readers to imagine seemingly utopian alternatives. Overall, they ask: how can we give a new critical meaning to the concept of the public good?PostprintPeer reviewe

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

Unifying (Machine) Vision via Counterfactual World Modeling

Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets. This complexity has held back progress in areas, such as robotics, where robust task-general perception remains a bottleneck. In contrast, "foundation models" of natural language have shown how large pre-trained neural networks can provide zero-shot solutions to a broad spectrum of apparently distinct tasks. Here we introduce Counterfactual World Modeling (CWM), a framework for constructing a visual foundation model: a unified, unsupervised network that can be prompted to perform a wide variety of visual computations. CWM has two key components, which resolve the core issues that have hindered application of the foundation model concept to vision. The first is structured masking, a generalization of masked prediction methods that encourages a prediction model to capture the low-dimensional structure in visual data. The model thereby factors the key physical components of a scene and exposes an interface to them via small sets of visual tokens. This in turn enables CWM's second main idea -- counterfactual prompting -- the observation that many apparently distinct visual representations can be computed, in a zero-shot manner, by comparing the prediction model's output on real inputs versus slightly modified ("counterfactual") inputs. We show that CWM generates high-quality readouts on real-world images and videos for a diversity of tasks, including estimation of keypoints, optical flow, occlusions, object segments, and relative depth. Taken together, our results show that CWM is a promising path to unifying the manifold strands of machine vision in a conceptually simple foundation

Genome-Wide Analysis of MEF2 Transcriptional Program Reveals Synaptic Target Genes and Neuronal Activity-Dependent Polyadenylation Site Selection

Although many transcription factors are known to control important aspects of neural development, the genome-wide programs that are directly regulated by these factors are not known. We have characterized the genetic program that is activated by MEF2, a key regulator of activity-dependent synapse development. These MEF2 target genes have diverse functions at synapses, revealing a broad role for MEF2 in synapse development. Several of the MEF2 targets are mutated in human neurological disorders including epilepsy and autism spectrum disorders, suggesting that these disorders may be caused by disruption of an activity-dependent gene program that controls synapse development. Our analyses also reveal that neuronal activity promotes alternative polyadenylation site usage at many of the MEF2 target genes, leading to the production of truncated mRNAs that may have different functions than their full-length counterparts. Taken together, these analyses suggest that the ubiquitously expressed transcription factor MEF2 regulates an intricate transcriptional program in neurons that controls synapse development

Dynamics of Wetting Fronts in Porous Media

We propose a new phenomenological approach for describing the dynamics of wetting front propagation in porous media. Unlike traditional models, the proposed approach is based on dynamic nature of the relation between capillary pressure and medium saturation. We choose a modified phase-field model of solidification as a particular case of such dynamic relation. We show that in the traveling wave regime the results obtained from our approach reproduce those derived from the standard model of flow in porous media. In more general case, the proposed approach reveals the dependence of front dynamics upon the flow regime.Comment: 4 pages, 2 figures, revte

Lamellipodia are crucial for haptotactic sensing and response

Haptotaxis is the process by which cells respond to gradients of substrate-bound cues, such as extracellular matrix proteins (ECM); however, the cellular mechanism of this response remains poorly understood and has mainly been studied by comparing cell behavior on uniform ECMs with different concentrations of components. To study haptotaxis in response to gradients, we utilized microfluidic chambers to generate gradients of the ECM protein fibronectin, and imaged the cell migration response. Lamellipodia are fan-shaped protrusions that are common in migrating cells. Here, we define a new function for lamellipodia and the cellular mechanism required for haptotaxis – differential actin and lamellipodial protrusion dynamics lead to biased cell migration. Modest differences in lamellipodial dynamics occurring over time periods of seconds to minutes are summed over hours to produce differential whole cell movement towards higher concentrations of fibronectin. We identify a specific subset of lamellipodia regulators as being crucial for haptotaxis. Numerous studies have linked components of this pathway to cancer metastasis and, consistent with this, we find that expression of the oncogenic Rac1 P29S mutation abrogates haptotaxis. Finally, we show that haptotaxis also operates through this pathway in 3D environments