176 research outputs found
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Physical intuition is pivotal for intelligent agents to perform complex
tasks. In this paper we investigate the passive acquisition of an intuitive
understanding of physical principles as well as the active utilisation of this
intuition in the context of generalised object stacking. To this end, we
provide: a simulation-based dataset featuring 20,000 stack configurations
composed of a variety of elementary geometric primitives richly annotated
regarding semantics and structural stability. We train visual classifiers for
binary stability prediction on the ShapeStacks data and scrutinise their
learned physical intuition. Due to the richness of the training data our
approach also generalises favourably to real-world scenarios achieving
state-of-the-art stability prediction on a publicly available benchmark of
block towers. We then leverage the physical intuition learned by our model to
actively construct stable stacks and observe the emergence of an intuitive
notion of stackability - an inherent object affordance - induced by the active
stacking task. Our approach performs well even in challenging conditions where
it considerably exceeds the stack height observed during training or in cases
where initially unstable structures must be stabilised via counterbalancing.Comment: revised version to appear at ECCV 201
Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes
Visually predicting the stability of block towers is a popular task in the
domain of intuitive physics. While previous work focusses on prediction
accuracy, a one-dimensional performance measure, we provide a broader analysis
of the learned physical understanding of the final model and how the learning
process can be guided. To this end, we introduce neural stethoscopes as a
general purpose framework for quantifying the degree of importance of specific
factors of influence in deep neural networks as well as for actively promoting
and suppressing information as appropriate. In doing so, we unify concepts from
multitask learning as well as training with auxiliary and adversarial losses.
We apply neural stethoscopes to analyse the state-of-the-art neural network for
stability prediction. We show that the baseline model is susceptible to being
misled by incorrect visual cues. This leads to a performance breakdown to the
level of random guessing when training on scenarios where visual cues are
inversely correlated with stability. Using stethoscopes to promote meaningful
feature extraction increases performance from 51% to 90% prediction accuracy.
Conversely, training on an easy dataset where visual cues are positively
correlated with stability, the baseline model learns a bias leading to poor
performance on a harder dataset. Using an adversarial stethoscope, the network
is successfully de-biased, leading to a performance increase from 66% to 88%
Iterative SE(3)-Transformers
When manipulating three-dimensional data, it is possible to ensure that
rotational and translational symmetries are respected by applying so-called
SE(3)-equivariant models. Protein structure prediction is a prominent example
of a task which displays these symmetries. Recent work in this area has
successfully made use of an SE(3)-equivariant model, applying an iterative
SE(3)-equivariant attention mechanism. Motivated by this application, we
implement an iterative version of the SE(3)-Transformer, an SE(3)-equivariant
attention-based model for graph data. We address the additional complications
which arise when applying the SE(3)-Transformer in an iterative fashion,
compare the iterative and single-pass versions on a toy problem, and consider
why an iterative model may be beneficial in some problem settings. We make the
code for our implementation available to the community
Making subaltern shikaris: histories of the hunted in colonial central India
Academic histories of hunting or shikar in India have almost entirely focused on the sports hunting of British colonists and Indian royalty. This article attempts to balance this elite bias by focusing on the meaning of shikar in the construction of the Gond ‘tribal’ identity in late nineteenth and early twentieth-century colonial central India. Coining the term ‘subaltern shikaris’ to refer to the class of poor, rural hunters, typically ignored in this historiography, the article explores how the British managed to use hunting as a means of state penetration into central India’s forest interior, where they came to regard their Gond forest-dwelling subjects as essentially and eternally primitive hunting tribes. Subaltern shikaris were employed by elite sportsmen and were also paid to hunt in the colonial regime’s vermin eradication programme, which targeted tigers, wolves, bears and other species identified by the state as ‘dangerous beasts’. When offered economic incentives, forest dwellers usually willingly participated in new modes of hunting, even as impact on wildlife rapidly accelerated and became unsustainable. Yet as non-indigenous approaches to nature became normative, there was sometimes also resistance from Gond communities. As overkill accelerated, this led to exclusion of local peoples from natural resources, to their increasing incorporation into dominant political and economic systems, and to the eventual collapse of hunting as a livelihood. All of this raises the question: To what extent were subaltern subjects, like wildlife, ‘the hunted’ in colonial India
Inference for stochastic chemical kinetics using moment equations and system size expansion
Quantitative mechanistic models are valuable tools for disentangling biochemical pathways and for achieving a comprehensive understanding of biological systems. However, to be quantitative the parameters of these models have to be estimated from experimental data. In the presence of significant stochastic fluctuations this is a challenging task as stochastic simulations are usually too time-consuming and a macroscopic description using reaction rate equations (RREs) is no longer accurate. In this manuscript, we therefore consider moment-closure approximation (MA) and the system size expansion (SSE), which approximate the statistical moments of stochastic processes and tend to be more precise than macroscopic descriptions. We introduce gradient-based parameter optimization methods and uncertainty analysis methods for MA and SSE. Efficiency and reliability of the methods are assessed using simulation examples as well as by an application to data for Epo-induced JAK/STAT signaling. The application revealed that even if merely population-average data are available, MA and SSE improve parameter identifiability in comparison to RRE. Furthermore, the simulation examples revealed that the resulting estimates are more reliable for an intermediate volume regime. In this regime the estimation error is reduced and we propose methods to determine the regime boundaries. These results illustrate that inference using MA and SSE is feasible and possesses a high sensitivity
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