Learning Human Pose Estimation Features with Convolutional Networks

This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models. Unconstrained human pose estimation is one of the hardest problems in computer vision, and our new architecture and learning schema shows significant improvement over the current state-of-the-art results. The main contribution of this paper is showing, for the first time, that a specific variation of deep learning is able to outperform all existing traditional architectures on this task. The paper also discusses several lessons learned while researching alternatives, most notably, that it is possible to learn strong low-level feature detectors on features that might even just cover a few pixels in the image. Higher-level spatial models improve somewhat the overall result, but to a much lesser extent then expected. Many researchers previously argued that the kinematic structure and top-down information is crucial for this domain, but with our purely bottom up, and weak spatial model, we could improve other more complicated architectures that currently produce the best results. This mirrors what many other researchers, like those in the speech recognition, object recognition, and other domains have experienced

Missing covariates in competing risks analysis.

Studies often follow individuals until they fail from one of a number of competing failure types. One approach to analyzing such competing risks data involves modeling the cause-specific hazards as functions of baseline covariates. A common issue that arises in this context is missing values in covariates. In this setting, we first establish conditions under which complete case analysis (CCA) is valid. We then consider application of multiple imputation to handle missing covariate values, and extend the recently proposed substantive model compatible version of fully conditional specification (SMC-FCS) imputation to the competing risks setting. Through simulations and an illustrative data analysis, we compare CCA, SMC-FCS, and a recent proposal for imputing missing covariates in the competing risks setting

Cooperative activation of IP3 receptors by sequential binding of IP3 and Ca2+ safeguards against spontaneous activity

AbstractBackground: Ca2+ waves allow effective delivery of intracellular Ca2+ signals to cytosolic targets. Propagation of these regenerative Ca2+ signals probably results from the activation of intracellular Ca2+ channels by the increase in cytosolic [Ca2+] that follows the opening of these channels. Such positive feedback is potentially explosive. Mechanisms that limit the spontaneous opening of intracellular Ca2+ channels are therefore likely to have evolved in parallel with the mechanism of Ca2+-induced Ca2+ release.Results: Maximal rates of 45Ca2+ efflux from permeabilised hepatocytes superfused with medium in which the [Ca2+] was clamped were cooperatively stimulated by inositol 1,4,5-trisphosphate (IP3). A minimal interval of ∼400 msec between IP3 addition and the peak rate of Ca2+ mobilisation indicate that channel opening does not immediately follow binding of IP3. Although the absolute latency of Ca2+ release was unaffected by further increasing the IP3 concentration, it was reduced by increased [Ca2+].Conclusions: We propose that the closed conformation of the IP3 receptor is very stable and therefore minimally susceptible to spontaneous activation; at least three (probably four) IP3 molecules may be required to provide enough binding energy to drive the receptor into a stable open conformation. We suggest that a further defence from noise is provided by an extreme form of coincidence detection. Binding of IP3 to each of its four receptor subunits unmasks a site to which Ca2+ must bind before the channel can open. As IP3 binding may also initiate receptor inactivation, there may be only a narrow temporal window during which each receptor subunit must bind both of its agonists if the channel is to open rather than inactivate

Systematic Visual Reasoning through Object-Centric Relational Abstraction

Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs. This capacity depends in large part on our ability to represent complex visual inputs in terms of both objects and relations. Recent work in computer vision has introduced models with the capacity to extract object-centric representations, leading to the ability to process multi-object visual inputs, but falling short of the systematic generalization displayed by human reasoning. Other recent models have employed inductive biases for relational abstraction to achieve systematic generalization of learned abstract rules, but have generally assumed the presence of object-focused inputs. Here, we combine these two approaches, introducing Object-Centric Relational Abstraction (OCRA), a model that extracts explicit representations of both objects and abstract relations, and achieves strong systematic generalization in tasks (including a novel dataset, CLEVR-ART, with greater visual complexity) involving complex visual displays

Vacancies, disorder-induced smearing of the electronic structure, and its implications for the superconductivity of anti-perovskite MgC0.93_{0.93}Ni2.85_{2.85}

The anti-perovskite superconductor MgC$_{0.93}$Ni$_{2.85}$ was studied using high-resolution x-ray Compton scattering combined with electronic structure calculations. Compton scattering measurements were used to determine experimentally a Fermi surface that showed good agreement with that of our supercell calculations, establishing the presence of the predicted hole and electron Fermi surface sheets. Our calculations indicate that the Fermi surface is smeared by the disorder due to the presence of vacancies on the C and Ni sites, but does not drastically change shape. The 20\% reduction in the Fermi level density-of-states would lead to a significant ($\sim 70\%$) suppression of the superconducting $T_c$ for pair-forming electron-phonon coupling. However, we ascribe the observed much smaller $T_c$ reduction at our composition (compared to the stoichiometric compound) to the suppression of pair-breaking spin fluctuations.Comment: 11 pages, 3 figure

Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval

Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes or careful engineering to work well. In this paper, we instead propose a generative model for learning multilingual text embeddings which can be used to retrieve or score sentence pairs. Our model operates on parallel data in $N$ languages and, through an approximation we introduce, efficiently encourages source separation in this multilingual setting, separating semantic information that is shared between translations from stylistic or language-specific variation. We show careful large-scale comparisons between contrastive and generation-based approaches for learning multilingual text embeddings, a comparison that has not been done to the best of our knowledge despite the popularity of these approaches. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval -- the last of which we introduce in this paper. Overall, our Variational Multilingual Source-Separation Transformer (VMSST) model outperforms both a strong contrastive and generative baseline on these tasks.Comment: Published as a long paper at ACL 202

Multiple imputation of missing covariates for the Cox proportional hazards cure model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134146/1/sim7048_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134146/2/sim7048.pd