@article{EiteljoergeAdamElsneretal.2019,
  author    = {Eiteljoerge, Sarah Fe Vivian and Adam, Maurits and Elsner, Birgit and Mani, Nivedita},
  title     = {Consistency of co-occurring actions influences young children's word learning learning},
  series = {Royal Society Open Science},
  volume    = {6},
  journal   = {Royal Society Open Science},
  number    = {8},
  publisher = {Royal Society},
  address   = {London},
  issn      = {2054-5703},
  doi       = {10.1098/rsos.190097},
  pages     = {17},
  year      = {2019},
  abstract  = {Communication with young children is often multimodal in nature, involving, for example, language and actions. The simultaneous presentation of information from both domains may boost language learning by highlighting the connection between an object and a word, owing to temporal overlap in the presentation of multimodal input. However, the overlap is not merely temporal but can also covary in the extent to which particular actions co-occur with particular words and objects, e.g. carers typically produce a hopping action when talking about rabbits and a snapping action for crocodiles. The frequency with which actions and words co-occurs in the presence of the referents of these words may also impact young children's word learning. We, therefore, examined the extent to which consistency in the co-occurrence of particular actions and words impacted children's learning of novel word-object associations. Children (18 months, 30 months and 36-48 months) and adults were presented with two novel objects and heard their novel labels while different actions were performed on these objects, such that the particular actions and word-object pairings always co-occurred (Consistent group) or varied across trials (Inconsistent group). At test, participants saw both objects and heard one of the labels to examine whether participants recognized the target object upon hearing its label. Growth curve models revealed that 18-month-olds did not learn words for objects in either condition, and 30-month-old and 36- to 48-month-old children learned words for objects only in the Consistent condition, in contrast to adults who learned words for objects independent of the actions presented. Thus, consistency in the multimodal input influenced word learning in early childhood but not in adulthood. In terms of a dynamic systems account of word learning, our study shows how multimodal learning settings interact with the child's perceptual abilities to shape the learning experience.},
  language  = {en}
}
@article{BlanchardFlaskaHandyetal.2016,
  author    = {Blanchard, Gilles and Flaska, Marek and Handy, Gregory and Pozzi, Sara and Scott, Clayton},
  title     = {Classification with asymmetric label noise: Consistency and maximal denoising},
  series = {Electronic journal of statistics},
  volume    = {10},
  journal   = {Electronic journal of statistics},
  publisher = {Institute of Mathematical Statistics},
  address   = {Cleveland},
  issn      = {1935-7524},
  doi       = {10.1214/16-EJS1193},
  pages     = {2780 -- 2824},
  year      = {2016},
  abstract  = {In many real-world classification problems, the labels of training examples are randomly corrupted. Most previous theoretical work on classification with label noise assumes that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are known. In this work, we give conditions that are necessary and sufficient for the true class-conditional distributions to be identifiable. These conditions are weaker than those analyzed previously, and allow for the classes to be nonseparable and the noise levels to be asymmetric and unknown. The conditions essentially state that a majority of the observed labels are correct and that the true class-conditional distributions are "mutually irreducible," a concept we introduce that limits the similarity of the two distributions. For any label noise problem, there is a unique pair of true class-conditional distributions satisfying the proposed conditions, and we argue that this pair corresponds in a certain sense to maximal denoising of the observed distributions. Our results are facilitated by a connection to "mixture proportion estimation," which is the problem of estimating the maximal proportion of one distribution that is present in another. We establish a novel rate of convergence result for mixture proportion estimation, and apply this to obtain consistency of a discrimination rule based on surrogate loss minimization. Experimental results on benchmark data and a nuclear particle classification problem demonstrate the efficacy of our approach.},
  language  = {en}
}
@article{GebserSchaubThieleetal.2011,
  author    = {Gebser, Martin and Schaub, Torsten H. and Thiele, Sven and Veber, Philippe},
  title     = {Detecting inconsistencies in large biological networks with answer set programming},
  series = {Theory and practice of logic programming},
  volume    = {11},
  journal   = {Theory and practice of logic programming},
  number    = {5-6},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {1471-0684},
  doi       = {10.1017/S1471068410000554},
  pages     = {323 -- 360},
  year      = {2011},
  abstract  = {We introduce an approach to detecting inconsistencies in large biological networks by using answer set programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on answer set programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.},
  language  = {en}
}