Question

Poe

"What was Molyneux's Question A Question About?"

Molyneux asked whether a newly sighted person could distinguish a sphere from a cube by sight alone, given that she was antecedently able to do so by touch. This, we contend, is a question about general ideas. To answer it, we must ask (a) whether spatial locations identified by touch can be identified also by sight, and (b) whether the integration of spatial locations into an idea of shape persists through changes of modality. Posed this way, Molyneux’s Question goes substantially beyond question (a), about spatial locations, alone; for a positive answer to (a) leaves open whether a perceiver might cross-identify locations, but not be able to identify the shapes that collections of locations comprise. We further emphasize that MQ targets general ideas so as to distinguish it from corresponding questions about experiences of shape and about the property of tangible (vs. visual) shape. After proposing a generalized formulation of MQ, we extend earlier work (“Many Molyneux Questions,” Australasian Journal of Philosophy 2020) by showing that MQ does not admit a single answer across the board. Some integrative data-processes transfer across modalities; others do not. Seeing where and how such transfer succeeds and fails in individual cases has much to offer to our understanding of perception and its modalities

What was Molyneux's Question A Question About?

Molyneux asked whether a newly sighted person could distinguish a sphere from a cube by sight alone, given that she was antecedently able to do so by touch. This, we contend, is a question about general ideas. To answer it, we must ask (a) whether spatial locations identified by touch can be identified also by sight, and (b) whether the integration of spatial locations into an idea of shape persists through changes of modality. Posed this way, Molyneux’s Question goes substantially beyond question (a), about spatial locations, alone; for a positive answer to (a) leaves open whether a perceiver might cross-identify locations, but not be able to identify the shapes that collections of locations comprise. We further emphasize that MQ targets general ideas so as to distinguish it from corresponding questions about experiences of shape and about the property of tangible (vs. visual) shape. After proposing a generalized formulation of MQ, we extend earlier work (“Many Molyneux Questions,” Australasian Journal of Philosophy 2020) by showing that MQ does not admit a single answer across the board. Some integrative data-processes transfer across modalities; others do not. Seeing where and how such transfer succeeds and fails in individual cases has much to offer to our understanding of perception and its modalities

Quick Question

Poetry by Matt Del Busto. Winner in the 2018 Manuscripts Poetry Contest

Question Type Guided Attention in Visual Question Answering

Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions. Image descriptors have structures at multiple spatial scales, while lexical inputs inherently follow a temporal sequence and naturally cluster into semantically different question types. A lot of previous works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. In this work, we propose Question Type-guided Attention (QTA). It utilizes the information of question type to dynamically balance between bottom-up and top-down visual features, respectively extracted from ResNet and Faster R-CNN networks. We experiment with multiple VQA architectures with extensive input ablation studies over the TDIUC dataset and show that QTA systematically improves the performance by more than 5% across multiple question type categories such as "Activity Recognition", "Utility" and "Counting" on TDIUC dataset. By adding QTA on the state-of-art model MCB, we achieve 3% improvement for overall accuracy. Finally, we propose a multi-task extension to predict question types which generalizes QTA to applications that lack of question type, with minimal performance loss

Question Dependent Recurrent Entity Network for Question Answering

Question Answering is a task which requires building models capable of providing answers to questions expressed in human language. Full question answering involves some form of reasoning ability. We introduce a neural network architecture for this task, which is a form of $Memory\ Network$, that recognizes entities and their relations to answers through a focus attention mechanism. Our model is named $Question\ Dependent\ Recurrent\ Entity\ Network$ and extends $Recurrent\ Entity\ Network$ by exploiting aspects of the question during the memorization process. We validate the model on both synthetic and real datasets: the $bAbI$ question answering dataset and the $CNN\ \&\ Daily\ News$$reading\ comprehension$ dataset. In our experiments, the models achieved a State-of-The-Art in the former and competitive results in the latter.Comment: 14 page

Question Isotropy

The "cosmological principle" was set up early without realizing its implications for the horizon problem, and almost entirely without support from observational data. Consistent signals of anisotropy have been found in data on electromagnetic propagation, polarizations of QSOs and $CMB$ temperature maps. The axis of Virgo is found again and again in signals breaking isotropy, from independent observables in independent energy regimes. There are no satisfactory explanations of these effects in conventional astrophysics. Axion-photon mixing and propagation in axion condensates are capable of encompassing the data.Comment: Published in Axions 2010: AIP Conf.Proc.1274:72-77,2010, edited by David Tanne