The modifiable areal unit problem in regional economics

There is a very well known fundamental problem in spatial data analysis namely that all results of quantitative methods are potencially influenced by the way of spatial delimitation. This problem is mostly called modifiable areal unit problem (MAUP). However, beside the rich tradition in the empirical spatial data analysis, the effect of MAUP on putting forward and testing a theory and the effect on model-building is an issue rarely investigated. The MAUP creates the need for the investigation of the connection between theories and data and the micro-macro dualism. My paper presents the epistemological background of the problem and gives illustrations of the negative consequences of ignoring them in regional macroeconomics.

Experimental realization of a programmable quantum-state discriminator and a phase-covariant quantum multimeter

We present an optical implementation of two programmable quantum measurement devices. The first one serves for unambiguous discrimination of two nonorthogonal states of a qubit. The particular pair of states to be discriminated is specified by the quantum state of a program qubit. The second device can perform von Neumann measurements on a single qubit in any basis located on the equator of the Bloch sphere. Again, the basis is selected by the state of a program qubit. In both cases the data and program qubits are represented by polarization states of photons. The experimental apparatus exploits the fact that two Bell states can be distinguished solely by means of linear optics. The outcome corresponding to the remaining two Bell states represents an inconclusive result.Comment: 7 pages, 7 figure

A Knowledge-Grounded Multimodal Search-Based Conversational Agent

Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB

Improving Context Modelling in Multimodal Dialogue Generation

In this work, we investigate the task of textual response generation in a multimodal task-oriented dialogue system. Our work is based on the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017) in the fashion domain. We introduce a multimodal extension to the Hierarchical Recurrent Encoder-Decoder (HRED) model and show that this extension outperforms strong baselines in terms of text-based similarity metrics. We also showcase the shortcomings of current vision and language models by performing an error analysis on our system's output