Twitter Watch: Leveraging Social Media to Monitor and Predict Collective-Efficacy of Neighborhoods. (arXiv:1911.06359v1 [cs.SI])

Sociologists associate the spatial variation of crime within an urban setting, with the concept of collective efficacy. The collective efficacy of a neighborhood is defined as social cohesion among neighbors combined with their willingness to intervene on behalf of the common good. Sociologists measure collective efficacy by conducting survey studies designed to measure individuals’ perception…

Arguing Ecosystem Values with Paraconsistent Logics. (arXiv:1911.06367v1 [cs.LO])

The valuation of ecosystem services prompts dialogical settings where non-trivially inconsistent arguments are often invoked. Here, I propose an approach to the valuation of ecosystem services circumscribed to a logic-based argumentation framework that caters for valid inconsistencies. This framework accounts for preference formation processes underpinned by a paraconsistent model of logical entailment. The value of…

Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior. (arXiv:1911.06379v1 [stat.ML])

In this paper we address the problem of solving ill-posed inverse problems in imaging where the prior is a neural generative model. Specifically we consider the decoupled case where the prior is trained once and can be reused for many different log-concave degradation models without retraining. Whereas previous MAP-based approaches to this problem lead to…

Entanglement-assisted Quantum Codes from Cyclic Codes. (arXiv:1911.06384v1 [cs.IT])

Entanglement-assisted quantum (QUENTA) codes are a subclass of quantum error-correcting codes which use entanglement as a resource. These codes can provide error correction capability higher than the codes derived from the traditional stabilizer formalism. In this paper, it is shown a general method to construct QUENTA codes from cyclic codes. Afterwards, the method is applied…

Estimating adaptive cruise control model parameters from on-board radar units. (arXiv:1911.06454v1 [stat.AP])

Two new methods are presented for estimating car-following model parameters using data collected from the Adaptive Cruise Control (ACC) enabled vehicles. The vehicle is assumed to follow a constant time headway relative velocity model in which the parameters are unknown and to be determined. The first technique is a batch method that uses a least-squares…

Graph Transformer Networks. (arXiv:1911.06455v1 [cs.LG])

Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link prediction. However, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs. The limitations especially become problematic when learning representations on a misspecified graph or…

Optimal Mini-Batch Size Selection for Fast Gradient Descent. (arXiv:1911.06459v1 [cs.LG])

This paper presents a methodology for selecting the mini-batch size that minimizes Stochastic Gradient Descent (SGD) learning time for single and multiple learner problems. By decoupling algorithmic analysis issues from hardware and software implementation details, we reveal a robust empirical inverse law between mini-batch size and the average number of SGD updates required to converge…

Fourier Spectrum Discrepancies in Deep Network Generated Images. (arXiv:1911.06465v1 [eess.IV])

Advancements in deep generative models such as generative adversarial networks and variational autoencoders have resulted in the ability to generate realistic images that are visually indistinguishable from real images. In this paper, we present an analysis of the high-frequency Fourier modes of real and deep network generated images and the effects of resolution and image…