The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

Integrating Feedback into the Transportation Planning Mode

This research develops and applies a new structure for the transportation planning model that includes feedback between demand, assignment, and traffic control. New methods, combined with a renewed interest in transportation planning models prompted by the Clean Air Act of 1990 and the Intermodal Surface Transportation Efficiency Act of 1991, warrant a reconsideration of the traditional "four-step" transportation planning model. This paper presents an algorithm for feedback which results in consistent travel times as input to travel demand and output from route assignment. The model, including six stages of Trip Generation, Destination Choice, Mode Choice, Departure Time Choice, Route Assignment and Intersection Control is briefly outlined. This is followed by an application comparing a base year 1990 application with a forecast year of 2010. The 2010 forecast is solved both with and without feedback for comparison purposes. Incorporation of feedback gives significantly different results than the standard model. l.

Coalescing Binary Neutron Stars

Coalescing compact binaries with neutron star or black hole components provide the most promising sources of gravitational radiation for detection by the LIGO/VIRGO/GEO/TAMA laser interferometers now under construction. This fact has motivated several different theoretical studies of the inspiral and hydrodynamic merging of compact binaries. Analytic analyses of the inspiral waveforms have been performed in the Post-Newtonian approximation. Analytic and numerical treatments of the coalescence waveforms from binary neutron stars have been performed using Newtonian hydrodynamics and the quadrupole radiation approximation. Numerical simulations of coalescing black hole and neutron star binaries are also underway in full general relativity. Recent results from each of these approaches will be described and their virtues and limitations summarized.Comment: Invited Topical Review paper to appear in Classical and Quantum Gravity, 35 pages, including 5 figure

Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to lean a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further proposed upon the adversarial training to strengthen the correlations between inputs and corresponding outputs. Our approach is generative to learn hash functions such that the learned hash codes can maximally correlate each input-output correspondence, meanwhile can also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method achieves better retrieval results than the state-of-the-arts.Comment: To appeared on IEEE Trans. Image Processing. arXiv admin note: text overlap with arXiv:1703.10593 by other author