Interacting Individuals Leading to Zipf's Law

We present a general approach to explain the Zipf's law of city distribution. If the simplest interaction (pairwise) is assumed, individuals tend to form cities in agreement with the well-known statisticsComment: 4 pages 2 figure

Local density of states of a d-wave superconductor with inhomogeneous antiferromagnetic correlations

The tunneling spectrum of an inhomogeneously doped extended Hubbard model is calculated at the mean field level. Self-consistent solutions admit both superconducting and antiferromagnetic order, which coexist inhomogeneously because of spatial randomness in the doping. The calculations find that, as a function of doping, there is a continuous cross over from a disordered ``pinned smectic'' state to a relatively homogeneous d-wave state with pockets of antiferromagnetic order. The density of states has a robust d-wave gap, and increasing antiferromagnetic correlations lead to a suppression of the coherence peaks. The spectra of isolated nanoscale antiferromagnetic domains are studied in detail, and are found to be very different from those of macroscopic antiferromagnets. Although no single set of model parameters reproduces all details of the experimental spectrum in BSCCO, many features, notably the collapse of the coherence peaks and the occurence of a low-energy shoulder in the local spectrum, occur naturally in these calculations.Comment: 9 pages, 5 figure

Diagnostic Prediction Using Discomfort Drawings with IBTM

In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients to express discomfort and pain related symptoms. These drawings have proven to be an effective method to collect patient data and make diagnostic decisions in real-life practice. A dataset from real-world patient cases is collected for which medical experts provide diagnostic labels. Next, we use a factorized multimodal topic model, Inter-Battery Topic Model (IBTM), to train a system that can make diagnostic predictions given an unseen discomfort drawing. The number of output diagnostic labels is determined by using mean-shift clustering on the discomfort drawing. Experimental results show reasonable predictions of diagnostic labels given an unseen discomfort drawing. Additionally, we generate synthetic discomfort drawings with IBTM given a diagnostic label, which results in typical cases of symptoms. The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, C

Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving

Stratum ventilation can energy efficiently provide good inhaled indoor air quality with a proper operation (e.g., fresh outdoor air ratio). However, the non-uniform CO2 distribution in a stratum-ventilated room challenges the provision of targeted indoor air quality. This study proposes an optimization on the fresh outdoor air ratio of stratum ventilation for both the targeted indoor air quality and maximal energy saving. A model of CO2 concentration in the breathing zone is developed by coupling CO2 removal efficiency in the breathing zone and mass conservation laws. With the developed model, the ventilation parameters corresponding to different fresh outdoor air ratios are quantified to achieve the targeted indoor air quality (i.e., targeted CO2 concentration in the breathing zone). Using the fresh outdoor air ratios and corresponding ventilation parameters as inputs, energy performance evaluations of the air conditioning system are conducted by building energy simulations. The fresh outdoor air ratio with the minimal energy consumption is determined as the optimal one. Experiments show that the mean absolute error of the developed model of CO2 concentration in the breathing zone is 1.9%. The effectiveness of the proposed optimization is demonstrated using TRNSYS that the energy consumption of the air conditioning system with stratum ventilation is reduced by 6.4% while achieving the targeted indoor air quality. The proposed optimization is also promising for other ventilation modes for targeted indoor air quality and improved energy efficiency

Topological Mott Insulators

We consider extended Hubbard models with repulsive interactions on a Honeycomb lattice and the transitions from the semi-metal phase at half-filling to Mott insulating phases. In particular, due to the frustrating nature of the second-neighbor repulsive interactions, topological Mott phases displaying the quantum Hall and the quantum spin Hall effects are found for spinless and spinful fermion models, respectively. We present the mean-field phase diagram and consider the effects of fluctuations within the random phase approximation (RPA). Functional renormalization group analysis also show that these states can be favored over the topologically trivial Mott insulating states.Comment: 5 Pages, 4 figure