Sensory processing and world modeling for an active ranging device

In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented

Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, {and} based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Extend the debt as it is not deeply out-of-the-money

In this paper, we modify the extendible debts model proposed in Longstaff (1990) to help relieve the moral hazard problem induced in the original model. In Longstaff¡¦s model, extending the maturity of the defaulted debts gives the borrower an incentive to default even if the borrower is insolvent. In this paper, we argue that the debt should not be extended if it is defaulted severely. We have shown that the extendible debt valuation can be obtained by the compound option pricing besides the PDE approach. We also have derived the fair interest rate of the extendible debts in this paper.

Simulation of the two-dimensional Potts model using nonextensive statistics

The standard Potts model is investigated in the framework of nonextensive statistical mechanics. We performed Monte Carlo simulations on two-dimensional lattices with linear sizes ranging from 16 to 64 using the Metropolis algorithm, where the classical Boltzmann-Gibbs transition probabilities were modified for the nonextensive case. We found that the Potts model undergoes a phase transition in the nonextensive scenario. We established the order of the phase transition and we computed the critical temperature for different values of the Tsallis entropic index.Comment: 5 pages, 8 figures, REVTe

TagF-mediated repression of bacterial type VI secretion systems involves a direct interaction with the cytoplasmic protein Fha

The bacterial type VI secretion system (T6SS) delivers effectors into eukaryotic host cells or toxins into bacterial competitor for survival and fitness. The T6SS is positively regulated by the threonine phosphorylation pathway (TPP) and negatively by the T6SS-accessory protein TagF. Here, we studied the mechanisms underlying TagF-mediated T6SS repression in two distinct bacterial pathogens, Agrobacterium tumefaciens and Pseudomonas aeruginosa. We found that in A. tumefaciens, T6SS toxin secretion and T6SS-dependent antibacterial activity are suppressed by a two-domain chimeric protein consisting of TagF and PppA, a putative phosphatase. Remarkably, this TagF domain is sufficient to post-translationally repress the T6SS, and this inhibition is independent of TPP. This repression requires interaction with a cytoplasmic protein, Fha, critical for activating T6SS assembly. In P. aeruginosa, PppA and TagF are two distinct proteins that repress T6SS in a TPP-dependent and -independent pathways, respectively. P. aeruginosa TagF interacts with Fha1, suggesting that formation of this complex represents a conserved TagF-mediated regulatory mechanism. Using TagF variants with substitutions of conserved amino acid residues at predicted protein-protein interaction interfaces, we uncovered evidence that the TagF-Fha interaction is critical for TagF-mediated T6SS repression in both bacteria. TagF inhibits T6SS without affecting T6SS protein abundance in A. tumefaciens, but TagF overexpression reduces the protein levels of all analyzed T6SS components in P. aeruginosa. Our results indicate that TagF interacts with Fha, which in turn could impact different stages of T6SS assembly in different bacteria, possibly reflecting an evolutionary divergence in T6SS control