Learning Generative Models with Visual Attention

Attention has long been proposed by psychologists as important for effectively dealing with the enormous sensory stimulus available in the neocortex. Inspired by the visual attention models in computational neuroscience and the need of object-centric data for generative models, we describe for generative learning framework using attentional mechanisms. Attentional mechanisms can propagate signals from region of interest in a scene to an aligned canonical representation, where generative modeling takes place. By ignoring background clutter, generative models can concentrate their resources on the object of interest. Our model is a proper graphical model where the 2D Similarity transformation is a part of the top-down process. A ConvNet is employed to provide good initializations during posterior inference which is based on Hamiltonian Monte Carlo. Upon learning images of faces, our model can robustly attend to face regions of novel test subjects. More importantly, our model can learn generative models of new faces from a novel dataset of large images where the face locations are not known.Comment: In the proceedings of Neural Information Processing Systems, 201

Quasi-Newton Methods for Markov Chain Monte Carlo

The performance of Markov chain Monte Carlo methods is often sensitive to the scaling and correlations between the random variables of interest. An important source of information about the local correlation and scale is given by the Hessian matrix of the target distribution, but this is often either computationally expensive or infeasible. In this paper we propose MCMC samplers that make use of quasi-Newton approximations, which approximate the Hessian of the target distribution from previous samples and gradients generated by the sampler. A key issue is that MCMC samplers that depend on the history of previous states are in general not valid. We address this problem by using limited memory quasi-Newton methods, which depend only on a fixed window of previous samples. On several real world datasets, we show that the quasi-Newton sampler is more effective than standard Hamiltonian Monte Carlo at a fraction of the cost of MCMC methods that require higher-order derivatives.

Solid-phase DNA sequencing reactions performed in micro-capillary reactors and solid-phase reversible immobilization in microfluidic chips for purification of dye-labeled DNA sequencing fragments

The research presented in this dissertation involves micro-capillary reactors for solid phase DNA sequencing, the identification of dye terminator sequencing fragments with time-resolved methods, and purification of dye-labeled DNA fragments using solid- phase reversible immobilization in microfluidic chips. Solid-phase micro-reactors have been prepared for DNA sequencing applications using slab gel electrophoresis. A PCR product was immobilized to the interior wall of a fused-silica capillary tube via a biotin-streptavidin linkage. Solid-phase sequencing was carried out in micro-capillary reactors using a four-lane, single color dye primer chemistry strategy. The read length was found to be 589 bases. The complementary DNA fragments generated in the small volume (~62 nL) reactor were directly injected into the gel-filled capillary for size separation with detection accomplished using near-IR laser-induced fluorescence. A set of terminators labeled with near-IR heavy-atom modified tricarbocyanine dyes were investigated for a terminator sequencing protocol in conjunction with slab gel electrophoresis. This protocol gave 605 bp read lengths. A one color, two lifetime format of DNA sequencing was implemented. A pixel-by-pixel analysis was employed to identify each of the bases in the run. The resulting read accuracy for the two-dye capillary run was 90.6%. The use of photoactivated polycarbonate (PC) for purification of dye-labeled terminator sequencing fragments using solid-phase reversible immobilization (SPRI) was investigated. SPRI cleanup of dye-terminator sequencing fragments using a photoactivated PC microchannel and slab gel electrophoresis produce a read length of 620 bases with a calling accuracy of 98.9%. The PC-SPRI cleanup format was also integrated to a capillary gel electrophoresis system. In this case, the immobilization microchannel contained microposts to increase the loading level of DNAs to improve signal intensity without the need for pre-concentration

Organizational Structure-Satisfactory Social Law Determination in Multiagent Workflow Systems

The multiagent workflow systems can be formalized from an organizational structure viewpoint, which includes three parts: the interaction structure among agents, the temporal flow of activities, and the critical resource sharing relations among activities. While agents execute activities, they should decide their strategies to satisfy the constraints brought by the organizational structure of multiagent workflow system. To avoid collisions in the multiagent workflow system, this paper presents a method to determine social laws in the system to restrict the strategies of agents and activities; the determined social laws can satisfy the characteristics of organization structures so as to minimize the conflicts among agents and activities. Moreover, we also deal with the social law adjustment mechanism for the alternations of interaction relations, temporal flows, and critical resource sharing relations. It is proved that our model can produce useful social laws for organizational structure of multiagent workflow systems, i.e., the conflicts brought by the constraints of organization structure can be minimized