Approaches to the implementation of binary relation inference network.

by C.W. Tong.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 96-98).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- The Availability of Parallel Processing Machines --- p.2Chapter 1.1.1 --- Neural Networks --- p.5Chapter 1.2 --- Parallel Processing in the Continuous-Time Domain --- p.6Chapter 1.3 --- Binary Relation Inference Network --- p.10Chapter 2 --- Binary Relation Inference Network --- p.12Chapter 2.1 --- Binary Relation Inference Network --- p.12Chapter 2.1.1 --- Network Structure --- p.14Chapter 2.2 --- Shortest Path Problem --- p.17Chapter 2.2.1 --- Problem Statement --- p.17Chapter 2.2.2 --- A Binary Relation Inference Network Solution --- p.18Chapter 3 --- A Binary Relation Inference Network Prototype --- p.21Chapter 3.1 --- The Prototype --- p.22Chapter 3.1.1 --- The Network --- p.22Chapter 3.1.2 --- Computational Element --- p.22Chapter 3.1.3 --- Network Response Time --- p.27Chapter 3.2 --- Improving Response --- p.29Chapter 3.2.1 --- Removing Feedback --- p.29Chapter 3.2.2 --- Selecting Minimum with Diodes --- p.30Chapter 3.3 --- Speeding Up the Network Response --- p.33Chapter 3.4 --- Conclusion --- p.35Chapter 4 --- VLSI Building Blocks --- p.36Chapter 4.1 --- The Site --- p.37Chapter 4.2 --- The Unit --- p.40Chapter 4.2.1 --- A Minimum Finding Circuit --- p.40Chapter 4.2.2 --- A Tri-state Comparator --- p.44Chapter 4.3 --- The Computational Element --- p.45Chapter 4.3.1 --- Network Performances --- p.46Chapter 4.4 --- Discussion --- p.47Chapter 5 --- A VLSI Chip --- p.48Chapter 5.1 --- Spatial Configuration --- p.49Chapter 5.2 --- Layout --- p.50Chapter 5.2.1 --- Computational Elements --- p.50Chapter 5.2.2 --- The Network --- p.52Chapter 5.2.3 --- I/O Requirements --- p.53Chapter 5.2.4 --- Optional Modules --- p.53Chapter 5.3 --- A Scalable Design --- p.54Chapter 6 --- The Inverse Shortest Paths Problem --- p.57Chapter 6.1 --- Problem Statement --- p.59Chapter 6.2 --- The Embedded Approach --- p.63Chapter 6.2.1 --- The Formulation --- p.63Chapter 6.2.2 --- The Algorithm --- p.65Chapter 6.3 --- Implementation Results --- p.66Chapter 6.4 --- Other Implementations --- p.67Chapter 6.4.1 --- Sequential Machine --- p.67Chapter 6.4.2 --- Parallel Machine --- p.68Chapter 6.5 --- Discussion --- p.68Chapter 7 --- Closed Semiring Optimization Circuits --- p.71Chapter 7.1 --- Transitive Closure Problem --- p.72Chapter 7.1.1 --- Problem Statement --- p.72Chapter 7.1.2 --- Inference Network Solutions --- p.73Chapter 7.2 --- Closed Semirings --- p.76Chapter 7.3 --- Closed Semirings and the Binary Relation Inference Network --- p.79Chapter 7.3.1 --- Minimum Spanning Tree --- p.80Chapter 7.3.2 --- VLSI Implementation --- p.84Chapter 7.4 --- Conclusion --- p.86Chapter 8 --- Conclusions --- p.87Chapter 8.1 --- Summary of Achievements --- p.87Chapter 8.2 --- Future Work --- p.89Chapter 8.2.1 --- VLSI Fabrication --- p.89Chapter 8.2.2 --- Network Robustness --- p.90Chapter 8.2.3 --- Inference Network Applications --- p.91Chapter 8.2.4 --- Architecture for the Bellman-Ford Algorithm --- p.91Bibliography --- p.92Appendices --- p.99Chapter A --- Detailed Schematic --- p.99Chapter A.1 --- Schematic of the Inference Network Structures --- p.99Chapter A.1.1 --- Unit with Self-Feedback --- p.99Chapter A.1.2 --- Unit with Self-Feedback Removed --- p.100Chapter A.1.3 --- Unit with a Compact Minimizer --- p.100Chapter A.1.4 --- Network Modules --- p.100Chapter A.2 --- Inference Network Interface Circuits --- p.100Chapter B --- Circuit Simulation and Layout Tools --- p.107Chapter B.1 --- Circuit Simulation --- p.107Chapter B.2 --- VLSI Circuit Design --- p.110Chapter B.3 --- VLSI Circuit Layout --- p.111Chapter C --- The Conjugate-Gradient Descent Algorithm --- p.113Chapter D --- Shortest Path Problem on MasPar --- p.11

Explore Biological Pathways from Noisy Array Data by Directed Acyclic Boolean Networks

We consider the structure of directed acyclic Boolean (DAB) networks as a tool for exploring biological pathways. In a DAB network, the basic objects are binary elements and their Boolean duals. A DAB is characterized by two kinds of pairwise relations: similarity and prerequisite. The latter is a partial order relation, namely, the on-status of one element is necessary for the on-status of another element. A DAB network is uniquely determined by the state space of its elements. We arrange samples from the state space of a DAB network in a binary array and introduce a random mechanism of measurement error. Our inference strategy consists of two stages. First, we consider each pair of elements and try to identify their most likely relation. In the meantime, we assign a score, s-p-score, to this relation. Second, we rank the s-p-scores obtained from the first stage. We expect that relations with smaller s-p-scores are more likely to be true, and those with larger s-p-scores are more likely to be false. The key idea is the definition of s-scores (referring to similarity), p-scores (referring to prerequisite), and s-p-scores. As with classical statistical tests, control of false negatives and false positives are our primary concerns. We illustrate the method by a simulated example, the classical arginine biosynthetic pathway, and show some exploratory results on a published microarray expression dataset of yeast Saccharomyces cerevisiae obtained from experiments with activation and genetic perturbation of the pheromone response MAPK pathway

Probabilistic Label Relation Graphs with Ising Models

We consider classification problems in which the label space has structure. A common example is hierarchical label spaces, corresponding to the case where one label subsumes another (e.g., animal subsumes dog). But labels can also be mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy and exclusion) graph was introduced in [7]. This combined a conditional random field (CRF) with a deep neural network (DNN), resulting in state of the art results when applied to visual object classification problems where the training labels were drawn from different levels of the ImageNet hierarchy (e.g., an image might be labeled with the basic level category "dog", rather than the more specific label "husky"). In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e.g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear). We call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can be converted to an Ising model, which allows us to use existing off-the-shelf inference methods (in contrast to the HEX method, which needed specialized inference algorithms). Experimental results show significant improvements in a number of large-scale visual object classification tasks, outperforming the previous HEX model.Comment: International Conference on Computer Vision (2015

Combining link and content-based information in a Bayesian inference model for entity search

An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented. The model supports the explicit combination of primitive data type and object-level semantics under a single computational framework. A flexible query model is supported capable to reason with the availability of simple semantics in querie

The random subgraph model for the analysis of an ecclesiastical network in Merovingian Gaul

In the last two decades many random graph models have been proposed to extract knowledge from networks. Most of them look for communities or, more generally, clusters of vertices with homogeneous connection profiles. While the first models focused on networks with binary edges only, extensions now allow to deal with valued networks. Recently, new models were also introduced in order to characterize connection patterns in networks through mixed memberships. This work was motivated by the need of analyzing a historical network where a partition of the vertices is given and where edges are typed. A known partition is seen as a decomposition of a network into subgraphs that we propose to model using a stochastic model with unknown latent clusters. Each subgraph has its own mixing vector and sees its vertices associated to the clusters. The vertices then connect with a probability depending on the subgraphs only, while the types of edges are assumed to be sampled from the latent clusters. A variational Bayes expectation-maximization algorithm is proposed for inference as well as a model selection criterion for the estimation of the cluster number. Experiments are carried out on simulated data to assess the approach. The proposed methodology is then applied to an ecclesiastical network in Merovingian Gaul. An R code, called Rambo, implementing the inference algorithm is available from the authors upon request.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS691 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modeling heterogeneity in random graphs through latent space models: a selective review

We present a selective review on probabilistic modeling of heterogeneity in random graphs. We focus on latent space models and more particularly on stochastic block models and their extensions that have undergone major developments in the last five years

Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019