Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

物件導向軟體之架構(Object-Oriented Software Construction)探討

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

679Background: Pancreatic Adenocarcinoma (PDAC) is often diagnosed at an advanced stage. We sought to develop a model for early PDAC prediction in the general population, using electronic health re...

Development and validation of a pancreatic cancer prediction model from electronic health records using machine learning.

We present the formal framework for a novel approach for specifying and automatically implementing systems such as digital circuits and network protocols. The goal is to reduce the design time and effort required to build correct, efficient, complex systems and to eliminate the need for the designer to deal directly with global synchronization and concurrency issues. Our compiler automatically transforms modular and asynchronous specifications of circuits written in our specification language, into tightly coupled, fully synchronous implementations in synthesizable Verilog. We formally state the correctness theorems and give an outline of the correctness proofs for two of the three main techniques that our compiler implements.

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A Formal Framework for Modular Synchronous System Design

Leveraging concepts from state machine refinement proofs, we use prophecy variables, which predict information about the future program execution, to enable forward reasoning for backward dataflow analyses. Drawing prophecy and history variables (concepts from the dynamic execution of the program) from the same lattice as the static program analysis results, we require the analysis results to satisfy both the dataflow equations and the transition relations in the operational semantics of underlying programming language. This approach eliminates explicit abstraction and concretization functions and promotes a more direct connection between the analysis and program executions, with the connection taking the form of a bisimulation relation between concrete executions and an augmented operational semantics over the analysis results. We present several classical dataflow analyses with this approach (live variables, very busy expressions, defined variables, and reaching definitions) along with proofs that highlight how this approach can enable more streamlined reasoning. To the best of our knowledge, we are the first to use prophecy variables for dataflow analysis.

Dataflow Analysis With Prophecy and History Variables.

In this position paper, we describe our vision of the future of machine-based programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.

The Three Pillars of Machine-Based Programming.

We present a dataflow model for modelling parallel Unix shell pipelines. To accurately capture the semantics of complex Unix pipelines, the dataflow model is order-aware, i.e., the order in which a node in the dataflow graph consumes inputs from different edges plays a central role in the semantics of the computation and therefore in the resulting parallelization. We use this model to capture the semantics of transformations that exploit data parallelism available in Unix shell computations and prove their correctness. We additionally formalize the translations from the Unix shell to the dataflow model and from the dataflow model back to a parallel shell script. We implement our model and transformations as the compiler and optimization passes of a system parallelizing shell pipelines, and use it to evaluate the speedup achieved on 47 pipelines.

Martin Rinard

Papers

Development and validation of a pancreatic cancer prediction model from electronic health records using machine learning.

A Formal Framework for Modular Synchronous System Design

Dataflow Analysis With Prophecy and History Variables.

The Three Pillars of Machine-Based Programming.

An Order-Aware Dataflow Model for Parallel Unix Pipelines