Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

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

In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance. Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms. We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

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A morphable model for the synthesis of 3D faces

Emotion and Adaptation

1. Introduction and Historical Background 1.1 Why make human figure models? 1.2 Historical roots 1.3 What is currently possible? 1.4 Manipulation, animation, and simulation 1.5 What did we leave out? 2. Mody Modeling 2.1 Geometric body modeling 2.2 Representing articulated figures 2.3 A flexible torso model 2.4 Shoulder complex 2.5 Clothing models 2.6 The anthropometry database 2.7 The anthropometry spreadsheet 2.8 Strength and torque display 3. Spatial Interaction 3.1 Direct manipulation 3.2 Manipulation with constraints 3.3 Inverse kinematic positioning 4. Behavioral Control of Articulated Figures 4.1 An interactive system for postural control 4.2 Interactive manipulation with behaviors 4.3 The animation interface 4.4 Human figure motions 4.5 Virtual human control 5. Simulation with Societies of Behaviors 5.1 Forward simulation with behaviors 5.2 Locomotion 5.3 Strength guided motion 5.4 Collision-free path and motion planning 5.5 Posture planning 6. Task-level Specifications 6.1 Performance simulation with simple commands 6.2 Natural language expressions of kinematics and space 6.3 Task-level simulation 6.4 A framework for instruction understanding 7. Epilogue 7.1 A road map toward the future 7.2 Conclusion

/pdf/simulating-humans-computer-graphics-animation-and-control-2dk1fy50vu.pdf

Simulating humans: computer graphics animation and control

We describe an implemented system which automatically generates and animates conversations between multiple human-like agents with appropriate and synchronized speech, intonation, facial expressions, and hand gestures. Conversation is created by a dialogue planner that produces the text as well as the intonation of the utterances. The speaker/listener relationship, the text, and the intonation in turn drive facial expressions, lip motions, eye gaze, head motion, and arm gestures generators. Coordinated arm, wrist, and hand motions are invoked to create semantically meaningful gestures. Throughout we will use examples from an actual synthesized, fully animated conversation.

/pdf/animated-conversation-rule-based-generation-of-facial-1druct0zs6.pdf

Animated conversation: rule-based generation of facial expression, gesture & spoken intonation for multiple conversational agents

In this paper we develop a set of inverse kinematics algorithms suitable for an anthropomorphic arm or leg. We use a combination of analytical and numerical methods to solve generalized inverse kinematics problems including position, orientation, and aiming constraints. Our combination of analytical and numerical methods results in faster and more reliable algorithms than conventional inverse Jacobian and optimization-based techniques. Additionally, unlike conventional numerical algorithms, our methods allow the user to interactively explore all possible solutions using an intuitive set of parameters that define the redundancy of the system.

/pdf/real-time-inverse-kinematics-techniques-for-anthropomorphic-13bs0edx7c.pdf

Real-time inverse kinematics techniques for anthropomorphic limbs

Simulating the motion of realistic, large, dense crowds of autonomous agents is still a challenge for the computer graphics community. Typical approaches either resemble particle simulations (where agents lack orientation controls) or are conservative in the range of human motion possible (agents lack psychological state and aren't allowed to 'push' each other). Our HiDAC system (for High-Density Autonomous Crowds) focuses on the problem of simulating the local motion and global wayfinding behaviors of crowds moving in a natural manner within dynamically changing virtual environments. By applying a combination of psychological and geometrical rules with a social and physical forces model, HiDAC exhibits a wide variety of emergent behaviors from agent line formation to pushing behavior and its consequences; relative to the current situation, personalities of the individuals and perceived social density.

/pdf/controlling-individual-agents-in-high-density-crowd-1zkmq0407w.pdf

Controlling individual agents in high-density crowd simulation

A system capable of analyzing image sequences of human motion is described. The system is structured as a ·feedback loop between high and low levels: predictions are made at the semantic level, and verifications are sought at the image level. The domain of human motion lends itself to a model-driven analysis, and the system includes a detailed model of the human body. All information extracted from the image is interpreted through a constraint network based on the structure of the human model. A constraint propagation operator is defined and its theoretical,properties developed. An implementation of this operator is described, and results of the analysis system for a short image sequence are presented.

/pdf/image-analysis-of-human-motion-using-constraint-propagation-lre5u12xfg.pdf

Norman I. Badler

Papers

Simulating humans: computer graphics animation and control

Animated conversation: rule-based generation of facial expression, gesture & spoken intonation for multiple conversational agents

Real-time inverse kinematics techniques for anthropomorphic limbs

Controlling individual agents in high-density crowd simulation

Image Analysis of Human Motion Using Constraint Propagation