On pattern classification algorithms - Introduction and survey

Pattern recognition algorithms, and mathematical techniques of estimation, decision making, and optimization theor

Two conversational languages for control theoretical computations in the time sharing mode

Two conversational languages for control theory applications on direct-access time sharing compute

Learning with a probabilistic teacher

Learning scheme for solving unsupervised learning problems with correct estimate convergence and for state estimates of Gauss-Markov sequences with additive and multiplicative observed nois

A Dual intepretation of Standard Constraints in Parametric Scheduling

The problem of parametric scheduling in hard real-time systems, ( in the presence of linear relative constraints between the start and execution times of tasks ) was posed in the litreature. In an earlier paper, a polynomial time algorithm is presented for the case when the constraints are restricted to be standard ( defined in paper ) and the execution time vectors belong to an axis-parallel hyper-rectangle. In this paper, we extend their results in two directions. We first present a polynomial time algorithm for the case when the execution time vectors belong to arbitrary convex domains. We then show that the set of standard constraints can be extended to include arbitrary network constraints. Our insights into the problem occur primarily as a result of studying the dual polytope of the constraint system. (Also cross-refernced as UMIACS-TR-2000-11

Text-based Editing of Talking-head Video

Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis

AD (Attacker Defender) Game

Information Dynamics is a framework for agent-based systems that gives a central position to the role of information, time, and the value of information. We illustrate system design in the Information Dynamics Framework by developing an intelligence game called AD involving attackers, defenders and targets operating in some space of locations. The goal of the attackers is to destroy all targets. Target destruction takes place when the number of attackers in the target's neighborhood exceeds the number of defenders in this neighborhood by a value WINNING_DIFFERENCE. The goal of defenders is to prevent attackers from achieving their goal. (Also UMIACS-TR-2001-45

State of the Art on Neural Rendering

Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo-realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state-of-the-art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photo-realistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. This state-of-the-art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems

Playing the role of a ‘boundary organisation’: getting smarter with networking

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