From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

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Nonlinear dimensionality reduction by locally linear embedding.

From the Publisher:
With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter.


0201000296B04062001

The Design and Analysis of Computer Algorithms

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

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Community detection in graphs

In this article we study the amortized efficiency of the "move-to-front" and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes e(i) time, we show that move-to-front is within a constant factor of optimum among a wide class of list maintenance rules. Other natural heuristics, such as the transpose and frequency count rules, do not share this property. We generalize our results to show that move-to-front is within a constant factor of optimum as long as the access cost is a convex function. We also study paging, a setting in which the access cost is not convex. The paging rule corresponding to move-to-front is the "least recently used" replacement rule. We analyze the amortized complexity of LRU, showing that its efficiency differs from that of the off- line paging rule (Belady's MlN algorithm) by a factor that depends on the size of fast memory. No on-line paging algorithm has better amortized performance.

Techniques and Amortized Efficiency Of List Ellis Horowitz Update and Paging Rules

Almost-Optimum Parallel Speed-Ups of Algorithms for Bipartite Matching and Related Problems ; CU-CS-425-89

We describe a new class of scheduling problem with precedence constraints, the disconnected staged scheduling problem (dssp). dssp is a nonpreemptive multiprocessor deadline scheduling problem; we seek to maximize the aggregate value of jobs that complete by a specified deadline. It arises in many commercially-important domains including bioinformatics and seismic signal processing. Our interest in dssp began with the practical problem of scheduling computer animation rendering jobs. Each job represents a brief film clip and consists of several stages that must be processed in order (e.g., physical simulation, model baking, frame rendering, and clip assembly). Each stage in turn consists of computational tasks that may be run in parallel; all tasks in a stage must finish before any task in the next stage can start. A job completes if and only if all of its tasks complete. Precedence constraints exist among tasks within a job, but not among tasks in different jobs. Jobs run overnight and yield value only if they complete before the artists who submitted them return the following morning. Demand frequently exceeds available CPU capacity, making it impossible to complete all submitted jobs by the deadline. The set of jobs is known in advance but their computational demands (e.g., the run times of tasks) are not precisely known. Existing scheduling practices rely on priority schedulers, which are not well suited to dssp because ordinal priorities cannot adequately express the value of jobs. Furthermore priority schedulers make job selection decisions as byproducts of task sequencing decisions. Our approach is to assign to jobs completion rewards whose sums and ratios are meaningful, and to perform job selection and task sequencing separately. We present both theoretical analysis and empirical evaluation of our dssp solution. Our empirical results are based on an eight-week trace of 2,388 jobs collected in a 1,000-CPU production system that rendered part of film Shrek 2 in 2004. We show that our two-phase method improves aggregate reward and achieves near-optimal performance under

Deadline scheduling for animation rendering

We introduce the zip tree,1 a form of randomized binary search tree that integrates previous ideas into one practical, performant, and pleasant-to-implement package. A zip tree is a binary search tree in which each node has a numeric rank and the tree is (max)-heap-ordered with respect to ranks, with rank ties broken in favor of smaller keys. Zip trees are essentially treaps [8], except that ranks are drawn from a geometric distribution instead of a uniform distribution, and we allow rank ties. These changes enable us to use fewer random bits per node. We perform insertions and deletions by unmerging and merging paths (unzipping and zipping) rather than by doing rotations, which avoids some pointer changes and improves efficiency. The methods of zipping and unzipping take inspiration from previous top-down approaches to insertion and deletion by Stephenson [10], Martinez and Roura [5], and Sprugnoli [9]. From a theoretical standpoint, this work provides two main results. First, zip trees require only O(log log n) bits (with high probability) to represent the largest rank in an n-node binary search tree; previous data structures require O(log n) bits for the largest rank. Second, zip trees are naturally isomorphic to skip lists [7], and simplify Dean and Jones’ mapping between skip lists

Zip Trees

We present algorithms that run in linear time on pointer machines for a collection of problems, each of which either directly or indirectly requires the evaluat ion of a function defined on paths in a tree. These problems previously had linear-time algorithms but only for random-access machines (RAMs); the best pointer-machine algorithms were super-linear by an inverse-Ackermann-function factor. Our algorithms are also simpler, in some cases substantially, t han the previous linear-time RAM algorithms. Our improvements come primarily from three new ideas: a refin ed analysis of path compression that gives a linear bound if the compressions favor certain nodes, a pointer-based radix sort as a replacement for table-based methods, and a more careful partitioning of a tree into easily managed parts. Our algorithms compute nearest common ancestors off-line, verify and construct minimum spanning trees, do interval analysis on a flowgraph, find the dominators of a flowg raph, and build the component tree of a weighted tree.

Robert E. Tarjan

Papers

Techniques and Amortized Efficiency Of List Ellis Horowitz Update and Paging Rules

Almost-Optimum Parallel Speed-Ups of Algorithms for Bipartite Matching and Related Problems ; CU-CS-425-89

Deadline scheduling for animation rendering

Zip Trees

Linear-Time Pointer-Machine Algorithms for Path-Evaluation Problems on Trees and Graphs