Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference

We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. Each node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks (we show how they can be generated using clustering and conditioning algorithms). The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based. The complexity of a Q-DAG evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the arithmetic expression it represents. The intended value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms due to the simplicity of the Q-DAG evaluation algorithm. Interestingly enough, Q-DAGs were found to serve other purposes: simple techniques for reducing Q-DAGs tend to subsume relatively complex optimization techniques for belief-network inference, such as network-pruning and computation-caching.Comment: See http://www.jair.org/ for any accompanying file

Quasipolynomial simulation of DNNF by a non-determinstic read-once branching program

We prove that dnnfs can be simulated by Non-deterministic Read-Once Branching Programs (nrobps) of quasi-polynomial size. As a result, all the exponential lower bounds for nrobps immediately apply for dnnfs

On the read-once property of branching programs and CNFs of bounded treewidth

for non-deterministic (syntactic) read-once branching programs (nrobps) on functions expressible as cnfs with treewidth at most k of their primal graphs. This lower bound rules out the possibility of fixed-parameter space complexity of nrobps parameterized by k. We use lower bound for nrobps to obtain a quasi-polynomial separation between Free Binary Decision Diagrams and Decision Decomposable Negation Normal Forms, essentially matching the existing upper bound introduced by Beame et al. (Proceedings of the twenty-ninth conference on uncertainty in artificial intelligence, Bellevue, 2013) and thus proving the tightness of the latter

Model-Based Diagnosis using Structured System Descriptions

This paper presents a comprehensive approach for model-based diagnosis which includes proposals for characterizing and computing preferred diagnoses, assuming that the system description is augmented with a system structure (a directed graph explicating the interconnections between system components). Specifically, we first introduce the notion of a consequence, which is a syntactically unconstrained propositional sentence that characterizes all consistency-based diagnoses and show that standard characterizations of diagnoses, such as minimal conflicts, correspond to syntactic variations on a consequence. Second, we propose a new syntactic variation on the consequence known as negation normal form (NNF) and discuss its merits compared to standard variations. Third, we introduce a basic algorithm for computing consequences in NNF given a structured system description. We show that if the system structure does not contain cycles, then there is always a linear-size consequence in NNF which can be computed in linear time. For arbitrary system structures, we show a precise connection between the complexity of computing consequences and the topology of the underlying system structure. Finally, we present an algorithm that enumerates the preferred diagnoses characterized by a consequence. The algorithm is shown to take linear time in the size of the consequence if the preference criterion satisfies some general conditions.Comment: See http://www.jair.org/ for any accompanying file

Machine Learning Methods for Septic Shock Prediction

Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model is built by obtaining a risk score from fitting the Cox Hazard model on multiple input features. The score is added to the list of features and the Random Forest ensemble classifier is trained to produce the model. The Cox Enhanced Random Forest (CERF) proposed method is evaluated by comparing its predictive accuracy to those of extant methods

The Language of Search

This paper is concerned with a class of algorithms that perform exhaustive search on propositional knowledge bases. We show that each of these algorithms defines and generates a propositional language. Specifically, we show that the trace of a search can be interpreted as a combinational circuit, and a search algorithm then defines a propositional language consisting of circuits that are generated across all possible executions of the algorithm. In particular, we show that several versions of exhaustive DPLL search correspond to such well-known languages as FBDD, OBDD, and a precisely-defined subset of d-DNNF. By thus mapping search algorithms to propositional languages, we provide a uniform and practical framework in which successful search techniques can be harnessed for compilation of knowledge into various languages of interest, and a new methodology whereby the power and limitations of search algorithms can be understood by looking up the tractability and succinctness of the corresponding propositional languages

Parameterized Compilation Lower Bounds for Restricted CNF-formulas

We show unconditional parameterized lower bounds in the area of knowledge compilation, more specifically on the size of circuits in decomposable negation normal form (DNNF) that encode CNF-formulas restricted by several graph width measures. In particular, we show that - there are CNF formulas of size $n$ and modular incidence treewidth $k$ whose smallest DNNF-encoding has size $n^{\Omega(k)}$, and - there are CNF formulas of size $n$ and incidence neighborhood diversity $k$ whose smallest DNNF-encoding has size $n^{\Omega(\sqrt{k})}$. These results complement recent upper bounds for compiling CNF into DNNF and strengthen---quantitatively and qualitatively---known conditional low\-er bounds for cliquewidth. Moreover, they show that, unlike for many graph problems, the parameters considered here behave significantly differently from treewidth

A Knowledge Compilation Map

We propose a perspective on knowledge compilation which calls for analyzing different compilation approaches according to two key dimensions: the succinctness of the target compilation language, and the class of queries and transformations that the language supports in polytime. We then provide a knowledge compilation map, which analyzes a large number of existing target compilation languages according to their succinctness and their polytime transformations and queries. We argue that such analysis is necessary for placing new compilation approaches within the context of existing ones. We also go beyond classical, flat target compilation languages based on CNF and DNF, and consider a richer, nested class based on directed acyclic graphs (such as OBDDs), which we show to include a relatively large number of target compilation languages

Cliquewidth and knowledge compilation

In this paper we study the role of cliquewidth in succinct representation of Boolean functions. Our main statement is the following: Let Z be a Boolean circuit having cliquewidth k. Then there is another circuit Z * computing the same function as Z having treewidth at most 18k + 2 and which has at most 4|Z| gates where |Z| is the number of gates of Z. In this sense, cliquewidth is not more ‘powerful’ than treewidth for the purpose of representation of Boolean functions. We believe this is quite a surprising fact because it contrasts the situation with graphs where an upper bound on the treewidth implies an upper bound on the cliquewidth but not vice versa. We demonstrate the usefulness of the new theorem for knowledge compilation. In particular, we show that a circuit Z of cliquewidth k can be compiled into a Decomposable Negation Normal Form (dnnf) of size O(918k k 2|Z|) and the same runtime. To the best of our knowledge, this is the first result on efficient knowledge compilation parameterized by cliquewidth of a Boolean circuit

Tipificación de patrones espacio - temporales y en relación con las crecidas de N03 –, sólidos en suspensión y sales disueltas en la cuenca del río Flumen (Huesca)

23 páginas[ES] Durante las crecidas de los ríos se producen cambios en las concentraciones de los contaminantes que influyen en la calidad de las aguas superficiales. Desde febrero de 2010 hasta octubre de 2012 se recogieron muestras de agua durante doce crecidas que tuvieron lugar en la zona sur de la cuenca del río Flumen para tipificar las variaciones de compuestos disuel- tos y sólidos en suspensión. Mediante la comparación de estos análisis con datos de muestras adicionales recogidas en temporada de regadío y de no regadío, así como del compendio de datos hidrometeorológicos y el estudio de los bucles de histéresis, se obtuvo una visión global sobre los mecanis- mos que afectan a la trayectoria y el transporte de nitratos (NO3–), sólidos en suspensión (TSS) y sales disueltas. Los resultados obtenidos indicaron cam- bios en la calidad del agua en función de los usos del suelo y de las va - riaciones estacionales y eventuales relacionadas con los episodios de crecida.[EN] During river floods, changes in the pollutants concentra- tions take place that influence the surface water quality. From February 2010 to October 2012, water samples from 12 floods that occurred in the southern part of the Flumen River basin were collected to characterize the relationships between dissolved compounds and suspended solids and water discharge By comparing these analyses with the collection of additional samples during irrigation and non-irrigation season, the compendium of hydrometeorological data and the study of hysteresis loops, an overview of the mechanisms that affect the trends and transport of nitrates (NO3–), suspended solids (TSS) and dissolved salts was obtained. Results showed changes in water quality based on land use and seasonal and eventual variations in relation to flood events.Este trabajo fue financiado con una Ayuda de Investigación del Instituto de Estudios Altoaragoneses en su convocatoria de 2013. Asimismo, contó con el apoyo proporcionado por el proyecto AGUAFLASH (EU Interreg IV B SUDOE Programme [SOE1/P2/F146]) y el proyecto LIFE09 ENV/ES/000431 CREAMAgua, este último liderado por la comarca de Los Monegros a la que agradecemos su interés y su colaboración. A la Agencia Estatal de Meteorología (AEMET) y a la Oficina del Regante del Gobierno de Aragón, la facilitación de datos; a Mercedes García y Alberto Barcos su asistencia en laboratorio, y a Silvia G. Eisman, su ayuda esencial en los trabajos de campo.Peer reviewe