Inference with Constrained Hidden Markov Models in PRISM

A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. Defining HMMs with side-constraints in Constraint Logic Programming have advantages in terms of more compact expression and pruning opportunities during inference. We present a PRISM-based framework for extending HMMs with side-constraints and show how well-known constraints such as cardinality and all different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment

Analytical tools for optimizing the error correction performance of arithmetic codes

International audienceIn joint source-channel arithmetic coding (JSCAC) schemes, additional redundancy may be introduced into an arithmetic source code in order to be more robust against transmission errors. The purpose of this work is to provide analytical tools to predict and evaluate the effectiveness of that redundancy. Integer binary Arithmetic Coding (AC) is modeled by a reduced-state automaton in order to obtain a bit-clock trellis describing the encoding process. Considering AC as a trellis code, distance spectra are then derived. In particular, an algorithm to compute the free distance of an arithmetic code is proposed. The obtained code properties allow to compute upper bounds on both bit error and symbol error probabilities and thus provide an objective criterion to analyze the behavior of JSCAC schemes when used on noisy channels. This criterion is then exploited to design efficient error-correcting arithmetic codes. Simulation results highlight the validity of the theoretical error bounds and show that for equivalent rate and complexity, a simple optimization yields JSCACs that outperform classical tandem schemes at low to medium SNR

Named Entity Recognition in multilingual handwritten texts

[ES] En nuestro trabajo presentamos un único modelo basado en aprendizaje profundo para la transcripción automática y el reconocimiento de entidades nombradas de textos manuscritos. Este modelo aprovecha las capacidades de generalización de sistemas de reconocimiento, combinando redes neuronales artificiales y n-gramas de caracteres. Se discute la evaluación de dicho sistema y, como consecuencia, se propone una nueva medida de evaluación. Con el fin de mejorar los resultados con respecto a dicha métrica, se evalúan diferentes estrategias de corrección de errores.[EN] In our work we present a single Deep Learning based model for the automatic transcription and Named Entity Recognition of handwritten texts. Such model leverages the generalization capabilities of recognition systems, combining Artificial Neural Networks and n-gram character models. The evaluation of said system is discussed and, as a consequence, a new evaluation metric is proposed. As a means to improve the results in regards to such metric, different error correction strategies are assessed.Villanova Aparisi, D. (2021). Named Entity Recognition in multilingual handwritten texts. Universitat Politècnica de València. http://hdl.handle.net/10251/174942TFG

Accelerating dynamic programming

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 129-136).Dynamic Programming (DP) is a fundamental problem-solving technique that has been widely used for solving a broad range of search and optimization problems. While DP can be invoked when more specialized methods fail, this generality often incurs a cost in efficiency. We explore a unifying toolkit for speeding up DP, and algorithms that use DP as subroutines. Our methods and results can be summarized as follows. - Acceleration via Compression. Compression is traditionally used to efficiently store data. We use compression in order to identify repeats in the table that imply a redundant computation. Utilizing these repeats requires a new DP, and often different DPs for different compression schemes. We present the first provable speedup of the celebrated Viterbi algorithm (1967) that is used for the decoding and training of Hidden Markov Models (HMMs). Our speedup relies on the compression of the HMM's observable sequence. - Totally Monotone Matrices. It is well known that a wide variety of DPs can be reduced to the problem of finding row minima in totally monotone matrices. We introduce this scheme in the context of planar graph problems. In particular, we show that planar graph problems such as shortest paths, feasible flow, bipartite perfect matching, and replacement paths can be accelerated by DPs that exploit a total-monotonicity property of the shortest paths. - Combining Compression and Total Monotonicity. We introduce a method for accelerating string edit distance computation by combining compression and totally monotone matrices.(cont.) In the heart of this method are algorithms for computing the edit distance between two straight-line programs. These enable us to exploits the compressibility of strings, even if each string is compressed using a different compression scheme. - Partial Tables. In typical DP settings, a table is filled in its entirety, where each cell corresponds to some subproblem. In some cases, by changing the DP, it is possible to compute asymptotically less cells of the table. We show that [theta](n³) subproblems are both necessary and sufficient for computing the similarity between two trees. This improves all known solutions and brings the idea of partial tables to its full extent. - Fractional Subproblems. In some DPs, the solution to a subproblem is a data structure rather than a single value. The entire data structure of a subproblem is then processed and used to construct the data structure of larger subproblems. We suggest a method for reusing parts of a subproblem's data structure. In some cases, such fractional parts remain unchanged when constructing the data structure of larger subproblems. In these cases, it is possible to copy this part of the data structure to the larger subproblem using only a constant number of pointer changes. We show how this idea can be used for finding the optimal tree searching strategy in linear time. This is a generalization of the well known binary search technique from arrays to trees.by Oren Weimann.Ph.D

A Comparison of File Organization Techniques

This thesis compares the file organization techniques that are implemented on two different types of computer systems, the large-scale and the small-scale. File organizations from representative computers in each class are examined in detail: the IBM System/370 (OS/370) and the Harris 1600 Distributed Processing System with the Extended Communications Operating System (ECOS). In order to establish the basic framework for comparison, an introduction to file organizations is presented. Additionally, the functional requirements for file organizations are described by their characteristics and user demands. Concluding remarks compare file organization techniques and discuss likely future developments of file systems

Viterbi algorithm in continuous-phase frequency shift keying

The Viterbi algorithm, an application of dynamic programming, is widely used for estimation and detection problems in digital communications and signal processing. It is used to detect signals in communication channels with memory, and to decode sequential error-control codes that are used to enhance the performance of digital communication systems. The Viterbi algorithm is also used in speech and character recognition tasks where the speech signals or characters are modeled by hidden Markov models. This project explains the basics of the Viterbi algorithm as applied to systems in digital communication systems, and speech and character recognition. It also focuses on the operations and the practical memory requirements to implement the Viterbi algorithm in real-time. A forward error correction technique known as convolutional coding with Viterbi decoding was explored. In this project, the basic Viterbi decoder behavior model was built and simulated. The convolutional encoder, BPSK and AWGN channel were implemented in MATLAB code. The BER was tested to evaluate the decoding performance. The theory of Viterbi Algorithm is introduced based on convolutional coding. The application of Viterbi Algorithm in the Continuous-Phase Frequency Shift Keying (CPFSK) is presented. Analysis for the performance is made and compared with the conventional coherent estimator. The main issue of this thesis is to implement the RTL level model of Viterbi decoder. The RTL Viterbi decoder model includes the Branch Metric block, the Add-Compare-Select block, the trace-back block, the decoding block and next state block. With all done, we further understand about the Viterbi decoding algorithm

Graph Processing in Main-Memory Column Stores

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe GRAPHITE, a hybrid graph-relational data management system. GRAPHITE is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of GRAPHITE we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that GRAPHITE can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes

The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction

With the advent of deep learning, research in many areas of machine learning is converging towards the same set of methods and models. For example, long short-term memory networks are not only popular for various tasks in natural language processing (NLP) such as speech recognition, machine translation, handwriting recognition, syntactic parsing, etc., but they are also applicable to seemingly unrelated fields such as robot control, time series prediction, and bioinformatics. Recent advances in contextual word embeddings like BERT boast with achieving state-of-the-art results on 11 NLP tasks with the same model. Before deep learning, a speech recognizer and a syntactic parser used to have little in common as systems were much more tailored towards the task at hand. At the core of this development is the tendency to view each task as yet another data mapping problem, neglecting the particular characteristics and (soft) requirements tasks often have in practice. This often goes along with a sharp break of deep learning methods with previous research in the specific area. This work can be understood as an antithesis to this paradigm. We show how traditional symbolic statistical machine translation models can still improve neural machine translation (NMT) while reducing the risk for common pathologies of NMT such as hallucinations and neologisms. Other external symbolic models such as spell checkers and morphology databases help neural grammatical error correction. We also focus on language models that often do not play a role in vanilla end-to-end approaches and apply them in different ways to word reordering, grammatical error correction, low-resource NMT, and document-level NMT. Finally, we demonstrate the benefit of hierarchical models in sequence-to-sequence prediction. Hand-engineered covering grammars are effective in preventing catastrophic errors in neural text normalization systems. Our operation sequence model for interpretable NMT represents translation as a series of actions that modify the translation state, and can also be seen as derivation in a formal grammar.EPSRC grant EP/L027623/1 EPSRC Tier-2 capital grant EP/P020259/

Inference and Learning with Planning Models

[ES] Inferencia y aprendizaje son los actos de razonar sobre evidencia recogida con el fin de alcanzar conclusiones lógicas sobre el proceso que la originó. En el contexto de un modelo de espacio de estados, inferencia y aprendizaje se refieren normalmente a explicar el comportamiento pasado de un agente, predecir sus acciones futuras, o identificar su modelo. En esta tesis, presentamos un marco para inferencia y aprendizaje en el modelo de espacio de estados subyacente al modelo de planificación clásica, y formulamos una paleta de problemas de inferencia y aprendizaje bajo este paraguas unificador. También desarrollamos métodos efectivos basados en planificación que nos permiten resolver estos problemas utilizando algoritmos de planificación genéricos del estado del arte. Mostraremos que un gran número de problemas de inferencia y aprendizaje claves que han sido tratados como desconectados se pueden formular de forma cohesiva y resolver siguiendo procedimientos homogéneos usando nuestro marco. Además, nuestro trabajo abre las puertas a nuevas aplicaciones para tecnología de planificación ya que resalta las características que hacen que el modelo de espacio de estados de planificación clásica sea diferente a los demás modelos.[CA] Inferència i aprenentatge són els actes de raonar sobre evidència arreplegada a fi d'aconseguir conclusions lògiques sobre el procés que la va originar. En el context d'un model d'espai d'estats, inferència i aprenentatge es referixen normalment a explicar el comportament passat d'un agent, predir les seues accions futures, o identificar el seu model. En esta tesi, presentem un marc per a inferència i aprenentatge en el model d'espai d'estats subjacent al model de planificació clàssica, i formulem una paleta de problemes d'inferència i aprenentatge davall este paraigua unificador. També desenrotllem mètodes efectius basats en planificació que ens permeten resoldre estos problemes utilitzant algoritmes de planificació genèrics de l'estat de l'art. Mostrarem que un gran nombre de problemes d'inferència i aprenentatge claus que han sigut tractats com desconnectats es poden formular de forma cohesiva i resoldre seguint procediments homogenis usant el nostre marc. A més, el nostre treball obri les portes a noves aplicacions per a tecnologia de planificació ja que ressalta les característiques que fan que el model d'espai d'estats de planificació clàssica siga diferent dels altres models.[EN] Inference and learning are the acts of reasoning about some collected evidence in order to reach a logical conclusion regarding the process that originated it. In the context of a state-space model, inference and learning are usually concerned with explaining an agent's past behaviour, predicting its future actions or identifying its model. In this thesis, we present a framework for inference and learning in the state-space model underlying the classical planning model, and formulate a palette of inference and learning problems under this unifying umbrella. We also develop effective planning-based approaches to solve these problems using off-the-shelf, state-of-the-art planning algorithms. We will show that several core inference and learning problems that previous research has treated as disconnected can be formulated in a cohesive way and solved following homogeneous procedures using the proposed framework. Further, our work opens the way for new applications of planning technology as it highlights the features that make the state-space model of classical planning different from other models.The work developed in this doctoral thesis has been possible thanks to the FPU16/03184 fellowship that I have enjoyed for the duration of my PhD studies. I have also been supported by my advisors’ grants TIN2017-88476-C2-1-R, TIN2014-55637-C2-2-R-AR, and RYC-2015-18009.Aineto García, D. (2022). Inference and Learning with Planning Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18535