Representing Information Collections for Visual Cognition

The importance of digital information collections is growing. Collections are typically represented with text-only, in a linear list format, which turns out to be a weak representation for cognition. We learned this from empirical research in cognitive psychology, and by conducting a study to develop an understanding of current practices and resulting breakdowns in human experiences of building and utilizing collections. Because of limited human attention and memory, participants had trouble finding specific elements in their collections, resulting in low levels of collection utilization. To address these issues, this research develops new collection representations for visual cognition. First, we present the image+text surrogate, a concise representation for a document, or portion thereof, which is easy to understand and think about. An information extraction algorithm is developed to automatically transform a document into a small set of image+text surrogates. After refinement, the average accuracy performance of the algorithm was 90%. Then, we introduce the composition space to represent collections, which helps people connect elements visually in a spatial format. To ensure diverse information from multiple sources to be presented evenly in the composition space, we developed a new control structure, the ResultDis- tributor. A user study has demonstrated that the participants were able to browse more diverse information using the ResultDistributor-enhanced composition space. Participants also found it easier and more entertaining to browse information in this representation. This research is applicable to represent the information resources in contexts such as search engines or digital libraries. The better representation will enhance the cognitive efficacy and enjoyment of people’s everyday tasks of information searching, browsing, collecting, and discovering

Map-based Cloning of an Anthracnose Resistance Gene in \u3ci\u3eMedicago truncatula\u3c/i\u3e

Anthracnose, caused by the fungal pathogen Colletotrichum trifolii, is one of the most destructive diseases of alfalfa worldwide. Cloning and characterization of the host resistance (R) genes against the pathogen will improve our knowledge of molecular mechanisms underlying host resistance and facilitate the development of resistant alfalfa cultivars. However, the intractable genetic system of cultivated alfalfa, owing to its tetrasomic inheritance and outcrossing nature, limits the ability to carry out genetic analysis in alfalfa. Nonetheless, the model legume Medicago truncatula, a close relative of alfalfa, provides a surrogate for cloning the counterparts of many agronomically important genes in alfalfa. In this study, we used genetic map-based approach to clone RCT1, a host resistance gene against C. trifolii race 1, in M. truncatula. The RCT1 locus was delimited within a physical interval spanning ~200 kilo-bases located on the top of M. truncatula linkage group 4. Complementation tests of three candidate genes on the susceptible alfalfa clones revealed that RCT1 is a member of the Toll-interleukin-1 receptor/nucleotide-binding site/leucine-rich repeat (TIR-NBS-LRR) class of plant R genes and confers broad spectrum anthracnose resistance. Thus, RCT1 offers a novel resource to develop anthracnose-resistant alfalfa cultivars. Furthermore, the cloning of RCT1 also makes a significant contribution to our understanding of host resistance against the fungal genus Colletotrichum

Multiple Alternative Sentene Compressions as a Tool for Automatic Summarization Tasks

Automatic summarization is the distillation of important information from a source into an abridged form for a particular user or task. Many current systems summarize texts by selecting sentences with important content. The limitation of extraction at the sentence level is that highly relevant sentences may also contain non-relevant and redundant content. This thesis presents a novel framework for text summarization that addresses the limitations of sentence-level extraction. Under this framework text summarization is performed by generating Multiple Alternative Sentence Compressions (MASC) as candidate summary components and using weighted features of the candidates to construct summaries from them. Sentence compression is the rewriting of a sentence in a shorter form. This framework provides an environment in which hypotheses about summarization techniques can be tested. Three approaches to sentence compression were developed under this framework. The first approach, HMM Hedge, uses the Noisy Channel Model to calculate the most likely compressions of a sentence. The second approach, Trimmer, uses syntactic trimming rules that are linguistically motivated by Headlinese, a form of compressed English associated with newspaper headlines. The third approach, Topiary, is a combination of fluent text with topic terms. The MASC framework for automatic text summarization has been applied to the tasks of headline generation and multi-document summarization, and has been used for initial work in summarization of novel genres and applications, including broadcast news, email threads, cross-language, and structured queries. The framework supports combinations of component techniques, fostering collaboration between development teams. Three results will be demonstrated under the MASC framework. The first is that an extractive summarization system can produce better summaries by automatically selecting from a pool of compressed sentence candidates than by automatically selecting from unaltered source sentences. The second result is that sentence selectors can construct better summaries from pools of compressed candidates when they make use of larger candidate feature sets. The third result is that for the task of Headline Generation, a combination of topic terms and compressed sentences performs better then either approach alone. Experimental evidence supports all three results

Genetic Engineering

Leading scientists from different countries around the world contributed valuable essays on the basic applications and safety, as well as the ethical and moral considerations, of the powerful genetic engineering tools now available for modifying the molecules, pathways, and phenotypes of species of agricultural, industrial and even medical importance. After three decades of perfecting such tools, we now see a refined technology, surprisingly unexpected applications, and matured guidelines to avoid unintentional damage to our and other species, as well as the environment, while trying to contribute to solve the biological, medical and technical challenges of society and industry. Chapters on thermo-stabilization of luciferase, engineering of the phenylpropanoid pathway in a species of high demand for the paper industry, more efficient regeneration of transgenic soybean, viral resistant plants, and a novel approach for rapidly screening properties of newly discovered animal growth hormones, illustrate the state-of-the-art science and technology of genetic engineering, but also serve to raise public awareness of the pros and cons that this young scientific discipline has to offer to mankind

Synergies between Numerical Methods for Kinetic Equations and Neural Networks

The overarching theme of this work is the efficient computation of large-scale systems. Here we deal with two types of mathematical challenges, which are quite different at first glance but offer similar opportunities and challenges upon closer examination. Physical descriptions of phenomena and their mathematical modeling are performed on diverse scales, ranging from nano-scale interactions of single atoms to the macroscopic dynamics of the earth\u27s atmosphere. We consider such systems of interacting particles and explore methods to simulate them efficiently and accurately, with a focus on the kinetic and macroscopic description of interacting particle systems. Macroscopic governing equations describe the time evolution of a system in time and space, whereas the more fine-grained kinetic description additionally takes the particle velocity into account. The study of discretizing kinetic equations that depend on space, time, and velocity variables is a challenge due to the need to preserve physical solution bounds, e.g. positivity, avoiding spurious artifacts and computational efficiency. In the pursuit of overcoming the challenge of computability in both kinetic and multi-scale modeling, a wide variety of approximative methods have been established in the realm of reduced order and surrogate modeling, and model compression. For kinetic models, this may manifest in hybrid numerical solvers, that switch between macroscopic and mesoscopic simulation, asymptotic preserving schemes, that bridge the gap between both physical resolution levels, or surrogate models that operate on a kinetic level but replace computationally heavy operations of the simulation by fast approximations. Thus, for the simulation of kinetic and multi-scale systems with a high spatial resolution and long temporal horizon, the quote by Paul Dirac is as relevant as it was almost a century ago. The first goal of the dissertation is therefore the development of acceleration strategies for kinetic discretization methods, that preserve the structure of their governing equations. Particularly, we investigate the use of convex neural networks, to accelerate the minimal entropy closure method. Further, we develop a neural network-based hybrid solver for multi-scale systems, where kinetic and macroscopic methods are chosen based on local flow conditions. Furthermore, we deal with the compression and efficient computation of neural networks. In the meantime, neural networks are successfully used in different forms in countless scientific works and technical systems, with well-known applications in image recognition, and computer-aided language translation, but also as surrogate models for numerical mathematics. Although the first neural networks were already presented in the 1950s, the scientific discipline has enjoyed increasing popularity mainly during the last 15 years, since only now sufficient computing capacity is available. Remarkably, the increasing availability of computing resources is accompanied by a hunger for larger models, fueled by the common conception of machine learning practitioners and researchers that more trainable parameters equal higher performance and better generalization capabilities. The increase in model size exceeds the growth of available computing resources by orders of magnitude. Since $2012$, the computational resources used in the largest neural network models doubled every $3.4$ months\footnote{\url{https://openai.com/blog/ai-and-compute/}}, opposed to Moore\u27s Law that proposes a $2$-year doubling period in available computing power. To some extent, Dirac\u27s statement also applies to the recent computational challenges in the machine-learning community. The desire to evaluate and train on resource-limited devices sparked interest in model compression, where neural networks are sparsified or factorized, typically after training. The second goal of this dissertation is thus a low-rank method, originating from numerical methods for kinetic equations, to compress neural networks already during training by low-rank factorization. This dissertation thus considers synergies between kinetic models, neural networks, and numerical methods in both disciplines to develop time-, memory- and energy-efficient computational methods for both research areas

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Structural patterns for document engineering: from an empirical bottom-up analysis to an ontological theory

This thesis aims at investigating a new approach to document analysis based on the idea of structural patterns in XML vocabularies. My work is founded on the belief that authors do naturally converge to a reasonable use of markup languages and that extreme, yet valid instances are rare and limited. Actual documents, therefore, may be used to derive classes of elements (patterns) persisting across documents and distilling the conceptualization of the documents and their components, and may give ground for automatic tools and services that rely on no background information (such as schemas) at all. The central part of my work consists in introducing from the ground up a formal theory of eight structural patterns (with three sub-patterns) that are able to express the logical organization of any XML document, and verifying their identifiability in a number of different vocabularies. This model is characterized by and validated against three main dimensions: terseness (i.e. the ability to represent the structure of a document with a small number of objects and composition rules), coverage (i.e. the ability to capture any possible situation in any document) and expressiveness (i.e. the ability to make explicit the semantics of structures, relations and dependencies). An algorithm for the automatic recognition of structural patterns is then presented, together with an evaluation of the results of a test performed on a set of more than 1100 documents from eight very different vocabularies. This language-independent analysis confirms the ability of patterns to capture and summarize the guidelines used by the authors in their everyday practice. Finally, I present some systems that work directly on the pattern-based representation of documents. The ability of these tools to cover very different situations and contexts confirms the effectiveness of the model

The Granite, 1973

The yearbook of the New Hampshire College of Agriculture and the Mechanic Arts, 1909-1922, the University of New Hampshire, 1923-https://scholars.unh.edu/granite_yearbook/1063/thumbnail.jp

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval