Sparse spatial selection for novelty-based search result diversification

Abstract. Novelty-based diversification approaches aim to produce a diverse ranking by directly comparing the retrieved documents. However, since such approaches are typically greedy, they require O(n 2) documentdocument comparisons in order to diversify a ranking of n documents. In this work, we propose to model novelty-based diversification as a similarity search in a sparse metric space. In particular, we exploit the triangle inequality property of metric spaces in order to drastically reduce the number of required document-document comparisons. Thorough experiments using three TREC test collections show that our approach is at least as effective as existing novelty-based diversification approaches, while improving their efficiency by an order of magnitude.

Modelling Efficient Novelty-based Search Result Diversification in Metric Spaces

Novelty-based diversification provides a way to tackle ambiguous queries by re-ranking a set of retrieved documents. Current approaches are typically greedy, requiring O(n2) document–document comparisons in order to diversify a ranking of n documents. In this article, we introduce a new approach for novelty-based search result diversification to reduce the overhead incurred by document–document comparisons. To this end, we model novelty promotion as a similarity search in a metric space, exploiting the properties of this space to efficiently identify novel documents. We investigate three different approaches: pivoting-based, clustering-based, and permutation-based. In the first two, a novel document is one that lies outside the range of a pivot or outside a cluster. In the latter, a novel document is one that has a different signature (i.e., the documentʼs relative distance to a distinguished set of fixed objects called permutants) compared to previously selected documents. Thorough experiments using two TREC test collections for diversity evaluation, as well as a large sample of the query stream of a commercial search engine show that our approaches perform at least as effectively as well-known novelty-based diversification approaches in the literature, while dramatically improving their efficiency.Fil: Gil Costa, Graciela Verónica. Yahoo; México. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis; ArgentinaFil: Santos, Rodrygo L. T.. University Of Glasgow; Reino UnidoFil: Macdonald, Craig. University Of Glasgow; Reino UnidoFil: Ounis, Iadh. University Of Glasgow; Reino Unid

Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions

Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future. It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities. Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community\u27s desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications

A Survey on Intent-based Diversification for Fuzzy Keyword Search

Keyword search is an interesting phenomenon, it is the process of finding important and relevant information from various data repositories. Structured and semistructured data can precisely be stored. Fully unstructured documents can annotate and be stored in the form of metadata. For the total web search, half of the web search is for information exploration process. In this paper, the earlier works for semantic meaning of keywords based on their context in the specified documents are thoroughly analyzed. In a tree data representation, the nodes are objects and could hold some intention. These nodes act as anchors for a Smallest Lowest Common Ancestor (SLCA) based pruning process. Based on their features, nodes are clustered. The feature is a distinctive attribute, it is the quality, property or traits of something. Automatic text classification algorithms are the modern way for feature extraction. Summarization and segmentation produce n consecutive grams from various forms of documents. The set of items which describe and summarize one important aspect of a query is known as the facet. Instead of exact string matching a fuzzy mapping based on semantic correlation is the new trend, whereas the correlation is quantified by cosine similarity. Once the outlier is detected, nearest neighbors of the selected points are mapped to the same hash code of the intend nodes with high probability. These methods collectively retrieve the relevant data and prune out the unnecessary data, and at the same time create a hash signature for the nearest neighbor search. This survey emphasizes the need for a framework for fuzzy oriented keyword search

Novel candidate genes underlying extreme trophic specialization in Caribbean pupfishes

The genetic changes responsible for evolutionary transitions from generalist to specialist phenotypes are poorly understood. Here we examine the genetic basis of craniofacial traits enabling novel trophic specialization in a sympatric radiation of Cyprinodon pupfishes endemic to San Salvador Island, Bahamas. This recent radiation consists of a generalist species and two novel specialists: a small-jawed "snail-eater" and a large-jawed "scale-eater." We genotyped 12 million single nucleotide polymorphisms (SNPs) by whole-genome resequencing of 37 individuals of all three species from nine populations and integrated genome-wide divergence scans with association mapping to identify divergent regions containing putatively causal SNPs affecting jaw size-the most rapidly diversifying trait in this radiation. A mere 22 fixed variants accompanied extreme ecological divergence between generalist and scale-eater species. We identified 31 regions (20 kb) containing variants fixed between specialists that were significantly associated with variation in jaw size which contained 11 genes annotated for skeletal system effects and 18 novel candidate genes never previously associated with craniofacial phenotypes. Six of these 31 regions showed robust signs of hard selective sweeps after accounting for demographic history. Our data are consistent with predictions based on quantitative genetic models of adaptation, suggesting that the effect sizes of regions influencing jaw phenotypes are positively correlated with distance between fitness peaks on a complex adaptive landscape

Functional segregation of hippocampal subdivisions in learning and memory

The hippocampus is well known for its function in declarative memories, especially in episodic memories and spatial navigation. Considering strikingly different features along its longitudinal axis from dorsal to ventral hippocampus, it has been proposed that hippocampal subdivisions might have distinct functional roles. Among several hypotheses, a particular prominent one is that dorsal hippocampus is required for cognitive functions, while ventral hippocampus is involved in emotional learning and stress responses, but their precise roles in learning and memory have remained controversial. In this thesis, I further explore the idea of a functional segregation, focusing on the roles of dorsal and ventral hippocampus in different types of declarative memories. Therefore, I use chemogenetic silencing to locally interfere with subdivision function in reinforced and incidental learning at various time points after memory acquisition and at memory retrieval. First, I compare the functions of dorsal and ventral hippocampus in single-trial learning. Then, I am addressing their roles in the formation of associations to previously acquired memories. Moreover, applying chemogenetic silencing and powerful recently developed techniques to genetically target learning-related neuronal populations, I study the localization of single-trial and association memories within the hippocampus. I show how in all hippocampus-dependent tasks both dorsal and ventral hippocampus is required, but with distinct contributions and irrespective of emotional relevance. Specifically, ventral hippocampus is involved in forming and recalling primary associations, whereas dorsal hippocampus is particularly important during a window of 5h post new learning. During this window dorsal hippocampus recalls memories and forms secondary associations learned on top of previously acquired memories. Thereby, the subdivisions provide a mechanism to recall previously acquired memories and to form associations to them without interference of memories, but instead with the possibility to independently use the distinct memory components. In a supplementary part, I have started to investigate the function of the transversal hippocampal axis, in particular the dentate gyrus in association learning. This study allows a first insight into a possible mechanism that might shape memory assemblies to form associations