Multi-Stage Search Architectures for Streaming Documents

The web is becoming more dynamic due to the increasing engagement and contribution of Internet users in the age of social media. A more dynamic web presents new challenges for web search--an important application of Information Retrieval (IR). A stream of new documents constantly flows into the web at a high rate, adding to the old content. In many cases, documents quickly lose their relevance. In these time-sensitive environments, finding relevant content in response to user queries requires a real-time search service; immediate availability of content for search and a fast ranking, which requires an optimized search architecture. These aspects of today's web are at odds with how academic IR researchers have traditionally viewed the web, as a collection of static documents. Moreover, search architectures have received little attention in the IR literature. Therefore, academic IR research, for the most part, does not provide a mechanism to efficiently handle a high-velocity stream of documents, nor does it facilitate real-time ranking. This dissertation addresses the aforementioned shortcomings. We present an efficient mech- anism to index a stream of documents, thereby enabling immediate availability of content. Our indexer works entirely in main memory and provides a mechanism to control inverted list con- tiguity, thereby enabling faster retrieval. Additionally, we consider document ranking with a machine-learned model, dubbed "Learning to Rank" (LTR), and introduce a novel multi-stage search architecture that enables fast retrieval and allows for more design flexibility. The stages of our architecture include candidate generation (top k retrieval), feature extraction, and docu- ment re-ranking. We compare this architecture with a traditional monolithic architecture where candidate generation and feature extraction occur together. As we lay out our architecture, we present optimizations to each stage to facilitate low-latency ranking. These optimizations include a fast approximate top k retrieval algorithm, document vectors for feature extraction, architecture- conscious implementations of tree ensembles for LTR using predication and vectorization, and algorithms to train tree-based LTR models that are fast to evaluate. We also study the efficiency- effectiveness tradeoffs of these techniques, and empirically evaluate our end-to-end architecture on microblog document collections. We show that our techniques improve efficiency without degrading quality

Runtime Optimizations for Prediction with Tree-Based Models

Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, given an already-trained model. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processor architectures. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures and significantly improve the speed of tree-based models over hard-coded if-else blocks. Our work contributes to the exploration of architecture-conscious runtime implementations of machine learning algorithms

Effects of coir fibres modified with Ca(OH)2 and Mg(OH)2 nanopeprintss on mechanical properties of lime-treated marine clay

In this study, coir fibres were modified with nanoparticles of calcium hydroxide and magnesium hydroxide via a chemical treatment in order to increase the tensile strength of the fibres and their interaction with soil. To evaluate the modification and its effects, unconfined compressive strength tests, indirect tensile strength tests, flexural strength tests and triaxial compressive strength tests were carried out at 7, 28 and 90 days of curing age on lime-treated soil reinforced with modified and unmodified fibres. The obtained results showed that nano-modification of fibres enhanced the mechanical properties of the lime-treated clay soil due to the tensile strength of the augmented fibres. The results showed that the compressive strength, the indirect tensile strength and the flexural strength of samples treated with modified coir fibres increased by 64%, 122% and 56%, respectively compared to that of samples treated with unmodified fibres. Moreover, an increase in the effective internal friction angle and the cohesion intercept was observed. Also, the results of scanning electron microscopy and energy dispersive X-ray confirmed a desired alteration in morphology of the fibres

Reinforcement benefits of nanomodified coir fiber in lime-treated marine clay

In this study, reinforcing effect of nanomodified coir fibers with ferric hydroxide, Fe(OH)3, and aluminum hydroxide, Al(OH)3, on shear strength of limed marine clay soil was investigated. Accordingly, triaxial compression strength (TCS) testing was carried out to determine the shear strength parameters of the reinforced soil. Also, wetting/drying cycle testing was conducted to assess the durability of samples. The results from the experimental investigation show that the lime and nanomodified fibers improved the shear strength and durability through the intended modification on natural coir fiber. Moreover, an increase in the effective stress internal friction angle and the cohesion intercept were observed. To confirm the morphology alteration in fibers, scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) tests were performed. Nanomodification of fibers increased their tensile strength and caused a better interaction with the limed matrix by an enhanced interfacial adhesion. The tensile strength and friction at the interface was the dominant mechanism controlling the reinforcement benefit

Evaluation of antidepressant-like effect of hydroalcoholic extract of Passiflora incarnata in animal models of depression in male mice

Introduction: Passiflora incarnata (PI) is one of the commonest herbal anti-anxiety and sedative agents. The aim of the present study was to investigate the antidepressant effect of hydroalcoholic extract of PI in forced swim test (FST) and tail suspension test (TST) in male mice. Methods: In this experimental study, 48 male mice were randomly divided into 6 groups of 8: Negative and positive control groups received normal saline (10 ml/kg), fluoxetine (20 mg/kg) and imipramine (30 mg/kg), respectively and treatment groups received extracts of PI (200, 400 and 800 mg/kg). Immobility, swimming and climbing behaviors were recorded during 6-min. Results: All doses of PI extract compared to control group significantly reduced the duration of immobility time in both of two tests (p&lt;0.001). Also, these extracts increased swimming time (p&lt;0.001) without significant change of climbing time. Conclusion: PI has considerable antidepressant-like effect in animal models of depression. However, further studies are needed to determine its exact mechanism of action.</p

Run-Time Monitoring of Timing Constraints: A Survey of Methods and Tools

Abstract-Despite the availability of static analysis methods to achieve a correct-by-construction design for different systems in terms of timing behavior, violations of timing constraints can still occur at run-time due to different reasons. The aim of monitoring of system performance with respect to the timing constraints is to detect the violations of timing specifications, or to predict them based on the current system performance data. Considerable work has been dedicated to suggesting efficient performance monitoring approaches during the past years. This paper presents a survey and classification of those approaches in order to help researchers gain a better view over different methods and developments in monitoring of timing behavior of systems. Classifications of the mentioned approaches are given based on different items that are seen as important in developing a monitoring system, i.e., the use of additional hardware, the data collection approach, etc. Moreover, a description of how these different methods work is presented in this paper along with the advantages and downsides of each of them

A transformer model for learning spatiotemporal contextual representation in fMRI data

AbstractRepresentation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative representation can especially benefit the analysis of fMRI data because of the complexities and dynamic dependencies present in such datasets. In this work, we propose a framework based on transformer models to learn an embedding of the fMRI data by taking the spatiotemporal contextual information in the data into account. This approach takes the multivariate BOLD time series of the regions of the brain as well as their functional connectivity network simultaneously as the input to create a set of meaningful features that can in turn be used in various downstream tasks such as classification, feature extraction, and statistical analysis. The proposed spatiotemporal framework uses the attention mechanism as well as the graph convolution neural network to jointly inject the contextual information regarding the dynamics in time series data and their connectivity into the representation. We demonstrate the benefits of this framework by applying it to two resting-state fMRI datasets, and provide further discussion on various aspects and advantages of it over a number of other commonly adopted architectures