On the Intrinsic Locality Properties of Web Reference Streams

There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged. In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web. Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality. We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi

Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time. In this paper, we present a novel scalable Monte Carlo Tree Search (MCTS) based NAS agent, named AlphaX, to tackle these two aspects. AlphaX improves the search efficiency by adaptively balancing the exploration and exploitation at the state level, and by a Meta-Deep Neural Network (DNN) to predict network accuracies for biasing the search toward a promising region. To amortize the network evaluation cost, AlphaX accelerates MCTS rollouts with a distributed design and reduces the number of epochs in evaluating a network by transfer learning, which is guided with the tree structure in MCTS. In 12 GPU days and 1000 samples, AlphaX found an architecture that reaches 97.84\% top-1 accuracy on CIFAR-10, and 75.5\% top-1 accuracy on ImageNet, exceeding SOTA NAS methods in both the accuracy and sampling efficiency. Particularly, we also evaluate AlphaX on NASBench-101, a large scale NAS dataset; AlphaX is 3x and 2.8x more sample efficient than Random Search and Regularized Evolution in finding the global optimum. Finally, we show the searched architecture improves a variety of vision applications from Neural Style Transfer, to Image Captioning and Object Detection.Comment: To appear in the Thirty-Fourth AAAI conference on Artificial Intelligence (AAAI-2020

El Bronx Imaginario Social: “Habitar 11” Arquitectura + Revitalización

Artículo de investigaciónSe trabaja un artículo de grado donde se analizan los diferentes factores, con el fin de desarrollar un proyecto de grado que parte del concepto “arquitectura + revitalización” en el sector del Bronx en la ciudad de Bogotá. El tema central del desarrollo de la investigación esta basado en proyectar un modelo o prototipo de alojamiento transitorio, a partir de modelos o referentes de vivienda flexible, modular y transitoria, como mecanismo de revitalización arquitectónica, el cual busca volver a dar vida a la ciudad construida, mitigando de forma directa el desconocimiento del contexto, el desplazamiento y la segregación social que atañe al sector.1. RESUMEN 2. INTRODUCCIÓN 3. METODOLOGIA 4. RESULTADOS 5. DISCUSIÓN 6. CONCLUSIONES REFERENCIAS ANEXOSPregradoArquitect

Definition of a support infrastructure for replicating and aggregating families of software engineering experiments

Experimental software engineering includes several processes, the most representative being run experiments, run replications and synthesize the results of multiple replications. Of these processes, only the first is relatively well established in software engineering. Problems of information management and communication among researchers are one of the obstacles to progress in the replication and synthesis processes. Software engineering experimentation has expanded considerably over the last few years. This has brought with it the invention of experimental process support proposals. However, few of these proposals provide integral support, including replication and synthesis processes. Most of the proposals focus on experiment execution. This paper proposes an infrastructure providing integral support for the experimental research process, specializing in the replication and synthesis of a family of experiments. The research has been divided into stages or phases, whose transition milestones are marked by the attainment of their goals. Each goal exactly matches an artifact or product. Within each stage, we will adopt cycles of successive approximations (generateand- test cycles), where each approximation includes a diferent viewpoint or input. Each cycle will end with the product approval

The function of the TGF-beta and Toll signalling pathways in Tribolium dorsoventral patterning

Dorsoventral (DV) patterning in Drosophila melanogaster is one of the most well-known gene regulatory networks (GRN) in biology. To investigate if this GRN is conserved during insect evolution, functional analysis of TGF-beta and Toll pathways in the short-germ beetle Tribolium castaneum was performed. In the first part, the function of several BMP/Dpp extracellular modulators, including the products of Tolloid (Tld) and Twisted-gastrulation/Crossveinless (Tsg-Cv), was investigated in Tribolium via parental RNAi (pRNAi). While Tc-tld pRNAi knock-down decreases embryonic BMP activity, Tc-tsg(cv) knock-down completely abolishes it. These observations are strikingly different from those in Drosophila, where tsg is only required for a subset of Dpp activity. These results suggest that Tsg/Cv-like proteins are essential for BMP signalling in Tribolium. Since duplicated copies of tsg(cv)- and tld-related genes are present in the Drosophila melanogaster genome, duplication followed by sub-functionalization of these modulators might have changed the BMP/Dpp gradient during evolution of the dipteran Drosophila lineage. In the second part, a functional analysis of the Toll pathway was performed. This analysis addressed the question of why the Tribolium Dorsal nuclear gradient is not stable, but rapidly shrinks and disappears, in contrast to the stable Drosophila gradient. Negative feedback accounts for this dynamic behavior: Tc-Dorsal and one of its target genes (Tc-Twist) activate transcription of the I-KappaB homolog Tc-cactus, which in turn terminates Dorsal function. Despite its transient role, Tc-Dorsal is strictly required to initiate DV polarity, as in Drosophila. However, unlike Drosophila, embryos lacking Tc-Dorsal display a periodic pattern of DV cell fates along the AP axis, indicating that a self-organizing ectodermal patterning system operates independently of mesoderm or maternal DV polarity cues. The presence of self-organizing patterning systems in short-germ insects like Tribolium is in agreement with a regulative type of embryogenesis proposed by classical fragmentation studies on hemimetabolous insects. These results also elucidate how extraembryonic tissues are organized in short-germ embryos, and how patterning information is transmitted from the early embryo to the growth zone. Altogether, the functional analysis of the TGF-betA and Toll pathways in Tribolium dorsoventral patterning suggests that extensive changes in this GRN have occurred during insect evolution

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear