The julia content distribution network

Abstract — Peer-to-peer content distribution networks are currently being used widely, drawing upon a large fraction of the Internet bandwidth. Unfortunately, these applications are not designed to be network-friendly. They optimize download time by using all available bandwidth. As a result, long haul bottleneck links are becoming congested and the load on the network is not well balanced. In this paper, we introduce the Julia content distribution network. The innovation of Julia is in its reduction of the overall communication cost, which in turn improves network load balance and reduces the usage of long haul links. Compared with the state-of-the-art BitTorrent content distribution network, we find that while Julia achieves slightly slower average finishing times relative to BitTorrent, Julia nevertheless reduces the total communication cost in the network by approximately 33%. Furthermore, the Julia protocol achieves a better load balancing of the network resources, especially over trans-Atlantic links. We evaluated the Julia protocol using real WAN deployment and by extensive simulation. The WAN experimentation was carried over the PlanetLab wide area testbed using over 250 machines. Simulations were performed using the the GT-ITM topology generator with 1200 nodes. A surprisingly good match was exhibited between the two evaluation methods (itself an interesting result), an encouraging indication of the ability of our simulation to predict scaling behavior. I

BioSimulator.jl: Stochastic simulation in Julia

Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, $\tau$-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. BioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.Comment: 27 pages, 5 figures, 3 table

Formalising the multidimensional nature of social networks

Individuals interact with conspecifics in a number of behavioural contexts or dimensions. Here, we formalise this by considering a social network between n individuals interacting in b behavioural dimensions as a nxnxb multidimensional object. In addition, we propose that the topology of this object is driven by individual needs to reduce uncertainty about the outcomes of interactions in one or more dimension. The proposal grounds social network dynamics and evolution in individual selection processes and allows us to define the uncertainty of the social network as the joint entropy of its constituent interaction networks. In support of these propositions we use simulations and natural 'knock-outs' in a free-ranging baboon troop to show (i) that such an object can display a small-world state and (ii) that, as predicted, changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalisation of social networks provides a framework within which to predict network dynamics and evolution under the assumption that it is driven by individuals seeking to reduce the uncertainty of their social environment.Comment: 16 pages, 4 figure

#ausvotes: How Twitter covered the 2010 Australian federal election

While the 2007 Australian federal election was notable for the use of social media by the Australian Labor Party in campaigning, the 2010 election took place in a media landscape in which social media–especially Twitter–had become much more embedded in both political journalism and independent political commentary. This article draws on the computer-aided analysis of election-related Twitter messages, collected under the #ausvotes hashtag, to describe the key patterns of activity and thematic foci of the election’s coverage in this particular social media site. It introduces novel metrics for analysing public communication via Twitter, and describes the related methods. What emerges from this analysis is the role of the #ausvotes hashtag as a means of gathering an ad hoc ‘issue public’– a finding which is likely to be replicated for other hashtag communities

Clue: Cross-modal Coherence Modeling for Caption Generation

We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image--caption coherence relations, we annotate 10,000 instances from publicly-available image--caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.Comment: Accepted as a long paper to ACL 202

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu/~junhua.mao/m-RNN.html .Comment: Add a simple strategy to boost the performance of image captioning task significantly. More details are shown in Section 8 of the paper. The code and related data are available at https://github.com/mjhucla/mRNN-CR ;. arXiv admin note: substantial text overlap with arXiv:1410.109

A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms. To this end, we developed "ForneyLab" (https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message passing-based inference in FFGs. We show by example how ForneyLab enables automatic derivation of Bayesian signal processing algorithms, including algorithms for parameter estimation and model comparison. Crucially, due to the modular makeup of the FFG framework, both the model specification and inference methods are readily extensible in ForneyLab. In order to test this framework, we compared variational message passing as implemented by ForneyLab with automatic differentiation variational inference (ADVI) and Monte Carlo methods as implemented by state-of-the-art tools "Edward" and "Stan". In terms of performance, extensibility and stability issues, ForneyLab appears to enjoy an edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate Reasonin