OpenRaiser/Intern-Atlas huggingface dataset:
Task Specific Adversarial Cost Function
Venue: arXiv cs.CV 2016
Year: 2016

The cost function used to train a generative model should fit the purpose of the model. If the model is intended for tasks such as generating perceptually correct samples, it is beneficial to maximise the likelihood of a sample drawn from the model, Q, coming from the same distribution as the training data, P. This is equivalent to minimising the Kullback-Leibler (KL) distance, KL[Q||P]. However, if the model is intended for tasks such as retrieval or classification it is beneficial to maximise the likelihood that a sample drawn from the training data is captured by the model, equivalent to minimising KL[P||Q]. The cost function used in adversarial training optimises the Jensen-Shannon entropy which can be seen as an even interpolation between KL[Q||P] and KL[P||Q]. Here, we propose an alternative adversarial cost function which allows easy tuning of the model for either task. Our task specific cost function is evaluated on a dataset of hand-written characters in the following tasks: Generation, retrieval and one-shot learning.
Transforming how water is managed in the West
Venue: arXiv cs.CY 2016
Year: 2016

California is challenged by its worst drought in 600 years and faces future water uncertainty. Pioneering new data infrastructure to integrate water use data across California's more than a thousand water providers will support water managers in ensuring water reliability. The California Data Collaborative is a coalition of municipal water utilities serving ten percent of California's population who are delivering on that promise by centralizing customer water use data in a recently completed pilot project. This project overview describes tools that have shown promising early results in improving water efficiency programs and optimizing system operations. Longer term, these tools will help navigate future uncertainty and support water managers in ensuring water reliability no matter what the future holds. The uniquely publicly-owned data infrastructure deployed in this project is envisioned to enable the world's first "marketplace of civic analytics" to power the volume of water efficiency measurements water managers require at a radically more cost effective price. More broadly, this data-utility approach is adaptable to domains other than water and shows specific potential for the broader universe of natural resources.
Global Fishing Watch: Bringing Transparency to Global Commercial Fisheries
Venue: arXiv cs.CY 2016
Year: 2016

Across all major industrial fishing sectors, overfishing due to overcapacity and lack of compliance in fishery governance has led to a decline in biomass of many global fish stocks. Overfishing threatens ocean biodiversity, global food security, and the livelihoods of law abiding fishermen. To address this issue, Global Fishing Watch (GFW) was created to bring transparency to global fisheries using computer science and big data analytics. A product of a partnership between Oceana, SkyTruth and Google, GFW uses the Automatic Identification System, or AIS, to analyze the movement of vessels at sea. AIS provides vessel location data, and GFW uses this information to track global vessel movement and apply algorithms to classify vessel behavior as "fishing" or "non-fishing" activity. Now publicly available, anyone with an internet connection can monitor when and where trackable commercial fishing appears to be occurring around the world. Hundreds of millions of people around the world depend on our ocean for their livelihoods, and many more rely on it for food. Collectively, the various applications of GFW will help reduce overfishing and illegal fishing, restore the ocean's abundance, and ensure sustainability through better monitoring and governance of our marine resources.
iRescU - Data for Social Good Saving Lives Bridging the Gaps in Sudden Cardiac Arrest Survival
Venue: arXiv cs.CY 2016
Year: 2016

Currently every day in the USA 1000 people die of sudden cardiac arrest (SCA) outside of hospitals or ambulances - before emergency medical help arrives - in the streets, workplaces, schools and homes of our cities, adults and children. Brain death commences in 3 minutes, and often the ambulance just can't be there in time. Citizen cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use can save precious minutes and lives. Using public access AED's saves lives in SCA- however AEDs are used in <2% of cardiac arrests, though could save lives in 80% if available, findable, functioning, and used. The systems problem to solve is that there is no comprehensive or real time accessible database of the AED locations, and also it is not known that they are actually being positioned where they are needed. The iRescU project is designed to bridge this gap in SCA survival, by substantially augmenting the AED database. Utilizing a combination of AED crowd sourcing and geolocation integrated with existing 911 services and SCA events and projected events based on machine learning data information to help make the nearest AED accessible and available in the setting of a SCA emergency and to identify the areas of greatest need for AEDs to be positioned in the community. Helping to save lives and address preventable death with a social good approach and applied big data.
The Subset Assignment Problem for Data Placement in Caches
Venue: arXiv cs.DS 2016
Year: 2016

We introduce the subset assignment problem in which items of varying sizes are placed in a set of bins with limited capacity. Items can be replicated and placed in any subset of the bins. Each (item, subset) pair has an associated cost. Not assigning an item to any of the bins is not free in general and can potentially be the most expensive option. The goal is to minimize the total cost of assigning items to subsets without exceeding the bin capacities. This problem is motivated by the design of caching systems composed of banks of memory with varying cost/performance specifications. The ability to replicate a data item in more than one memory bank can benefit the overall performance of the system with a faster recovery time in the event of a memory failure. For this setting, the number $n$ of data objects (items) is very large and the number $d$ of memory banks (bins) is a small constant (on the order of $3$ or $4$). Therefore, the goal is to determine an optimal assignment in time that minimizes dependence on $n$. The integral version of this problem is NP-hard since it is a generalization of the knapsack problem. We focus on an efficient solution to the LP relaxation as the number of fractionally assigned items will be at most $d$. If the data objects are small with respect to the size of the memory banks, the effect of excluding the fractionally assigned data items from the cache will be small. We give an algorithm that solves the LP relaxation and runs in time $O({3^d \choose d+1} \text{poly}(d) n \log(n) \log(nC) \log(Z))$, where $Z$ is the maximum item size and $C$ the maximum storage cost.
Character Sequence Models for ColorfulWords
Venue: arXiv cs.CL 2016
Year: 2016

We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale color--name pairs obtained from an online color design forum, we evaluate our model on a "color Turing test" and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at colorlab.us.
Graph-Theoretic Approaches to Two-Sender Index Coding
Venue: arXiv cs.IT 2016
Year: 2016

Consider a communication scenario over a noiseless channel where a sender is required to broadcast messages to multiple receivers, each having side information about some messages. In this scenario, the sender can leverage the receivers' side information during the encoding of messages in order to reduce the required transmissions. This type of encoding is called index coding. In this paper, we study index coding with two cooperative senders, each with some subset of messages, and multiple receivers, each requesting one unique message. The index coding in this setup is called two-sender unicast index coding (TSUIC). The main aim of TSUIC is to minimize the total number of transmissions required by the two senders. Based on graph-theoretic approaches, we prove that TSUIC is equivalent to single-sender unicast index coding (SSUIC) for some special cases. Moreover, we extend the existing schemes for SSUIC, viz., the cycle-cover scheme, the clique-cover scheme, and the local-chromatic scheme to the corresponding schemes for TSUIC.
Models of Level-0 Behavior for Predicting Human Behavior in Games
Venue: arXiv cs.GT 2016
Year: 2016

Behavioral game theory seeks to describe the way actual people (as compared to idealized, "rational" agents) act in strategic situations. Our own recent work has identified iterative models (such as quantal cognitive hierarchy) as the state of the art for predicting human play in unrepeated, simultaneous-move games (Wright & Leyton-Brown 2012, 2016). Iterative models predict that agents reason iteratively about their opponents, building up from a specification of nonstrategic behavior called level-0. The modeler is in principle free to choose any description of level-0 behavior that makes sense for the setting. However, almost all existing work specifies this behavior as a uniform distribution over actions. In most games it is not plausible that even nonstrategic agents would choose an action uniformly at random, nor that other agents would expect them to do so. A more accurate model for level-0 behavior has the potential to dramatically improve predictions of human behavior, since a substantial fraction of agents may play level-0 strategies directly, and furthermore since iterative models ground all higher-level strategies in responses to the level-0 strategy. Our work considers models of the way in which level-0 agents construct a probability distribution over actions, given an arbitrary game. Using a Bayesian optimization package called SMAC (Hutter, Hoos, & Leyton-Brown, 2010, 2011, 2012), we systematically evaluated a large space of such models, each of which makes its prediction based only on general features that can be computed from any normal form game. In the end, we recommend a model that achieved excellent performance across the board: a linear weighting of features that requires the estimation of four weights. We evaluated the effects of combining this new level-0 model with several iterative models, and observed large improvements in the models' predictive accuracies.
Better Data Visibility & Data Use Result in Lower Cost and Improved Performance in Medicine Supply Chains
Venue: arXiv cs.CY 2016
Year: 2016

In 2013-2014, Tanzania embarked on a major revamp of the management of its public health supply chains for medicines and other health supplies. These upgrades include the establishment of a national electronic logistics management information system (eLMIS) and the introduction of a Logistics Management Unit (LMU) to use the eLMIS for managing all key public health commodities. This paper describes results from the "round one" evaluation of the impact of those key management upgrades roughly one year after their introduction. The study has three main components: (1) analysis of reporting, data use, management practices, and supply chain outcomes; (2) a cost and cost-effectiveness analysis and (3) a return on investment analysis to measure savings generated by the new systems. The study used a non-experimental pre- and post-design to compare the previous system with the upgraded management system. The quantitative analysis found that stock out rates for all product goods dropped from 32% to 23%, with the frequency of stock-outs greater than 7 days dropping from 24% to 15%. Annual supply chain costs increased from $66million to $76million. Performance improved from the 2014 baseline findings of 68% to 77%, but cost per value of commodities adjusted for performance decreased from 58% at baseline to 50% in year 1.
HyperNetworks
Venue: arXiv cs.LG 2016
Year: 2016

This work explores hypernetworks: an approach of using a one network, also known as a hypernetwork, to generate the weights for another network. Hypernetworks provide an abstraction that is similar to what is found in nature: the relationship between a genotype - the hypernetwork - and a phenotype - the main network. Though they are also reminiscent of HyperNEAT in evolution, our hypernetworks are trained end-to-end with backpropagation and thus are usually faster. The focus of this work is to make hypernetworks useful for deep convolutional networks and long recurrent networks, where hypernetworks can be viewed as relaxed form of weight-sharing across layers. Our main result is that hypernetworks can generate non-shared weights for LSTM and achieve near state-of-the-art results on a variety of sequence modelling tasks including character-level language modelling, handwriting generation and neural machine translation, challenging the weight-sharing paradigm for recurrent networks. Our results also show that hypernetworks applied to convolutional networks still achieve respectable results for image recognition tasks compared to state-of-the-art baseline models while requiring fewer learnable parameters.
MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization
Venue: arXiv cs.DC 2016
Year: 2016

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors $W$ and $H$, for the given input matrix $A$, such that $A \approx W H$. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel algorithms to solve the problem for big data sets. The main contribution of this work is a new, high-performance parallel computational framework for a broad class of NMF algorithms that iteratively solves alternating non-negative least squares (NLS) subproblems for $W$ and $H$. It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). The framework is flexible and able to leverage a variety of NMF and NLS algorithms, including Multiplicative Update, Hierarchical Alternating Least Squares, and Block Principal Pivoting. Our implementation allows us to benchmark and compare different algorithms on massive dense and sparse data matrices of size that spans for few hundreds of millions to billions. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements. The code and the datasets used for conducting the experiments are available online.
Multi Model Data mining approach for Heart failure prediction
Venue: arXiv cs.LG 2016
Year: 2016

Developing predictive modelling solutions for risk estimation is extremely challenging in health-care informatics. Risk estimation involves integration of heterogeneous clinical sources having different representation from different health-care provider making the task increasingly complex. Such sources are typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel computing tools collectively termed big data tools are in need which can synthesize and assist the physician to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel approach for combining the predictive ability of multiple models for better prediction accuracy. We demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study. Results show that the proposed multi-model predictive architecture is able to provide better accuracy than best model approach. By modelling the error of predictive models we are able to choose sub set of models which yields accurate results. More information was modelled into system by multi-level mining which has resulted in enhanced predictive accuracy.
Structure-Aware Classification using Supervised Dictionary Learning
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the data points. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed from the training data or learned and adapted along the dictionary learning process. The combination of these two terms promotes the discriminative power of the learned sparse representations and leads to improved classification accuracy. The proposed method was evaluated on several different datasets, representing both single-label and multi-label classification problems, and demonstrated better performance compared with other dictionary based approaches.
Data Rate for Distributed Consensus of Multi-agent Systems with High Order Oscillator Dynamics
Venue: arXiv cs.SY 2016
Year: 2016

Distributed consensus with data rate constraint is an important research topic of multi-agent systems. Some results have been obtained for consensus of multi-agent systems with integrator dynamics, but it remains challenging for general high-order systems, especially in the presence of unmeasurable states. In this paper, we study the quantized consensus problem for a special kind of high-order systems and investigate the corresponding data rate required for achieving consensus. The state matrix of each agent is a 2m-th order real Jordan block admitting m identical pairs of conjugate poles on the unit circle; each agent has a single input, and only the first state variable can be measured. The case of harmonic oscillators corresponding to m=1 is first investigated under a directed communication topology which contains a spanning tree, while the general case of m >= 2 is considered for a connected and undirected network. In both cases it is concluded that the sufficient number of communication bits to guarantee the consensus at an exponential convergence rate is an integer between $m$ and $2m$, depending on the location of the poles.
Training Dependency Parsers with Partial Annotation
Venue: arXiv cs.CL 2016
Year: 2016

Recently, these has been a surge on studying how to obtain partially annotated data for model supervision. However, there still lacks a systematic study on how to train statistical models with partial annotation (PA). Taking dependency parsing as our case study, this paper describes and compares two straightforward approaches for three mainstream dependency parsers. The first approach is previously proposed to directly train a log-linear graph-based parser (LLGPar) with PA based on a forest-based objective. This work for the first time proposes the second approach to directly training a linear graph-based parse (LGPar) and a linear transition-based parser (LTPar) with PA based on the idea of constrained decoding. We conduct extensive experiments on Penn Treebank under three different settings for simulating PA, i.e., random dependencies, most uncertain dependencies, and dependencies with divergent outputs from the three parsers. The results show that LLGPar is most effective in learning from PA and LTPar lags behind the graph-based counterparts by large margin. Moreover, LGPar and LTPar can achieve best performance by using LLGPar to complete PA into full annotation (FA).
Self-stabilizing Byzantine Clock Synchronization with Optimal Precision
Venue: arXiv cs.DC 2016
Year: 2016

We revisit the approach to Byzantine fault-tolerant clock synchronization based on approximate agreement introduced by Lynch and Welch. Our contribution is threefold: (1) We provide a slightly refined variant of the algorithm yielding improved bounds on the skew that can be achieved and the sustainable frequency offsets. (2) We show how to extend the technique to also synchronize clock rates. This permits less frequent communication without significant loss of precision, provided that clock rates change sufficiently slowly. (3) We present a coupling scheme that allows to make these algorithms self-stabilizing while preserving their high precision. The scheme utilizes a low-precision, but self-stabilizing algorithm for the purpose of recovery.
Redefining Binarization and the Visual Archetype
Venue: arXiv cs.CV 2016
Year: 2016

Although binarization is considered passe, it still remains a highly popular research topic. In this paper we propose a rethinking of what binarization is. We introduce the notion of the visual archetype as the ideal form of any one document. Binarization can be defined as the restoration of the visual archetype for a class of images. This definition broadens the scope of what binarization means but also suggests ground-truth should focus on the foreground.
Keyless authentication in the presence of a simultaneously transmitting adversary
Venue: arXiv cs.IT 2016
Year: 2016

If Alice must communicate with Bob over a channel shared with the adversarial Eve, then Bob must be able to validate the authenticity of the message. In particular we consider the model where Alice and Eve share a discrete memoryless multiple access channel with Bob, thus allowing simultaneous transmissions from Alice and Eve. By traditional random coding arguments, we demonstrate an inner bound on the rate at which Alice may transmit, while still granting Bob the ability to authenticate. Furthermore this is accomplished in spite of Alice and Bob lacking a pre-shared key, as well as allowing Eve prior knowledge of both the codebook Alice and Bob share and the messages Alice transmits.
Approximate Hierarchical Clustering via Sparsest Cut and Spreading Metrics
Venue: arXiv cs.DS 2016
Year: 2016

Dasgupta recently introduced a cost function for the hierarchical clustering of a set of points given pairwise similarities between them. He showed that this function is NP-hard to optimize, but a top-down recursive partitioning heuristic based on an alpha_n-approximation algorithm for uniform sparsest cut gives an approximation of O(alpha_n log n) (the current best algorithm has alpha_n=O(sqrt{log n})). We show that the aforementioned sparsest cut heuristic in fact obtains an O(alpha_n)-approximation for hierarchical clustering. The algorithm also applies to a generalized cost function studied by Dasgupta. Moreover, we obtain a strong inapproximability result, showing that the hierarchical clustering objective is hard to approximate to within any constant factor assuming the Small-Set Expansion (SSE) Hypothesis. Finally, we discuss approximation algorithms based on convex relaxations. We present a spreading metric SDP relaxation for the problem and show that it has integrality gap at most O(sqrt{log n}). The advantage of the SDP relative to the sparsest cut heuristic is that it provides an explicit lower bound on the optimal solution and could potentially yield an even better approximation for hierarchical clustering. In fact our analysis of this SDP served as the inspiration for our improved analysis of the sparsest cut heuristic. We also show that a spreading metric LP relaxation gives an O(log n)-approximation.