OpenRaiser/Intern-Atlas huggingface dataset:
Good Code Sets from Complementary Pairs via Discrete Frequency Chips
Venue: arXiv cs.IT 2017
Year: 2017

It is shown that replacing the sinusoidal chip in Golay complementary code pairs by special classes of waveforms that satisfy two conditions, symmetry/anti-symmetry and quazi-orthogonality in the convolution sense, renders the complementary codes immune to frequency selective fading and also allows for concatenating them in time using one frequency band/channel. This results in a zero-sidelobe region around the mainlobe and an adjacent region of small cross-correlation sidelobes. The symmetry/anti-symmetry property results in the zero-sidelobe region on either side of the mainlobe, while quasi-orthogonality of the two chips keeps the adjacent region of cross-correlations small. Such codes are constructed using discrete frequency-coding waveforms (DFCW) based on linear frequency modulation (LFM) and piecewise LFM (PLFM) waveforms as chips for the complementary code pair, as they satisfy both the symmetry/anti-symmetry and quasi-orthogonality conditions. It is also shown that changing the slopes/chirp rates of the DFCW waveforms (based on LFM and PLFM waveforms) used as chips with the same complementary code pair results in good code sets with a zero-sidelobe region. It is also shown that a second good code set with a zero-sidelobe region could be constructed from the mates of the complementary code pair, while using the same DFCW waveforms as their chips. The cross-correlation between the two sets is shown to contain a zero-sidelobe region and an adjacent region of small cross-correlation sidelobes. Thus, the two sets are quasi-orthogonal and could be combined to form a good code set with twice the number of codes without affecting their cross-correlation properties. Or a better good code set with the same number codes could be constructed by choosing the best candidates form the two sets. Such code sets find utility in multiple input-multiple output (MIMO) radar applications.
From MPI to MPI+OpenACC: Conversion of a legacy FORTRAN PCG solver for the spherical Laplace equation
Venue: arXiv cs.MS 2017
Year: 2017

A real-world example of adding OpenACC to a legacy MPI FORTRAN Preconditioned Conjugate Gradient code is described, and timing results for multi-node multi-GPU runs are shown. The code is used to obtain three-dimensional spherical solutions to the Laplace equation. Its application is finding potential field solutions of the solar corona, a useful tool in space weather modeling. We highlight key tips, strategies, and challenges faced when adding OpenACC. Performance results are shown for running the code with MPI-only on multiple CPUs, and with MPI+OpenACC on multiple GPUs and CPUs.
Implementation and Evaluation of a Framework to calculate Impact Measures for Wikipedia Authors
Venue: arXiv cs.DL 2017
Year: 2017

Wikipedia, an open collaborative website, can be edited by anyone, even anonymously, thus becoming victim to ill-intentioned changes. Therefore, ranking Wikipedia authors by calculating impact measures based on the edit history can help to identify reputational users or harmful activity such as vandalism \cite{Adler:2008:MAC:1822258.1822279}. However, processing millions of edits on one system can take a long time. The author implements an open source framework to calculate such rankings in a distributed way (MapReduce) and evaluates its performance on various sized datasets. A reimplementation of the contribution measures by \citeauthor{Adler:2008:MAC:1822258.1822279} demonstrates its extensibility and usability, as well as problems of handling huge datasets and their possible resolutions. The results put different performance optimizations into perspective and show that horizontal scaling can decrease the total processing time.
Abstraction of Linear Consensus Networks with Guaranteed Systemic Performance Measures
Venue: arXiv cs.SY 2017
Year: 2017

A proper abstraction of a large-scale linear consensus network with a dense coupling graph is one whose number of coupling links is proportional to its number of subsystems and its performance is comparable to the original network. Optimal design problems for an abstracted network are more amenable to efficient optimization algorithms. From the implementation point of view, maintaining such networks are usually more favorable and cost effective due to their reduced communication requirements across a network. Therefore, approximating a given dense linear consensus network by a suitable abstract network is an important analysis and synthesis problem. In this paper, we develop a framework to compute an abstraction of a given large-scale linear consensus network with guaranteed performance bounds using a nearly-linear time algorithm. First, the existence of abstractions of a given network is proven. Then, we present an efficient and fast algorithm for computing a proper abstraction of a given network. Finally, we illustrate the effectiveness of our theoretical findings via several numerical simulations.
Learning Neural Word Salience Scores
Venue: arXiv cs.CL 2017
Year: 2017

Measuring the salience of a word is an essential step in numerous NLP tasks. Heuristic approaches such as tfidf have been used so far to estimate the salience of words. We propose \emph{Neural Word Salience} (NWS) scores, unlike heuristics, are learnt from a corpus. Specifically, we learn word salience scores such that, using pre-trained word embeddings as the input, can accurately predict the words that appear in a sentence, given the words that appear in the sentences preceding or succeeding that sentence. Experimental results on sentence similarity prediction show that the learnt word salience scores perform comparably or better than some of the state-of-the-art approaches for representing sentences on benchmark datasets for sentence similarity, while using only a fraction of the training and prediction times required by prior methods. Moreover, our NWS scores positively correlate with psycholinguistic measures such as concreteness, and imageability implying a close connection to the salience as perceived by humans.
Push-Pull Block Puzzles are Hard
Venue: arXiv cs.CC 2017
Year: 2017

This paper proves that push-pull block puzzles in 3D are PSPACE-complete to solve, and push-pull block puzzles in 2D with thin walls are NP-hard to solve, settling an open question by Zubaran and Ritt. Push-pull block puzzles are a type of recreational motion planning problem, similar to Sokoban, that involve moving a `robot' on a square grid with $1 \times 1$ obstacles. The obstacles cannot be traversed by the robot, but some can be pushed and pulled by the robot into adjacent squares. Thin walls prevent movement between two adjacent squares. This work follows in a long line of algorithms and complexity work on similar problems. The 2D push-pull block puzzle shows up in the video games Pukoban as well as The Legend of Zelda: A Link to the Past, giving another proof of hardness for the latter. This variant of block-pushing puzzles is of particular interest because of its connections to reversibility, since any action (e.g., push or pull) can be inverted by another valid action (e.g., pull or push).
Tensor Representation in High-Frequency Financial Data for Price Change Prediction
Venue: arXiv cs.CE 2017
Year: 2017

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.
Passivity based design of sliding modes for optimal Load Frequency Control
Venue: arXiv cs.SY 2017
Year: 2017

This paper proposes a distributed sliding mode control strategy for optimal Load Frequency Control (OLFC) in power networks, where besides frequency regulation also minimization of generation costs is achieved (economic dispatch). We study a nonlinear power network partitioned into control areas, where each area is modelled by an equivalent generator including voltage and second order turbine-governor dynamics. The turbine-governor dynamics suggest the design of a sliding manifold, such that the turbine-governor system enjoys a suitable passivity property, once the sliding manifold is attained. This work offers a new perspective on OLFC by means of sliding mode control, and in comparison with existing literature, we relax required dissipation conditions on the generation side and assumptions on the system parameters.
OpenSIP: Toward Software-Defined SIP Networking
Venue: arXiv cs.NI 2017
Year: 2017

VoIP is becoming a low-priced and efficient replacement for PSTN in communication industries. With a widely growing adoption rate, SIP is an application layer signaling protocol, standardized by the IETF, for creating, modifying, and terminating VoIP sessions. Generally speaking, SIP routes a call request to its destination by using SIP proxies. With the increasing use of SIP, traditional configurations pose certain drawbacks, such as ineffective routing, un-optimized management of proxy resources (including CPU and memory), and overload conditions. This paper presents OpenSIP to upgrade the SIP network framework with emerging technologies, such as SDN and NFV. SDN provides for management that decouples the data and control planes along with a software-based centralized control that results in effective routing and resource management. Moreover, NFV assists SDN by virtualizing various network devices and functions. However, current SDN elements limit the inspected fields to layer 2-4 headers, whereas SIP routing information resides in the layer-7 header. A benefit of OpenSIP is that it enforces policies on SIP networking that are agnostic to higher layers with the aid of a Deep Packet Inspection (DPI) engine. Among the benefits of OpenSIP is programmability, cost reduction, unified management, routing, as well as efficient load balancing. The present study implements OpenSIP on a real testbed which includes Open vSwitch and the Floodlight controller. The results show that the proposed architecture has a low overhead and satisfactory performance and, in addition, can take advantage of a flexible scale-out design during application deployment.
PSD Estimation of Multiple Sound Sources in a Reverberant Room Using a Spherical Microphone Array
Venue: arXiv cs.SD 2017
Year: 2017

We propose an efficient method to estimate source power spectral densities (PSDs) in a multi-source reverberant environment using a spherical microphone array. The proposed method utilizes the spatial correlation between the spherical harmonics (SH) coefficients of a sound field to estimate source PSDs. The use of the spatial cross-correlation of the SH coefficients allows us to employ the method in an environment with a higher number of sources compared to conventional methods. Furthermore, the orthogonality property of the SH basis functions saves the effort of designing specific beampatterns of a conventional beamformer-based method. We evaluate the performance of the algorithm with different number of sources in practical reverberant and non-reverberant rooms. We also demonstrate an application of the method by separating source signals using a conventional beamformer and a Wiener post-filter designed from the estimated PSDs.
Visualizing and Improving Scattering Networks
Venue: arXiv cs.CV 2017
Year: 2017

Scattering Transforms (or ScatterNets) introduced by Mallat are a promising start into creating a well-defined feature extractor to use for pattern recognition and image classification tasks. They are of particular interest due to their architectural similarity to Convolutional Neural Networks (CNNs), while requiring no parameter learning and still performing very well (particularly in constrained classification tasks). In this paper we visualize what the deeper layers of a ScatterNet are sensitive to using a 'DeScatterNet'. We show that the higher orders of ScatterNets are sensitive to complex, edge-like patterns (checker-boards and rippled edges). These complex patterns may be useful for texture classification, but are quite dissimilar from the patterns visualized in second and third layers of Convolutional Neural Networks (CNNs) - the current state of the art Image Classifiers. We propose that this may be the source of the current gaps in performance between ScatterNets and CNNs (83% vs 93% on CIFAR-10 for ScatterNet+SVM vs ResNet). We then use these visualization tools to propose possible enhancements to the ScatterNet design, which show they have the power to extract features more closely resembling CNNs, while still being well-defined and having the invariance properties fundamental to ScatterNets.
SLO-aware Colocation of Data Center Tasks Based on Instantaneous Processor Requirements
Venue: arXiv cs.DC 2017
Year: 2017

In a cloud data center, a single physical machine simultaneously executes dozens of highly heterogeneous tasks. Such colocation results in more efficient utilization of machines, but, when tasks' requirements exceed available resources, some of the tasks might be throttled down or preempted. We analyze version 2.1 of the Google cluster trace that shows short-term (1 second) task CPU usage. Contrary to the assumptions taken by many theoretical studies, we demonstrate that the empirical distributions do not follow any single distribution. However, high percentiles of the total processor usage (summed over at least 10 tasks) can be reasonably estimated by the Gaussian distribution. We use this result for a probabilistic fit test, called the Gaussian Percentile Approximation (GPA), for standard bin-packing algorithms. To check whether a new task will fit into a machine, GPA checks whether the resulting distribution's percentile corresponding to the requested service level objective, SLO is still below the machine's capacity. In our simulation experiments, GPA resulted in colocations exceeding the machines' capacity with a frequency similar to the requested SLO.
The Devil is in the Tails: Fine-grained Classification in the Wild
Venue: arXiv cs.CV 2017
Year: 2017

The world is long-tailed. What does this mean for computer vision and visual recognition? The main two implications are (1) the number of categories we need to consider in applications can be very large, and (2) the number of training examples for most categories can be very small. Current visual recognition algorithms have achieved excellent classification accuracy. However, they require many training examples to reach peak performance, which suggests that long-tailed distributions will not be dealt with well. We analyze this question in the context of eBird, a large fine-grained classification dataset, and a state-of-the-art deep network classification algorithm. We find that (a) peak classification performance on well-represented categories is excellent, (b) given enough data, classification performance suffers only minimally from an increase in the number of classes, (c) classification performance decays precipitously as the number of training examples decreases, (d) surprisingly, transfer learning is virtually absent in current methods. Our findings suggest that our community should come to grips with the question of long tails.
Knowledge Transfer Between Artificial Intelligence Systems
Venue: arXiv cs.AI 2017
Year: 2017

We consider the fundamental question: how a legacy "student" Artificial Intelligent (AI) system could learn from a legacy "teacher" AI system or a human expert without complete re-training and, most importantly, without requiring significant computational resources. Here "learning" is understood as an ability of one system to mimic responses of the other and vice-versa. We call such learning an Artificial Intelligence knowledge transfer. We show that if internal variables of the "student" Artificial Intelligent system have the structure of an $n$-dimensional topological vector space and $n$ is sufficiently high then, with probability close to one, the required knowledge transfer can be implemented by simple cascades of linear functionals. In particular, for $n$ sufficiently large, with probability close to one, the "student" system can successfully and non-iteratively learn $k\ll n$ new examples from the "teacher" (or correct the same number of mistakes) at the cost of two additional inner products. The concept is illustrated with an example of knowledge transfer from a pre-trained convolutional neural network to a simple linear classifier with HOG features.
Using Cross-Model EgoSupervision to Learn Cooperative Basketball Intention
Venue: arXiv cs.CV 2017
Year: 2017

We present a first-person method for cooperative basketball intention prediction: we predict with whom the camera wearer will cooperate in the near future from unlabeled first-person images. This is a challenging task that requires inferring the camera wearer's visual attention, and decoding the social cues of other players. Our key observation is that a first-person view provides strong cues to infer the camera wearer's momentary visual attention, and his/her intentions. We exploit this observation by proposing a new cross-model EgoSupervision learning scheme that allows us to predict with whom the camera wearer will cooperate in the near future, without using manually labeled intention labels. Our cross-model EgoSupervision operates by transforming the outputs of a pretrained pose-estimation network, into pseudo ground truth labels, which are then used as a supervisory signal to train a new network for a cooperative intention task. We evaluate our method, and show that it achieves similar or even better accuracy than the fully supervised methods do.
360 Panorama Cloning on Sphere
Venue: arXiv cs.GR 2017
Year: 2017

In this paper, we address a novel problem of cloning a patch of the source spherical panoramic image to the target spherical panoramic image, which we call 360 panorama cloning. Considering the sphere geometry constraint embedded in spherical panoramic images, we develop a coordinate-based method that directly clones in the spherical domain. Our method neither differentiates the polar regions and equatorial regions, nor identifies the boundaries in the unrolled planar-formatted panorama. We discuss in depth two unique issues in panorama cloning, i.e. preserving the patch's orientation, and handling the large-patch cloning (covering over 180 field of view) which may suffer from discoloration artifacts. As experimental results demonstrate, our method is able to get visually pleasing cloning results and achieve real time cloning performance.
BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer. However, the improved performance of additional layers in a deep network comes at the cost of added latency and energy usage in feedforward inference. As networks continue to get deeper and larger, these costs become more prohibitive for real-time and energy-sensitive applications. To address this issue, we present BranchyNet, a novel deep network architecture that is augmented with additional side branch classifiers. The architecture allows prediction results for a large portion of test samples to exit the network early via these branches when samples can already be inferred with high confidence. BranchyNet exploits the observation that features learned at an early layer of a network may often be sufficient for the classification of many data points. For more difficult samples, which are expected less frequently, BranchyNet will use further or all network layers to provide the best likelihood of correct prediction. We study the BranchyNet architecture using several well-known networks (LeNet, AlexNet, ResNet) and datasets (MNIST, CIFAR10) and show that it can both improve accuracy and significantly reduce the inference time of the network.
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Venue: arXiv cs.IR 2017
Year: 2017

Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are Long-Short-Term-Memory networks (LSTMs) \cite{hochreiter1997long}. Deep neural networks, due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in real-world, it is hard to get large labeled data, mainly due to heavy cost associated with manual annotation. Towards this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.
Stocator: A High Performance Object Store Connector for Spark
Venue: arXiv cs.DC 2017
Year: 2017

We present Stocator, a high performance object store connector for Apache Spark, that takes advantage of object store semantics. Previous connectors have assumed file system semantics, in particular, achieving fault tolerance and allowing speculative execution by creating temporary files to avoid interference between worker threads executing the same task and then renaming these files. Rename is not a native object store operation; not only is it not atomic, but it is implemented using a costly copy operation and a delete. Instead our connector leverages the inherent atomicity of object creation, and by avoiding the rename paradigm it greatly decreases the number of operations on the object store as well as enabling a much simpler approach to dealing with the eventually consistent semantics typical of object stores. We have implemented Stocator and shared it in open source. Performance testing shows that it is as much as 18 times faster for write intensive workloads and performs as much as 30 times fewer operations on the object store than the legacy Hadoop connectors, reducing costs both for the client and the object storage service provider.