OpenRaiser/Intern-Atlas huggingface dataset:
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++
Venue: arXiv cs.MS 2015
Year: 2015

As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important. The implementation of the derivatives that make these algorithms so powerful, however, is a substantial user burden and the practicality of these algorithms depends critically on tools like automatic differentiation that remove the implementation burden entirely. The Stan Math Library is a C++, reverse-mode automatic differentiation library designed to be usable, extensive and extensible, efficient, scalable, stable, portable, and redistributable in order to facilitate the construction and utilization of such algorithms. Usability is achieved through a simple direct interface and a cleanly abstracted functional interface. The extensive built-in library includes functions for matrix operations, linear algebra, differential equation solving, and most common probability functions. Extensibility derives from a straightforward object-oriented framework for expressions, allowing users to easily create custom functions. Efficiency is achieved through a combination of custom memory management, subexpression caching, traits-based metaprogramming, and expression templates. Partial derivatives for compound functions are evaluated lazily for improved scalability. Stability is achieved by taking care with arithmetic precision in algebraic expressions and providing stable, compound functions where possible. For portability, the library is standards-compliant C++ (03) and has been tested for all major compilers for Windows, Mac OS X, and Linux.
IllinoisSL: A JAVA Library for Structured Prediction
Venue: arXiv cs.LG 2015
Year: 2015

IllinoisSL is a Java library for learning structured prediction models. It supports structured Support Vector Machines and structured Perceptron. The library consists of a core learning module and several applications, which can be executed from command-lines. Documentation is provided to guide users. In Comparison to other structured learning libraries, IllinoisSL is efficient, general, and easy to use.
Synchronous Subsequentiality and Approximations to Undecidable Problems
Venue: arXiv cs.FL 2015
Year: 2015

We introduce the class of synchronous subsequential relations, a subclass of the synchronous relations which embodies some properties of subsequential relations. If we take relations of this class as forming the possible transitions of an infinite automaton, then most decision problems (apart from membership) still remain undecidable (as they are for synchronous and subsequential rational relations), but on the positive side, they can be approximated in a meaningful way we make precise in this paper. This might make the class useful for some applications, and might serve to establish an intermediate position in the trade-off between issues of expressivity and (un)decidability.
Computing the Geodesic Centers of a Polygonal Domain
Venue: arXiv cs.CG 2015
Year: 2015

We present an algorithm that computes the geodesic center of a given polygonal domain. The running time of our algorithm is $O(n^{12+ε})$ for any $ε>0$, where $n$ is the number of corners of the input polygonal domain. Prior to our work, only the very special case where a simple polygon is given as input has been intensively studied in the 1980s, and an $O(n \log n)$-time algorithm is known by Pollack et al. Our algorithm is the first one that can handle general polygonal domains having one or more polygonal holes.
Sparsity-based Correction of Exponential Artifacts
Venue: arXiv cs.LG 2015
Year: 2015

This paper describes an exponential transient excision algorithm (ETEA). In biomedical time series analysis, e.g., in vivo neural recording and electrocorticography (ECoG), some measurement artifacts take the form of piecewise exponential transients. The proposed method is formulated as an unconstrained convex optimization problem, regularized by smoothed l1-norm penalty function, which can be solved by majorization-minimization (MM) method. With a slight modification of the regularizer, ETEA can also suppress more irregular piecewise smooth artifacts, especially, ocular artifacts (OA) in electroencephalog- raphy (EEG) data. Examples of synthetic signal, EEG data, and ECoG data are presented to illustrate the proposed algorithms.
An IMS DSL Developed at Ericsson
Venue: arXiv cs.SE 2015
Year: 2015

In this paper, we present how we created a Domain Specific Language (DSL) dedicated to IP Multimedia Subsystem (IMS) at Ericsson. First, we introduce IMS and how developers are burdened by its complexity when integrating it in their application. Then we describe the principles we followed to create our new IMS DSL from its core in the Scala language to its syntax. We then present how we integrated it in two existing projects and show how it can save time for developers and how readable the syntax of the IMS DSL is.
Composite charging games in networks of electric vehicles
Venue: arXiv cs.GT 2015
Year: 2015

An important scenario for smart grids which encompass distributed electrical networks is given by the simultaneous presence of aggregators and individual consumers. In this work, an aggregator is seen as an entity (a coalition) which is able to manage jointly the energy demand of a large group of consumers or users. More precisely, the demand consists in charging an electrical vehicle (EV) battery. The way the EVs user charge their batteries matters since it strongly impacts the network, especially the distribution network costs (e.g., in terms of Joule losses or transformer ageing). Since the charging policy is chosen by the users or the aggregators, the charging problem is naturally distributed. It turns out that one of the tools suited to tackle this heterogenous scenario has been introduced only recently namely, through the notion of composite games. This paper exploits for the first time in the literature of smart grids the notion of composite game and equilibrium. By assuming a rectangular charging profile for an EV, a composite equilibrium analysis is conducted, followed by a detailed analysis of a case study which assumes three possible charging periods or time-slots. Both the provided analytical and numerical results allow one to better understand the relationship between the size (which is a measure) of the coalition and the network sum-cost. In particular, a social dilemma, a situation where everybody prefers unilaterally defecting to cooperating, while the consequence is the worst for all, is exhibited.
Speech Dereverberation in the STFT Domain
Venue: arXiv cs.SD 2015
Year: 2015

Reverberation is damaging to both the quality and the intelligibility of a speech signal. We propose a novel single-channel method of dereverberation based on a linear filter in the Short Time Fourier Transform domain. Each enhanced frame is constructed from a linear sum of nearby frames based on the channel impulse response. The results show that the method can resolve any reverberant signal with knowledge of the impulse response to a non-reverberant signal.
Adaptive Sequential Optimization with Applications to Machine Learning
Venue: arXiv cs.LG 2015
Year: 2015

A framework is introduced for solving a sequence of slowly changing optimization problems, including those arising in regression and classification applications, using optimization algorithms such as stochastic gradient descent (SGD). The optimization problems change slowly in the sense that the minimizers change at either a fixed or bounded rate. A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples needed from the distributions underlying each problem in order to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. Experiments with synthetic and real data are used to confirm that this approach performs well.
Sparsification Upper and Lower Bounds for Graph Problems and Not-All-Equal SAT
Venue: arXiv cs.CC 2015
Year: 2015

We present several sparsification lower and upper bounds for classic problems in graph theory and logic. For the problems 4-Coloring, (Directed) Hamiltonian Cycle, and (Connected) Dominating Set, we prove that there is no polynomial-time algorithm that reduces any n-vertex input to an equivalent instance, of an arbitrary problem, with bitsize O(n^{2-e}) for e > 0, unless NP is in coNP/poly and the polynomial-time hierarchy collapses. These results imply that existing linear-vertex kernels for k-Nonblocker and k-Max Leaf Spanning Tree (the parametric duals of (Connected) Dominating Set) cannot be improved to have O(k^{2-e}) edges, unless NP is in coNP/poly. We also present a positive result and exhibit a non-trivial sparsification algorithm for d-Not-All-Equal SAT. We give an algorithm that reduces an n-variable input with clauses of size at most d to an equivalent input with O(n^{d-1}) clauses, for any fixed d. Our algorithm is based on a linear-algebraic proof of Lovasz that bounds the number of hyperedges in critically 3-chromatic d-uniform n-vertex hypergraphs by n choose d-1. We show that our kernel is tight under the assumption that NP is not a subset of coNP/poly.
Minimax Regret 1-Median Problem in Dynamic Path Networks
Venue: arXiv cs.DS 2015
Year: 2015

This paper considers the minimax regret 1-median problem in dynamic path networks. In our model, we are given a dynamic path network consisting of an undirected path with positive edge lengths, uniform positive edge capacity, and nonnegative vertex supplies. Here, each vertex supply is unknown but only an interval of supply is known. A particular assignment of supply to each vertex is called a scenario. Given a scenario s and a sink location x in a dynamic path network, let us consider the evacuation time to x of a unit supply given on a vertex by s. The cost of x under s is defined as the sum of evacuation times to x for all supplies given by s, and the median under s is defined as a sink location which minimizes this cost. The regret for x under s is defined as the cost of x under s minus the cost of the median under s. Then, the problem is to find a sink location such that the maximum regret for all possible scenarios is minimized. We propose an O(n^3) time algorithm for the minimax regret 1-median problem in dynamic path networks with uniform capacity, where n is the number of vertices in the network.
A Path Generation Approach to Embedding of Virtual Networks
Venue: arXiv cs.NI 2015
Year: 2015

As the virtualization of networks continues to attract attention from both industry and academia, the Virtual Network Embedding (VNE) problem remains a focus of researchers. This paper proposes a one-shot, unsplittable flow VNE solution based on column generation. We start by formulating the problem as a path-based mathematical program called the primal, for which we derive the corresponding dual problem. We then propose an initial solution which is used, first, by the dual problem and then by the primal problem to obtain a final solution. Unlike most approaches, our focus is not only on embedding accuracy but also on the scalability of the solution. In particular, the one-shot nature of our formulation ensures embedding accuracy, while the use of column generation is aimed at enhancing the computation time to make the approach more scalable. In order to assess the performance of the proposed solution, we compare it against four state of the art approaches as well as the optimal link-based formulation of the one-shot embedding problem. Experiments on a large mix of Virtual Network (VN) requests show that our solution is near optimal (achieving about 95% of the acceptance ratio of the optimal solution), with a clear improvement over existing approaches in terms of VN acceptance ratio and average Substrate Network (SN) resource utilization, and a considerable improvement (92% for a SN of 50 nodes) in time complexity compared to the optimal solution.
Warp: Lightweight Multi-Key Transactions for Key-Value Stores
Venue: arXiv cs.DC 2015
Year: 2015

Traditional NoSQL systems scale by sharding data across multiple servers and by performing each operation on a small number of servers. Because transactions on multiple keys necessarily require coordination across multiple servers, NoSQL systems often explicitly avoid making transactional guarantees in order to avoid such coordination. Past work on transactional systems control this coordination by either increasing the granularity at which transactions are ordered, sacrificing serializability, or by making clock synchronicity assumptions. This paper presents a novel protocol for providing serializable transactions on top of a sharded data store. Called acyclic transactions, this protocol allows multiple transactions to prepare and commit simultaneously, improving concurrency in the system, while ensuring that no cycles form between concurrently-committing transactions. We have fully implemented acyclic transactions in a document store called Warp. Experiments show that Warp achieves 4 times higher throughput than Sinfonia's mini-transactions on the standard TPC-C benchmark with no aborts. Further, the system achieves 75% of the throughput of the non-transactional key-value store it builds upon.
An Efficient Local Strategy to Control Information Spreading in Network
Venue: arXiv cs.SI 2015
Year: 2015

In social networks, control of rumor spread is an active area of research. SIR model is generally used to study the rumor dynamics in network while considering the rumor as an epidemic. In disease spreading model, epidemic is controlled by removing central nodes in the network. Full network information is needed for such removal. To have the information of complete network is difficult proposition. As a consequence, the search of an algorithm that may control epidemic without needing global information is a matter of great interest. In this paper, an immunization strategy is proposed that uses only local information available at a node, viz. degree of the node and average degree of its neighbour nodes. Proposed algorithm has been evaluated for scale-free network using SIR model. Numerical results show that proposed method has less complexity and gives significantly better results in comparison with other strategies while using only local information.
Approaching Single-Hop Performance in Multi-Hop Networks: End-To-End Known-Interference Cancellation (E2E-KIC)
Venue: arXiv cs.NI 2015
Year: 2015

To improve the efficiency of wireless data communications, new physical-layer transmission methods based on known-interference cancellation (KIC) have been developed. These methods share the common idea that the interference can be cancelled when the content of it is known. Existing work on KIC mainly focuses on single-hop or two-hop networks, with physical-layer network coding (PNC) and full-duplex (FD) communications as typical examples. This paper extends the idea of KIC to general multi-hop networks, and proposes an end-to-end KIC (E2E-KIC) transmission method together with its MAC design. With E2E-KIC, multiple nodes in a flow passing through a few nodes in an arbitrary topology can simultaneously transmit and receive on the same channel. We first present a theoretical analysis on the effectiveness of E2E-KIC in an idealized case. Then, to support E2E-KIC in multi-hop networks with arbitrary topology, we propose an E2E-KIC-supported MAC protocol (E2E-KIC MAC), which is based on an extension of the Request-to-Send/Clear-to-Send (RTS/CTS) mechanism in the IEEE 802.11 MAC. We also analytically analyze the performance of the proposed E2E-KIC MAC in the presence of hidden terminals. Simulation results illustrate that the proposed E2E-KIC MAC protocol can improve the network throughput and reduce the end-to-end delay.
Deformation Lamps: A Projection Technique to Make a Static Object Dynamic
Venue: arXiv cs.GR 2015
Year: 2015

Light projection is a powerful technique to edit appearances of objects in the real world. Based on pixel-wise modification of light transport, previous techniques have successfully modified static surface properties such as surface color, dynamic range, gloss and shading. Here, we propose an alternative light projection technique that adds a variety of illusory, yet realistic distortions to a wide range of static 2D and 3D projection targets. The key idea of our technique, named Deformation Lamps, is to project only dynamic luminance information, which effectively activates the motion (and shape) processing in the visual system, while preserving the color and texture of the original object. Although the projected dynamic luminance information is spatially inconsistent with the color and texture of the target object, the observer's brain automatically com- bines these sensory signals in such a way as to correct the inconsistency across visual attributes. We conducted a psychophysical experiment to investigate the characteristics of the inconsistency correction, and found that the correction was dependent critically on the retinal magnitude of inconsistency. Another experiment showed that perceived magnitude of image deformation by our techniques was underestimated. The results ruled out the possibility that the effect by our technique stemmed simply from the physical change of object appearance by light projection. Finally, we discuss how our techniques can make the observers perceive a vivid and natural movement, deformation, or oscillation of a variety of static objects, including drawn pictures, printed photographs, sculptures with 3D shading, objects with natural textures including human bodies.
Exploiting Multi-Hop Relaying to Overcome Blockage in Directional mmWave Small Cells
Venue: arXiv cs.NI 2015
Year: 2015

With vast amounts of spectrum available in the millimeter wave (mmWave) band, small cells at mmWave frequencies densely deployed underlying the conventional homogeneous macrocell network have gained considerable interest from academia, industry, and standards bodies. Due to high propagation loss at higher frequencies, mmWave communications are inherently directional, and concurrent transmissions (spatial reuse) under low inter-link interference can be enabled to significantly improve network capacity. On the other hand, mmWave links are easily blocked by obstacles such as human body and furniture. In this paper, we develop a Multi-Hop Relaying Transmission scheme, termed as MHRT, to steer blocked flows around obstacles by establishing multi-hop relay paths. InMHRT, a relay path selection algorithmis proposed to establish relay paths for blocked flows for better use of concurrent transmissions. After relay path selection, we use a multi-hop transmission scheduling algorithm to compute near-optimal schedules by fully exploiting the spatial reuse. Through extensive simulations under various traffic patterns and channel conditions, we demonstrate MHRT achieves superior performance in terms of network throughput and connection robustness compared with other existing protocols, especially under serious blockage conditions. The performance of MHRT with different hop limitations is also simulated and analyzed for a better choice of themaximum hop number in practice.
End-to-End Text-Dependent Speaker Verification
Venue: arXiv cs.LG 2015
Year: 2015

In this paper we present a data-driven, integrated approach to speaker verification, which maps a test utterance and a few reference utterances directly to a single score for verification and jointly optimizes the system's components using the same evaluation protocol and metric as at test time. Such an approach will result in simple and efficient systems, requiring little domain-specific knowledge and making few model assumptions. We implement the idea by formulating the problem as a single neural network architecture, including the estimation of a speaker model on only a few utterances, and evaluate it on our internal "Ok Google" benchmark for text-dependent speaker verification. The proposed approach appears to be very effective for big data applications like ours that require highly accurate, easy-to-maintain systems with a small footprint.
Detecting Overlapping Communities from Local Spectral Subspaces
Venue: arXiv cs.SI 2015
Year: 2015

Based on the definition of local spectral subspace, we propose a novel approach called LOSP for local overlapping community detection. Instead of using the invariant subspace spanned by the dominant eigenvectors of the entire network, we run the power method for a few steps to approximate the leading eigenvectors that depict the embedding of the local neighborhood structure around seeds of interest. We then seek a sparse approximate indicator vector in the local spectral subspace spanned by these vectors such that the seeds are in its support. We evaluate LOSP on five large real world networks across various domains with labeled ground-truth communities and compare the results with the state-of-the-art community detection approaches. LOSP identifies the members of a target community with high accuracy from very few seed members, and outperforms the local Heat Kernel or PageRank diffusions as well as the global baselines. Two candidate definitions of the local spectral subspace are analyzed, and different community scoring functions for determining the community boundary, including two new metrics, are thoroughly evaluated. The structural properties of different seed sets and the impact of the seed set size are discussed. We observe low degree seeds behave better, and LOSP is robust even when started from a single random seed. Using LOSP as a subroutine and starting from each ego connected component, we try the harder yet significant task of identifying all communities a single vertex is in. Experiments show that the proposed method achieves high F1 measures on the detected multiple local overlapping communities containing the seed vertex.
Moss: A Scalable Tool for Efficiently Sampling and Counting 4- and 5-Node Graphlets
Venue: arXiv cs.SI 2015
Year: 2015

Counting the frequencies of 3-, 4-, and 5-node undirected motifs (also know as graphlets) is widely used for understanding complex networks such as social and biology networks. However, it is a great challenge to compute these metrics for a large graph due to the intensive computation. Despite recent efforts to count triangles (i.e., 3-node undirected motif counting), little attention has been given to developing scalable tools that can be used to characterize 4- and 5-node motifs. In this paper, we develop computational efficient methods to sample and count 4- and 5- node undirected motifs. Our methods provide unbiased estimators of motif frequencies, and we derive simple and exact formulas for the variances of the estimators. Moreover, our methods are designed to fit vertex centric programming models, so they can be easily applied to current graph computing systems such as Pregel and GraphLab. We conduct experiments on a variety of real-word datasets, and experimental results show that our methods are several orders of magnitude faster than the state-of-the-art methods under the same estimation errors.
Representation Benefits of Deep Feedforward Networks
Venue: arXiv cs.LG 2015
Year: 2015

This note provides a family of classification problems, indexed by a positive integer $k$, where all shallow networks with fewer than exponentially (in $k$) many nodes exhibit error at least $1/6$, whereas a deep network with 2 nodes in each of $2k$ layers achieves zero error, as does a recurrent network with 3 distinct nodes iterated $k$ times. The proof is elementary, and the networks are standard feedforward networks with ReLU (Rectified Linear Unit) nonlinearities.