OpenRaiser/Intern-Atlas huggingface dataset:
Topic-focused Dynamic Information Filtering in Social Media
Venue: arXiv cs.IR 2015
Year: 2015

With the quick development of online social media such as twitter or sina weibo in china, many users usually track hot topics to satisfy their desired information need. For a hot topic, new opinions or ideas will be continuously produced in the form of online data stream. In this scenario, how to effectively filter and display information for a certain topic dynamically, will be a critical problem. We call the problem as Topic-focused Dynamic Information Filtering (denoted as TDIF for short) in social media. In this paper, we start open discussions on such application problems. We first analyze the properties of the TDIF problem, which usually contains several typical requirements: relevance, diversity, recency and confidence. Recency means that users want to follow the recent opinions or news. Additionally, the confidence of information must be taken into consideration. How to balance these factors properly in online data stream is very important and challenging. We propose a dynamic preservation strategy on the basis of an existing feature-based utility function, to solve the TDIF problem. Additionally, we propose new dynamic diversity measures, to get a more reasonable evaluation for such application problems. Extensive exploratory experiments have been conducted on TREC public twitter dataset, and the experimental results validate the effectiveness of our approach.
On the gap between RIP-properties and sparse recovery conditions
Venue: arXiv cs.IT 2015
Year: 2015

We consider the problem of recovering sparse vectors from underdetermined linear measurements via $\ell_p$-constrained basis pursuit. Previous analyses of this problem based on generalized restricted isometry properties have suggested that two phenomena occur if $p\neq 2$. First, one may need substantially more than $s \log(en/s)$ measurements (optimal for $p=2$) for uniform recovery of all $s$-sparse vectors. Second, the matrix that achieves recovery with the optimal number of measurements may not be Gaussian (as for $p=2$). We present a new, direct analysis which shows that in fact neither of these phenomena occur. Via a suitable version of the null space property we show that a standard Gaussian matrix provides $\ell_q/\ell_1$-recovery guarantees for $\ell_p$-constrained basis pursuit in the optimal measurement regime. Our result extends to several heavier-tailed measurement matrices. As an application, we show that one can obtain a consistent reconstruction from uniform scalar quantized measurements in the optimal measurement regime.
Route Planning in Transportation Networks
Venue: arXiv cs.DS 2015
Year: 2015

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.
Deep Spatial Pyramid: The Devil is Once Again in the Details
Venue: arXiv cs.CV 2015
Year: 2015

In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achieve a simple, efficient, yet highly accurate image classification system. We first list 5 important factors, based on both existing researches and ideas proposed in this paper. These important detailed factors include: 1) $\ell_2$ matrix normalization is more effective than unnormalized or $\ell_2$ vector normalization, 2) the proposed natural deep spatial pyramid is very effective, and 3) a very small $K$ in Fisher Vectors surprisingly achieves higher accuracy than normally used large $K$ values. Along with other choices (convolutional activations and multiple scales), the proposed DSP framework is not only intuitive and efficient, but also achieves excellent classification accuracy on many benchmark datasets. For example, DSP's accuracy on SUN397 is 59.78%, significantly higher than previous state-of-the-art (53.86%).
Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms
Venue: arXiv cs.DS 2015
Year: 2015

Tensor rank and low-rank tensor decompositions have many applications in learning and complexity theory. Most known algorithms use unfoldings of tensors and can only handle rank up to $n^{\lfloor p/2 \rfloor}$ for a $p$-th order tensor in $\mathbb{R}^{n^p}$. Previously no efficient algorithm can decompose 3rd order tensors when the rank is super-linear in the dimension. Using ideas from sum-of-squares hierarchy, we give the first quasi-polynomial time algorithm that can decompose a random 3rd order tensor decomposition when the rank is as large as $n^{3/2}/\textrm{polylog} n$. We also give a polynomial time algorithm for certifying the injective norm of random low rank tensors. Our tensor decomposition algorithm exploits the relationship between injective norm and the tensor components. The proof relies on interesting tools for decoupling random variables to prove better matrix concentration bounds, which can be useful in other settings.
The adaptable buffer algorithm for high quantile estimation in non-stationary data streams
Venue: arXiv cs.CV 2015
Year: 2015

The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many semi-automatic surveillance analytics systems which detect abnormalities in close-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the memory for storing observations is limited. We make several major contributions: (i) we derive an important theoretical result which shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data, (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings, (iii) we introduce a novel algorithm which implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity, and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic data sets and three large `real-world' streams acquired in the course of operation of an existing commercial surveillance system. Our findings convincingly demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high valued and the available buffer capacity severely limited.
On the Role of Conductance, Geography and Topology in Predicting Hashtag Virality
Venue: arXiv cs.SI 2015
Year: 2015

We focus on three aspects of the early spread of a hashtag in order to predict whether it will go viral: the network properties of the subset of users tweeting the hashtag, its geographical properties, and, most importantly, its conductance-related properties. One of our significant contributions is to discover the critical role played by the conductance based features for the successful prediction of virality. More specifically, we show that the first derivative of the conductance gives an early indication of whether the hashtag is going to go viral or not. We present a detailed experimental evaluation of the effect of our various categories of features on the virality prediction task. When compared to the baselines and the state of the art techniques proposed in the literature our feature set is able to achieve significantly better accuracy on a large dataset of 7.7 million users and all their tweets over a period of month, as well as on existing datasets.
Weighted Independent Sets in a Subclass of $P_6$-free Graphs
Venue: arXiv cs.DM 2015
Year: 2015

The Maximum Weight Independent Set (MWIS) problem on graphs with vertex weights asks for a set of pairwise nonadjacent vertices of maximum total weight. The complexity of the MWIS problem for $P_6$-free graphs is unknown. In this note, we show that the MWIS problem can be solved in time $O(n^3m)$ for ($P_6$, banner)-free graphs by analyzing the structure of subclasses of these class of graphs. This extends the existing results for ($P_5$, banner)-free graphs, and ($P_6$, $C_4$)-free graphs. Here, $P_t$ denotes the chordless path on $t$ vertices, and a banner is the graph obtained from a chordless cycle on four vertices by adding a vertex that has exactly one neighbor on the cycle.
An Anonymous Social Network of Opinions
Venue: arXiv cs.SI 2015
Year: 2015

Research interest on Online Social Networks (OSNs), has increased dramatically over the last decade, mainly because online networks provide a vast source of social information. Graph structure, user connections, growth, information exposure and diffusion, are some of the most frequently researched subjects. However, some areas of these networks, such as anonymity, equality and bias are overlooked or even unconsidered. In the related bibliography, such features seem to be influential to social interactions. Based on these studies, we aim at determining how universal anonymity affects bias, user equality, information propagation, sharing and exposure, connection establishment, as well as network structure. Thus, we propose a new Anonymous Online Social Network, which will facilitate a variety of monitoring and data analysis.
A Generalized Spatial Correlation Model for 3D MIMO Channels based on the Fourier Coefficients of Power Spectrums
Venue: arXiv cs.IT 2015
Year: 2015

Previous studies have confirmed the adverse impact of fading correlation on the mutual information (MI) of two-dimensional (2D) multiple-input multiple-output (MIMO) systems. More recently, the trend is to enhance the system performance by exploiting the channel's degrees of freedom in the elevation, which necessitates the derivation and characterization of three-dimensional (3D) channels in the presence of spatial correlation. In this paper, an exact closed-form expression for the Spatial Correlation Function (SCF) is derived for 3D MIMO channels. This novel SCF is developed for a uniform linear array of antennas with nonisotropic antenna patterns. The proposed method resorts to the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre and associated Legendre polynomials. The resulting expression depends on the underlying arbitrary angular distributions and antenna patterns through the Fourier Series (FS) coefficients of power azimuth and elevation spectrums. The novelty of the proposed method lies in the SCF being valid for any 3D propagation environment. The developed SCF determines the covariance matrices at the transmitter and the receiver that form the Kronecker channel model. In order to quantify the effects of correlation on the system performance, the information-theoretic deterministic equivalents of the MI for the Kronecker model are utilized in both mono-user and multi-user cases. Numerical results validate the proposed analytical expressions and elucidate the dependence of the system performance on azimuth and elevation angular spreads and antenna patterns. Some useful insights into the behaviour of MI as a function of downtilt angles are provided. The derived model will help evaluate the performance of correlated 3D MIMO channels in the future.
A Proof of the Convergence of the Hegselmann-Krause Dynamics on the Circle
Venue: arXiv cs.SY 2015
Year: 2015

In this note, we give a complete proof that Hegselmann-Krause systems converge on the circle following the proof strategy developed by Hegarty, Martinsson, and Wedin.
Efficient Approximation Algorithms for Computing \emph{k} Disjoint Restricted Shortest Paths
Venue: arXiv cs.DM 2015
Year: 2015

Network applications, such as multimedia streaming and video conferencing, impose growing requirements over Quality of Service (QoS), including bandwidth, delay, jitter, etc. Meanwhile, networks are expected to be load-balanced, energy-efficient, and resilient to some degree of failures. It is observed that the above requirements could be better met with multiple disjoint QoS paths than a single one. Let $G=(V,\, E)$ be a digraph with nonnegative integral cost and delay on every edge, $s,\, t\in V$ be two specified vertices, and $D\in\mathbb{Z}_{0}^{+}$ be a delay bound (or some other constraint), the \emph{$k$ Disjoint Restricted Shortest Path} ($k$\emph{RSP})\emph{ Problem} is computing $k$ disjoint paths between $s$ and $t$ with total cost minimized and total delay bounded by $D$. Few efficient algorithms have been developed because of the hardness of the problem. In this paper, we propose efficient algorithms with provable performance guarantees for the $k$RSP problem. We first present a pseudo-polynomial-time approximation algorithm with a bifactor approximation ratio of $(1,\,2)$, then improve the algorithm to polynomial time with a bifactor ratio of $(1+ε,\,2+ε)$ for any fixed $ε>0$, which is better than the current best approximation ratio $(O(1+γ),\, O(1+\frac{1}γ)\})$ for any fixed $γ>0$ \cite{orda2004efficient}. To the best of our knowledge, this is the first constant-factor algorithm that almost strictly obeys the constraint for the $k$RSP problem.
Joint Source-Channel Secrecy Using Hybrid Coding
Venue: arXiv cs.IT 2015
Year: 2015

The secrecy performance of a source-channel model is studied in the context of lossy source compression over a noisy broadcast channel. The source is causally revealed to the eavesdropper during decoding. The fidelity of the transmission to the legitimate receiver and the secrecy performance at the eavesdropper are both measured by a distortion metric. Two achievability schemes using the technique of hybrid coding are analyzed and compared with an operationally separate source-channel coding scheme. A numerical example is provided and the comparison results show that the hybrid coding schemes outperform the operationally separate scheme.
On Fortification of Projection Games
Venue: arXiv cs.CC 2015
Year: 2015

A recent result of Moshkovitz \cite{Moshkovitz14} presented an ingenious method to provide a completely elementary proof of the Parallel Repetition Theorem for certain projection games via a construction called fortification. However, the construction used in \cite{Moshkovitz14} to fortify arbitrary label cover instances using an arbitrary extractor is insufficient to prove parallel repetition. In this paper, we provide a fix by using a stronger graph that we call fortifiers. Fortifiers are graphs that have both $\ell_1$ and $\ell_2$ guarantees on induced distributions from large subsets. We then show that an expander with sufficient spectral gap, or a bi-regular extractor with stronger parameters (the latter is also the construction used in an independent update \cite{Moshkovitz15} of \cite{Moshkovitz14} with an alternate argument), is a good fortifier. We also show that using a fortifier (in particular $\ell_2$ guarantees) is necessary for obtaining the robustness required for fortification.
Lossy Compression with Privacy Constraints: Optimality of Polar Codes
Venue: arXiv cs.IT 2015
Year: 2015

A lossy source coding problem with privacy constraint is studied in which two correlated discrete sources $X$ and $Y$ are compressed into a reconstruction $\hat{X}$ with some prescribed distortion $D$. In addition, a privacy constraint is specified as the equivocation between the lossy reconstruction $\hat{X}$ and $Y$. This models the situation where a certain amount of source information from one user is provided as utility (given by the fidelity of its reconstruction) to another user or the public, while some other correlated part of the source information $Y$ must be kept private. In this work, we show that polar codes are able, possibly with the aid of time sharing, to achieve any point in the optimal rate-distortion-equivocation region identified by Yamamoto, thus providing a constructive scheme that obtains the optimal tradeoff between utility and privacy in this framework.
Median and Mode Ellipse Parameterization for Robust Contour Fitting
Venue: arXiv cs.CV 2015
Year: 2015

Problems that require the parameterization of closed contours arise frequently in computer vision applications. This article introduces a new curve parameterization algorithm that is able to fit a closed curve to a set of points while being robust to the presence of outliers and occlusions in the data. This robustness property makes this algorithm applicable to computer vision applications where misclassification of features may lead to outliers. The algorithm starts by fitting ellipses to numerous five point subsets from the source data. The closed curve is parameterized by determining the median perimeter of the set of ellipses. The resulting curve is not an ellipse, allowing arbitrary closed contours to be parameterized. The use of the modal perimeter rather than the median perimeter is also explored. A detailed comparison is made between the proposed curve fitting algorithm and existing robust ellipse fitting algorithms. Finally, the utility of the algorithm for computer vision applications is demonstrated through the parameterization of the boundary of fuel droplets during combustion. The performance of the proposed algorithm and the performance of existing algorithms are compared to a ground truth segmentation of the fuel droplet images, which demonstrates improved performance for both area quantification and edge deviation.
Content-Centric Multicast Beamforming in Cache-Enabled Cloud Radio Access Networks
Venue: arXiv cs.IT 2015
Year: 2015

Multicast transmission and wireless caching are effective ways of reducing air and backhaul traffic load in wireless networks. This paper proposes to incorporate these two key ideas for content-centric multicast transmission in a cloud radio access network (RAN) where multiple base stations (BSs) are connected to a central processor (CP) via finite-capacity backhaul links. Each BS has a cache with finite storage size and is equipped with multiple antennas. The BSs cooperatively transmit contents, which are either stored in the local cache or fetched from the CP, to multiple users in the network. Users requesting a same content form a multicast group and are served by a same cluster of BSs cooperatively using multicast beamforming. Assuming fixed cache placement, this paper investigates the joint design of multicast beamforming and content-centric BS clustering by formulating an optimization problem of minimizing the total network cost under the quality-of-service (QoS) constraints for each multicast group. The network cost involves both the transmission power and the backhaul cost. We model the backhaul cost using the mixed $\ell_0/\ell_2$-norm of beamforming vectors. To solve this non-convex problem, we first approximate it using the semidefinite relaxation (SDR) method and concave smooth functions. We then propose a difference of convex functions (DC) programming algorithm to obtain suboptimal solutions and show the connection of three smooth functions. Simulation results validate the advantage of multicasting and show the effects of different cache size and caching policies in cloud RAN.
Service-Oriented Logic Programming
Venue: arXiv cs.LO 2015
Year: 2015

We develop formal foundations for notions and mechanisms needed to support service-oriented computing. Our work builds on recent theoretical advancements in the algebraic structures that capture the way services are orchestrated and in the processes that formalize the discovery and binding of services to given client applications by means of logical representations of required and provided services. We show how the denotational and the operational semantics specific to conventional logic programming can be generalized using the theory of institutions to address both static and dynamic aspects of service-oriented computing. Our results rely upon a strong analogy between the discovery of a service that can be bound to an application and the search for a clause that can be used for computing an answer to a query; they explore the manner in which requests for external services can be described as service queries, and explain how the computation of their answers can be performed through service-oriented derivatives of unification and resolution, which characterize the binding of services and the reconfiguration of applications.
Compute-and-Forward Can Buy Secrecy Cheap
Venue: arXiv cs.CR 2015
Year: 2015

We consider a Gaussian multiple access channel with $K$ transmitters, a (intended) receiver and an external eavesdropper. The transmitters wish to reliably communicate with the receiver while concealing their messages from the eavesdropper. This scenario has been investigated in prior works using two different coding techniques; the random i.i.d. Gaussian coding and the signal alignment coding. Although, the latter offers promising results in a very high SNR regime, extending these results to the finite SNR regime is a challenging task. In this paper, we propose a new lattice alignment scheme based on the compute-and-forward framework which works at any finite SNR. We show that our achievable secure sum rate scales with $\log(\mathrm{SNR})$ and hence, in most SNR regimes, our scheme outperforms the random coding scheme in which the secure sum rate does not grow with power. Furthermore, we show that our result matches the prior work in the infinite SNR regime. Additionally, we analyze our result numerically.