OpenRaiser/Intern-Atlas huggingface dataset:
Classification with Sparse Overlapping Groups
Venue: arXiv cs.LG 2014
Year: 2014

Classification with a sparsity constraint on the solution plays a central role in many high dimensional machine learning applications. In some cases, the features can be grouped together so that entire subsets of features can be selected or not selected. In many applications, however, this can be too restrictive. In this paper, we are interested in a less restrictive form of structured sparse feature selection: we assume that while features can be grouped according to some notion of similarity, not all features in a group need be selected for the task at hand. When the groups are comprised of disjoint sets of features, this is sometimes referred to as the "sparse group" lasso, and it allows for working with a richer class of models than traditional group lasso methods. Our framework generalizes conventional sparse group lasso further by allowing for overlapping groups, an additional flexiblity needed in many applications and one that presents further challenges. The main contribution of this paper is a new procedure called Sparse Overlapping Group (SOG) lasso, a convex optimization program that automatically selects similar features for classification in high dimensions. We establish model selection error bounds for SOGlasso classification problems under a fairly general setting. In particular, the error bounds are the first such results for classification using the sparse group lasso. Furthermore, the general SOGlasso bound specializes to results for the lasso and the group lasso, some known and some new. The SOGlasso is motivated by multi-subject fMRI studies in which functional activity is classified using brain voxels as features, source localization problems in Magnetoencephalography (MEG), and analyzing gene activation patterns in microarray data analysis. Experiments with real and synthetic data demonstrate the advantages of SOGlasso compared to the lasso and group lasso.
Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves
Venue: arXiv cs.LG 2014
Year: 2014

Unsupervised ranking faces one critical challenge in evaluation applications, that is, no ground truth is available. When PageRank and its variants show a good solution in related subjects, they are applicable only for ranking from link-structure data. In this work, we focus on unsupervised ranking from multi-attribute data which is also common in evaluation tasks. To overcome the challenge, we propose five essential meta-rules for the design and assessment of unsupervised ranking approaches: scale and translation invariance, strict monotonicity, linear/nonlinear capacities, smoothness, and explicitness of parameter size. These meta-rules are regarded as high level knowledge for unsupervised ranking tasks. Inspired by the works in [8] and [14], we propose a ranking principal curve (RPC) model, which learns a one-dimensional manifold function to perform unsupervised ranking tasks on multi-attribute observations. Furthermore, the RPC is modeled to be a cubic Bézier curve with control points restricted in the interior of a hypercube, thereby complying with all the five meta-rules to infer a reasonable ranking list. With control points as the model parameters, one is able to understand the learned manifold and to interpret the ranking list semantically. Numerical experiments of the presented RPC model are conducted on two open datasets of different ranking applications. In comparison with the state-of-the-art approaches, the new model is able to show more reasonable ranking lists.
On the Average Performance of Caching and Coded Multicasting with Random Demands
Venue: arXiv cs.IT 2014
Year: 2014

For a network with one sender, $n$ receivers (users) and $m$ possible messages (files), caching side information at the users allows to satisfy arbitrary simultaneous demands by sending a common (multicast) coded message. In the worst-case demand setting, explicit deterministic and random caching strategies and explicit linear coding schemes have been shown to be order optimal. In this work, we consider the same scenario where the user demands are random i.i.d., according to a Zipf popularity distribution. In this case, we pose the problem in terms of the minimum average number of equivalent message transmissions. We present a novel decentralized random caching placement and a coded delivery scheme which are shown to achieve order-optimal performance. As a matter of fact, this is the first order-optimal result for the caching and coded multicasting problem in the case of random demands.
On the distinctness of binary sequences derived from $2$-adic expansion of m-sequences over finite prime fields
Venue: arXiv cs.IT 2014
Year: 2014

Let $p$ be an odd prime with $2$-adic expansion $\sum_{i=0}^kp_i\cdot2^i$. For a sequence $\underline{a}=(a(t))_{t\ge 0}$ over $\mathbb{F}_{p}$, each $a(t)$ belongs to $\{0,1,\ldots, p-1\}$ and has a unique $2$-adic expansion $$a(t)=a_0(t)+a_1(t)\cdot 2+\cdots+a_{k}(t)\cdot2^k,$$ with $a_i(t)\in\{0, 1\}$. Let $\underline{a_i}$ denote the binary sequence $(a_i(t))_{t\ge 0}$ for $0\le i\le k$. Assume $i_0$ is the smallest index $i$ such that $p_{i}=0$ and $\underline{a}$ and $\underline{b}$ are two different m-sequences generated by a same primitive characteristic polynomial over $\mathbb{F}_p$. We prove that for $i\neq i_0$ and $0\le i\le k$, $\underline{a_i}=\underline{b_i}$ if and only if $\underline{a}=\underline{b}$, and for $i=i_0$, $\underline{a_{i_0}}=\underline{b_{i_0}}$ if and only if $\underline{a}=\underline{b}$ or $\underline{a}=-\underline{b}$. Then the period of $\underline{a_i}$ is equal to the period of $\underline{a}$ if $i\ne i_0$ and half of the period of $\underline{a}$ if $i=i_0$. We also discuss a possible application of the binary sequences $\underline{a_i}$.
Passive Dynamics in Mean Field Control
Venue: arXiv cs.SY 2014
Year: 2014

Mean-field models are a popular tool in a variety of fields. They provide an understanding of the impact of interactions among a large number of particles or people or other "self-interested agents", and are an increasingly popular tool in distributed control. This paper considers a particular randomized distributed control architecture introduced in our own recent work. In numerical results it was found that the associated mean-field model had attractive properties for purposes of control. In particular, when viewed as an input-output system, its linearization was found to be minimum phase. In this paper we take a closer look at the control model. The results are summarized as follows: (i) The Markov Decision Process framework of Todorov is extended to continuous time models, in which the "control cost" is based on relative entropy. This is the basis of the construction of a family of controlled Markovian generators. (ii) A decentralized control architecture is proposed in which each agent evolves as a controlled Markov process. A central authority broadcasts a common control signal to each agent. The central authority chooses this signal based on an aggregate scalar output of the Markovian agents. (iii) Provided the control-free system is a reversible Markov process, the following identity holds for the linearization, \[ \text{Real} (G(jω)) = \text{PSD}_Y(ω)\ge 0, \quad ω\in\Re, \] where the right hand side denotes the power spectral density for the output of any one of the individual (control-free) Markov processes.
Cyber Behavior of Microblog Users: Onlies Versus Others
Venue: arXiv cs.CY 2014
Year: 2014

Much research has been conducted to investigate personality and daily behavior of these only children ('Onlies') due to the Chinese one-child-per-family policy, and report the singleton generation to be more selfish. As Microblog becomes increasingly popular recently in China, we studied cyber behavior of Onlies and children with siblings ('Others') on Sina Microblog ('Weibo'), a leading Microblog service provider in China. Participants were 1792 Weibo users. Their recorded data on Weibo were downloaded to assess their cyber behaviors. The general results show that (1) Onlies have a smaller social circle; (2)Onlies are more significantly active on social platform.
On the Degrees-of-freedom of the 3-user MISO Broadcast Channel with Hybrid CSIT
Venue: arXiv cs.IT 2014
Year: 2014

The 3-user multiple-input single-output (MISO) broadcast channel (BC) with hybrid channel state information at the transmitter (CSIT) is considered. In this framework, there is perfect and instantaneous CSIT from a subset of users and delayed CSIT from the remaining users. We present new results on the degrees of freedom (DoF) of the 3-user MISO BC with hybrid CSIT. In particular, for the case of 2 transmit antennas, we show that with perfect CSIT from one user and delayed CSIT from the remaining two users, the optimal DoF is 5/3. For the case of 3 transmit antennas and the same hybrid CSIT setting, it is shown that a higher DoF of 9/5 is achievable and this result improves upon the best known bound. Furthermore, with 3 transmit antennas, and the hybrid CSIT setting in which there is perfect CSIT from two users and delayed CSIT from the third one, a novel scheme is presented which achieves 9/4 DoF. Our results also reveal new insights on how to utilize hybrid channel knowledge for multi-user scenarios.
PuppetDroid: A User-Centric UI Exerciser for Automatic Dynamic Analysis of Similar Android Applications
Venue: arXiv cs.CR 2014
Year: 2014

Popularity and complexity of malicious mobile applications are rising, making their analysis difficult and labor intensive. Mobile application analysis is indeed inherently different from desktop application analysis: In the latter, the interaction of the user (i.e., victim) is crucial for the malware to correctly expose all its malicious behaviors. We propose a novel approach to analyze (malicious) mobile applications. The goal is to exercise the user interface (UI) of an Android application to effectively trigger malicious behaviors, automatically. Our key intuition is to record and reproduce the UI interactions of a potential victim of the malware, so as to stimulate the relevant behaviors during dynamic analysis. To make our approach scale, we automatically re-execute the recorded UI interactions on apps that are similar to the original ones. These characteristics make our system orthogonal and complementary to current dynamic analysis and UI-exercising approaches. We developed our approach and experimentally shown that our stimulation allows to reach a higher code coverage than automatic UI exercisers, so to unveil interesting malicious behaviors that are not exposed when using other approaches. Our approach is also suitable for crowdsourcing scenarios, which would push further the collection of new stimulation traces. This can potentially change the way we conduct dynamic analysis of (mobile) applications, from fully automatic only, to user-centric and collaborative too.
Vesselness via Multiple Scale Orientation Scores
Venue: arXiv cs.CV 2014
Year: 2014

The multi-scale Frangi vesselness filter is an established tool in (retinal) vascular imaging. However, it cannot cope with crossings or bifurcations, since it only looks for elongated structures. Therefore, we disentangle crossing structures in the image via (multiple scale) invertible orientation scores. The described vesselness filter via scale-orientation scores performs considerably better at enhancing vessels throughout crossings and bifurcations than the Frangi version. Both methods are evaluated on a public dataset. Performance is measured by comparing ground truth data to the segmentation results obtained by basic thresholding and morphological component analysis of the filtered images.
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity
Venue: arXiv cs.CV 2014
Year: 2014

We propose a new method, {\it binary fused compressive sensing} (BFCS), to recover sparse piece-wise smooth signals from 1-bit compressive measurements. The proposed algorithm is a modification of the previous {\it binary iterative hard thresholding} (BIHT) algorithm, where, in addition to the sparsity constraint, the total-variation of the recovered signal is upper constrained. As in BIHT, the data term of the objective function is an one-sided $\ell_1$ (or $\ell_2$) norm. Experiments on the recovery of sparse piece-wise smooth signals show that the proposed algorithm is able to take advantage of the piece-wise smoothness of the original signal, achieving more accurate recovery than BIHT.
Analysing Membership Profile Privacy Issues in Online Social Networks
Venue: arXiv cs.SI 2014
Year: 2014

A social networking site is an on-line service that attracts a society of subscribers and provides such users with a multiplicity of tools for distribution personal data and creating subscribers generated content directed to a given users interest and personal life. Operators of online social networks are gradually giving out potentially sensitive information about users and their relationships with advertisers, application developers, and data-mining researchers. Some criminals too uses information gathered through membership profile in social networks to break peoples PINs and passwords. In this paper, we looked at the field structure of membership profiles in ten popular social networking sites. We also analysed how private information can easily be made public in such sites. At the end recommendations and countermeasures were made on how to safe guard subscribers personal data.
Study of the Dynamic Coupling Term (μ) in Parallel Force/Velocity Actuated Systems
Venue: arXiv cs.RO 2014
Year: 2014

Presented in this paper is an actuator concept, called a Parallel Force/Velocity Actuator (PFVA), that combines two fundamentally distinct actuators (one using low gear reduction or even direct drive, which we will call a Force Actuator (FA) and the other with a high reduction gear train that we will refer to as a Velocity Actuator (VA)). The objective of this work is to evaluate the effect of the relative scale factor, RSF, (ratio of gear reductions) between these inputs on their dynamic coupling. We conceptually describe a Parallel Force/Velocity Actuator (PFVA) based on a Dual-Input-Single- Output (DISO) epicyclic gear train. We then present an analytical formulation for the variation of the dynamic coupling term w.r.t. RSF. Conclusions from this formulation are illustrated through a numerical example involving a 1-DOF four-bar linkage. It is shown, both analytically and numerically, that as we increase the RSF, the two inputs to the PFVA are decoupled w.r.t. the inertia torques. This understanding can serve as an important design guideline for PFVAs. The paper also presents two limitations of this study and suggests future work based on these caveats.
Revisiting Trace and Testing Equivalences for Nondeterministic and Probabilistic Processes
Venue: arXiv cs.LO 2014
Year: 2014

Two of the most studied extensions of trace and testing equivalences to nondeterministic and probabilistic processes induce distinctions that have been questioned and lack properties that are desirable. Probabilistic trace-distribution equivalence differentiates systems that can perform the same set of traces with the same probabilities, and is not a congruence for parallel composition. Probabilistic testing equivalence, which relies only on extremal success probabilities, is backward compatible with testing equivalences for restricted classes of processes, such as fully nondeterministic processes or generative/reactive probabilistic processes, only if specific sets of tests are admitted. In this paper, new versions of probabilistic trace and testing equivalences are presented for the general class of nondeterministic and probabilistic processes. The new trace equivalence is coarser because it compares execution probabilities of single traces instead of entire trace distributions, and turns out to be compositional. The new testing equivalence requires matching all resolutions of nondeterminism on the basis of their success probabilities, rather than comparing only extremal success probabilities, and considers success probabilities in a trace-by-trace fashion, rather than cumulatively on entire resolutions. It is fully backward compatible with testing equivalences for restricted classes of processes; as a consequence, the trace-by-trace approach uniformly captures the standard probabilistic testing equivalences for generative and reactive probabilistic processes. The paper discusses in full details the new equivalences and provides a simple spectrum that relates them with existing ones in the setting of nondeterministic and probabilistic processes.
Circulant graphs and GCD and LCM of Subsets
Venue: arXiv cs.CC 2014
Year: 2014

Given two sets $A$ and $B$ of integers, we consider the problem of finding a set $S \subseteq A$ of the smallest possible cardinality such the greatest common divisor of the elements of $S \cup B$ equals that of those of $A \cup B$. The particular cases of $B = \emptyset$ and $\#B = 1$ are of special interest and have some links with graph theory. We also consider the corresponding question for the least common multiple of the elements. We establish NP-completeness and approximation results for these problems by relating them to the Minimum Cover Problem.
Information Aggregation in Exponential Family Markets
Venue: arXiv cs.AI 2014
Year: 2014

We consider the design of prediction market mechanisms known as automated market makers. We show that we can design these mechanisms via the mold of \emph{exponential family distributions}, a popular and well-studied probability distribution template used in statistics. We give a full development of this relationship and explore a range of benefits. We draw connections between the information aggregation of market prices and the belief aggregation of learning agents that rely on exponential family distributions. We develop a very natural analysis of the market behavior as well as the price equilibrium under the assumption that the traders exhibit risk aversion according to exponential utility. We also consider similar aspects under alternative models, such as when traders are budget constrained.
A Hybrid Estimation of Distribution Algorithm with Random Walk local Search for Multi-mode Resource-Constrained Project Scheduling problems
Venue: arXiv cs.OH 2014
Year: 2014

Multi-mode resource-constrained project scheduling problems (MRCPSPs) are classified as NP-hard problems, in which a task has different execution modes characterized by different resource requirements. Estimation of distribution algorithm (EDA) has shown an effective performance for solving such real-world optimization problems but it fails to find the desired optima. This paper integrates a novel hybrid local search technique with EDA to enhance their local search ability. The new local search is based on delete-then-insert operator and a random walk (DIRW) to enhance exploitation abilities of EDA in the neighborhoods of the search space. The proposed algorithm is capable to explore and exploit the search mechanism in the search space through its outer and inner loops. The proposed algorithm is tested and evaluated using benchmark test problems of the project scheduling problem library PSPLIB. Simulation results of the proposed algorithm are compared with the classical EDA algorithm. The obtained results showed that the effectiveness of the proposed algorithm and outperformed the compared EDA algorithm.
Dynamic Rate and Channel Selection in Cognitive Radio Systems
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we investigate dynamic channel and rate selection in cognitive radio systems which exploit a large number of channels free from primary users. In such systems, transmitters may rapidly change the selected (channel, rate) pair to opportunistically learn and track the pair offering the highest throughput. We formulate the problem of sequential channel and rate selection as an online optimization problem, and show its equivalence to a {\it structured} Multi-Armed Bandit problem. The structure stems from inherent properties of the achieved throughput as a function of the selected channel and rate. We derive fundamental performance limits satisfied by {\it any} channel and rate adaptation algorithm, and propose algorithms that achieve (or approach) these limits. In turn, the proposed algorithms optimally exploit the inherent structure of the throughput. We illustrate the efficiency of our algorithms using both test-bed and simulation experiments, in both stationary and non-stationary radio environments. In stationary environments, the packet successful transmission probabilities at the various channel and rate pairs do not evolve over time, whereas in non-stationary environments, they may evolve. In practical scenarios, the proposed algorithms are able to track the best channel and rate quite accurately without the need of any explicit measurement and feedback of the quality of the various channels.
On Power and Load Coupling in Cellular Networks for Energy Optimization
Venue: arXiv cs.IT 2014
Year: 2014

We consider the problem of minimization of sum transmission energy in cellular networks where coupling occurs between cells due to mutual interference. The coupling relation is characterized by the signal-to-interference-and-noise-ratio (SINR) coupling model. Both cell load and transmission power, where cell load measures the average level of resource usage in the cell, interact via the coupling model. The coupling is implicitly characterized with load and power as the variables of interest using two equivalent equations, namely, non-linear load coupling equation (NLCE) and non-linear power coupling equation (NPCE), respectively. By analyzing the NLCE and NPCE, we prove that operating at full load is optimal in minimizing sum energy, and provide an iterative power adjustment algorithm to obtain the corresponding optimal power solution with guaranteed convergence, where in each iteration a standard bisection search is employed. To obtain the algorithmic result, we use the properties of the so-called standard interference function; the proof is non-standard because the NPCE cannot even be expressed as a closed-form expression with power as the implicit variable of interest. We present numerical results illustrating the theoretical findings for a real-life and large-scale cellular network, showing the advantage of our solution compared to the conventional solution of deploying uniform power for base stations.
Evaluation of node importance in complex networks
Venue: arXiv cs.SI 2014
Year: 2014

The assessment of node importance has been a fundamental issue in the research of complex networks. In this paper, we propose to use the Shannon-Parry measure (SPM) to evaluate the importance of a node quantitatively, because SPM is the stationary distribution of the most unprejudiced random walk on the network. We demonstrate the accuracy and robustness of SPM compared with several popular methods in the Zachary karate club network and three toy networks. We apply SPM to analyze the city importance of China Railways High-speed (CRH) network, and obtain reasonable results. Since SPM can be used effectively in weighted and directed network, we believe it is a relevant method to identify key nodes in networks.
A new inexact iterative hard thresholding algorithm for compressed sensing
Venue: arXiv cs.IT 2014
Year: 2014

Compressed sensing (CS) demonstrates that a sparse, or compressible signal can be acquired using a low rate acquisition process below the Nyquist rate, which projects the signal onto a small set of vectors incoherent with the sparsity basis. In this paper, we propose a new framework for compressed sensing recovery problem using iterative approximation method via L0 minimization. Instead of directly solving the unconstrained L0 norm optimization problem, we use the linearization and proximal points techniques to approximate the penalty function at each iteration. The proposed algorithm is very simple, efficient, and proved to be convergent. Numerical simulation demonstrates our conclusions and indicates that the algorithm can improve the reconstruction quality.