OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Auctions vs. Anonymous Pricing
Venue: arXiv cs.GT 2015
Year: 2015

For selling a single item to agents with independent but non-identically distributed values, the revenue optimal auction is complex. With respect to it, Hartline and Roughgarden (2009) showed that the approximation factor of the second-price auction with an anonymous reserve is between two and four. We consider the more demanding problem of approximating the revenue of the ex ante relaxation of the auction problem by posting an anonymous price (while supplies last) and prove that their worst-case ratio is e. As a corollary, the upper-bound of anonymous pricing or anonymous reserves versus the optimal auction improves from four to $e$. We conclude that, up to an $e$ factor, discrimination and simultaneity are unimportant for driving revenue in single-item auctions.
Energy management in communication networks: a journey through modelling and optimization glasses
Venue: arXiv cs.NI 2015
Year: 2015

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed.
Unfolding-Based Process Discovery
Venue: arXiv cs.LO 2015
Year: 2015

This paper presents a novel technique for process discovery. In contrast to the current trend, which only considers an event log for discovering a process model, we assume two additional inputs: an independence relation on the set of logged activities, and a collection of negative traces. After deriving an intermediate net unfolding from them, we perform a controlled folding giving rise to a Petri net which contains both the input log and all independence-equivalent traces arising from it. Remarkably, the derived Petri net cannot execute any trace from the negative collection. The entire chain of transformations is fully automated. A tool has been developed and experimental results are provided that witness the significance of the contribution of this paper.
Low-Risk Mechanisms for the Kidney Exchange Game
Venue: arXiv cs.GT 2015
Year: 2015

In this paper we consider the pairwise kidney exchange game. This game naturally appears in situations that some service providers benefit from pairwise allocations on a network, such as the kidney exchanges between hospitals. Ashlagi et al. present a $2$-approximation randomized truthful mechanism for this problem. This is the best known result in this setting with multiple players. However, we note that the variance of the utility of an agent in this mechanism may be as large as $Ω(n^2)$, which is not desirable in a real application. In this paper we resolve this issue by providing a $2$-approximation randomized truthful mechanism in which the variance of the utility of each agent is at most $2+ε$. Interestingly, we could apply our technique to design a deterministic mechanism such that, if an agent deviates from the mechanism, she does not gain more than $2\lceil \log_2 m\rceil$. We call such a mechanism an almost truthful mechanism. Indeed, in a practical scenario, an almost truthful mechanism is likely to imply a truthful mechanism. We believe that our approach can be used to design low risk or almost truthful mechanisms for other problems.
Fast Signal Separation of 2D Sparse Mixture via Approximate Message-Passing
Venue: arXiv cs.IT 2015
Year: 2015

Approximate message-passing (AMP) method is a simple and efficient framework for the linear inverse problems. In this letter, we propose a faster AMP to solve the \emph{$L_1$-Split-Analysis} for the 2D sparsity separation, which is referred to as \emph{MixAMP}. We develop the MixAMP based on the factor graphical modeling and the min-sum message-passing. Then, we examine MixAMP for two types of the sparsity separation: separation of the direct-and-group sparsity, and that of the direct-and-finite-difference sparsity. This case study shows that the MixAMP method offers computational advantages over the conventional first-order method, TFOCS.
A simplified 1.5-approximation algorithm for augmenting edge-connectivity of a graph from 1 to 2
Venue: arXiv cs.DS 2015
Year: 2015

The Tree Augmentation Problem (TAP) is: given a connected graph $G=(V,{\cal E})$ and an edge set $E$ on $V$ find a minimum size subset of edges $F \subseteq E$ such that $(V,{\cal E} \cup F)$ is $2$-edge-connected. In the conference version \cite{EFKN-APPROX} was sketched a $1.5$-approximation algorithm for the problem. Since a full proof was very complex and long, the journal version was cut into two parts. In the first part \cite{EFKN-TALG} was only proved ratio $1.8$. An attempt to simplify the second part produced an error in \cite{EKN-IPL}. Here we give a correct, different, and self contained proof of the ratio $1.5$, that is also substantially simpler and shorter than the previous proofs.
Optimal Continuous Time Markov Decisions
Venue: arXiv cs.SY 2015
Year: 2015

In the context of Markov decision processes running in continuous time, one of the most intriguing challenges is the efficient approximation of finite horizon reachability objectives. A multitude of sophisticated model checking algorithms have been proposed for this. However, no proper benchmarking has been performed thus far. This paper presents a novel and yet simple solution: an algorithm originally developed for a restricted subclass of models and a subclass of schedulers can be twisted so as to become competitive with the more sophisticated algorithms in full generality. As the second main contribution, we perform a comparative evaluation of the core algorithmic concepts on an extensive set of benchmarks varying over all key parameters: model size, amount of non-determinism, time horizon, and precision.
On Existence and Properties of Approximate Pure Nash Equilibria in Bandwidth Allocation Games
Venue: arXiv cs.GT 2015
Year: 2015

In \emph{bandwidth allocation games} (BAGs), the strategy of a player consists of various demands on different resources. The player's utility is at most the sum of these demands, provided they are fully satisfied. Every resource has a limited capacity and if it is exceeded by the total demand, it has to be split between the players. Since these games generally do not have pure Nash equilibria, we consider approximate pure Nash equilibria, in which no player can improve her utility by more than some fixed factor $α$ through unilateral strategy changes. There is a threshold $α_δ$ (where $δ$ is a parameter that limits the demand of each player on a specific resource) such that $α$-approximate pure Nash equilibria always exist for $α\geq α_δ$, but not for $α< α_δ$. We give both upper and lower bounds on this threshold $α_δ$ and show that the corresponding decision problem is ${\sf NP}$-hard. We also show that the $α$-approximate price of anarchy for BAGs is $α+1$. For a restricted version of the game, where demands of players only differ slightly from each other (e.g. symmetric games), we show that approximate Nash equilibria can be reached (and thus also be computed) in polynomial time using the best-response dynamic. Finally, we show that a broader class of utility-maximization games (which includes BAGs) converges quickly towards states whose social welfare is close to the optimum.
Softwarized 5G Networks Resiliency with Selfhealing
Venue: arXiv cs.NI 2015
Year: 2015

The meaning of 5G is still a subject of discussion in the industry. However, the softwarization of networks is expected to shape the design, operation and management of 5G networks. The opportunity is then crucial for Telcos, vendors and IT players to consider the management of 5G networks during its design time and avoid the build it first, manage it later paradigm. However, network softwarization comes with its own set of challenges, including robustness, scalability and resilience. In this paper, we focus on the importance of resiliency and propose a Self-Healing based framework for 5G networks to ensure services and resources availability.
A Bloom filter based semi-index on $q$-grams
Venue: arXiv cs.DS 2015
Year: 2015

We present a simple $q$-gram based semi-index, which allows to look for a pattern typically only in a small fraction of text blocks. Several space-time tradeoffs are presented. Experiments on Pizza & Chili datasets show that our solution is up to three orders of magnitude faster than the Claude et al. \cite{CNPSTjda10} semi-index at a comparable space usage.
SenseDroid: A Context-Aware Information Exchange Framework for Mobile Sensor Networks Using Android Phones
Venue: arXiv cs.NI 2015
Year: 2015

Mobile phones and smartphones have evolved to be very powerful devices that have the potential to be utilized in many application areas apart from generic communication. With each passing year, we see increasingly powerful smartphones being manufactured, which have a plethora of powerful embedded sensors like microphone, camera, digital compass, GPS, accelerometer, temperature sensors and many more. Moreover, the ability to easily program today's smartphones, enables us to exploit these sensors, in a wide variety of application such as personal safety, emergency and calamity response, situation awareness, remote activity monitoring, transportation and environment monitoring. In this paper, we survey the existing mobile phone sensing methodologies and application areas. We also formulate the architectural framework of our project, SenseDroid, its utility, limitations and possible future applications.
LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes
Venue: arXiv cs.CV 2015
Year: 2015

One popular approach to interactively segment the foreground object of interest from an image is to annotate a bounding box that covers the foreground object. Then, a binary labeling is performed to achieve a refined segmentation. One major issue of the existing algorithms for such interactive image segmentation is their preference of an input bounding box that tightly encloses the foreground object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object. We propose a new Markov Random Fields (MRF) model for segmentation with loosely bounded boxes, including a global similarity constraint to better distinguish the foreground and background, and an additional energy term to encourage consistent labeling of similar-appearance pixels. This MRF model is then solved by an iterated max-flow algorithm. In the experiments, we evaluate LooseCut in three publicly-available image datasets, and compare its performance against several state-of-the-art interactive image segmentation algorithms. We also show that LooseCut can be used for enhancing the performance of unsupervised video segmentation and image saliency detection.
A Placement Vulnerability Study in Multi-tenant Public Clouds
Venue: arXiv cs.CR 2015
Year: 2015

Public infrastructure-as-a-service clouds, such as Amazon EC2, Google Compute Engine (GCE) and Microsoft Azure allow clients to run virtual machines (VMs) on shared physical infrastructure. This practice of multi-tenancy brings economies of scale, but also introduces the risk of sharing a physical server with an arbitrary and potentially malicious VM. Past works have demonstrated how to place a VM alongside a target victim (co-location) in early-generation clouds and how to extract secret information via side- channels. Although there have been numerous works on side-channel attacks, there have been no studies on placement vulnerabilities in public clouds since the adoption of stronger isolation technologies such as Virtual Private Clouds (VPCs). We investigate this problem of placement vulnerabilities and quantitatively evaluate three popular public clouds for their susceptibility to co-location attacks. We find that adoption of new technologies (e.g., VPC) makes many prior attacks, such as cloud cartography, ineffective. We find new ways to reliably test for co-location across Amazon EC2, Google GCE, and Microsoft Azure. We also found ways to detect co-location with victim web servers in a multi-tiered cloud application located behind a load balancer. We use our new co-residence tests and multiple customer accounts to launch VM instances under different strategies that seek to maximize the likelihood of co-residency. We find that it is much easier (10x higher success rate) and cheaper (up to $114 less) to achieve co-location in these three clouds when compared to a secure reference placement policy.
Efficient Quantum Algorithms for (Gapped) Group Testing and Junta Testing
Venue: arXiv cs.CC 2015
Year: 2015

In the $k$-junta testing problem, a tester has to efficiently decide whether a given function $f:\{0,1\}^n\rightarrow \{0,1\}$ is a $k$-junta (i.e., depends on at most $k$ of its input bits) or is $ε$-far from any $k$-junta. Our main result is a quantum algorithm for this problem with query complexity $\tilde O(\sqrt{k/ε})$ and time complexity $\tilde O(n\sqrt{k/ε})$. This quadratically improves over the query complexity of the previous best quantum junta tester, due to Atıcı and Servedio. Our tester is based on a new quantum algorithm for a gapped version of the combinatorial group testing problem, with an up to quartic improvement over the query complexity of the best classical algorithm. For our upper bound on the time complexity we give a near-linear time implementation of a shallow variant of the quantum Fourier transform over the symmetric group, similar to the Schur-Weyl transform. We also prove a lower bound of $Ω(k^{1/3})$ queries for junta-testing (for constant $ε$).
Continuous Partial Quorums for Consistency-Latency Tuning in Distributed NoSQL Storage Systems
Venue: arXiv cs.DC 2015
Year: 2015

NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases when the need for scalability outweighs the need for transactions. Several of these systems provide quorum-based replication and present the application developer with a choice of multiple client-side "consistency levels" that determine the number of replicas accessed by reads and writes, which in turn affects both latency and the consistency observed by the client application. Since using a fixed combination of read and write consistency levels for a given application provides only a limited number of discrete options, we investigate techniques that allow more fine-grained tuning of the consistency-latency trade-off, as may be required to support consistency-based service level agreements (SLAs). We propose a novel technique called \emph{continuous partial quorums} (CPQ) that assigns the consistency level on a per-operation basis by choosing randomly between two options, such as eventual and strong consistency, with a tunable probability. We evaluate our technique experimentally using Apache Cassandra and demonstrate that it outperforms an alternative tuning technique that delays operations artificially.
Using Bayesian Network Representations for Effective Sampling from Generative Network Models
Venue: arXiv cs.AI 2015
Year: 2015

Bayesian networks (BNs) are used for inference and sampling by exploiting conditional independence among random variables. Context specific independence (CSI) is a property of graphical models where additional independence relations arise in the context of particular values of random variables (RVs). Identifying and exploiting CSI properties can simplify inference. Some generative network models (models that generate social/information network samples from a network distribution P(G)), with complex interactions among a set of RVs, can be represented with probabilistic graphical models, in particular with BNs. In the present work we show one such a case. We discuss how a mixed Kronecker Product Graph Model can be represented as a BN, and study its BN properties that can be used for efficient sampling. Specifically, we show that instead of exhibiting CSI properties, the model has deterministic context-specific dependence (DCSD). Exploiting this property focuses the sampling method on a subset of the sampling space that improves efficiency.
Predicting Small Group Accretion in Social Networks: A topology based incremental approach
Venue: arXiv cs.SI 2015
Year: 2015

Small Group evolution has been of central importance in social sciences and also in the industry for understanding dynamics of team formation. While most of research works studying groups deal at a macro level with evolution of arbitrary size communities, in this paper we restrict ourselves to studying evolution of small group (size $\leq20$) which is governed by contrasting sociological phenomenon. Given a previous history of group collaboration between a set of actors, we address the problem of predicting likely future group collaborations. Unfortunately, predicting groups requires choosing from $n \choose r$ possibilities (where $r$ is group size and $n$ is total number of actors), which becomes computationally intractable as group size increases. However, our statistical analysis of a real world dataset has shown that two processes: an external actor joining an existing group (incremental accretion (IA)) or collaborating with a subset of actors of an exiting group (subgroup accretion (SA)), are largely responsible for future group formation. This helps to drastically reduce the $n\choose r$ possibilities. We therefore, model the attachment of a group for different actors outside this group. In this paper, we have built three topology based prediction models to study these phenomena. The performance of these models is evaluated using extensive experiments over DBLP dataset. Our prediction results shows that the proposed models are significantly useful for future group predictions both for IA and SA.
DeepFont: Identify Your Font from An Image
Venue: arXiv cs.CV 2015
Year: 2015

As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers. We study the Visual Font Recognition (VFR) problem, and advance the state-of-the-art remarkably by developing the DeepFont system. First of all, we build up the first available large-scale VFR dataset, named AdobeVFR, consisting of both labeled synthetic data and partially labeled real-world data. Next, to combat the domain mismatch between available training and testing data, we introduce a Convolutional Neural Network (CNN) decomposition approach, using a domain adaptation technique based on a Stacked Convolutional Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text images combined with synthetic data preprocessed in a specific way. Moreover, we study a novel learning-based model compression approach, in order to reduce the DeepFont model size without sacrificing its performance. The DeepFont system achieves an accuracy of higher than 80% (top-5) on our collected dataset, and also produces a good font similarity measure for font selection and suggestion. We also achieve around 6 times compression of the model without any visible loss of recognition accuracy.
Capacity and performance analysis for multi-user system under distributed opportunistic scheduling in a time dependent channel
Venue: arXiv cs.IT 2015
Year: 2015

Consider the problem of a multi-user multiple access channel. While several multi-user coding techniques exist, in practical scenarios, not all users can be scheduled simultaneously. Thus, a key problem is which users to schedule in a given time slot. Under realistic approach for time dependency of the channel, we adopt a distributed scheduling algorithm in which each user, in the beginning of each slot, estimates his channel gain and compares it to a threshold, and if exceeding it the user can transmit. In this work we are interested in the expected capacity of the system and the delay and quality of service of the data accumulated at the users under this scheduling scheme. First we derive the expected capacity under scheduling (distributed and centralized) for this time dependent environment and show that its scaling law is $O(σ_g\sqrt{2\log K}+μ_g)$, were $σ_g, μ_g$ are the good channel parameters (assuming Gaussian capacity approximation, e.g., under MIMO) and $K$ is the number of users. Then we turn to the performance analysis of such system while assuming the users are not necessarily fully backlogged, and focus specifically on the queueing problem and the strong dependence between the queues which leave no alternative but to turn to approximate models for this system. We adopt the celebrated model of Ephremides and Zhu to give new results on the convergence of the probability of collision to its average value (as the number of users grows), and hence for the ensuing system performance metrics, such as throughput and delay. We further utilize this finding to suggest a much simpler approximate model, which accurately describes the system behavior when the number of queues is large. The system performance as predicted by the approximate models shows excellent agreement with simulation results.
Optimal Multi-Robot Path Planning on Graphs: Complete Algorithms and Effective Heuristics
Venue: arXiv cs.RO 2015
Year: 2015

We study the problem of optimal multi-robot path planning on graphs MPP over four distinct minimization objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance, the total arrival time, and the total distance. In a related paper, we show that these objectives are distinct and NP-hard to optimize. In this work, we focus on efficiently algorithmic solutions for solving these optimal MPP problems. Toward this goal, we first establish a one-to-one solution mapping between MPP and network-flow. Based on this equivalence and integer linear programming (ILP), we design novel and complete algorithms for optimizing over each of the four objectives. In particular, our exact algorithm for computing optimal makespan solutions is a first such that is capable of solving extremely challenging problems with robot-vertex ratio as high as 100%. Then, we further improve the computational performance of these exact algorithms through the introduction of principled heuristics, at the expense of some optimality loss. The combination of ILP model based algorithms and the heuristics proves to be highly effective, allowing the computation of 1.x-optimal solutions for problems containing hundreds of robots, densely populated in the environment, often in just seconds.