OpenRaiser/Intern-Atlas huggingface dataset:
Face Detection with a 3D Model
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model with six parameters. The computational complexity of the search in the six dimensional pose space is addressed by proposing meaningful 3D pose candidates by image-based regression from detected face keypoint locations. The 3D pose candidates are evaluated using a parameter sensitive classifier based on difference features relative to the 3D pose. A compatible subset of candidates is then obtained by non-maximal suppression. Experiments on two standard face detection datasets show that the proposed 3D model based approach obtains results comparable to or better than state of the art.
Joint Channel Assignment and Opportunistic Routing for Maximizing Throughput in Cognitive Radio Networks
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we consider the joint opportunistic routing and channel assignment problem in multi-channel multi-radio (MCMR) cognitive radio networks (CRNs) for improving aggregate throughput of the secondary users. We first present the nonlinear programming optimization model for this joint problem, taking into account the feature of CRNs-channel uncertainty. Then considering the queue state of a node, we propose a new scheme to select proper forwarding candidates for opportunistic routing. Furthermore, a new algorithm for calculating the forwarding probability of any packet at a node is proposed, which is used to calculate how many packets a forwarder should send, so that the duplicate transmission can be reduced compared with MAC-independent opportunistic routing & encoding (MORE) [11]. Our numerical results show that the proposed scheme performs significantly better that traditional routing and opportunistic routing in which channel assignment strategy is employed.
From advertising profits to bandwidth prices-A quantitative methodology for negotiating premium peering
Venue: arXiv cs.NI 2015
Year: 2015

We have developed a first of its kind methodology for deriving bandwidth prices for premium direct peering between Access ISPs (A-ISPs) and Content and Service Providers (CSPs) that want to deliver content and services in premium quality. Our methodology establishes a direct link between service profitability, e.g., from advertising, user- and subscriber-loyalty, interconnection costs, and finally bandwidth price for peering. Unlike existing work in both the networking and economics literature, our resulting computational model built around Nash bargaining, can be used for deriving quantitative results comparable to actual market prices. We analyze the US market and derive prices for video that compare favorably with existing prices for transit and paid peering. We also observe that the fair prices returned by the model for high-profit/low-volume services such as search, are orders of magnitude higher than current bandwidth prices. This implies that resolving existing (fierce) interconnection tussles may require per service, instead of wholesale, peering between A-ISPs and CSPs. Our model can be used for deriving initial benchmark prices for such negotiations.
Control of parallel non-observable queues: asymptotic equivalence and optimality of periodic policies
Venue: arXiv cs.PF 2015
Year: 2015

We consider a queueing system composed of a dispatcher that routes deterministically jobs to a set of non-observable queues working in parallel. In this setting, the fundamental problem is which policy should the dispatcher implement to minimize the stationary mean waiting time of the incoming jobs. We present a structural property that holds in the classic scaling of the system where the network demand (arrival rate of jobs) grows proportionally with the number of queues. Assuming that each queue of type $r$ is replicated $k$ times, we consider a set of policies that are periodic with period $k \sum_r p_r$ and such that exactly $p_r$ jobs are sent in a period to each queue of type $r$. When $k\to\infty$, our main result shows that all the policies in this set are equivalent, in the sense that they yield the same mean stationary waiting time, and optimal, in the sense that no other policy having the same aggregate arrival rate to \emph{all} queues of a given type can do better in minimizing the stationary mean waiting time. This property holds in a strong probabilistic sense. Furthermore, the limiting mean waiting time achieved by our policies is a convex function of the arrival rate in each queue, which facilitates the development of a further optimization aimed at solving the fundamental problem above for large systems.
Triangle counting in dynamic graph streams
Venue: arXiv cs.DS 2015
Year: 2015

Estimating the number of triangles in graph streams using a limited amount of memory has become a popular topic in the last decade. Different variations of the problem have been studied, depending on whether the graph edges are provided in an arbitrary order or as incidence lists. However, with a few exceptions, the algorithms have considered {\em insert-only} streams. We present a new algorithm estimating the number of triangles in {\em dynamic} graph streams where edges can be both inserted and deleted. We show that our algorithm achieves better time and space complexity than previous solutions for various graph classes, for example sparse graphs with a relatively small number of triangles. Also, for graphs with constant transitivity coefficient, a common situation in real graphs, this is the first algorithm achieving constant processing time per edge. The result is achieved by a novel approach combining sampling of vertex triples and sparsification of the input graph. In the course of the analysis of the algorithm we present a lower bound on the number of pairwise independent 2-paths in general graphs which might be of independent interest. At the end of the paper we discuss lower bounds on the space complexity of triangle counting algorithms that make no assumptions on the structure of the graph.
Trace Inclusion for One-Counter Nets Revisited
Venue: arXiv cs.LO 2015
Year: 2015

One-Counter nets (OCN) consist of a nondeterministic finite control and a single integer counter that cannot be fully tested for zero. They form a natural subclass of both One-Counter Automata, which allow zero-tests and Petri Nets/VASS, which allow multiple such weak counters. The trace inclusion problem has recently been shown to be undecidable for OCN. In this paper, we contrast the complexity of two natural restrictions which imply decidability. First, we show that trace inclusion between an OCN and a deterministic OCN is NL-complete, even with arbitrary binary-encoded initial counter-values as part of the input. Secondly, we show Ackermannian completeness of for the trace universality problem of nondeterministic OCN. This problem is equivalent to checking trace inclusion between a finite and a OCN-process.
Reflections on Tiles (in Self-Assembly)
Venue: arXiv cs.CG 2015
Year: 2015

We define the Reflexive Tile Assembly Model (RTAM), which is obtained from the abstract Tile Assembly Model (aTAM) by allowing tiles to reflect across their horizontal and/or vertical axes. We show that the class of directed temperature-1 RTAM systems is not computationally universal, which is conjectured but unproven for the aTAM, and like the aTAM, the RTAM is computationally universal at temperature 2. We then show that at temperature 1, when starting from a single tile seed, the RTAM is capable of assembling n x n squares for n odd using only n tile types, but incapable of assembling n x n squares for n even. Moreover, we show that n is a lower bound on the number of tile types needed to assemble n x n squares for n odd in the temperature-1 RTAM. The conjectured lower bound for temperature-1 aTAM systems is 2n-1. Finally, we give preliminary results toward the classification of which finite connected shapes in Z^2 can be assembled (strictly or weakly) by a singly seeded (i.e. seed of size 1) RTAM system, including a complete classification of which finite connected shapes be strictly assembled by a "mismatch-free" singly seeded RTAM system.
Piko: A Design Framework for Programmable Graphics Pipelines
Venue: arXiv cs.GR 2015
Year: 2015

We present Piko, a framework for designing, optimizing, and retargeting implementations of graphics pipelines on multiple architectures. Piko programmers express a graphics pipeline by organizing the computation within each stage into spatial bins and specifying a scheduling preference for these bins. Our compiler, Pikoc, compiles this input into an optimized implementation targeted to a massively-parallel GPU or a multicore CPU. Piko manages work granularity in a programmable and flexible manner, allowing programmers to build load-balanced parallel pipeline implementations, to exploit spatial and producer-consumer locality in a pipeline implementation, and to explore tradeoffs between these considerations. We demonstrate that Piko can implement a wide range of pipelines, including rasterization, Reyes, ray tracing, rasterization/ray tracing hybrid, and deferred rendering. Piko allows us to implement efficient graphics pipelines with relative ease and to quickly explore design alternatives by modifying the spatial binning configurations and scheduling preferences for individual stages, all while delivering real-time performance that is within a factor six of state-of-the-art rendering systems.
Quality Sensitive Price Competition in Spectrum Oligopoly: Part II
Venue: arXiv cs.GT 2015
Year: 2015

We investigate a spectrum oligopoly market where each primary seeks to sell secondary access to its channel at multiple locations. Transmission qualities of a channel evolve randomly. Each primary needs to select a price and a set of non-interfering locations (which is an independent set in the conflict graph of the region) at which to offer its channel without knowing the transmission qualities of the channels of its competitors. We formulate the above problem as a non-cooperative game. We consider two scenarios-i) when the region is small, ii) when the region is large. In the first setting, we focus on a class of conflict graphs, known as mean valid graphs which commonly arise when the region is small. We explicitly compute a symmetric Nash equilibrium (NE); the NE is threshold type in that primaries only choose independent set whose cardinality is greater than a certain threshold. The threshold on the cardinality increases with increase in quality of the channel on sale. We show that the symmetric NE strategy profile is unique in a special class of conflict graphs (linear graph). In the second setting, we consider node symmetric conflict graphs which arises when the number of locations is large (potentially, infinite). We explicitly compute a symmetric NE that randomizes equally among the maximum independent sets at a given channel state vector. In the NE a primary only selects the maximum independent set at a given channel state vector. We show that the two symmetric NEs computed in two settings exhibit important structural difference.
Detecting "protein words" through unsupervised word segmentation
Venue: arXiv cs.CE 2015
Year: 2015

Unsupervised word segmentation methods were applied to analyze the protein sequence. Protein sequences, such as 'MTMDKSELVQKA...', were used as input to these methods. Segmented 'protein word' sequences, such as 'MTM DKSE LVQKA', were then obtained. We compare the 'protein words' produced by unsupervised segmentation and the protein secondary structure segmentation. An interesting finding is that the unsupervised word segmentation is more efficient than secondary structure segmentation in expressing information. Our experiment also suggests there may be some 'protein ruins' in current noncoding regions.
Generalized sphere-packing and sphere-covering bounds on the size of codes for combinatorial channels
Venue: arXiv cs.IT 2015
Year: 2015

Many of the classic problems of coding theory are highly symmetric, which makes it easy to derive sphere-packing upper bounds and sphere-covering lower bounds on the size of codes. We discuss the generalizations of sphere-packing and sphere-covering bounds to arbitrary error models. These generalizations become especially important when the sizes of the error spheres are nonuniform. The best possible sphere-packing and sphere-covering bounds are solutions to linear programs. We derive a series of bounds from approximations to packing and covering problems and study the relationships and trade-offs between them. We compare sphere-covering lower bounds with other graph theoretic lower bounds such as Turán's theorem. We show how to obtain upper bounds by optimizing across a family of channels that admit the same codes. We present a generalization of the local degree bound of Kulkarni and Kiyavash and use it to improve the best known upper bounds on the sizes of single deletion correcting codes and single grain error correcting codes.
NScale: Neighborhood-centric Large-Scale Graph Analytics in the Cloud
Venue: arXiv cs.DB 2015
Year: 2015

There is an increasing interest in executing complex analyses over large graphs, many of which require processing a large number of multi-hop neighborhoods or subgraphs. Examples include ego network analysis, motif counting, personalized recommendations, and others. These tasks are not well served by existing vertex-centric graph processing frameworks, where user programs are only able to directly access the state of a single vertex. This paper introduces NSCALE, a novel end-to-end graph processing framework that enables the distributed execution of complex subgraph-centric analytics over large-scale graphs in the cloud. NSCALE enables users to write programs at the level of subgraphs rather than at the level of vertices. Unlike most previous graph processing frameworks, which apply the user program to the entire graph, NSCALE allows users to declaratively specify subgraphs of interest. Our framework includes a novel graph extraction and packing (GEP) module that utilizes a cost-based optimizer to partition and pack the subgraphs of interest into memory on as few machines as possible. The distributed execution engine then takes over and runs the user program in parallel, while respecting the scope of the various subgraphs. Our experimental results show orders-of-magnitude improvements in performance and drastic reductions in the cost of analytics compared to vertex-centric approaches.
Transalg: a Tool for Translating Procedural Descriptions of Discrete Functions to SAT
Venue: arXiv cs.AI 2015
Year: 2015

In this paper we present the Transalg system, designed to produce SAT encodings for discrete functions, written as programs in a specific language. Translation of such programs to SAT is based on propositional encoding methods for formal computing models and on the concept of symbolic execution. We used the Transalg system to make SAT encodings for a number of cryptographic functions.
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Venue: arXiv cs.DS 2015
Year: 2015

Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature). Our results advance the state of art on several different topics: the theory of crowdsourcing markets, principal-agent problems, multi-armed bandits, and dynamic pricing.
Computational homogenization of fibrous piezoelectric materials
Venue: arXiv cs.CE 2015
Year: 2015

Flexible piezoelectric devices made of polymeric materials are widely used for micro- and nano-electro-mechanical systems. In particular, numerous recent applications concern energy harvesting. Due to the importance of computational modeling to understand the influence that microscale geometry and constitutive variables exert on the macroscopic behavior, a numerical approach is developed here for multiscale and multiphysics modeling of thin piezoelectric sheets made of aligned arrays of polymeric nanofibers, manufactured by electrospinning. At the microscale, the representative volume element consists in piezoelectric polymeric nanofibers, assumed to feature a piezoelastic behavior and subjected to electromechanical contact constraints. The latter are incorporated into the virtual work equations by formulating suitable electric, mechanical and coupling potentials and the constraints are enforced by using the penalty method. From the solution of the micro-scale boundary value problem, a suitable scale transition procedure leads to identifying the performance of a macroscopic thin piezoelectric shell element.
Pattern Recognition in Narrative: Tracking Emotional Expression in Context
Venue: arXiv cs.AI 2015
Year: 2015

Using geometric data analysis, our objective is the analysis of narrative, with narrative of emotion being the focus in this work. The following two principles for analysis of emotion inform our work. Firstly, emotion is revealed not as a quality in its own right but rather through interaction. We study the 2-way relationship of Ilsa and Rick in the movie Casablanca, and the 3-way relationship of Emma, Charles and Rodolphe in the novel {\em Madame Bovary}. Secondly, emotion, that is expression of states of mind of subjects, is formed and evolves within the narrative that expresses external events and (personal, social, physical) context. In addition to the analysis methodology with key aspects that are innovative, the input data used is crucial. We use, firstly, dialogue, and secondly, broad and general description that incorporates dialogue. In a follow-on study, we apply our unsupervised narrative mapping to data streams with very low emotional expression. We map the narrative of Twitter streams. Thus we demonstrate map analysis of general narratives.
Kernelization Complexity of Possible Winner and Coalitional Manipulation Problems in Voting
Venue: arXiv cs.GT 2015
Year: 2015

In the Possible Winner problem in computational social choice theory, we are given a set of partial preferences and the question is whether a distinguished candidate could be made winner by extending the partial preferences to linear preferences. Previous work has provided, for many common voting rules, fixed parameter tractable algorithms for the Possible Winner problem, with number of candidates as the parameter. However, the corresponding kernelization question is still open and in fact, has been mentioned as a key research challenge. In this paper, we settle this open question for many common voting rules. We show that the Possible Winner problem for maximin, Copeland, Bucklin, ranked pairs, and a class of scoring rules that include the Borda voting rule do not admit a polynomial kernel with the number of candidates as the parameter. We show however that the Coalitional Manipulation problem which is an important special case of the Possible Winner problem does admit a polynomial kernel for maximin, Copeland, ranked pairs, and a class of scoring rules that includes the Borda voting rule, when the number of manipulators is polynomial in the number of candidates. A significant conclusion of our work is that the Possible Winner problem is harder than the Coalitional Manipulation problem since the Coalitional Manipulation problem admits a polynomial kernel whereas the Possible Winner problem does not admit a polynomial kernel.
Sparsity Based Methods for Overparameterized Variational Problems
Venue: arXiv cs.CV 2015
Year: 2015

Two complementary approaches have been extensively used in signal and image processing leading to novel results, the sparse representation methodology and the variational strategy. Recently, a new sparsity based model has been proposed, the cosparse analysis framework, which may potentially help in bridging sparse approximation based methods to the traditional total-variation minimization. Based on this, we introduce a sparsity based framework for solving overparameterized variational problems. The latter has been used to improve the estimation of optical flow and also for general denoising of signals and images. However, the recovery of the space varying parameters involved was not adequately addressed by traditional variational methods. We first demonstrate the efficiency of the new framework for one dimensional signals in recovering a piecewise linear and polynomial function. Then, we illustrate how the new technique can be used for denoising and segmentation of images.
Voting with Coarse Beliefs
Venue: arXiv cs.GT 2015
Year: 2015

The classic Gibbard-Satterthwaite theorem says that every strategy-proof voting rule with at least three possible candidates must be dictatorial. Similar impossibility results hold even if we consider a weaker notion of strategy-proofness where voters believe that the other voters' preferences are i.i.d.~(independent and identically distributed). In this paper, we take a bounded-rationality approach to this problem and consider a setting where voters have "coarse" beliefs (a notion that has gained popularity in the behavioral economics literature). In particular, we construct good voting rules that satisfy a notion of strategy-proofness with respect to coarse i.i.d.~beliefs, thus circumventing the above impossibility results.
Event-Triggered Stabilization of Linear Systems Under Bounded Bit Rates
Venue: arXiv cs.SY 2015
Year: 2015

This paper addresses the problem of exponential practical stabilization of linear time-invariant systems with disturbances using event-triggered control and bounded communication bit rate. We consider both the case of instantaneous communication with finite precision data at each transmission and the case of non-instantaneous communication with bounded communication rate. Given a prescribed rate of convergence, the proposed event-triggered control implementations opportunistically determine the transmission instants and the finite precision data to be transmitted on each transmission. We show that our design exponentially practically stabilizes the origin while guaranteeing a uniform positive lower bound on the inter-transmission and inter-reception times, ensuring that the number of bits transmitted on each transmission is upper bounded uniformly in time, and allowing for the possibility of transmitting fewer bits at any given time if more bits than prescribed were transmitted earlier. We also characterize the necessary and sufficient average data rate for exponential practical stabilization. Several simulations illustrate the results.