OpenRaiser/Intern-Atlas huggingface dataset:
Asymptotic Granularity Reduction and Its Application
Venue: arXiv cs.CC 2014
Year: 2014

It is well known that the inverse function of y = x with the derivative y' = 1 is x = y, the inverse function of y = c with the derivative y' = 0 is inexistent, and so on. Hence, on the assumption that the noninvertibility of the univariate increasing function y = f(x) with x > 0 is in direct proportion to the growth rate reflected by its derivative, the authors put forward a method of comparing difficulties in inverting two functions on a continuous or discrete interval called asymptotic granularity reduction (AGR) which integrates asymptotic analysis with logarithmic granularities, and is an extension and a complement to polynomial time (Turing) reduction (PTR). Prove by AGR that inverting y = x ^ x (mod p) is computationally harder than inverting y = g ^ x (mod p), and inverting y = g ^ (x ^ n) (mod p) is computationally equivalent to inverting y = g ^ x (mod p), which are compatible with the results from PTR. Besides, apply AGR to the comparison of inverting y = x ^ n (mod p) with y = g ^ x (mod p), y = g ^ (g1 ^ x) (mod p) with y = g ^ x (mod p), and y = x ^ n + x + 1 (mod p) with y = x ^ n (mod p) in difficulty, and observe that the results are consistent with existing facts, which further illustrates that AGR is suitable for comparison of inversion problems in difficulty. Last, prove by AGR that inverting y = (x ^ n)(g ^ x) (mod p) is computationally equivalent to inverting y = g ^ x (mod p) when PTR can not be utilized expediently. AGR with the assumption partitions the complexities of problems more detailedly, and finds out some new evidence for the security of cryptosystems.
Extreme-Value Theorems for Optimal Multidimensional Pricing
Venue: arXiv cs.GT 2014
Year: 2014

We provide a near-optimal, computationally efficient algorithm for the unit-demand pricing problem, where a seller wants to price n items to optimize revenue against a unit-demand buyer whose values for the items are independently drawn from known distributions. For any chosen accuracy eps>0 and item values bounded in [0,1], our algorithm achieves revenue that is optimal up to an additive error of at most eps, in polynomial time. For values sampled from Monotone Hazard Rate (MHR) distributions, we achieve a (1-eps)-fraction of the optimal revenue in polynomial time, while for values sampled from regular distributions the same revenue guarantees are achieved in quasi-polynomial time. Our algorithm for bounded distributions applies probabilistic techniques to understand the statistical properties of revenue distributions, obtaining a reduction in the search space of the algorithm via dynamic programming. Adapting this approach to MHR and regular distributions requires the proof of novel extreme value theorems for such distributions. As a byproduct, our techniques establish structural properties of approximately-optimal and near-optimal solutions. We show that, for values independently distributed according to MHR distributions, pricing all items at the same price achieves a constant fraction of the optimal revenue. Moreover, for all eps >0, g(1/eps) distinct prices suffice to obtain a (1-eps)-fraction of the optimal revenue, where g(1/eps) is quadratic in 1/eps and independent of n. Similarly, for all eps>0 and n>0, at most g(1/(eps log n)) distinct prices suffice if the values are independently distributed according to regular distributions, where g() is a polynomial function. Finally, when the values are i.i.d. from some MHR distribution, we show that, if n is a sufficiently large function of 1/eps, a single price suffices to achieve a (1-eps)-fraction of the optimal revenue.
The First-Order Theory of Ground Tree Rewrite Graphs
Venue: arXiv cs.LO 2014
Year: 2014

We prove that the complexity of the uniform first-order theory of ground tree rewrite graphs is in ATIME(2^{2^{poly(n)}},O(n)). Providing a matching lower bound, we show that there is some fixed ground tree rewrite graph whose first-order theory is hard for ATIME(2^{2^{poly(n)}},poly(n)) with respect to logspace reductions. Finally, we prove that there exists a fixed ground tree rewrite graph together with a single unary predicate in form of a regular tree language such that the resulting structure has a non-elementary first-order theory.
Measurement-Adaptive Cellular Random Access Protocols
Venue: arXiv cs.NI 2014
Year: 2014

This work considers a single-cell random access channel (RACH) in cellular wireless networks. Communications over RACH take place when users try to connect to a base station during a handover or when establishing a new connection. Within the framework of Self-Organizing Networks (SONs), the system should self- adapt to dynamically changing environments (channel fading, mobility, etc.) without human intervention. For the performance improvement of the RACH procedure, we aim here at maximizing throughput or alternatively minimizing the user dropping rate. In the context of SON, we propose protocols which exploit information from measurements and user reports in order to estimate current values of the system unknowns and broadcast global action-related values to all users. The protocols suggest an optimal pair of user actions (transmission power and back-off probability) found by minimizing the drift of a certain function. Numerical results illustrate considerable benefits of the dropping rate, at a very low or even zero cost in power expenditure and delay, as well as the fast adaptability of the protocols to environment changes. Although the proposed protocol is designed to minimize primarily the amount of discarded users per cell, our framework allows for other variations (power or delay minimization) as well.
Polyceptron: A Polyhedral Learning Algorithm
Venue: arXiv cs.LG 2014
Year: 2014

In this paper we propose a new algorithm for learning polyhedral classifiers which we call as Polyceptron. It is a Perception like algorithm which updates the parameters only when the current classifier misclassifies any training data. We give both batch and online version of Polyceptron algorithm. Finally we give experimental results to show the effectiveness of our approach.
Type Expressiveness and Its Application in Separation of Behavior Programming and Data Management Programming
Venue: arXiv cs.PL 2014
Year: 2014

A new behavior descriptive entity type called spec is proposed, which combines the traditional interface with test rules and test cases, to completely specify the desired behavior of each method, and to enforce the behavior-wise correctness of all compiled units. Using spec, a new programming paradigm is proposed, which allows the separation programming space into 1) a behavior domain to aggregate all behavior programming in the format of specs, 2) a object domain to bind each concrete spec to its data representation in a particular address space, and 3) a realization domain to connect the behavior domain and the object domain. Such separation guarantees the strictness of behavior satisfaction at compile time, while allows flexibility of dynamical binding of actual implementation at runtime. A new convention call type expressiveness to allow data exchange between different programming languages and between different software environments is also proposed.
DANCE: A Framework for the Distributed Assessment of Network Centralities
Venue: arXiv cs.NI 2014
Year: 2014

The analysis of large-scale complex networks is a major challenge in the Big Data domain. Given the large-scale of the complex networks researchers commonly deal with nowadays, the use of localized information (i.e. restricted to a limited neighborhood around each node of the network) for centrality-based analysis is gaining momentum in the recent literature. In this context, we propose a framework for the Distributed Assessment of Network Centralities (DANCE) in complex networks. DANCE offers a single environment that allows the use of different localized centrality proposals, which can be tailored to specific applications. This environment can be thus useful given the vast potential applicability of centrality-based analysis on large-scale complex networks found in different areas, such as Biology, Physics, Sociology, or Computer Science. Since the localized centrality proposals DANCE implements employ only localized information, DANCE can easily benefit from parallel processing environments and run on different computing architectures. To illustrate this, we present a parallel implementation of DANCE and show how it can be applied to the analysis of large-scale complex networks using different kinds of network centralities. This implementation is made available to complex network researchers and practitioners interested in using it through a scientific web portal.
Complex-Valued Autoencoders
Venue: arXiv cs.NE 2014
Year: 2014

Autoencoders are unsupervised machine learning circuits whose learning goal is to minimize a distortion measure between inputs and outputs. Linear autoencoders can be defined over any field and only real-valued linear autoencoder have been studied so far. Here we study complex-valued linear autoencoders where the components of the training vectors and adjustable matrices are defined over the complex field with the $L_2$ norm. We provide simpler and more general proofs that unify the real-valued and complex-valued cases, showing that in both cases the landscape of the error function is invariant under certain groups of transformations. The landscape has no local minima, a family of global minima associated with Principal Component Analysis, and many families of saddle points associated with orthogonal projections onto sub-space spanned by sub-optimal subsets of eigenvectors of the covariance matrix. The theory yields several iterative, convergent, learning algorithms, a clear understanding of the generalization properties of the trained autoencoders, and can equally be applied to the hetero-associative case when external targets are provided. Partial results on deep architecture as well as the differential geometry of autoencoders are also presented. The general framework described here is useful to classify autoencoders and identify general common properties that ought to be investigated for each class, illuminating some of the connections between information theory, unsupervised learning, clustering, Hebbian learning, and autoencoders.
Structural Routability of n-Pairs Information Networks
Venue: arXiv cs.IT 2014
Year: 2014

Information does not generally behave like a conservative fluid flow in communication networks with multiple sources and sinks. However, it is often conceptually and practically useful to be able to associate separate data streams with each source-sink pair, with only routing and no coding performed at the network nodes. This raises the question of whether there is a nontrivial class of network topologies for which achievability is always equivalent to routability, for any combination of source signals and positive channel capacities. This chapter considers possibly cyclic, directed, errorless networks with n source-sink pairs and mutually independent source signals. The concept of downward dominance is introduced and it is shown that, if the network topology is downward dominated, then the achievability of a given combination of source signals and channel capacities implies the existence of a feasible multicommodity flow.
QoS Aware and Survivable Network Design for Planned Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

We study the problem of wireless sensor network design by deploying a minimum number of additional relay nodes (to minimize network cost) at a subset of given potential relay locations, in order to convey the data from already existing sensor nodes (hereafter called source nodes) to a Base Station, while meeting a certain specified hop count bound (the hop count bound is chosen to ensure a pre-determined probability of the data being delivered to the BS within a given maximum delay). We study two variations of the problem. First we sudy the problem of guaranteed QoS connected network design, where the objective is to have at least one path from each source to the BS with the specified hop count bound. We show that the problem is NP-Hard. For a problem in which the number of existing sensor nodes and potential relay locations is n, we propose an O(n) approximation algorithm of polynomial time complexity. Results show that the algorithm performs efficiently (in over 90% of the tested scenarios, it gave solutions that were either optimal or were worse than optimal by just one relay) in various randomly generated network scenarios. Next, we study the problem of survivable network design with guaranteed QoS, i.e, the requirement is to have at least k > 1 node disjoint hop constrained paths from each source to the BS. We show that the problem is NP-Hard. We also show that the problem of finding a feasible solution to this optimization problem is NP-Complete. We propose two polynomial time heuristics for this problem, and compare their performance on various randomly generated network scenarios.
Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia
Venue: arXiv cs.MM 2014
Year: 2014

This paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach.
Unitary Precoding and Basis Dependency of MMSE Performance for Gaussian Erasure Channels
Venue: arXiv cs.IT 2014
Year: 2014

We consider the transmission of a Gaussian vector source over a multi-dimensional Gaussian channel where a random or a fixed subset of the channel outputs are erased. Within the setup where the only encoding operation allowed is a linear unitary transformation on the source, we investigate the MMSE performance, both in average, and also in terms of guarantees that hold with high probability as a function of the system parameters. Under the performance criterion of average MMSE, necessary conditions that should be satisfied by the optimal unitary encoders are established and explicit solutions for a class of settings are presented. For random sampling of signals that have a low number of degrees of freedom, we present MMSE bounds that hold with high probability. Our results illustrate how the spread of the eigenvalue distribution and the unitary transformation contribute to these performance guarantees. The performance of the discrete Fourier transform (DFT) is also investigated. As a benchmark, we investigate the equidistant sampling of circularly wide-sense stationary (c.w.s.s.) signals, and present the explicit error expression that quantifies the effects of the sampling rate and the eigenvalue distribution of the covariance matrix of the signal. These findings may be useful in understanding the geometric dependence of signal uncertainty in a stochastic process. In particular, unlike information theoretic measures such as entropy, we highlight the basis dependence of uncertainty in a signal with another perspective. The unitary encoding space restriction exhibits the most and least favorable signal bases for estimation.
Approximating Fixation Probabilities in the Generalized Moran Process
Venue: arXiv cs.CC 2014
Year: 2014

We consider the Moran process, as generalized by Lieberman, Hauert and Nowak (Nature, 433:312--316, 2005). A population resides on the vertices of a finite, connected, undirected graph and, at each time step, an individual is chosen at random with probability proportional to its assigned 'fitness' value. It reproduces, placing a copy of itself on a neighbouring vertex chosen uniformly at random, replacing the individual that was there. The initial population consists of a single mutant of fitness $r>0$ placed uniformly at random, with every other vertex occupied by an individual of fitness 1. The main quantities of interest are the probabilities that the descendants of the initial mutant come to occupy the whole graph (fixation) and that they die out (extinction); almost surely, these are the only possibilities. In general, exact computation of these quantities by standard Markov chain techniques requires solving a system of linear equations of size exponential in the order of the graph so is not feasible. We show that, with high probability, the number of steps needed to reach fixation or extinction is bounded by a polynomial in the number of vertices in the graph. This bound allows us to construct fully polynomial randomized approximation schemes (FPRAS) for the probability of fixation (when $r\geq 1$) and of extinction (for all $r>0$).
Adaptive Redundancy Management for Durable P2P Backup
Venue: arXiv cs.DC 2014
Year: 2014

We design and analyze the performance of a redundancy management mechanism for Peer-to-Peer backup applications. Armed with the realization that a backup system has peculiar requirements -- namely, data is read over the network only during restore processes caused by data loss -- redundancy management targets data durability rather than attempting to make each piece of information availabile at any time. In our approach each peer determines, in an on-line manner, an amount of redundancy sufficient to counter the effects of peer deaths, while preserving acceptable data restore times. Our experiments, based on trace-driven simulations, indicate that our mechanism can reduce the redundancy by a factor between two and three with respect to redundancy policies aiming for data availability. These results imply an according increase in storage capacity and decrease in time to complete backups, at the expense of longer times required to restore data. We believe this is a very reasonable price to pay, given the nature of the application. We complete our work with a discussion on practical issues, and their solutions, related to which encoding technique is more suitable to support our scheme.
On paths-based criteria for polynomial time complexity in proof-nets
Venue: arXiv cs.LO 2014
Year: 2014

Girard's Light linear logic (LLL) characterized polynomial time in the proof-as-program paradigm with a bound on cut elimination. This logic relied on a stratification principle and a "one-door" principle which were generalized later respectively in the systems L^4 and L^3a. Each system was brought with its own complex proof of Ptime soundness. In this paper we propose a broad sufficient criterion for Ptime soundness for linear logic subsystems, based on the study of paths inside the proof-nets, which factorizes proofs of soundness of existing systems and may be used for future systems. As an additional gain, our bound stands for any reduction strategy whereas most bounds in the literature only stand for a particular strategy.
Nash Codes for Noisy Channels
Venue: arXiv cs.GT 2014
Year: 2014

This paper studies the stability of communication protocols that deal with transmission errors. We consider a coordination game between an informed sender and an uninformed decision maker, the receiver, who communicate over a noisy channel. The sender's strategy, called a code, maps states of nature to signals. The receiver's best response is to decode the received channel output as the state with highest expected receiver payoff. Given this decoding, an equilibrium or "Nash code" results if the sender encodes every state as prescribed. We show two theorems that give sufficient conditions for Nash codes. First, a receiver-optimal code defines a Nash code. A second, more surprising observation holds for communication over a binary channel which is used independently a number of times, a basic model of information transmission: Under a minimal "monotonicity" requirement for breaking ties when decoding, which holds generically, EVERY code is a Nash code.
Cooperative oligopoly games with boundedly rational firms
Venue: arXiv cs.GT 2014
Year: 2014

We analyze cooperative Cournot games with boundedly rational firms. Due to cogni- tive constraints, the members of a coalition cannot accurately predict the coalitional structure of the non-members. Thus, they compute their value using simple heuris- tics. In particular, they assign various non-equilibrium probability distributions over the outsiders' set of partitions. We construct the characteristic function of a coalition in such an environment and we analyze the core of the corresponding games. We show that the core is non-empty provided the number of firms in the market is sufficiently large. Moreover, we show that if two distributions over the set of partitions are related via first-order dominance, then the core of the game under the dominated distribution is a subset of the core under the dominant distribution.
On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages
Venue: arXiv cs.RO 2014
Year: 2014

We extend our study of Motion Planning via Manifold Samples (MMS), a general algorithmic framework that combines geometric methods for the exact and complete analysis of low-dimensional configuration spaces with sampling-based approaches that are appropriate for higher dimensions. The framework explores the configuration space by taking samples that are entire low-dimensional manifolds of the configuration space capturing its connectivity much better than isolated point samples. The contributions of this paper are as follows: (i) We present a recursive application of MMS in a six-dimensional configuration space, enabling the coordination of two polygonal robots translating and rotating amidst polygonal obstacles. In the adduced experiments for the more demanding test cases MMS clearly outperforms PRM, with over 20-fold speedup in a coordination-tight setting. (ii) A probabilistic completeness proof for the most prevalent case, namely MMS with samples that are affine subspaces. (iii) A closer examination of the test cases reveals that MMS has, in comparison to standard sampling-based algorithms, a significant advantage in scenarios containing high-dimensional narrow passages. This provokes a novel characterization of narrow passages which attempts to capture their dimensionality, an attribute that had been (to a large extent) unattended in previous definitions.