OpenRaiser/Intern-Atlas huggingface dataset:
A Martingale Approach and Time-Consistent Sampling-based Algorithms for Risk Management in Stochastic Optimal Control
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we consider a class of stochastic optimal control problems with risk constraints that are expressed as bounded probabilities of failure for particular initial states. We present here a martingale approach that diffuses a risk constraint into a martingale to construct time-consistent control policies. The martingale stands for the level of risk tolerance over time. By augmenting the system dynamics with the controlled martingale, the original risk-constrained problem is transformed into a stochastic target problem. We extend the incremental Markov Decision Process (iMDP) algorithm to approximate arbitrarily well an optimal feedback policy of the original problem by sampling in the augmented state space and computing proper boundary conditions for the reformulated problem. We show that the algorithm is both probabilistically sound and asymptotically optimal. The performance of the proposed algorithm is demonstrated on motion planning and control problems subject to bounded probability of collision in uncertain cluttered environments.
Value of information in noncooperative games
Venue: arXiv cs.GT 2015
Year: 2015

In some games, additional information hurts a player, e.g., in games with first-mover advantage, the second-mover is hurt by seeing the first-mover's move. What properties of a game determine whether it has such negative "value of information" for a particular player? Can a game have negative value of information for all players? To answer such questions, we generalize the definition of marginal utility of a good to define the marginal utility of a parameter vector specifying a game. So rather than analyze the global structure of the relationship between a game's parameter vector and player behavior, as in previous work, we focus on the local structure of that relationship. This allows us to prove that generically, every game can have negative marginal value of information, unless one imposes a priori constraints on allowed changes to the game's parameter vector. We demonstrate these and related results numerically, and discuss their implications.
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
Venue: arXiv cs.LG 2015
Year: 2015

In recent years, there has been growing focus on the study of automated recommender systems. Music recommendation systems serve as a prominent domain for such works, both from an academic and a commercial perspective. A fundamental aspect of music perception is that music is experienced in temporal context and in sequence. In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions. The model is learned online and is uniquely adapted for each listener. To reduce exploration time, DJ-MC exploits user feedback to initialize a model, which it subsequently updates by reinforcement. We evaluate our framework with human participants using both real song and playlist data. Our results indicate that DJ-MC's ability to recommend sequences of songs provides a significant improvement over more straightforward approaches, which do not take transitions into account.
Actor-Critic Algorithms for Learning Nash Equilibria in N-player General-Sum Games
Venue: arXiv cs.GT 2015
Year: 2015

We consider the problem of finding stationary Nash equilibria (NE) in a finite discounted general-sum stochastic game. We first generalize a non-linear optimization problem from Filar and Vrieze [2004] to a $N$-player setting and break down this problem into simpler sub-problems that ensure there is no Bellman error for a given state and an agent. We then provide a characterization of solution points of these sub-problems that correspond to Nash equilibria of the underlying game and for this purpose, we derive a set of necessary and sufficient SG-SP (Stochastic Game - Sub-Problem) conditions. Using these conditions, we develop two actor-critic algorithms: OFF-SGSP (model-based) and ON-SGSP (model-free). Both algorithms use a critic that estimates the value function for a fixed policy and an actor that performs descent in the policy space using a descent direction that avoids local minima. We establish that both algorithms converge, in self-play, to the equilibria of a certain ordinary differential equation (ODE), whose stable limit points coincide with stationary NE of the underlying general-sum stochastic game. On a single state non-generic game (see Hart and Mas-Colell [2005]) as well as on a synthetic two-player game setup with $810,000$ states, we establish that ON-SGSP consistently outperforms NashQ ([Hu and Wellman, 2003] and FFQ [Littman, 2001] algorithms.
Exponential-Krylov methods for ordinary differential equations
Venue: arXiv cs.NA 2015
Year: 2015

This paper develops a new class of exponential-type integrators where all the matrix exponentiations are performed in a single Krylov space of low dimension. The new family, called Lightly Implicit Krylov-Exponential (LIKE), is well suited for solving large scale systems of ODEs or semi-discrete PDEs. The time discretization and the Krylov space approximation are treated as a single computational process, and the Krylov space properties are an integral part of the new LIKE order condition theory developed herein. Consequently, LIKE methods require a small number of basis vectors determined solely by the temporal order of accuracy. The subspace size is independent of the ODE under consideration, and there is no need to monitor the errors in linear system solutions at each stage. Numerical results illustrate the favorable properties of new family of methods.
Function spaces for second-order polynomial time
Venue: arXiv cs.CC 2015
Year: 2015

In the context of second-order polynomial-time computability, we prove that there is no general function space construction. We proceed to identify restrictions on the domain or the codomain that do provide a function space with polynomial-time function evaluation containing all polynomial-time computable functions of that type. As side results we show that a polynomial-time counterpart to admissibility of a representation is not a suitable criterion for natural representations, and that the Weihrauch degrees embed into the polynomial-time Weihrauch degrees.
Broadcast Classical-Quantum Capacity Region of Two-Phase Bidirectional Relaying Channel
Venue: arXiv cs.IT 2015
Year: 2015

We study a three-node quantum network which enables bidirectional communication between two nodes with a half-duplex relay node. A decode-and-forward protocol is used to perform the communication in two phases. In the first phase, the messages of two nodes are transmitted to the relay node. In the second phase, the relay node broadcasts a re-encoded composition to the two nodes. We determine the capacity region of the broadcast phase.
Channels with cost constraints: strong converse and dispersion
Venue: arXiv cs.IT 2015
Year: 2015

This paper shows the strong converse and the dispersion of memoryless channels with cost constraints and performs refined analysis of the third order term in the asymptotic expansion of the maximum achievable channel coding rate, showing that it is equal to $\frac 1 2 \frac {\log n}{n}$ in most cases of interest. The analysis is based on a non-asymptotic converse bound expressed in terms of the distribution of a random variable termed the $\mathsf b$-tilted information density, which plays a role similar to that of the $\mathsf d$-tilted information in lossy source coding. We also analyze the fundamental limits of lossy joint-source-channel coding over channels with cost constraints.
Stable Roommates Problem with Random Preferences
Venue: arXiv cs.DS 2015
Year: 2015

The stable roommates problem with $n$ agents has worst case complexity $O(n^2)$ in time and space. Random instances can be solved faster and with less memory, however. We introduce an algorithm that has average time and space complexity $O(n^\frac{3}{2})$ for random instances. We use this algorithm to simulate large instances of the stable roommates problem and to measure the probabilty $p_n$ that a random instance of size $n$ admits a stable matching. Our data supports the conjecture that $p_n = Θ(n^{-1/4})$.
Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
Venue: arXiv cs.CV 2015
Year: 2015

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDMLDCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g. LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.
A Unified Approach for Network Information Theory
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we take a unified approach for network information theory and prove a coding theorem, which can recover most of the achievability results in network information theory that are based on random coding. The final single-letter expression has a very simple form, which was made possible by many novel elements such as a unified framework that represents various network problems in a simple and unified way, a unified coding strategy that consists of a few basic ingredients but can emulate many known coding techniques if needed, and new proof techniques beyond the use of standard covering and packing lemmas. For example, in our framework, sources, channels, states and side information are treated in a unified way and various constraints such as cost and distortion constraints are unified as a single joint-typicality constraint. Our theorem can be useful in proving many new achievability results easily and in some cases gives simpler rate expressions than those obtained using conventional approaches. Furthermore, our unified coding can strictly outperform existing schemes. For example, we obtain a generalized decode-compress-amplify-and-forward bound as a simple corollary of our main theorem and show it strictly outperforms previously known coding schemes. Using our unified framework, we formally define and characterize three types of network duality based on channel input-output reversal and network flow reversal combined with packing-covering duality.
User Participation in an Academic Social Networking Service: A Survey of Open Group Users on Mendeley
Venue: arXiv cs.SI 2015
Year: 2015

Although there are a number of social networking services that specifically target scholars, little has been published about the actual practices and the usage of these so-called academic social networking services (ASNSs). To fill this gap, we explore the populations of academics who engage in social activities using an ASNS; as an indicator of further engagement, we also determine their various motivations for joining a group in ASNSs. Using groups and their members in Mendeley as the platform for our case study, we obtained 146 participant responses from our online survey about users' common activities, usage habits, and motivations for joining groups. Our results show that 1) participants did not engage with social-based features as frequently and actively as they engaged with research-based features, and 2) users who joined more groups seemed to have a stronger motivation to increase their professional visibility and to contribute the research articles they had read to the group reading list. Our results generate interesting insights into Mendeley's user populations, their activities, and their motivations relative to the social features of Mendeley. We also argue that further design of ASNSs is needed to take greater account of disciplinary differences in scholarly communication and to establish incentive mechanisms for encouraging user participation.
Linear Boolean classification, coding and "the critical problem"
Venue: arXiv cs.IT 2015
Year: 2015

The problem of constructing a minimal rank matrix over GF(2) whose kernel does not intersect a given set S is considered. In the case where S is a Hamming ball centered at 0, this is equivalent to finding linear codes of largest dimension. For a general set, this is an instance of "the critical problem" posed by Crapo and Rota in 1970. This work focuses on the case where S is an annulus. As opposed to balls, it is shown that an optimal kernel is composed not only of dense but also of sparse vectors, and the optimal mixture is identified in various cases. These findings corroborate a proposed conjecture that for annulus of inner and outer radius nq and np respectively, the optimal relative rank is given by (1-q)H(p/(1-q)), an extension of the Gilbert-Varshamov bound H(p) conjectured for Hamming balls of radius np.
Convergence of Tâtonnement in Fisher Markets
Venue: arXiv cs.GT 2015
Year: 2015

Analyzing simple and natural price-adjustment processes that converge to a market equilibrium is a fundamental question in economics. Such an analysis may have implications in economic theory, computational economics, and distributed systems. Tâtonnement, proposed by Walras in 1874, is a process by which prices go up in response to excess demand, and down in response to excess supply. This paper analyzes the convergence of a time-discrete tâtonnement process, a problem that recently attracted considerable attention of computer scientists. We prove that the simple tâtonnement process that we consider converges (efficiently) to equilibrium prices and allocation in markets with nested CES-Leontief utilities, generalizing some of the previous convergence proofs for more restricted types of utility functions.
Capacity of Binary State Symmetric Channel with and without Feedback and Transmission Cost
Venue: arXiv cs.IT 2015
Year: 2015

We consider a unit memory channel, called Binary State Symmetric Channel (BSSC), in which the channel state is the modulo2 addition of the current channel input and the previous channel output. We derive closed form expressions for the capacity and corresponding channel input distribution, of this BSSC with and without feedback and transmission cost. We also show that the capacity of the BSSC is not increased by feedback, and it is achieved by a first order symmetric Markov process.
Customizable Contraction Hierarchies
Venue: arXiv cs.DS 2015
Year: 2015

We consider the problem of quickly computing shortest paths in weighted graphs given auxiliary data derived in an expensive preprocessing phase. By adding a fast weight-customization phase, we extend Contraction Hierarchies by Geisberger et al to support the three-phase workflow introduced by Delling et al. Our Customizable Contraction Hierarchies use nested dissection orders as suggested by Bauer et al. We provide an in-depth experimental analysis on large road and game maps that clearly shows that Customizable Contraction Hierarchies are a very practicable solution in scenarios where edge weights often change.
Cognitive Forwarding Control in Wireless Ad-Hoc Networks with Slow Fading Channels
Venue: arXiv cs.NI 2015
Year: 2015

We propose a decentralized stochastic control solution for the broadcast message dissemination problem in wireless ad hoc networks with slow fading channels. We formulate the control problem as a dynamic robust game which is well justified by two key observations; first, the shared nature of the wireless medium which inevitably cross-couples the nodes' forwarding decisions, thus binding them together as strategic players; second, the stochastic dynamics associated with the link qualities which renders the transmission costs noisy, thus motivating a robust formulation. Given the non stationarity induced by the fading process, an online solution for the formulated game would then require an adaptive procedure capable of both convergence to and tracking strategic equilibria as the environment changes. To this end, we deploy the strategic and non stationary learning algorithm of regret tracking, the temporally adaptive variant of the celebrated regret matching algorithm, to guarantee the emergence and active tracking of the correlated equilibria in the dynamic robust forwarding game. We also make provision for exploiting the channel state information, when available, to enhance the convergence speed of the learning algorithm by conducting an accurate transmission cost estimation. This cost estimate can basically serve as a model which spares the algorithm from extra action exploration, thus rendering the learning process more sample efficient. Simulation results reveal that our proposed solution excels in terms of both the number of transmissions and load distribution while also maintaining near perfect delivery ratio, especially in dense crowded environments.
The Analysis of Kademlia for random IDs
Venue: arXiv cs.DS 2015
Year: 2015

Kademlia is the de facto standard searching algorithm for P2P (peer-to-peer) networks on the Internet. In our earlier work, we introduced two slightly different models for Kademlia and studied how many steps it takes to search for a target node by using Kademlia's searching algorithm. The first model, in which nodes of the network are labelled with deterministic IDs, had been discussed in that paper. The second one, in which nodes are labelled with random IDs, which we call the Random ID Model, was only briefly mentioned. Refined results with detailed proofs for this model are given in this paper. Our analysis shows that with high probability it takes about $c \log n$ steps to locate any node, where $n$ is the total number of nodes in the network and $c$ is a constant that does not depend on $n$.
Small Cell In-Band Wireless Backhaul in Massive MIMO Systems: A Cooperation of Next-Generation Techniques
Venue: arXiv cs.IT 2015
Year: 2015

Massive multiple-inputmultiple-output (MIMO) systems, dense small-cells (SCs), and full duplex are three candidate techniques for next-generation communication systems. The cooperation of next-generation techniques could offer more benefits, e.g., SC in-band wireless backhaul in massive MIMO systems. In this paper, three strategies of SC in-band wireless backhaul in massive MIMO systems are introduced and compared, i.e., complete time-division duplex (CTDD), zero-division duplex (ZDD), and ZDD with interference rejection (ZDD-IR). Simulation results demonstrate that SC in-band wireless backhaul has the potential to improve the throughput for massive MIMO systems. Specifically, among the three strategies, CTDD is the simplest one and could achieve decent throughput improvement. Depending on conditions, with the self-interference cancellation capability at SCs, ZDD could achieve better throughput than CTDD, even with residual self-interference. Moreover, ZDD-IR requires the additional interference rejection process at the BS compared to ZDD, but it could generally achieve better throughput than CTDD and ZDD.
R-dimensional ESPRIT-type algorithms for strictly second-order non-circular sources and their performance analysis
Venue: arXiv cs.IT 2015
Year: 2015

High-resolution parameter estimation algorithms designed to exploit the prior knowledge about incident signals from strictly second-order (SO) non-circular (NC) sources allow for a lower estimation error and can resolve twice as many sources. In this paper, we derive the R-D NC Standard ESPRIT and the R-D NC Unitary ESPRIT algorithms that provide a significantly better performance compared to their original versions for arbitrary source signals. They are applicable to shift-invariant R-D antenna arrays and do not require a centrosymmetric array structure. Moreover, we present a first-order asymptotic performance analysis of the proposed algorithms, which is based on the error in the signal subspace estimate arising from the noise perturbation. The derived expressions for the resulting parameter estimation error are explicit in the noise realizations and asymptotic in the effective signal-to-noise ratio (SNR), i.e., the results become exact for either high SNRs or a large sample size. We also provide mean squared error (MSE) expressions, where only the assumptions of a zero mean and finite SO moments of the noise are required, but no assumptions about its statistics are necessary. As a main result, we analytically prove that the asymptotic performance of both R-D NC ESPRIT-type algorithms is identical in the high effective SNR regime. Finally, a case study shows that no improvement from strictly non-circular sources can be achieved in the special case of a single source.
Verification of Markov Decision Processes using Learning Algorithms
Venue: arXiv cs.LO 2015
Year: 2015

We present a general framework for applying machine-learning algorithms to the verification of Markov decision processes (MDPs). The primary goal of these techniques is to improve performance by avoiding an exhaustive exploration of the state space. Our framework focuses on probabilistic reachability, which is a core property for verification, and is illustrated through two distinct instantiations. The first assumes that full knowledge of the MDP is available, and performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP, and yields probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. The latter is the first extension of statistical model-checking for unbounded properties in MDPs. In contrast with other related approaches, we do not restrict our attention to time-bounded (finite-horizon) or discounted properties, nor assume any particular properties of the MDP. We also show how our techniques extend to LTL objectives. We present experimental results showing the performance of our framework on several examples.