OpenRaiser/Intern-Atlas huggingface dataset:
Super-resolution community detection for layer-aggregated multilayer networks
Venue: arXiv cs.SI 2017
Year: 2017

Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the tradeoffs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with $N$ nodes and $L$ layers, which are drawn from an ensemble of Erdős-Rényi networks. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit $K^*$. When layers are aggregated via a summation, we obtain $K^*\varpropto \mathcal{O}(\sqrt{NL}/T)$, where $T$ is the number of layers across which the community persists. Interestingly, if $T$ is allowed to vary with $L$ then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that $T/L$ decays more slowly than $ \mathcal{O}(L^{-1/2})$. Moreover, we find that thresholding the summation can in some cases cause $K^*$ to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. That is, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.
Resource-Performance Trade-off Analysis for Mobile Robot Design
Venue: arXiv cs.RO 2017
Year: 2017

The design of mobile autonomous robots is challenging due to the limited on-board resources such as processing power and energy. A promising approach is to generate intelligent schedules that reduce the resource consumption while maintaining best performance, or more interestingly, to trade off reduced resource consumption for a slightly lower but still acceptable level of performance. In this paper, we provide a framework to aid designers in exploring such resource-performance trade-offs and finding schedules for mobile robots, guided by questions such as "what is the minimum resource budget required to achieve a given level of performance?" The framework is based on a quantitative multi-objective verification technique which, for a collection of possibly conflicting objectives, produces the Pareto front that contains all the optimal trade-offs that are achievable. The designer then selects a specific Pareto point based on the resource constraints and desired performance level, and a correct-by-construction schedule that meets those constraints is automatically generated. We demonstrate the efficacy of this framework on several robotic scenarios in both simulations and experiments with encouraging results.
Hybrid Beamforming for Massive MIMO - A Survey
Venue: arXiv cs.IT 2017
Year: 2017

Hybrid multiple-antenna transceivers, which combine large-dimensional analog pre/postprocessing with lower-dimensional digital processing, are the most promising approach for reducing the hardware cost and training overhead in massive MIMO systems. This paper provides a comprehensive survey of the various incarnations of such structures that have been proposed in the literature. We provide a taxonomy in terms of the required channel state information (CSI), namely whether the processing adapts to the instantaneous or the average (second-order) CSI; while the former provides somewhat better signal-to-noise and interference ratio (SNIR), the latter has much lower overhead for CSI acquisition. We furthermore distinguish hardware structures of different complexities. Finally, we point out the special design aspects for operation at millimeter-wave frequencies.
Asynchronous and Dynamic Coverage Control Scheme for Persistent Surveillance Missions
Venue: arXiv cs.MA 2017
Year: 2017

A decomposition-based coverage control scheme is proposed for multi-agent, persistent surveillance missions operating in a communication-constrained, dynamic environment. The proposed approach decouples high-level task assignment from low-level motion planning in a modular framework. Coverage assignments and surveillance parameters are managed by a central base station, and transmitted to mobile agents via unplanned and asynchronous exchanges. Coverage updates promote load balancing, while maintaining geometric and temporal characteristics that allow effective pairing with generic path planners. Namely, the proposed scheme guarantees that (i) coverage regions are connected and collectively cover the environment, (ii) subregions may only go uncovered for bounded periods of time, (iii) collisions (or sensing overlaps) are inherently avoided, and (iv) under static event likelihoods, the collective coverage regions converge to a Pareto-optimal configuration. This management scheme is then paired with a generic path planner satisfying loose assumptions. The scheme is illustrated through simulated surveillance missions.
ReasoNet: Learning to Stop Reading in Machine Comprehension
Venue: arXiv cs.LG 2017
Year: 2017

Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets have achieved exceptional performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset.
Multiparty Session Actors
Venue: arXiv cs.PL 2017
Year: 2017

Actor coordination armoured with a suitable protocol description language has been a pressing problem in the actors community. We study the applicability of multiparty session type (MPST) protocols for verification of actor programs. We incorporate sessions to actors by introducing minimum additions to the model such as the notion of actor roles and protocol mailboxes. The framework uses Scribble, which is a protocol description language based on multiparty session types. Our programming model supports actor-like syntax and runtime verification mechanism guaranteeing communication safety of the participating entities. An actor can implement multiple roles in a similar way as an object can implement multiple interfaces. Multiple roles allow for cooperative inter-concurrency in a single actor. We demonstrate our framework by designing and implementing a session actor library in Python and its runtime verification mechanism. Benchmark results demonstrate that the runtime checks induce negligible overhead. We evaluate the applicability of our verification framework to specify actor interactions by implementing twelve examples from an actor benchmark suit.
Functional Clones and Expressibility of Partition Functions
Venue: arXiv cs.DM 2017
Year: 2017

We study functional clones, which are sets of non-negative pseudo-Boolean functions (functions $\{0,1\}^k\to\mathbb{R}_{\geq 0}$) closed under (essentially) multiplication, summation and limits. Functional clones naturally form a lattice under set inclusion and are closely related to counting Constraint Satisfaction Problems (CSPs). We identify a sublattice of interesting functional clones and investigate the relationships and properties of the functional clones in this sublattice.
Popular Matchings with Multiple Partners
Venue: arXiv cs.GT 2017
Year: 2017

Our input is a bipartite graph $G = (A \cup B,E)$ where each vertex in $A \cup B$ has a preference list strictly ranking its neighbors. The vertices in $A$ and in $B$ are called students and courses, respectively. Each student $a$ seeks to be matched to $\mathsf{cap}(a) \ge 1$ courses while each course $b$ seeks $\mathsf{cap}(b) \ge 1$ many students to be matched to it. The Gale-Shapley algorithm computes a pairwise-stable matching (one with no blocking edge) in $G$ in linear time. We consider the problem of computing a popular matching in $G$ -- a matching $M$ is popular if $M$ cannot lose an election to any matching where vertices cast votes for one matching versus another. Our main contribution is to show that a max-size popular matching in $G$ can be computed by the 2-level Gale-Shapley algorithm in linear time. This is an extension of the classical Gale-Shapley algorithm and we prove its correctness via linear programming.
An information-based classification of Elementary Cellular Automata
Venue: arXiv cs.CC 2017
Year: 2017

A novel, information-based classification of elementary cellular automata is proposed that circumvents the problems associated with isolating whether complexity is in fact intrinsic to a dynamical rule, or if it arises merely as a product of a complex initial state. Transfer entropy variations processed by the system split the 256 elementary rules into three information classes, based on sensitivity to initial conditions. These classes form a hierarchy such that coarse-graining transitions observed among elementary cellular automata rules predominately occur within each information- based class, or much more rarely, down the hierarchy.
Super-resolving multiresolution images with band-independant geometry of multispectral pixels
Venue: arXiv cs.CV 2017
Year: 2017

A new resolution enhancement method is presented for multispectral and multi-resolution images, such as these provided by the Sentinel-2 satellites. Starting from the highest resolution bands, band-dependent information (reflectance) is separated from information that is common to all bands (geometry of scene elements). This model is then applied to unmix low-resolution bands, preserving their reflectance, while propagating band-independent information to preserve the sub-pixel details. A reference implementation is provided, with an application example for super-resolving Sentinel-2 data.
Viewport-Adaptive Navigable 360-Degree Video Delivery
Venue: arXiv cs.MM 2017
Year: 2017

The delivery and display of 360-degree videos on Head-Mounted Displays (HMDs) presents many technical challenges. 360-degree videos are ultra high resolution spherical videos, which contain an omnidirectional view of the scene. However only a portion of this scene is displayed on the HMD. Moreover, HMD need to respond in 10 ms to head movements, which prevents the server to send only the displayed video part based on client feedback. To reduce the bandwidth waste, while still providing an immersive experience, a viewport-adaptive 360-degree video streaming system is proposed. The server prepares multiple video representations, which differ not only by their bit-rate, but also by the qualities of different scene regions. The client chooses a representation for the next segment such that its bit-rate fits the available throughput and a full quality region matches its viewing. We investigate the impact of various spherical-to-plane projections and quality arrangements on the video quality displayed to the user, showing that the cube map layout offers the best quality for the given bit-rate budget. An evaluation with a dataset of users navigating 360-degree videos demonstrates that segments need to be short enough to enable frequent view switches.
Polynomial Time Corresponds to Solutions of Polynomial Ordinary Differential Equations of Polynomial Length (Journal version)
Venue: arXiv cs.CC 2017
Year: 2017

The outcomes of this paper are twofold. Implicit complexity. We provide an implicit characterization of polynomial time computation in terms of ordinary differential equations: we characterize the class PTIME of languages computable in polynomial time in terms of differential equations with polynomial right-hand side. This result gives a purely continuous elegant and simple characterization of PTIME. We believe it is the first time complexity classes are characterized using only ordinary differential equations. Our characterization extends to functions computable in polynomial time over the reals in the sense of computable analysis. Our results may provide a new perspective on classical complexity, by giving a way to define complexity classes, like PTIME, in a very simple way, without any reference to a notion of (discrete) machine. This may also provide ways to state classical questions about computational complexity via ordinary differential equations. Continuous-Time Models of Computation. Our results can also be interpreted in terms of analog computers or analog models of computation: As a side effect, we get that the 1941 General Purpose Analog Computer (GPAC) of Claude Shannon is provably equivalent to Turing machines both in terms of computability and complexity, a fact that has never been established before. This result provides arguments in favour of a generalised form of the Church-Turing Hypothesis, which states that any physically realistic (macroscopic) computer is equivalent to Turing machines both in terms of computability and complexity.
Don't Skype & Type! Acoustic Eavesdropping in Voice-Over-IP
Venue: arXiv cs.CR 2017
Year: 2017

Acoustic emanations of computer keyboards represent a serious privacy issue. As demonstrated in prior work, physical properties of keystroke sounds might reveal what a user is typing. However, previous attacks assumed relatively strong adversary models that are not very practical in many real-world settings. Such strong models assume: (i) adversary's physical proximity to the victim, (ii) precise profiling of the victim's typing style and keyboard, and/or (iii) significant amount of victim's typed information (and its corresponding sounds) available to the adversary. This paper presents and explores a new keyboard acoustic eavesdropping attack that involves Voice-over-IP (VoIP), called Skype & Type (S&T), while avoiding prior strong adversary assumptions. This work is motivated by the simple observation that people often engage in secondary activities (including typing) while participating in VoIP calls. As expected, VoIP software acquires and faithfully transmits all sounds, including emanations of pressed keystrokes, which can include passwords and other sensitive information. We show that one very popular VoIP software (Skype) conveys enough audio information to reconstruct the victim's input -- keystrokes typed on the remote keyboard. Our results demonstrate that, given some knowledge on the victim's typing style and keyboard model, the attacker attains top-5 accuracy of 91.7% in guessing a random key pressed by the victim. Furthermore, we demonstrate that S&T is robust to various VoIP issues (e.g., Internet bandwidth fluctuations and presence of voice over keystrokes), thus confirming feasibility of this attack. Finally, it applies to other popular VoIP software, such as Google Hangouts.
On Optimal Latency of Communications
Venue: arXiv cs.IT 2017
Year: 2017

In this paper we investigate the optimal latency of communications. Focusing on fixed rate communication without any feedback channel, this paper encompasses low-latency strategies with which one hop and multi-hop communication issues are treated from an information theoretic perspective. By defining the latency as the time required to make decisions, we prove that if short messages can be transmitted in parallel Gaussian channels, for example, via orthogonal frequency-division multiplexing (OFDM)-like signals, there exists an optimal low-latency strategy for every code. This can be achieved via early-detection schemes or asynchronous detections. We first provide the optimal achievable latency in additive white Gaussian noise (AWGN) channels for every channel code given a probability block error $ε$. This can be obtained via sequential ratio tests or a "genie" aided, \textit{e.g}. error-detecting codes. Results demonstrate the effectiveness of the approach. Next, we show how early-detection can be effective with OFDM signals while maintaining its spectral efficiency via random coding or pre-coding random matrices. Finally, we explore the optimal low-latency strategy in multi-hop relaying schemes. For amplify-and-forward (AF) and decode-and-forward (DF) relaying schemes there exist an optimal achievable latency. In particular, we first show that there exist a better low-latency strategy, for which AF relays could transmit while receiving. This can be achieved by using amplify and forward combined with early detection.
A Normalizing Computation Rule for Propositional Extensionality in Higher-Order Minimal Logic
Venue: arXiv cs.LO 2017
Year: 2017

The univalence axiom expresses the principle of extensionality for dependent type theory. However, if we simply add the univalence axiom to type theory, then we lose the property of canonicity - that every closed term computes to a canonical form. A computation becomes `stuck' when it reaches the point that it needs to evaluate a proof term that is an application of the univalence axiom. So we wish to find a way to compute with the univalence axiom. While this problem has been solved with the formulation of cubical type theory, where the computations are expressed using a nominal extension of lambda-calculus, it may be interesting to explore alternative solutions, which do not require such an extension. As a first step, we present here a system of propositional higher-order minimal logic (PHOML). There are three kinds of typing judgement in PHOML. There are terms which inhabit types, which are the simple types over $Ω$. There are proofs which inhabit propositions, which are the terms of type $Ω$. The canonical propositions are those constructed from $\bot$ by implication $\supset$. Thirdly, there are paths which inhabit equations $M =_A N$, where $M$ and $N$ are terms of type $A$. There are two ways to prove an equality: reflexivity, and propositional extensionality - logically equivalent propositions are equal. This system allows for some definitional equalities that are not present in cubical type theory, namely that transport along the trivial path is identity. We present a call-by-name reduction relation for this system, and prove that the system satisfies canonicity: every closed typable term head-reduces to a canonical form. This work has been formalised in Agda.
Personalized Prediction of Vehicle Energy Consumption based on Participatory Sensing
Venue: arXiv cs.HC 2017
Year: 2017

The advent of abundant on-board sensors and electronic devices in vehicles populates the paradigm of participatory sensing to harness crowd-sourced data gathering for intelligent transportation applications, such as distance-to-empty prediction and eco-routing. While participatory sensing can provide diverse driving data, there lacks a systematic study of effective utilization of the data for personalized prediction. There are considerable challenges on how to interpolate the missing data from a sparse dataset, which often arises from participatory sensing. This paper presents and compares various approaches for personalized vehicle energy consumption prediction, including a blackbox framework that identifies driver/vehicle/environment-dependent factors and a collaborative filtering approach based on matrix factorization. Furthermore, a case study of distance-to-empty prediction for electric vehicles by participatory sensing data is conducted and evaluated empirically, which shows that our approaches can significantly improve the prediction accuracy.
A new kernel-based approach to system identification with quantized output data
Venue: arXiv cs.SY 2017
Year: 2017

In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods to provide an estimate of the system. In particular, we design two methods based on the so-called Gibbs sampler that allow also to estimate the kernel hyperparameters by marginal likelihood maximization via the expectation-maximization method. Numerical simulations show the effectiveness of the proposed scheme, as compared to the state-of-the-art kernel-based methods when these are employed in system identification with quantized data.
Team-Optimal Distributed MMSE Estimation in General and Tree Networks
Venue: arXiv cs.SY 2017
Year: 2017

We construct team-optimal estimation algorithms over distributed networks for state estimation in the finite-horizon mean-square error (MSE) sense. Here, we have a distributed collection of agents with processing and cooperation capabilities. These agents observe noisy samples of a desired state through a linear model and seek to learn this state by interacting with each other. Although this problem has attracted significant attention and been studied extensively in fields including machine learning and signal processing, all the well-known strategies do not achieve team-optimal learning performance in the finite-horizon MSE sense. To this end, we formulate the finite-horizon distributed minimum MSE (MMSE) when there is no restriction on the size of the disclosed information, i.e., oracle performance, over an arbitrary network topology. Subsequently, we show that exchange of local estimates is sufficient to achieve the oracle performance only over certain network topologies. By inspecting these network structures, we propose recursive algorithms achieving the oracle performance through the disclosure of local estimates. For practical implementations we also provide approaches to reduce the complexity of the algorithms through the time-windowing of the observations. Finally, in the numerical examples, we demonstrate the superior performance of the introduced algorithms in the finite-horizon MSE sense due to optimal estimation.
Energy-Aware Wireless Relay Selection in Load-Coupled OFDMA Cellular Networks
Venue: arXiv cs.IT 2017
Year: 2017

We investigate transmission energy minimization via optimizing wireless relay selection in orthogonal-frequency-division multiple access (OFDMA) networks. We take into account the impact of the load of cells on transmission energy. We prove the NP-hardness of the energy-aware wireless relay selection problem. To tackle the computational complexity, a partial optimality condition is derived for providing insights in respect of designing an effective and efficient algorithm. Numerical results show that the resulting algorithm achieves high energy performance.