Spacecraft missions to small celestial bodies face sensitive, strongly non-Keplerian dynamics that motivate the employment of automated sampling-based trajectory planning. However, the scarcity of onboard computing resources necessitates careful formulation of heuristics for efficiently searching the reachable sets, which exhibit complex and finely-detailed structure. We examine a global search heuristic that combines aspects of simulated annealing and hill-climbing to locate sparse regions of the planning domain that simultaneously satisfy numerous geometric and timing constraints associated with remote sensing objectives for points of interest on the central body surface. Subsequently, we demonstrate the use of a receding-horizon implementation of this maneuver-planning strategy to produce mission profiles that fulfill sets of such goals.

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is their interpretability: the transfer functions provide visual means for inspecting the models and identifying domain-specific relations between inputs and outputs. However, in large-scale problems involving the prediction of many related tasks, learning independently additive models results in a loss of model interpretability, and can cause overfitting when training data is scarce. We introduce a novel multi-task learning approach which provides a corpus of accurate and interpretable additive models for a large number of related forecasting tasks. Our key idea is to share transfer functions across models in order to reduce the model complexity and ease the exploration of the corpus. We establish a connection with sparse dictionary learning and propose a new efficient fitting algorithm which alternates between sparse coding and transfer function updates. The former step is solved via an extension of Orthogonal Matching Pursuit, whose properties are analyzed using a novel recovery condition which extends existing results in the literature. The latter step is addressed using a traditional dictionary update rule. Experiments on real-world data demonstrate that our approach compares favorably to baseline methods while yielding an interpretable corpus of models, revealing structure among the individual tasks and being more robust when training data is scarce. Our framework therefore extends the well-known benefits of additive models to common regression settings possibly involving thousands of tasks.

This paper studies the form and complexity of inference in graphical models using the abstraction offered by algebraic structures. In particular, we broadly formalize inference problems in graphical models by viewing them as a sequence of operations based on commutative semigroups. We then study the computational complexity of inference by organizing various problems into an "inference hierarchy". When the underlying structure of an inference problem is a commutative semiring -- i.e. a combination of two commutative semigroups with the distributive law -- a message passing procedure called belief propagation can leverage this distributive law to perform polynomial-time inference for certain problems. After establishing the NP-hardness of inference in any commutative semiring, we investigate the relation between algebraic properties in this setting and further show that polynomial-time inference using distributive law does not (trivially) extend to inference problems that are expressed using more than two commutative semigroups. We then extend the algebraic treatment of message passing procedures to survey propagation, providing a novel perspective using a combination of two commutative semirings. This formulation generalizes the application of survey propagation to new settings.

Particle Swarm Optimization (PSO) is a nature-inspired meta-heuristic for solving continuous optimization problems. In the literature, the potential of the particles of swarm has been used to show that slightly modified PSO guarantees convergence to local optima. Here we show that under specific circumstances the unmodified PSO, even with swarm parameters known (from the literature) to be good, almost surely does not yield convergence to a local optimum is provided. This undesirable phenomenon is called stagnation. For this purpose, the particles' potential in each dimension is analyzed mathematically. Additionally, some reasonable assumptions on the behavior if the particles' potential are made. Depending on the objective function and, interestingly, the number of particles, the potential in some dimensions may decrease much faster than in other dimensions. Therefore, these dimensions lose relevance, i.e., the contribution of their entries to the decisions about attractor updates becomes insignificant and, with positive probability, they never regain relevance. If Brownian Motion is assumed to be an approximation of the time-dependent drop of potential, practical, i.e., large values for this probability are calculated. Finally, on chosen multidimensional polynomials of degree two, experiments are provided showing that the required circumstances occur quite frequently. Furthermore, experiments are provided showing that even when the very simple sphere function is processed the described stagnation phenomenon occurs. Consequently, unmodified PSO does not converge to any local optimum of the chosen functions for tested parameter settings.

Multi-context Systems (MCSs) are a formalism for systems consisting of knowledge bases (possibly heterogeneous and non-monotonic) that are interlinked via bridge rules, where the global system semantics emerges from the local semantics of the knowledge bases (also called contexts) in an equilibrium. While MCSs and related formalisms are inherently targeted for distributed set- tings, no truly distributed algorithms for their evaluation were available. We address this short- coming and present a suite of such algorithms which includes a basic algorithm DMCS, an ad- vanced version DMCSOPT that exploits topology-based optimizations, and a streaming algorithm DMCS-STREAMING that computes equilibria in packages of bounded size. The algorithms be- have quite differently in several respects, as experienced in thorough experimental evaluation of a system prototype. From the experimental results, we derive a guideline for choosing the appropriate algorithm and running mode in particular situations, determined by the parameter settings.

In this paper, we propose a Concept-level Emotion Cause Model (CECM), instead of the mere word-level models, to discover causes of microblogging users' diversified emotions on specific hot event. A modified topic-supervised biterm topic model is utilized in CECM to detect'emotion topics' in event-related tweets, and then context-sensitive topical PageRank is utilized to detect meaningful multiword expressions as emotion causes. Experimental results on a dataset from Sina Weibo, one of the largest microblogging websites in China, show CECM can better detect emotion causes than baseline methods.

Truly complex stochastic processes--the infinitary processes [1] whose mutual information between past and future diverges--arise in many physical and biological systems [2-5], such as those in critical states. They are implicated in many natural phenomena, from the geophysics of earthquakes [6] and physiological measurements of neural avalanches [7] to semantics in natural language [8] and cascading failure in power transmission grids [9]. Their apparent infinite memory makes empirical estimation and modeling particularly challenging. The difficulty is reflected in the computational complexity of inference [10]: the resources required to predict and model them diverge in sample size, in memory for storing model parameters, and in memory required for prediction. Resource scaling, an analog of the venerable technique of finite-size scaling in statistical mechanics, suggests that for infinitary processes we look for statistical signatures that track divergences. Since resource divergences are sensitive to a process's inherent randomness and organization, one hopes that their scaling forms are uniquely revealing indicators of process complexity and can guide the selection of appropriate models. To date, though, there are few tractable constructions with which to explore possible general relationships between prediction, complexity, and learning for infinitary processes.

This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of parallelization. Without exchanging elements from the distributed training sets and keeping inter-node communications at affordable levels, the local (per-node) learners consent to the desired quantity inferred globally, meaning the one obtained if the entire training data set were centrally available. Impact of the decentralized learning framework to contemporary wireless communications and networking tasks is illustrated through case studies including target tracking using wireless sensor networks, unveiling Internet traffic anomalies, power system state estimation, as well as spectrum cartography for wireless cognitive radio networks.

Abstract--This paper is concerned about sparse, continuous frequency estimation in line spectral estimation, and focused on developing gridless sparse methods which overcome grid mismatches and correspond to limiting scenarios of existing grid-based approaches, e.g., We generalize AST (atomic-norm soft thresholding) to the case of nonconsecutively sampled data (incomplete data) inspired by recent atomic norm based techniques. We present a gridless version of SPICE (gridless SPICE, or GLS), which is applicable to both complete and incomplete data without the knowledge of noise level. We further prove the equivalence between GLS and atomic norm-based techniques under different assumptions of noise. Moreover, we extend GLS to a systematic framework consisting of model order selection and robust frequency estimation, and present feasible algorithms for AST and GLS. Numerical simulations are provided to validate our theoretical analysis and demonstrate performance of our methods compared to existing ones. Spectral analysis of signals [1] is a major problem in statistical signal processing. In this paper we are concerned about the line spectral estimation problem which has wide applications in communications, radar, sonar, seismology, astronomy and so on. C is the measurement noise. The sinusoid numberK M, usually referred to as the model order, is typically unknown in practice. Following from [2], the case when the signal is observed on [M ] is referred to as the complete data case while the other case when only samples on Ω [M ] are available is called the incomplete data case (or missing data case), in which the samples on the complementary set of Ω, Ω, [M ]\ Ω, are called missing data. Manuscript November 2013; accepted by IEEE Transactions on Signal Processing March 2015. The authors are with the School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore (email: { yangzai, elhxie } @ntu.edu.sg). Frequency estimation and model order selection are two important topics in line spectral estimation. 's can be obtained by a simple least-squares method according to (1). This paper is mainly focused on frequency estimation but we also incorporate existing model order selection tools in our methods. Many methods have been proposed for frequency estimation. Common classical methods include periodogram (or beamforming), nonlinear least squares (NLS) and MUSIC but often have limitations (see the review in [1]). For example, the periodogram suffers from leakage problems and have difficulties in resolving closely separated frequencies [1]. It is worth noting that the recent iterative adaptive approach (IAA) [4], [5] reduces the leakage of periodogram.

Existing MAP inference algorithms for determinantal point processes (DPPs) need to calculate determinants or conduct eigenvalue decomposition generally at the scale of the full kernel, which presents a great challenge for real-world applications. In this paper, we introduce a class of DPPs, called BwDPPs, that are characterized by an almost block diagonal kernel matrix and thus can allow efficient block-wise MAP inference. Furthermore, BwDPPs are successfully applied to address the difficulty of selecting change-points in the problem of change-point detection (CPD), which results in a new BwDPP-based CPD method, named BwDppCpd. In BwDppCpd, a preliminary set of change-point candidates is first created based on existing well-studied metrics. Then, these change-point candidates are treated as DPP items, and DPP-based subset selection is conducted to give the final estimate of the change-points that favours both quality and diversity. The effectiveness of BwDppCpd is demonstrated through extensive experiments on five real-world datasets.