A Convex Formulation for Spectral Shrunk Clustering

Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reduction into the clustering process assisted by manifold learning in the original space. However, the manifold in reduced-dimensional subspace is likely to exhibit altered properties in contrast with the original space. Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance. Aiming to address this issue, we propose a novel convex algorithm that mines the manifold structure in the low-dimensional subspace. In addition, our unified learning process makes the manifold learning particularly tailored for the clustering. Compared with other related methods, the proposed algorithm results in more structured clustering result. To validate the efficacy of the proposed algorithm, we perform extensive experiments on several benchmark datasets in comparison with some state-of-the-art clustering approaches. The experimental results demonstrate that the proposed algorithm has quite promising clustering performance.Comment: AAAI201

The PACE 2017 Parameterized Algorithms and Computational Experiments Challenge: The Second Iteration

In this article, the Program Committee of the Second Parameterized Algorithms and Computational Experiments challenge (PACE 2017) reports on the second iteration of the PACE challenge. Track A featured the Treewidth problem and Track B the Minimum Fill-In problem. Over 44 participants on 17 teams from 11 countries submitted their implementations to the competition

Human-Agent Decision-making: Combining Theory and Practice

Extensive work has been conducted both in game theory and logic to model strategic interaction. An important question is whether we can use these theories to design agents for interacting with people? On the one hand, they provide a formal design specification for agent strategies. On the other hand, people do not necessarily adhere to playing in accordance with these strategies, and their behavior is affected by a multitude of social and psychological factors. In this paper we will consider the question of whether strategies implied by theories of strategic behavior can be used by automated agents that interact proficiently with people. We will focus on automated agents that we built that need to interact with people in two negotiation settings: bargaining and deliberation. For bargaining we will study game-theory based equilibrium agents and for argumentation we will discuss logic-based argumentation theory. We will also consider security games and persuasion games and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729

Delta-Decision Procedures for Exists-Forall Problems over the Reals

Solving nonlinear SMT problems over real numbers has wide applications in robotics and AI. While significant progress is made in solving quantifier-free SMT formulas in the domain, quantified formulas have been much less investigated. We propose the first delta-complete algorithm for solving satisfiability of nonlinear SMT over real numbers with universal quantification and a wide range of nonlinear functions. Our methods combine ideas from counterexample-guided synthesis, interval constraint propagation, and local optimization. In particular, we show how special care is required in handling the interleaving of numerical and symbolic reasoning to ensure delta-completeness. In experiments, we show that the proposed algorithms can handle many new problems beyond the reach of existing SMT solvers

A fast and tight heuristic for A∗ in road networks

We study exact, efficient and practical algorithms for route planning in large road networks. Routing applications often require integrating the current traffic situation, planning ahead with traffic predictions for the future, respecting forbidden turns, and many other features depending on the exact application. While Dijkstra’s algorithm can be used to solve these problems, it is too slow for many applications. A* is a classical approach to accelerate Dijkstra’s algorithm. A* can support many extended scenarios without much additional implementation complexity. However, A*’s performance depends on the availability of a good heuristic that estimates distances. Computing tight distance estimates is a challenge on its own. On road networks, shortest paths can also be quickly computed using hierarchical speedup techniques. They achieve speed and exactness but sacrifice A*’s flexibility. Extending them to certain practical applications can be hard. In this paper, we present an algorithm to efficiently extract distance estimates for A* from Contraction Hierarchies (CH), a hierarchical technique. We call our heuristic CH-Potentials. Our approach allows decoupling the supported extensions from the hierarchical speed-up technique. Additionally, we describe A* optimizations to accelerate the processing of low degree nodes, which often occur in road networks

РЕЗУЛЬТАТЫ МОДЕЛИРОВАНИЯ ПРОСТРАНСТВЕННОГО РАСПРЕДЕЛЕНИЯ НЕКОТОРЫХ ВИДОВ РУКОКРЫЛЫХ НА ТЕРРИТОРИИ ТВЕРСКОЙ ОБЛАСТИ С ИСПОЛЬЗОВАНИЕМ МЕТОДА МАКСИМАЛЬНОЙ ЭНТРОПИИ

Проведен анализ пространственного распределения рукокрылых в Тверской области методом математического моделирования MaxEnt, реализованный при помощи пакета прикладных программ Maxent (ver. 3.3.3k) и ArcGIS 10.2. Выявлено, что на вероятность обнаружения летучих мышей в регионе оказывают влияние сочетания ряда экологических факторов, среди которых наибольшее значение имеют: высота над уровнем моря, средние температуры самого теплого месяца и плотность населения. Хироптерофауна Тверского Верхневолжья распределена неравномерно, что обусловлено пространственной локализацией наиболее благоприятных мест обитания, преимущественно приуроченных к долинам крупных рек, озерам Валдайской возвышенности и водохранилищам. Отмечаются видовые особенности в требованиях к условиям обитани