Feedback Set Problems

Festa, Paola; Pardalos, Pãnos M.; Resende, Maurício G. C.

doi:10.1007/978-1-4757-3023-4_4

Cited by 131 publications

(52 citation statements)

References 50 publications

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“…Grounding sets are equivalent to well‐known sets in graph theory called feedback vertex sets (Festa, Pardalos, and Resende ). These are sets of vertices U that cover all circuits; that is, for any circuit c , there is at least one word of c belonging to U .…”

Section: Grounding Setsmentioning

confidence: 99%

The Latent Structure of Dictionaries

Vincent-Lamarre

Massé

Lopes³

et al. 2016

Topics in Cognitive Science

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How many words—and which ones—are sufficient to define all other words? When dictionaries are analyzed as directed graphs with links from defining words to defined words, they reveal a latent structure. Recursively removing all words that are reachable by definition but that do not define any further words reduces the dictionary to a Kernel of about 10% of its size. This is still not the smallest number of words that can define all the rest. About 75% of the Kernel turns out to be its Core, a “Strongly Connected Subset” of words with a definitional path to and from any pair of its words and no word's definition depending on a word outside the set. But the Core cannot define all the rest of the dictionary. The 25% of the Kernel surrounding the Core consists of small strongly connected subsets of words: the Satellites. The size of the smallest set of words that can define all the rest—the graph's “minimum feedback vertex set” or MinSet—is about 1% of the dictionary, about 15% of the Kernel, and part‐Core/part‐Satellite. But every dictionary has a huge number of MinSets. The Core words are learned earlier, more frequent, and less concrete than the Satellites, which are in turn learned earlier, more frequent, but more concrete than the rest of the Dictionary. In principle, only one MinSet's words would need to be grounded through the sensorimotor capacity to recognize and categorize their referents. In a dual‐code sensorimotor/symbolic model of the mental lexicon, the symbolic code could do all the rest through recombinatory definition.

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Section: Grounding Setsmentioning

confidence: 99%

The Latent Structure of Dictionaries

Vincent-Lamarre

Massé

Lopes³

et al. 2016

Topics in Cognitive Science

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“…Data retransmission: Each client upon hearing the suppress determines if it is being suppressed: If so, it does not transmit (until it receives finish), otherwise, the client retransmits its data. 6. Finish: As the nodes are suppressed, the number of concurrent transmissions received by APs decreases with each slot.…”

Section: Cooperative Packet Subtractionmentioning

confidence: 99%

“…Thus, to compute the largest set of suppressed nodes in this slot, the CS computes the maximum Induced Directed Acyclic Subgraph (IDAS) of G d . Algorithms for computing the maximum IDAS have been proposed before [6]. Although, those algorithms take into account the first and the second requirements discussed in Section 3.1, but they do not take into account the other two requirements.…”

Section: Phase 2: Computing the Maximum Vertex-induced Directed Acyclmentioning

confidence: 99%

Symphony

Bansal

Chen

Sinha

et al. 2013

Proceedings of the 19th Annual International Conference on Mobile Computing &Amp; Networking - MobiCom '13

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In this paper, we propose Symphony, a packet recovery architecture that encourages collisions among transmitters, and utilizes the unused capacity in the backbone to transmit recovered data packets and coordinate the efficient recovery of collided packets. Symphony improves the wireless throughput while incurring a low overhead on the typically under-utilized wired backbone. In Symphony, upon receiving the collided transmissions, the APs carefully suppress a subset of the transmissions. Realizing this idea in practice entails several challenges including identification of clients that have data to transmit and ensuring that the algorithm works despite imperfect time-synchronization and non-zero latency among APs. We present Symphony that addresses these challenges and show how it leverages Successive Interference Cancellation (SIC) to further increase the network throughput. Experiments performed on an USRP testbed shows that on an average, Symphony provides 43% and 187% higher throughput over Omniscient TDMA and IEEE 802.11, respectively. ns-3 based simulation results show that on an average, Symphony provides a throughput of up to 1.63x compared to omniscient TDMA and 5.6x compared to IEEE 802.11.

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“…The problem of finding a minimum cardinality set of nodes whose deletion resolves every cycle in general graphs is called feedback vertex set (FVS) problem [31]. The FVS problem is known to be NP-complete, even if an exact solution is achievable in polynomial time for particular categories of graphs.…”

Section: Detecting and Solving Violations Of The Regularity Restrictionmentioning

confidence: 99%

OWL 2 modeling and reasoning with complex human activities

Riboni

Bettini

2011

Pervasive and Mobile Computing

134

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In recent years, there has been a growing interest in the adoption of ontologies and ontological reasoning to automatically recognize complex context data such as human activities. In particular, the Web Ontology Language (OWL) emerged as the language of choice, being a standard for the Semantic Web, and supported by a number of tools for knowledge engineering and reasoning. However, the limitations of OWL 1 in terms of expressiveness have been recognized in various fields, and important research e↵orts have been made to extend the language while preserving decidability of its OWL 1 DL fragment. The result of such work is OWL 2. In this paper we investigate the use of OWL 2 for modeling complex activities and reasoning with them. We show that the new language constructors of OWL 2 overcome the main limitations of OWL 1 for the representation of activities; OWL 2 axioms can be used to represent certain rules and rule-based reasoning previously demanded to hybrid approaches, with the advantage of having a unique semantics, avoiding potential inconsistencies. Then, we propose a system architecture showing the integration of a novel OWL 2 activity ontology and reasoning modules with distributed modules for sensor data aggregation and reasoning. The feasibility of our solution is shown by an extensive experimental evaluation with simulations of di↵erent intelligent environments.

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Feedback Set Problems

Cited by 131 publications

References 50 publications

The Latent Structure of Dictionaries

The Latent Structure of Dictionaries

Symphony

OWL 2 modeling and reasoning with complex human activities

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