111 research outputs found
Sequential Decision Making with Untrustworthy Service Providers
In this paper, we deal with the sequential decision making problem of agents operating in computational economies, where there is uncertainty regarding the trustworthiness of service providers populating the environment. Specifically, we propose a generic Bayesian trust model, and formulate the optimal Bayesian solution to the exploration-exploitation problem facing the agents when repeatedly interacting with others in such environments. We then present a computationally tractable Bayesian reinforcement learning algorithm to approximate that solution by taking into account the expected value of perfect information of an agent's actions. Our algorithm is shown to dramatically outperform all previous finalists of the international Agent Reputation and Trust (ART) competition, including the winner from both years the competition has been run
Coreness of Cooperative Games with Truncated Submodular Profit Functions
Coreness represents solution concepts related to core in cooperative games,
which captures the stability of players. Motivated by the scale effect in
social networks, economics and other scenario, we study the coreness of
cooperative game with truncated submodular profit functions. Specifically, the
profit function is defined by a truncation of a submodular function
: if and
otherwise, where is a given threshold. In this paper, we
study the core and three core-related concepts of truncated submodular profit
cooperative game. We first prove that whether core is empty can be decided in
polynomial time and an allocation in core also can be found in polynomial time
when core is not empty. When core is empty, we show hardness results and
approximation algorithms for computing other core-related concepts including
relative least-core value, absolute least-core value and least average
dissatisfaction value
Exploring Quantum Neural Networks for the Discovery and Implementation of Quantum Error-Correcting Codes
We investigate the use of Quantum Neural Networks for discovering and
implementing quantum error-correcting codes. Our research showcases the
efficacy of Quantum Neural Networks through the successful implementation of
the Bit-Flip quantum error-correcting code using a Quantum Autoencoder,
effectively correcting bit-flip errors in arbitrary logical qubit states.
Additionally, we employ Quantum Neural Networks to restore states impacted by
Amplitude Damping by utilizing an approximative 4-qubit error-correcting
codeword. Our models required modification to the initially proposed Quantum
Neural Network structure to avoid barren plateaus of the cost function and
improve training time. Moreover, we propose a strategy that leverages Quantum
Neural Networks to discover new encryption protocols tailored for specific
quantum channels. This is exemplified by learning to generate logical qubits
explicitly for the bit-flip channel. Our modified Quantum Neural Networks
consistently outperformed the standard implementations across all tasks
Cooperative games with overlapping coalitions
In the usual models of cooperative game theory, the outcome of a coalition formation process is either the grand coalition or a coalition structure that consists of disjoint coalitions. However, in many domains where coalitions are associated with tasks, an agent may be involved in executing more than one task, and thus may distribute his resources among several coalitions. To tackle such scenarios, we introduce a model for cooperative games with overlapping coalitions—or overlapping coalition formation (OCF) games. We then explore the issue of stability in this setting. In particular, we introduce a notion of the core, which generalizes the corresponding notion in the traditional (non-overlapping) scenario. Then, under some quite general conditions, we characterize the elements of the core, and show that any element of the core maximizes the social welfare. We also introduce a concept of balancedness for overlapping coalitional games, and use it to characterize coalition structures that can be extended to elements of the core. Finally, we generalize the notion of convexity to our setting, and show that under some natural assumptions convex games have a non-empty core. Moreover, we introduce two alternative notions of stability in OCF that allow a wider range of deviations, and explore the relationships among the corresponding definitions of the core, as well as the classic (non-overlapping) core and the Aubin core. We illustrate the general properties of the three cores, and also study them from a computational perspective, thus obtaining additional insights into their fundamental structure
Well-promising outcomes with vacuum-assisted closure in an infected wound following laparotomy: A case report
Introducation: Negative pressure wound therapy (NPWT) represents an alternative method to optimize conditions for wound healing. Delayed wound closure is a significant health problem, which is directly associated with pain and suffering from patient's aspect, as well with social and financial burden. Presentation of case: We report a case of vacuum-assisted wound therapy with hypertonic solution distillation and continuous negative pressure application, in an infected wound after laparotomy for incisional hernia reconstruction with mesh placement. Negative pressure was initiated at the wound margins after failure of conventional treatment with great outcomes, achieving a total closure of the incision within two weeks.
Discussion: Each wound has particular characteristics which must be managed. Vacuum assisted closure (VAC) with continuous negative pressure and simultaneous wound instillation and cleanse can provide optimum results, reducing the cavity volume, by newly produced granulated tissue.
Conclusion: The simultaneous use of instillation and constant pressure seemed to be superior in comparison with NPWT alone. Compared to conventional methods, the use of VAC ends to better outcomes, in cases of infected wounds following laparotomy
The Least-core and Nucleolus of Path Cooperative Games
Cooperative games provide an appropriate framework for fair and stable profit
distribution in multiagent systems. In this paper, we study the algorithmic
issues on path cooperative games that arise from the situations where some
commodity flows through a network. In these games, a coalition of edges or
vertices is successful if it enables a path from the source to the sink in the
network, and lose otherwise. Based on dual theory of linear programming and the
relationship with flow games, we provide the characterizations on the CS-core,
least-core and nucleolus of path cooperative games. Furthermore, we show that
the least-core and nucleolus are polynomially solvable for path cooperative
games defined on both directed and undirected network
Computing Stable Coalitions: Approximation Algorithms for Reward Sharing
Consider a setting where selfish agents are to be assigned to coalitions or
projects from a fixed set P. Each project k is characterized by a valuation
function; v_k(S) is the value generated by a set S of agents working on project
k. We study the following classic problem in this setting: "how should the
agents divide the value that they collectively create?". One traditional
approach in cooperative game theory is to study core stability with the
implicit assumption that there are infinite copies of one project, and agents
can partition themselves into any number of coalitions. In contrast, we
consider a model with a finite number of non-identical projects; this makes
computing both high-welfare solutions and core payments highly non-trivial.
The main contribution of this paper is a black-box mechanism that reduces the
problem of computing a near-optimal core stable solution to the purely
algorithmic problem of welfare maximization; we apply this to compute an
approximately core stable solution that extracts one-fourth of the optimal
social welfare for the class of subadditive valuations. We also show much
stronger results for several popular sub-classes: anonymous, fractionally
subadditive, and submodular valuations, as well as provide new approximation
algorithms for welfare maximization with anonymous functions. Finally, we
establish a connection between our setting and the well-studied simultaneous
auctions with item bidding; we adapt our results to compute approximate pure
Nash equilibria for these auctions.Comment: Under Revie
Primary malignant melanoma of the oesophagus: a case report
Primary malignant melanoma of the oesophagus is a rare neoplasm comprising less than 0.2% of all primary oesophageal neoplasms. There are fewer than 250 reported cases in worldwide literature. Several reports suggest that it has a mean survival rate of 2.2% at 5 years and a median survival rate of 10 months. A 48 year old male presented to our surgical service complaining of a three month history of progressively worsening dysphagia with associated regurgitation and unintentional weight loss of 14 kg. There was no prior history of cutaneous or ocular melanoma. He was treated with a combination of subtotal oesophageal resection and immunomodulatory therapy. We present herein a case of primary malignant melanoma of the oesophagus including the associated clinical, pathological and radiological findings
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