Lightsolver challenges a leading deep learning solver for Max-2-SAT problems

Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decision problem that is known to be NP-hard. In this paper, we compare LightSolver's quantum-inspired algorithm to a leading deep-learning solver for the MAX-2-SAT problem. Experiments on benchmark data sets show that LightSolver achieves significantly smaller time-to-optimal-solution compared to a state-of-the-art deep-learning algorithm, where the gain in performance tends to increase with the problem size

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Matrix metalloproteinase 12 promotes tumor propagation in the lung

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Computer-Mediated Trust in Self-interested Expert Recommendations

International audienceImportant decisions are often based on a distributed process of information processing , from a knowledge base that is itself distributed among agents . The simplest such situation is that where a decision-maker seeks the recommendations of experts. Because experts may have vested interests in the consequences of their recommendations, decision-makers usually seek the advice of experts they trust . Trust, however, is a commodity that is usually built through repeated face time and social interaction , and thus cannot easily be built in a global world where we have immediate internet access to a vast pool of experts. In this article, we integrate findings from experimental psychology and formal tools from Artificial Intelligence to offer a preliminary roadmap for solving the problem of trust in this computer-mediated environment. We conclude the article by considering a diverse array of extended applications of such a solution