Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học tạp chí Department of Mathematic dành cho các bạn yêu thích môn toán học đề tài:Asymptotically optimal pairing strategy for Tic-Tac-Toe with numerous directions...
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article On the Asymptotic Optimality of Opportunistic Norm-Based User Selection with Hard SINR Constraint
Linear context-free rewriting systems (LCFRSs) are grammar formalisms with the capability of modeling discontinuous constituents. Many applications use LCFRSs where the fan-out (a measure of the discontinuity of phrases) is not allowed to be greater than 2. We present an efﬁcient algorithm for transforming LCFRS with fan-out at most 2 into a binary form, whenever this is possible. This results in asymptotical run-time improvement for known parsing algorithms for this class.
Linear Context-Free Rewriting Systems (LCFRSs) are a grammar formalism capable of modeling discontinuous phrases. Many parsing applications use LCFRSs where the fan-out (a measure of the discontinuity of phrases) does not exceed 2. We present an efﬁcient algorithm for optimal reduction of the length of production right-hand side in LCFRSs with fan-out at most 2. This results in asymptotical running time improvement for known parsing algorithms for this class.
Current estimates and measurements predict that Internet traffic will continue to
grow for many years to come. Driving this growth is the fact that the Internet has
moved from a convenience to a mission-critical platform for conducting and
succeeding in business. In addition, the provision of advanced broadband services
to end users will continue to cultivate and prolong this growth in the future.
The minimum Bayes risk (MBR) decoding objective improves BLEU scores for machine translation output relative to the standard Viterbi objective of maximizing model score. However, MBR targeting BLEU is prohibitively slow to optimize over k-best lists for large k. In this paper, we introduce and analyze an alternative to MBR that is equally effective at improving performance, yet is asymptotically faster — running 80 times faster than MBR in experiments with 1000-best lists.