24,984 research outputs found
Hashing based Answer Selection
Answer selection is an important subtask of question answering (QA), where
deep models usually achieve better performance. Most deep models adopt
question-answer interaction mechanisms, such as attention, to get vector
representations for answers. When these interaction based deep models are
deployed for online prediction, the representations of all answers need to be
recalculated for each question. This procedure is time-consuming for deep
models with complex encoders like BERT which usually have better accuracy than
simple encoders. One possible solution is to store the matrix representation
(encoder output) of each answer in memory to avoid recalculation. But this will
bring large memory cost. In this paper, we propose a novel method, called
hashing based answer selection (HAS), to tackle this problem. HAS adopts a
hashing strategy to learn a binary matrix representation for each answer, which
can dramatically reduce the memory cost for storing the matrix representations
of answers. Hence, HAS can adopt complex encoders like BERT in the model, but
the online prediction of HAS is still fast with a low memory cost. Experimental
results on three popular answer selection datasets show that HAS can outperform
existing models to achieve state-of-the-art performance
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Orthopedic Surgery Planning Based on the Integration of Reverse Engineering and Rapid Prototyping
This paper describes orthopedic surgical planning based on the integration of RE and RP.
Using symmetrical characteristics of the human body, CAD data of the original bone without
damages for the injured extent are generated from a mirror transformation of undamaged bone
data for the uninjured extent. The physical model before the injury is manufactured from RP
apparatus. Surgical planning, such as the selection of the proper implant, pre-forming of the
implant, decision of fixation positions and incision sizes, etc., is determined by a physical
simulation using the physical model. In order to examine the applicability and efficiency of
surgical planning technology for orthopedics, various case studies, such as a proximal tibia
plateau fracture, a distal tibia comminuted fracture and an iliac wing fracture of pelvis, are
carried out. As a result of the examination, it has been shown that the orthopedic surgical
planning based on the integration of RE and RP is an efficient surgical tool.Mechanical Engineerin
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
The 0-1 knapsack problem is a well-known combinatorial optimisation problem.
Approximation algorithms have been designed for solving it and they return
provably good solutions within polynomial time. On the other hand, genetic
algorithms are well suited for solving the knapsack problem and they find
reasonably good solutions quickly. A naturally arising question is whether
genetic algorithms are able to find solutions as good as approximation
algorithms do. This paper presents a novel multi-objective optimisation genetic
algorithm for solving the 0-1 knapsack problem. Experiment results show that
the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy
genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm
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