13,194 research outputs found
PerfBlower: Quickly Detecting Memory-Related Performance Problems via Amplification
Performance problems in managed languages are extremely difficult to find. Despite many efforts to find those problems, most existing work focuses on how to debug a user-provided test execution in which performance problems already manifest. It remains largely unknown how to effectively find performance bugs before software release. As a result, performance bugs often escape to production runs, hurting software reliability and user experience. This paper describes PerfBlower, a general performance testing framework that allows developers to quickly test Java programs to find memory-related performance problems. PerfBlower provides (1) a novel specification language ISL to describe a general class of performance problems that have observable symptoms; (2) an automated test oracle via emph{virtual amplification}; and (3) precise reference-path-based diagnostic information via object mirroring. Using this framework, we have amplified three different types of problems. Our experimental results demonstrate that (1) ISL is expressive enough to describe various memory-related performance problems; (2) PerfBlower successfully distinguishes executions with and without problems; 8 unknown problems are quickly discovered under small workloads; and (3) PerfBlower outperforms existing detectors and does not miss any bugs studied before in the literature
Assisted optimal state discrimination without entanglement
A fundamental problem in quantum information is to explore the roles of
different quantum correlations in a quantum information procedure. Recent work
[Phys. Rev. Lett., 107 (2011) 080401] shows that the protocol for assisted
optimal state discrimination (AOSD) may be implemented successfully without
entanglement, but with another correlation, quantum dissonance. However, both
the original work and the extension to discrimination of states [Phys. Rev.
A, 85 (2012) 022328] have only proved that entanglement can be absent in the
case with equal a \emph{priori} probabilities. By improving the protocol in
[Sci. Rep., 3 (2013) 2134], we investigate this topic in a simple case to
discriminate three nonorthogonal states of a qutrit, with positive real
overlaps. In our procedure, the entanglement between the qutrit and an
auxiliary qubit is found to be completely unnecessary. This result shows that
the quantum dissonance may play as a key role in optimal state discrimination
assisted by a qubit for more general cases.Comment: 6 pages, 3 figures. Accepted by EPL. We extended the protocol for
assisted optimal state discrimination to the case with positive real
overlaps, and presented a proof for the absence of entanglemen
Discourse-Aware Graph Networks for Textual Logical Reasoning
Textual logical reasoning, especially question-answering (QA) tasks with
logical reasoning, requires awareness of particular logical structures. The
passage-level logical relations represent entailment or contradiction between
propositional units (e.g., a concluding sentence). However, such structures are
unexplored as current QA systems focus on entity-based relations. In this work,
we propose logic structural-constraint modeling to solve the logical reasoning
QA and introduce discourse-aware graph networks (DAGNs). The networks first
construct logic graphs leveraging in-line discourse connectives and generic
logic theories, then learn logic representations by end-to-end evolving the
logic relations with an edge-reasoning mechanism and updating the graph
features. This pipeline is applied to a general encoder, whose fundamental
features are joined with the high-level logic features for answer prediction.
Experiments on three textual logical reasoning datasets demonstrate the
reasonability of the logical structures built in DAGNs and the effectiveness of
the learned logic features. Moreover, zero-shot transfer results show the
features' generality to unseen logical texts
Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN
The dominant object detection approaches treat each dataset separately and
fit towards a specific domain, which cannot adapt to other domains without
extensive retraining. In this paper, we address the problem of designing a
universal object detection model that exploits diverse category granularity
from multiple domains and predict all kinds of categories in one system.
Existing works treat this problem by integrating multiple detection branches
upon one shared backbone network. However, this paradigm overlooks the crucial
semantic correlations between multiple domains, such as categories hierarchy,
visual similarity, and linguistic relationship. To address these drawbacks, we
present a novel universal object detector called Universal-RCNN that
incorporates graph transfer learning for propagating relevant semantic
information across multiple datasets to reach semantic coherency. Specifically,
we first generate a global semantic pool by integrating all high-level semantic
representation of all the categories. Then an Intra-Domain Reasoning Module
learns and propagates the sparse graph representation within one dataset guided
by a spatial-aware GCN. Finally, an InterDomain Transfer Module is proposed to
exploit diverse transfer dependencies across all domains and enhance the
regional feature representation by attending and transferring semantic contexts
globally. Extensive experiments demonstrate that the proposed method
significantly outperforms multiple-branch models and achieves the
state-of-the-art results on multiple object detection benchmarks (mAP: 49.1% on
COCO).Comment: Accepted by AAAI2
A method based on multiscale base-scale entropy and random forests for roller bearings faults diagnosis
A method based on multiscale base-scale entropy (MBSE) and random forests (RF) for roller bearings faults diagnosis is presented in this study. Firstly, the roller bearings vibration signals were decomposed into base-scale entropy (BSE), sample entropy (SE) and permutation entropy (PE) values by using MBSE, multiscale sample entropy (MSE) and multiscale permutation entropy (MPE) under different scales. Then the computation time of the MBSE/MSE/MPE methods were compared. Secondly, the entropy values of BSE, SE, and PE under different scales were regarded as the input of RF and SVM optimized by particle swarm ion (PSO) and genetic algorithm (GA) algorithms for fulfilling the fault identification, and the classification accuracy was utilized to verify the effect of the MBSE/MSE/MPE methods by using RF/PSO/GA-SVM models. Finally, the experiment result shows that the computational efficiency and classification accuracy of MBSE method are superior to MSE and MPE with RF and SVM
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