363 research outputs found
Unifying Event Detection and Captioning as Sequence Generation via Pre-Training
Dense video captioning aims to generate corresponding text descriptions for a
series of events in the untrimmed video, which can be divided into two
sub-tasks, event detection and event captioning. Unlike previous works that
tackle the two sub-tasks separately, recent works have focused on enhancing the
inter-task association between the two sub-tasks. However, designing inter-task
interactions for event detection and captioning is not trivial due to the large
differences in their task specific solutions. Besides, previous event detection
methods normally ignore temporal dependencies between events, leading to event
redundancy or inconsistency problems. To tackle above the two defects, in this
paper, we define event detection as a sequence generation task and propose a
unified pre-training and fine-tuning framework to naturally enhance the
inter-task association between event detection and captioning. Since the model
predicts each event with previous events as context, the inter-dependency
between events is fully exploited and thus our model can detect more diverse
and consistent events in the video. Experiments on the ActivityNet dataset show
that our model outperforms the state-of-the-art methods, and can be further
boosted when pre-trained on extra large-scale video-text data. Code is
available at \url{https://github.com/QiQAng/UEDVC}
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
An ever increasing number of configuration parameters are provided to system
users. But many users have used one configuration setting across different
workloads, leaving untapped the performance potential of systems. A good
configuration setting can greatly improve the performance of a deployed system
under certain workloads. But with tens or hundreds of parameters, it becomes a
highly costly task to decide which configuration setting leads to the best
performance. While such task requires the strong expertise in both the system
and the application, users commonly lack such expertise.
To help users tap the performance potential of systems, we present
BestConfig, a system for automatically finding a best configuration setting
within a resource limit for a deployed system under a given application
workload. BestConfig is designed with an extensible architecture to automate
the configuration tuning for general systems. To tune system configurations
within a resource limit, we propose the divide-and-diverge sampling method and
the recursive bound-and-search algorithm. BestConfig can improve the throughput
of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce
the running time of Hive join job by about 50% and that of Spark join job by
about 80%, solely by configuration adjustment
Analyzing scenery images by monotonic tree
Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of techniques have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques are needed to support high-level queries. Research efforts are needed to bridge the gap between high-level semantics and low-level features. In this paper, we present a novel approach to support semantics-based image retrieval. Our approach is based on the monotonic tree, a derivation of the contour tree for use with discrete data. The structural elements of an image are modeled as branches (or subtrees) of the monotonic tree. These structural elements are classified and clustered on the basis of such properties as color, spatial location, harshness and shape. Each cluster corresponds to some semantic feature. This scheme is applied to the analysis and retrieval of scenery images. Comparisons of experimental results of this approach with conventional techniques using low-level features demonstrate the effectiveness of our approach.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42315/1/30080495.pd
Extended application of random-walk shielding-potential viscosity model of metals in wide temperature region
The transport properties of matter have been widely investigated. In
particular, shear viscosity over a wide parameter space is crucial for various
applications, such as designing inertial confinement fusion (ICF) targets and
determining the Rayleigh-Taylor instability. In this work, an extended
random-walk shielding-potential viscosity model (RWSP-VM) [Phys. Rev. E 106,
014142] based on the statistics of random-walk ions and the Debye shielding
effect is proposed to elevate the temperature limit of RWSP-VM in evaluating
the shear viscosity of metals. In the extended model, we reconsider the
collision diameter that is introduced by hard-sphere concept, hence, it is
applicable in both warm and hot temperature regions (10^1-10^7 eV) rather than
the warm temperature region (10^1-10^2 eV) in which RWSP-VM is applicable. The
results of Be, Al, Fe, and U show that the extended model provides a systematic
way to calculate the shear viscosity of arbitrary metals at the densities from
about 0.1 to 10 times the normal density (the density at room temperature and 1
standard atmosphere). This work will help to develop viscosity model in wide
region when combined with our previous low temperature viscosity model [AIP
Adv. 11, 015043].Comment: 6 pages, 5 figure
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