1,348 research outputs found
Building Efficient Large-Scale Big Data Processing Platforms
In the era of big data, many cluster platforms and resource management schemes are created to satisfy the increasing demands on processing a large volume of data. A general setting of big data processing jobs consists of multiple stages, and each stage represents generally defined data operation such as ltering and sorting. To parallelize the job execution in a cluster, each stage includes a number of identical tasks that can be concurrently launched at multiple servers. Practical clusters often involve hundreds or thousands of servers processing a large batch of jobs. Resource management, that manages cluster resource allocation and job execution, is extremely critical for the system performance.
Generally speaking, there are three main challenges in resource management of the new big data processing systems. First, while there are various pending tasks from dierent jobs and stages, it is difficult to determine which ones deserve the priority to obtain the resources for execution, considering the tasks\u27 different characteristics such as resource demand and execution time. Second, there exists dependency among the tasks that can be concurrently running. For any two consecutive stages of a job, the output data of the former stage is the input data of the later one. The resource management has to comply with such dependency. The third challenge is the inconsistent performance of the cluster nodes. In practice, run-time performance of every server is varying. The resource management needs to dynamically adjust the resource allocation according to the performance change of each server.
The resource management in the existing platforms and prior work often rely on fixed user-specific configurations, and assumes consistent performance in each node. The performance, however, is not satisfactory under various workloads. This dissertation aims to explore new approaches to improving the eciency of large-scale big data processing platforms. In particular, the run-time dynamic factors are carefully considered when the system allocates the resources. New algorithms are developed to collect run-time data and predict the characteristics of jobs and the cluster. We further develop resource management schemes that dynamically tune the resource allocation for each stage of every running job in the cluster. New findings and techniques in this dissertation will certainly provide valuable and inspiring insights to other similar problems in the research community
The leptonic future of the Higgs
Precision study of electroweak symmetry breaking strongly motivates the
construction of a lepton collider with center-of-mass energy of at least 240
GeV. Besides Higgsstrahlung (), such a collider would measure
weak boson pair production () with an astonishing precision. The
weak-boson-fusion production process () provides an
increasingly powerful handle at higher center-of-mass energies. High energies
also benefit the associated top-Higgs production () that is
crucial to constrain directly the top Yukawa coupling. The impact and
complementarity of differential measurements, at different center-of-mass
energies and for several beam polarization configurations, are studied in a
global effective-field-theory framework. We define a "global determinant
parameter" (GDP) which characterizes the overall strengthening of constraints
independently of the choice of operator basis. The reach of the CEPC, CLIC,
FCC-ee, and ILC designs is assessed.Comment: 55 pages, lots of figures, v2: references added, minor corrections,
extended discussions on quadratic EFT contributions and beam polarization
effects, matches published version in JHE
Beyond Higgs Couplings: Probing the Higgs with Angular Observables at Future Colliders
We study angular observables in the channel at future circular colliders such as CEPC
and FCC-ee. Taking into account the impact of realistic cut acceptance and
detector effects, we forecast the precision of six angular asymmetries at CEPC
(FCC-ee) with center-of-mass energy 240 GeV and 5 (30) integrated luminosity. We then determine the projected sensitivity to
a range of operators relevant for the Higgs-strahlung process in the
dimension-6 Higgs EFT. Our results show that angular observables provide
complementary sensitivity to rate measurements when constraining various tensor
structures arising from new physics. We further find that angular asymmetries
provide a novel means of both probing BSM corrections to the
coupling and constraining the "blind spot" in indirect limits on supersymmetric
scalar top partners.Comment: 28 pages, 9 figures. v2: references added, matches published version
in JHE
The Transfiguration of the Woman’s Body: A Study of Holy Bible and The Woman’s Bible
This article employs the method of critical discourse analysis to study the changing portrayal of the woman’s body. Our analysis shows the ways in which the woman’s body is constructed as silent, instrumentalized, sinful, and unclean in Holy Bible under the dominance of male discourses. Then we examined how the woman’s body is reconstructed as an independent and glorified entity in The Woman’s Bible, along with the subversion of male discourses by feminist renditions thereof. The study has implications for women who are fighting for their bodily rights
Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition
Open-set image recognition is a challenging topic in computer vision. Most of
the existing works in literature focus on learning more discriminative features
from the input images, however, they are usually insensitive to the high- or
low-frequency components in features, resulting in a decreasing performance on
fine-grained image recognition. To address this problem, we propose a
Complementary Frequency-varying Awareness Network that could better capture
both high-frequency and low-frequency information, called CFAN. The proposed
CFAN consists of three sequential modules: (i) a feature extraction module is
introduced for learning preliminary features from the input images; (ii) a
frequency-varying filtering module is designed to separate out both high- and
low-frequency components from the preliminary features in the frequency domain
via a frequency-adjustable filter; (iii) a complementary temporal aggregation
module is designed for aggregating the high- and low-frequency components via
two Long Short-Term Memory networks into discriminative features. Based on
CFAN, we further propose an open-set fine-grained image recognition method,
called CFAN-OSFGR, which learns image features via CFAN and classifies them via
a linear classifier. Experimental results on 3 fine-grained datasets and 2
coarse-grained datasets demonstrate that CFAN-OSFGR performs significantly
better than 9 state-of-the-art methods in most cases
Recursive Counterfactual Deconfounding for Object Recognition
Image recognition is a classic and common task in the computer vision field,
which has been widely applied in the past decade. Most existing methods in
literature aim to learn discriminative features from labeled images for
classification, however, they generally neglect confounders that infiltrate
into the learned features, resulting in low performances for discriminating
test images. To address this problem, we propose a Recursive Counterfactual
Deconfounding model for object recognition in both closed-set and open-set
scenarios based on counterfactual analysis, called RCD. The proposed model
consists of a factual graph and a counterfactual graph, where the relationships
among image features, model predictions, and confounders are built and updated
recursively for learning more discriminative features. It performs in a
recursive manner so that subtler counterfactual features could be learned and
eliminated progressively, and both the discriminability and generalization of
the proposed model could be improved accordingly. In addition, a negative
correlation constraint is designed for alleviating the negative effects of the
counterfactual features further at the model training stage. Extensive
experimental results on both closed-set recognition task and open-set
recognition task demonstrate that the proposed RCD model performs better than
11 state-of-the-art baselines significantly in most cases
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