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 ($e^+e^- \to hZ$), such a collider would measure weak boson pair production ($e^+e^- \to WW$) with an astonishing precision. The weak-boson-fusion production process ($e^+e^- \to \nu \bar{\nu} h$) provides an increasingly powerful handle at higher center-of-mass energies. High energies also benefit the associated top-Higgs production ($e^+e^-\to t\bar th$) 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 e+e−e^+ e^- Colliders

We study angular observables in the $e^+e^-\to Z H\to \ell^+ \ell^-\,b\bar{b}$ channel at future circular $e^+ e^-$ 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 $\sqrt{s} =$ 240 GeV and 5 (30) ${\rm ab}^{-1}$ 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 $H Z \gamma$ 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