254 research outputs found
Completion of the restructuring of China\u27s coast guard administration: challenges and opportunities
The dissertation is a study of the restructuring activities of entities of China’s maritime law enforcement by focusing on the establishment of China’s Coast Guard Administration. Because China’s Coast Guard Administration was just established two years ago, the aim of this dissertation is to analyse the current situation of China’s Coast Guard Administration, and then identify and analyse challenges and opportunities in China’s Coast Guard Administration. In order to provide a comprehensive cognition of the reasons for establishing China’s Coast Guard Administration, the dissertation initially provides a brief explanation of the drawbacks of the past situation of entities of maritime law enforcement in China. Then the current situation of China’s Coast Guard Administration is analysed by stating the framework and initial mandates of China’s Coast Guard Administration. Importantly, the legal foundations for previous departments are analysed and some of them are still available for China’s Coast Guard Administration. The legal analysis is divided into two levels, which are international laws and domestic laws. Significantly, a vast number of challenges faced by China’s Coast Guard Administration are identified and analysed by internal and external challenges. Besides, existing opportunities within China’s Coast Guard Administration are analysed as well as three different aspects. The concluding chapter summarizes the discussion and analysis of this dissertation, as well as confirming the challenges and opportunities are fulfilling the aim of this dissertation. Finally, several feasible recommendations are put forward which are based on the analysis of challenges and opportunities to promote the development of China’s Coast Guard Administration
Performance Analysis and Optimisation of In-network Caching for Information-Centric Future Internet
The rapid development in wireless technologies and multimedia services has radically shifted the major function of the current Internet from host-centric communication to service-oriented content dissemination, resulting a mismatch between the protocol design and the current usage patterns. Motivated by this significant change, Information-Centric Networking (ICN), which has been attracting ever-increasing attention from the communication networks research community, has emerged as a new clean-slate networking paradigm for future Internet. Through identifying and routing data by unified names, ICN aims at providing natural support for efficient information retrieval over the Internet. As a crucial characteristic of ICN, in-network caching enables users to efficiently access popular contents from on-path routers equipped with ubiquitous caches, leading to the enhancement of the service quality and reduction of network loads.
Performance analysis and optimisation has been and continues to be key research interests of ICN. This thesis focuses on the development of efficient and accurate analytical models for the performance evaluation of ICN caching and the design of optimal caching management schemes under practical network configurations.
This research starts with the proposition of a new analytical model for caching performance under the bursty multimedia traffic. The bursty characteristic is captured and the closed formulas for cache hit ratio are derived. To investigate the impact of topology and heterogeneous caching parameters on the performance, a comprehensive analytical model is developed to gain valuable insight into the caching performance with heterogeneous cache sizes, service intensity and content distribution under arbitrary topology. The accuracy of the proposed models is validated by comparing the analytical results with those obtained from extensive simulation experiments. The analytical models are then used as cost-efficient tools to investigate the key network and content parameters on the performance of caching in ICN.
Bursty traffic and heterogeneous caching features have significant influence on the performance of ICN. Therefore, in order to obtain optimal performance results, a caching resource allocation scheme, which leverages the proposed model and targets at minimising the total traffic within the network and improving hit probability at the nodes, is proposed. The performance results reveal that the caching allocation scheme can achieve better caching performance and network resource utilisation than the default homogeneous and random caching allocation strategy. To attain a thorough understanding of the trade-off between the economic aspect and service quality, a cost-aware Quality-of-Service (QoS) optimisation caching mechanism is further designed aiming for cost-efficiency and QoS guarantee in ICN. A cost model is proposed to take into account installation and operation cost of ICN under a realistic ISP network scenario, and a QoS model is presented to formulate the service delay and delay jitter in the presence of heterogeneous service requirements and general probabilistic caching strategy. Numerical results show the effectiveness of the proposed mechanism in achieving better service quality and lower network cost.
In this thesis, the proposed analytical models are used to efficiently and accurately evaluate the performance of ICN and investigate the key performance metrics. Leveraging the insights discovered by the analytical models, the proposed caching management schemes are able to optimise and enhance the performance of ICN. To widen the outcomes achieved in the thesis, several interesting yet challenging research directions are pointed out
HiQR: An efficient algorithm for high dimensional quadratic regression with penalties
This paper investigates the efficient solution of penalized quadratic
regressions in high-dimensional settings. We propose a novel and efficient
algorithm for ridge-penalized quadratic regression that leverages the matrix
structures of the regression with interactions. Building on this formulation,
we develop an alternating direction method of multipliers (ADMM) framework for
penalized quadratic regression with general penalties, including both single
and hybrid penalty functions. Our approach greatly simplifies the calculations
to basic matrix-based operations, making it appealing in terms of both memory
storage and computational complexity.Comment: 18 page
Why KDAC? A general activation function for knowledge discovery
Deep learning oriented named entity recognition (DNER) has gradually become
the paradigm of knowledge discovery, which greatly promotes domain
intelligence. However, the current activation function of DNER fails to treat
gradient vanishing, no negative output or non-differentiable existence, which
may impede knowledge exploration caused by the omission and incomplete
representation of latent semantics. To break through the dilemma, we present a
novel activation function termed KDAC. Detailly, KDAC is an aggregation
function with multiple conversion modes. The backbone of the activation region
is the interaction between exponent and linearity, and the both ends extend
through adaptive linear divergence, which surmounts the obstacle of gradient
vanishing and no negative output. Crucially, the non-differentiable points are
alerted and eliminated by an approximate smoothing algorithm. KDAC has a series
of brilliant properties, including nonlinear, stable near-linear transformation
and derivative, as well as dynamic style, etc. We perform experiments based on
BERT-BiLSTM-CNN-CRF model on six benchmark datasets containing different domain
knowledge, such as Weibo, Clinical, E-commerce, Resume, HAZOP and People's
daily. The evaluation results show that KDAC is advanced and effective, and can
provide more generalized activation to stimulate the performance of DNER. We
hope that KDAC can be exploited as a promising activation function to devote
itself to the construction of knowledge.Comment: Accepted by Neurocomputin
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality
Multimodal sentiment analysis (MSA) is an important way of observing mental
activities with the help of data captured from multiple modalities. However,
due to the recording or transmission error, some modalities may include
incomplete data. Most existing works that address missing modalities usually
assume a particular modality is completely missing and seldom consider a
mixture of missing across multiple modalities. In this paper, we propose a
simple yet effective meta-sampling approach for multimodal sentiment analysis
with missing modalities, namely Missing Modality-based Meta Sampling (M3S). To
be specific, M3S formulates a missing modality sampling strategy into the modal
agnostic meta-learning (MAML) framework. M3S can be treated as an efficient
add-on training component on existing models and significantly improve their
performances on multimodal data with a mixture of missing modalities. We
conduct experiments on IEMOCAP, SIMS and CMU-MOSI datasets, and superior
performance is achieved compared with recent state-of-the-art methods
Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms
Domain generalization asks for models trained over a set of training
environments to generalize well in unseen test environments. Recently, a series
of algorithms such as Invariant Risk Minimization (IRM) have been proposed for
domain generalization. However, Rosenfeld et al. (2021) shows that in a simple
linear data model, even if non-convexity issues are ignored, IRM and its
extensions cannot generalize to unseen environments with less than
training environments, where is the dimension of the spurious-feature
subspace. In this work, we propose Invariant-feature Subspace Recovery (ISR): a
new class of algorithms to achieve provable domain generalization across the
settings of classification and regression problems. First, in the binary
classification setup of Rosenfeld et al. (2021), we show that our first
algorithm, ISR-Mean, can identify the subspace spanned by invariant features
from the first-order moments of the class-conditional distributions, and
achieve provable domain generalization with training environments. Our
second algorithm, ISR-Cov, further reduces the required number of training
environments to using the information of second-order moments. Notably,
unlike IRM, our algorithms bypass non-convexity issues and enjoy global
convergence guarantees. Next, we extend ISR-Mean to the more general setting of
multi-class classification and propose ISR-Multiclass, which leverages class
information and provably recovers the invariant-feature subspace with training environments for -class classification. Finally, for
regression problems, we propose ISR-Regression that can identify the
invariant-feature subspace with training environments. Empirically, we
demonstrate the superior performance of our ISRs on synthetic benchmarks.
Further, ISR can be used as post-processing methods for feature extractors such
as neural nets.Comment: Submitted to JMLR. This journal version significantly extends our
ICML 2022 paper, arXiv:2201.1291
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation
Recent offline meta-reinforcement learning (meta-RL) methods typically
utilize task-dependent behavior policies (e.g., training RL agents on each
individual task) to collect a multi-task dataset. However, these methods always
require extra information for fast adaptation, such as offline context for
testing tasks. To address this problem, we first formally characterize a unique
challenge in offline meta-RL: transition-reward distribution shift between
offline datasets and online adaptation. Our theory finds that
out-of-distribution adaptation episodes may lead to unreliable policy
evaluation and that online adaptation with in-distribution episodes can ensure
adaptation performance guarantee. Based on these theoretical insights, we
propose a novel adaptation framework, called In-Distribution online Adaptation
with uncertainty Quantification (IDAQ), which generates in-distribution context
using a given uncertainty quantification and performs effective task belief
inference to address new tasks. We find a return-based uncertainty
quantification for IDAQ that performs effectively. Experiments show that IDAQ
achieves state-of-the-art performance on the Meta-World ML1 benchmark compared
to baselines with/without offline adaptation
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