178 research outputs found
The Prevention and Control of Economic Crime in China: A Critical Analysis of the Law and its Administration
Economic crime and corruption has been an issue throughout Chinese history. While there may be scope for discussion as to the significance of public confidence in the integrity of a government, in practical terms the government of China has had to focus attention on maintaining confidence in its integrity as an issue for stability. Since the establishment of the Chinese Communist Party (CCP) and its assumption of power and in particular after the ‘Opening’ of the Chinese economy, abusive conduct on the part of those in positions of privilege, primarily in governmental organisations, has arguably reached an unprecedented level. In turn, this is impeding development as far as it undermines public confidence, accelerates jealousy and forges an even wider gap between rich and poor, thereby threatening the stability and security of civil societies. More importantly, these abuses undermine the reputation of the CCP and the government. China naturally consider this as of key significance in attracting foreign investment and assuming its leading role in the world economy. While there have been many attempts to curb economic crime, the traditional capabilities of the law and particularly the criminal justice system have in general terms been found to be inadequate. This thesis examines the existing law relating to fraud and corruption, as a mechanism for reducing the incidence and impact of such abuses and offers appropriate recommendations for rendering it more efficient and efficacious. The author discusses the legal history of economic crime control, followed by the various initiatives that have been undertaken at different levels of government to curb economic crime and corruption since the foundation of the People’s Republic of China. This thesis also assesses the existing legal and institutional regime for the protection of victims of economic crime. China’s stand against corruption is then placed in the context of various international initiatives, in particular those involving the United Nations. The primary objective of this thesis is to assess the law and its administration in China in the fight against economic crime and corruption and to facilitate a better understanding and control of the issues relating to the prevention of this phenomenon, in the promotion of China’s economic and political stability
SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma
Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy. Due to its wide heterogeneity, PDAC acts aggressively and responds poorly to most chemotherapies, causing an urgent need for the development of new therapeutic strategies. Cell lines have been used as the foundation for drug development and disease modeling. CRISPR-Cas9 plays a key role in every step-in drug discovery: from target identification and validation to preclinical cancer cell testing. Using cell-line models and CRISPR-Cas9 technology together make drug target prediction feasible. However, there is still a large gap between predicted results and actionable targets in real tumors. Biological network models provide great modus to mimic genetic interactions in real biological systems, which can benefit gene perturbation studies and potential target identification for treating PDAC. Nevertheless, building a network model that takes cell-line data and CRISPR-Cas9 data as input to accurately predict potential targets that will respond well on real tissue remains unsolved.
Methods: We developed a novel algorithm 'Spectral Clustering for Network-based target Ranking' (SCNrank) that systematically integrates three types of data: expression profiles from tumor tissue, normal tissue and cell-line PDAC; protein-protein interaction network (PPI); and CRISPR-Cas9 data to prioritize potential drug targets for PDAC. The whole algorithm can be classified into three steps: 1. using STRING PPI network skeleton, SCNrank constructs tissue-specific networks with PDAC tumor and normal pancreas tissues from expression profiles; 2. With the same network skeleton, SCNrank constructs cell-line-specific networks using the cell-line PDAC expression profiles and CRISPR-Cas 9 data from pancreatic cancer cell-lines; 3. SCNrank applies a novel spectral clustering approach to reduce data dimension and generate gene clusters that carry common features from both networks. Finally, SCNrank applies a scoring scheme called 'Target Influence score' (TI), which estimates a given target's influence towards the cluster it belongs to, for scoring and ranking each drug target.
Results: We applied SCNrank to analyze 263 expression profiles, CRPSPR-Cas9 data from 22 different pancreatic cancer cell-lines and the STRING protein-protein interaction (PPI) network. With SCNrank, we successfully constructed an integrated tissue PDAC network and an integrated cell-line PDAC network, both of which contain 4414 selected genes that are overexpressed in tumor tissue samples. After clustering, 4414 genes are distributed into 198 clusters, which include 367 targets of FDA approved drugs. These drug targets are all scored and ranked by their TI scores, which we defined to measure their influence towards the network. We validated top-ranked targets in three aspects: Firstly, mapping them onto the existing clinical drug targets of PDAC to measure the concordance. Secondly, we performed enrichment analysis to these drug targets and the clusters there are within, to reveal functional associations between clusters and PDAC; Thirdly, we performed survival analysis for the top-ranked targets to connect targets with clinical outcomes. Survival analysis reveals that overexpression of three top-ranked genes, PGK1, HMMR and POLE2, significantly increases the risk of death in PDAC patients. SCNrank is an unbiased algorithm that systematically integrates multiple types of omics data to do potential drug target selection and ranking. SCNrank shows great capability in predicting drug targets for PDAC. Pancreatic cancer-associated gene candidates predicted by our SCNrank approach have the potential to guide genetics-based anti-pancreatic drug discovery
An adaptive model checking test for functional linear model
Numerous studies have been devoted to the estimation and inference problems
for functional linear models (FLM). However, few works focus on model checking
problem that ensures the reliability of results. Limited tests in this area do
not have tractable null distributions or asymptotic analysis under
alternatives. Also, the functional predictor is usually assumed to be fully
observed, which is impractical. To address these problems, we propose an
adaptive model checking test for FLM. It combines regular moment-based and
conditional moment-based tests, and achieves model adaptivity via the dimension
of a residual-based subspace. The advantages of our test are manifold. First,
it has a tractable chi-squared null distribution and higher powers under the
alternatives than its components. Second, asymptotic properties under different
underlying models are developed, including the unvisited local alternatives.
Third, the test statistic is constructed upon finite grid points, which
incorporates the discrete nature of collected data. We develop the desirable
relationship between sample size and number of grid points to maintain the
asymptotic properties. Besides, we provide a data-driven approach to estimate
the dimension leading to model adaptivity, which is promising in sufficient
dimension reduction. We conduct comprehensive numerical experiments to
demonstrate the advantages the test inherits from its two simple components
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
Despite the stunning ability to generate high-quality images by recent
text-to-image models, current approaches often struggle to effectively compose
objects with different attributes and relationships into a complex and coherent
scene. We propose T2I-CompBench, a comprehensive benchmark for open-world
compositional text-to-image generation, consisting of 6,000 compositional text
prompts from 3 categories (attribute binding, object relationships, and complex
compositions) and 6 sub-categories (color binding, shape binding, texture
binding, spatial relationships, non-spatial relationships, and complex
compositions). We further propose several evaluation metrics specifically
designed to evaluate compositional text-to-image generation. We introduce a new
approach, Generative mOdel fine-tuning with Reward-driven Sample selection
(GORS), to boost the compositional text-to-image generation abilities of
pretrained text-to-image models. Extensive experiments and evaluations are
conducted to benchmark previous methods on T2I-CompBench, and to validate the
effectiveness of our proposed evaluation metrics and GORS approach. Project
page is available at https://karine-h.github.io/T2I-CompBench/.Comment: Project page: https://karine-h.github.io/T2I-CompBench
Progressive-Hint Prompting Improves Reasoning in Large Language Models
The performance of Large Language Models (LLMs) in reasoning tasks depends
heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency
being critical methods that enhance this ability. However, these methods do not
fully exploit the answers generated by the LLM to guide subsequent responses.
This paper proposes a new prompting method, named Progressive-Hint Prompting
(PHP), that enables automatic multiple interactions between users and LLMs by
using previously generated answers as hints to progressively guide toward the
correct answers. PHP is orthogonal to CoT and self-consistency, making it easy
to combine with state-of-the-art techniques to further improve performance. We
conducted an extensive and comprehensive evaluation to demonstrate the
effectiveness of the proposed method. Our experimental results on six
benchmarks show that combining CoT and self-consistency with PHP significantly
improves accuracy while remaining highly efficient. For instance, with
text-davinci-003, we observed a 4.2% improvement on GSM8K with greedy decoding
compared to Complex CoT, and a 46.17% reduction in sample paths with
self-consistency. With GPT-4 and PHP, we achieve state-of-the-art performances
on SVAMP (91.9%), GSM8K (95.5%) and AQuA (79.9%).Comment: Tech Repor
Drag-A-Video: Non-rigid Video Editing with Point-based Interaction
Video editing is a challenging task that requires manipulating videos on both
the spatial and temporal dimensions. Existing methods for video editing mainly
focus on changing the appearance or style of the objects in the video, while
keeping their structures unchanged. However, there is no existing method that
allows users to interactively ``drag'' any points of instances on the first
frame to precisely reach the target points with other frames consistently
deformed. In this paper, we propose a new diffusion-based method for
interactive point-based video manipulation, called Drag-A-Video. Our method
allows users to click pairs of handle points and target points as well as masks
on the first frame of an input video. Then, our method transforms the inputs
into point sets and propagates these sets across frames. To precisely modify
the contents of the video, we employ a new video-level motion supervision to
update the features of the video and introduce the latent offsets to achieve
this update at multiple denoising timesteps. We propose a temporal-consistent
point tracking module to coordinate the movement of the points in the handle
point sets. We demonstrate the effectiveness and flexibility of our method on
various videos. The website of our work is available here:
https://drag-a-video.github.io/
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