A Case Study: Lessons from the Hong Kong Independent Commission Against Corruption

This article is a case study that examines the three-pronged approach (TPA) of the Independent Commission against Corruption (ICAC) in Hong Kong. Its functions and operations on anti-corruption matters will be assessed from an institution-oriented perspective. It is suggested that for decades, the TPA had been misunderstood in its role as a conventional investigation, prevention and education tool and that such misconceptions may lead to a failure in anti-corruption institutional reform. By better understanding the TPA and its simplistic traits of deterrence and trust then we may be able to remedy the misconceptions the public has about ICAC's strategies. Policy implications involve further improvements in anti-corruption agencies that will enhance their role in maintaining an environment free of corruption

Analytical technique for simplification of the encoder-decoder circuit for a perfect five-qubit error correction

Simpler encoding and decoding networks are necessary for more reliable quantum error correcting codes (QECCs). The simplification of the encoder-decoder circuit for a perfect five-qubit QECC can be derived analytically if the QECC is converted from its equivalent one-way entanglement purification protocol (1-EPP). In this work, the analytical method to simplify the encoder-decoder circuit is introduced and a circuit that is as simple as the existent simplest circuits is presented as an example. The encoder-decoder circuit presented here involves nine single- and two-qubit unitary operations, only six of which are controlled-NOT (CNOT) gates

A NOVEL OCCLUSION SIGN LANGUAGE RECOGNITION

[[abstract]]Sign language plays an important role in communicate with changers that hearing improved. However, the sign language in many countries and areas different and auto recognition system became the research way in recent year. In this paper, we devise a novel method for occlusion processing in Taiwan Sign Language recognition system. Our method employs adxl345 and Kinect to extract the feature of signer. Then the features are regulated by the dictionary of sparse coding. In final, the HMM model and result signs are recognized from the features that corrected by our method. In experimental result, we present the data that our employ. Then we describe closing test result and future work.[[sponsorship]]National Taipei University[[conferencetype]]國際[[conferencedate]]20150718~20150719[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Dual residence time for droplet to coalesce with liquid surface

When droplets approach a liquid surface, they have a tendency to merge in order to minimize surface energy. However, under certain conditions, they can exhibit a phenomenon called coalescence delay, where they remain separate for tens of milliseconds. This duration is known as the residence time or the non-coalescence time. Surprisingly, under identical parameters and initial conditions, the residence time for water droplets is not a constant value but exhibits dual peaks in its distribution. In this paper, we present the observation of the dual residence times through rigorous statistical analysis and investigate the quantitative variations in residence time by manipulating parameters such as droplet height, radius, and viscosity. Theoretical models and physical arguments are provided to explain their effects, particularly why a large viscosity or/and a small radius is detrimental to the appearance of the longer residence time peak.Comment: 7 pages, 6 figure

MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild

Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors. We present MixNet, a hybrid architecture that combines the strengths of CNNs and Transformers, capable of accurately detecting small text from challenging natural scenes, regardless of the orientations, styles, and lighting conditions. MixNet incorporates two key modules: (1) the Feature Shuffle Network (FSNet) to serve as the backbone and (2) the Central Transformer Block (CTBlock) to exploit the 1D manifold constraint of the scene text. We first introduce a novel feature shuffling strategy in FSNet to facilitate the exchange of features across multiple scales, generating high-resolution features superior to popular ResNet and HRNet. The FSNet backbone has achieved significant improvements over many existing text detection methods, including PAN, DB, and FAST. Then we design a complementary CTBlock to leverage center line based features similar to the medial axis of text regions and show that it can outperform contour-based approaches in challenging cases when small scene texts appear closely. Extensive experimental results show that MixNet, which mixes FSNet with CTBlock, achieves state-of-the-art results on multiple scene text detection datasets