Application of the LANS-alpha Model to Gravity Currents

This thesis aims to investigate how HERCULES performs when running a lock-exchange gravity current case. The LANS-alpha model with stratification is also tested as a subgrid model in HERCULES using the same gravity current case. Gravity currents have been studied using both direct numerical simulation (DNS) and large eddy simulation (LES). On the other hand, the LANS-alpha model has only been applied to several test cases which mainly focus on isotropic turbulence and wall-bounded unstratified flows. We begin by reviewing the characteristics of the turbulent structures in the gravity currents and the motivation to use the LANS-alpha model. This is followed by the implementation of the model in HERCULES with both grid-dependent alpha and flow-dependent alpha. For the numerical study, a gravity current is generated using a lock release in a horizontal channel. With a fine grid, the front location and the three-dimensionality of the gravity current can be resolved accurately using HERCULES. When the grid resolution is coarse, the LANS-alpha model can improve the results considerably using grid-dependent alpha with both subgrid terms. The flow-dependent alpha requires modification in its definition as the grid-dependent alpha outperforms it in resolving the front location and the small-scale, three-dimensional structures

Does the introduction of index futures stabilize stock markets? Further evidence from emerging markets

We examine how the introduction of index futures affects the stability of stock markets in seven emerging countries by studying the existence and the impact of positive feedback trading in both pre- and post-futures periods. Consistent with the findings in advanced markets, we find that positive feedback traders are already prevalent before the introduction of index futures in six out of the seven markets studied. After the introduction of index futures, signs of positive feedback trading emerge in only two markets (India and Poland). In contrast to the evidence in developed markets, positive feedback traders migrate from spot to futures markets in four markets, which suggests that the introduction of index futures may destabilize some emerging stock markets. Another interesting finding is that positive feedback trading becomes more intense when there is a market decline in the majority of the markets

Bright Soliton Solution of (1+1)-Dimensional Quantum System with Power-Law Dependent Nonlinearity

We study the nonlinear dynamics of (1+1)-dimensional quantum system in power-law dependent media based on the nonlinear Schrödinger equation (NLSE) incorporating power-law dependent nonlinearity, linear attenuation, self-steepening terms, and third-order dispersion term. The analytical bright soliton solution of this NLSE is derived via the F-expansion method. The key feature of the bright soliton solution is pictorially demonstrated, which together with typical analytical formulation of the soliton solution shows the applicability of our theoretical treatment

Dual-attention Focused Module for Weakly Supervised Object Localization

The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, the most discriminative regions usually conceal the other parts of the object, thereby impeding entire object recognition and localization. To tackle this problem, the Dual-attention Focused Module (DFM) is proposed to enhance object localization performance. Specifically, we present a dual attention module for information fusion, consisting of a position branch and a channel one. In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them. For the position mask map, we introduce a focused matrix to enhance it, which utilizes the principle that the pixels of an object are continuous. Between these two branches, the enhancement map is integrated with the mask map, aiming at partially compensating the lost information and diversifies the features. With the dual-attention module and focused matrix, the entire object region could be precisely recognized with implicit information. We demonstrate outperforming results of DFM in experiments. In particular, DFM achieves state-of-the-art performance in localization accuracy in ILSVRC 2016 and CUB-200-2011.Comment: 8 pages, 6 figures and 4 table

Printing of Fine Metal Electrodes for Organic Thin‐Film Transistors

Attributed to the excellent mechanical flexibility and compatibility with low‐cost and high‐throughput printing processes, the organic thin‐film transistor (OTFT) is a promising technology of choice for a wide range of flexible and large‐area electronics applications. Among various printing techniques, the drop‐on‐demand inkjet printing is one of the most versatile ones to form patterned electrodes with the advantages of mask‐less patterning, non‐contact, low cost, and scalability to large‐area manufacturing. However, the limited positional accuracy of the inkjet printer system and the spreading of the ink droplets on the substrate surface, which is influenced by both the ink properties and the substrate surface energy, make it difficult to obtain fine‐line morphologies and define the exact channel length as required, especially for relatively narrow‐line and short‐channel patterns. This chapter introduces the printing of uniform fine silver electrodes and down scaling of the channel length by controlling ink wetting on polymer substrate. All‐solution‐processed/printable OTFTs with short channels (<20 µm) are also demonstrated by incorporating fine inkjet‐printed silver electrodes into a low‐voltage (<3 V) OTFT architecture. This work would provide a commercially competitive manufacturing approach to developing printable low‐voltage OTFTs for low‐power electronics applications

TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision

End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on Region-of-Interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these limitations, we propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. Specifically, using query embedding per text instance, TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing without sacrificing flexibility or simplicity. Additionally, we design an Adaptive Global aGgregation (AGG) module to transfer global features into sequential features for reading arbitrarily-shaped texts, which overcomes the sub-optimization problem of RoI operations. Furthermore, potential corpus information is utilized from weak annotations to full labels through mixed supervision, further improving text detection and end-to-end text spotting results. Extensive experiments on various bilingual (i.e., English and Chinese) benchmarks demonstrate the superiority of our method. Especially on TDA-ReCTS dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.Comment: MIR 2023, 15 page

Example-based image colorization using locality consistent sparse representation

—Image colorization aims to produce a natural looking color image from a given grayscale image, which remains a challenging problem. In this paper, we propose a novel examplebased image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target grayscale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target grayscale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms state-ofthe-art methods, both visually and quantitatively using a user stud

Adaptive gradient-based block compressive sensing with sparsity for noisy images

This paper develops a novel adaptive gradient-based block compressive sensing (AGbBCS_SP) methodology for noisy image compression and reconstruction. The AGbBCS_SP approach splits an image into blocks by maximizing their sparsity, and reconstructs images by solving a convex optimization problem. In block compressive sensing, the commonly used square block shapes cannot always produce the best results. The main contribution of our paper is to provide an adaptive method for block shape selection, improving noisy image reconstruction performance. The proposed algorithm can adaptively achieve better results by using the sparsity of pixels to adaptively select block shape. Experimental results with different image sets demonstrate that our AGbBCS_SP method is able to achieve better performance, in terms of peak signal to noise ratio (PSNR) and computational cost, than several classical algorithms

Block compressive sensing for solder joint images with wavelet packet thresholding

This paper provides a novel method that can achieve better results in solder joint imagery compression and reconstruction. Wavelet packet decomposition is used to generate some frequency coefficients of images. The higher and lower frequency coefficients of the reconstruction signal are used separately to improve the reconstruction performance. A threshold that only relates to the higher frequency coefficients is defined to remove the noise in the reconstruction result in each iteration. A new control factor is further defined to control the threshold value. The control factor relates to the wavelet packet low-frequency coefficients and is updated by the wavelet packet low-frequency coefficients in each iteration. The experimental results reveal that the proposed algorithm is able to improve the performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared with classical algorithms in reconstruction of different types of solder joint images. When the sample rate is increased, the proposed method improves the reconstruction results and maintains low computational cost. The proposed algorithm can retain more image structure and achieve better results than some common methods