CP-Violation in the Two Higgs Doublet Model: from the LHC to EDMs

We study the prospective sensitivity to CP-violating Two Higgs Doublet Models from the 14 TeV LHC and future electric dipole moment (EDM) experiments. We concentrate on the search for a resonant heavy Higgs that decays to a $Z$ boson and a SM-like Higgs h, leading to the $Z(\ell\ell)h(b\bar{b})$ final state. The prospective LHC reach is analyzed using the Boosted Decision Tree method. We illustrate the complementarity between the LHC and low energy EDM measurements and study the dependence of the physics reach on the degree of deviation from the alignment limit. In all cases, we find that there exists a large part of parameter space that is sensitive to both EDMs and LHC searches.Comment: 21 pages, 34 figure

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

ENHANCING EFL ADOLESCENT LEARNERS’ VOCABULARY ACQUISITION VIA ONLINE SINGLE PLAYER ROLE-PLAY GAMES

With the booming of digital gaming industry, numerous researchers have placed the focus on the use of online role-play games in language learning. However, the research focus of most prior studies was on the commercially-driven “massive multiplayer online role-play games” in the afterschool settings. The use of online single-player role-play games in the class was less studied. The present study hereby investigated sixty-five eighth graders’ use of one online single-player RPG called OzHigh in vocabulary learning. The participants came from three classes in one public junior high school in central Taiwan. By means of a single group design, the participants underwent the game treatment, pre-test, post-test, delayed post-test, questionnaire, and the semi-structured interview. The results showed that the participants had significant improvement on their vocabulary performance. They also responded positively to their role-play game learning experience. Nevertheless, that did not mean that they held negative attitudes toward the traditional face-to-face method of vocabulary instruction. Instead, they confirmed the positive effects of both instructional methods and were aware of the varied learning purposes of these two methods. It is hoped that the findings of this study shed light on language teachers in their efforts to enhance their students’ vocabulary learning

Enzymatic Cross-Linking of Dynamic Thiol-Norbornene Click Hydrogels

Enzyme-mediated in situ forming hydrogels are attractive for many biomedical applications because gelation afforded by enzymatic reactions can be readily controlled not only by tuning macromer compositions, but also by adjusting enzyme kinetics. For example, horseradish peroxidase (HRP) has been used extensively for in situ cross-linking of macromers containing hydroxyl-phenol groups. The use of HRP to initiate thiol-allylether polymerization has also been reported, yet no prior study has demonstrated enzymatic initiation of thiol-norbornene gelation. In this study, we discovered that HRP can generate the thiyl radicals needed for initiating thiol-norbornene hydrogelation, which has only been demonstrated previously using photopolymerization. Enzymatic thiol-norbornene gelation not only overcomes light attenuation issue commonly observed in photopolymerized hydrogels, but also preserves modularity of the cross-linking. In particular, we prepared modular hydrogels from two sets of norbornene-modified macromers, 8-arm poly(ethylene glycol)-norbornene (PEG8NB) and gelatin-norbornene (GelNB). Bis-cysteine-containing peptides or PEG-tetra-thiol (PEG4SH) was used as a cross-linker for forming enzymatically and orthogonally polymerized hydrogel. For HRP-initiated PEG-peptide hydrogel cross-linking, gelation efficiency was significantly improved via adding tyrosine residues on the peptide cross-linkers. Interestingly, these additional tyrosine residues did not form permanent dityrosine cross-links following HRP-induced gelation. As a result, they remained available for tyrosinase-mediated secondary cross-linking, which dynamically increased hydrogel stiffness. In addition to material characterizations, we also found that both PEG- and gelatin-based hydrogels exhibited excellent cytocompatibility for dynamic 3D cell culture. The enzymatic thiol-norbornene gelation scheme presented here offers a new cross-linking mechanism for preparing modularly and dynamically cross-linked hydrogels

A Novel Model Considered Mass and Energy Conservation for Both Liquid and Vapor in Adsorption Refrigeration System.

In this paper, we proposed a dynamic model for a two-bed adsorption refrigeration system. Different from most existing researches which assume saturation vaper pressure in each device, the proposed method models the pressure in each device by considering both the liquid and vaper content in the device. Therefore, it can be more accurate in describing the system response and more suitable for studying the system instrumentation. The components included in this system model are: adsorption bed, evaporator, condenser, expansion valve, and etc. Each device is modeled based on the energy and mass conservation. Furthermore, the adsorption phenomenon is modeled by the “Freundlich equation,†and “linear driving force model.†The phase change of the refrigerant in evaporator and condenser is modeled by Hertz-Knudsen theory. In a case study, the pressure of the adsorption bed during the adsorption process is estimated to be 0.7kPa by the proposed model, while it was 1.6kPa by conventional method which assuming saturated vapor pressure. The coefficient-of-performance of the adsorption system is estimated to be 0.246 by this model, 0.36 by conventional method, and 0.28 by experimental data. The proposed model can estimate system performance more accurate than the conventional method. Moreover, the proposed model also inspire a new instrumentation strategy for the adsorption system, in which the system efficiency is improved and the pressure surge is avoided

A Unified CPU-GPU Protocol for GNN Training

Training a Graph Neural Network (GNN) model on large-scale graphs involves a high volume of data communication and computations. While state-of-the-art CPUs and GPUs feature high computing power, the Standard GNN training protocol adopted in existing GNN frameworks cannot efficiently utilize the platform resources. To this end, we propose a novel Unified CPU-GPU protocol that can improve the resource utilization of GNN training on a CPU-GPU platform. The Unified CPU-GPU protocol instantiates multiple GNN training processes in parallel on both the CPU and the GPU. By allocating training processes on the CPU to perform GNN training collaboratively with the GPU, the proposed protocol improves the platform resource utilization and reduces the CPU-GPU data transfer overhead. Since the performance of a CPU and a GPU varies, we develop a novel load balancer that balances the workload dynamically between CPUs and GPUs during runtime. We evaluate our protocol using two representative GNN sampling algorithms, with two widely-used GNN models, on three datasets. Compared with the standard training protocol adopted in the state-of-the-art GNN frameworks, our protocol effectively improves resource utilization and overall training time. On a platform where the GPU moderately outperforms the CPU, our protocol speeds up GNN training by up to 1.41x. On a platform where the GPU significantly outperforms the CPU, our protocol speeds up GNN training by up to 1.26x. Our protocol is open-sourced and can be seamlessly integrated into state-of-the-art GNN frameworks and accelerate GNN training. Our protocol particularly benefits those with limited GPU access due to its high demand.Comment: To appear in 21st ACM International Conference on Computing Frontiers (CF' 24