Collider probes of singlet fermionic dark matter scenarios for the Fermi gamma-ray excess

We investigate the collider signatures of the three benchmark points in the singlet fermionic dark matter model. The benchmark points, which were introduced previously to explain the Fermi gamma-ray excess by dark matter (DM) pair annihilation at the Galactic center, have definite predictions for future collider experiments such as the International Linear Collider and the High-Luminosity LHC. We consider four collider observables: (1) Higgs signal strength (essentially $hZZ$ coupling), (2) triple Higgs coupling, (3) exotic Higgs decay, and (4) direct production of a new scalar particle. The benchmark points are classified by the final states of the DM annihilation process: a pair of $b$ quarks, SM-like Higgs bosons, and new scalar particles. Each benchmark scenario has detectable new physics signals for the above collider observables that can be well tested in the future lepton and hadron colliders.Comment: 14 pages, 1 figure, 1 tabl

Multicultural families in Korean rural farming communities

노트 : Paper presented at the Fourth Annual East Asian Social Policy research network (EASP) International ConferenceRestructuring Care Responsibility: Dynamics of Welfare Mix in East Asia20-21 October 2007, The University of Tokyo, Japa

The Mx/G/1 queue with queue length dependent service times

We deal with the MX/G/1 queue where service times depend on the queue length at the service initiation. By using Markov renewal theory, we derive the queue length distribution at departure epochs. We also obtain the transient queue length distribution at time t and its limiting distribution and the virtual waiting time distribution. The numerical results for transient mean queue length and queue length distributions are given.Bong Dae Choi, Yeong Cheol Kim, Yang Woo Shin, and Charles E. M. Pearc

Economic Ripple Effect Analysis of New Converging Industry: Focusing on Inter-Industrial Analysis of Fintech Industry in South Korea, China and the United States

116–121The Fintech industry is the convergence of the financial industry and the ICT (Information and Communications Technology) industry. It not only replaces the services provided by traditional financial services such as remittances, settlements, and asset management, but also creates new industries combined with ICT technology such as Cloud funding, P2P, and Internet Professional Bank. Therefore, this research focuses on the economic ripple effect of the Fintech industry on the national economy by inter-industrial analysis. At the same time, in order to learn the different circumstances of Fintech industry worldwide, this study conducted comparative research on Fintech leading countries in the United States and China with South Korea. All the economic ripple effects of South Korea, China, and the United States Fintech industry are relatively low among all industries, and the industrial characteristics are analyzed to be 'intermediate primary production' type industry. This study, as the first attempt, analyzes the current economic impact of Fintech in each country, and inspires future development direction of Fintech industrial policy

PAS: Partial Additive Speech Data Augmentation Method for Noise Robust Speaker Verification

Background noise reduces speech intelligibility and quality, making speaker verification (SV) in noisy environments a challenging task. To improve the noise robustness of SV systems, additive noise data augmentation method has been commonly used. In this paper, we propose a new additive noise method, partial additive speech (PAS), which aims to train SV systems to be less affected by noisy environments. The experimental results demonstrate that PAS outperforms traditional additive noise in terms of equal error rates (EER), with relative improvements of 4.64% and 5.01% observed in SE-ResNet34 and ECAPA-TDNN. We also show the effectiveness of proposed method by analyzing attention modules and visualizing speaker embeddings.Comment: 5 pages, 2 figures, 1 table, accepted to CKAIA2023 as a conference pape

Oxygen Partial Pressure during Pulsed Laser Deposition: Deterministic Role on Thermodynamic Stability of Atomic Termination Sequence at SrRuO3/BaTiO3 Interface

With recent trends on miniaturizing oxide-based devices, the need for atomic-scale control of surface/interface structures by pulsed laser deposition (PLD) has increased. In particular, realizing uniform atomic termination at the surface/interface is highly desirable. However, a lack of understanding on the surface formation mechanism in PLD has limited a deliberate control of surface/interface atomic stacking sequences. Here, taking the prototypical SrRuO3/BaTiO3/SrRuO3 (SRO/BTO/SRO) heterostructure as a model system, we investigated the formation of different interfacial termination sequences (BaO-RuO2 or TiO2-SrO) with oxygen partial pressure (PO2) during PLD. We found that a uniform SrO-TiO2 termination sequence at the SRO/BTO interface can be achieved by lowering the PO2 to 5 mTorr, regardless of the total background gas pressure (Ptotal), growth mode, or growth rate. Our results indicate that the thermodynamic stability of the BTO surface at the low-energy kinetics stage of PLD can play an important role in surface/interface termination formation. This work paves the way for realizing termination engineering in functional oxide heterostructures.Comment: 27 pages, 6 figures, Supporting Informatio

Integrated Parameter-Efficient Tuning for General-Purpose Audio Models

The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability have achieved state-of-the-art performances through fine-tuning for downstream tasks. Nevertheless, re-training all the parameters of these massive models entails an enormous amount of time and cost, along with a huge carbon footprint. To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain. We also propose an integrated parameter-efficient tuning (IPET) framework by aggregating the embedding prompt (a prompt-based learning approach), and the adapter (an effective transfer learning method). We demonstrate the efficacy of the proposed framework using two backbone pre-trained audio models with different characteristics: the audio spectrogram transformer and wav2vec 2.0. The proposed IPET framework exhibits remarkable performance compared to fine-tuning method with fewer trainable parameters in four downstream tasks: sound event classification, music genre classification, keyword spotting, and speaker verification. Furthermore, the authors identify and analyze the shortcomings of the IPET framework, providing lessons and research directions for parameter efficient tuning in the audio domain.Comment: 5 pages, 3 figures, submit to ICASSP202

Convolution channel separation and frequency sub-bands aggregation for music genre classification

In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative. A music genre classification (MGC) system should be able to analyze these features. In this research, we propose a novel framework that can extract and aggregate both short- and long-term features hierarchically. Our framework is based on ECAPA-TDNN, where all the layers that extract short-term features are affected by the layers that extract long-term features because of the back-propagation training. To prevent the distortion of short-term features, we devised the convolution channel separation technique that separates short-term features from long-term feature extraction paths. To extract more diverse features from our framework, we incorporated the frequency sub-bands aggregation method, which divides the input spectrogram along frequency bandwidths and processes each segment. We evaluated our framework using the Melon Playlist dataset which is a large-scale dataset containing 600 times more data than GTZAN which is a widely used dataset in MGC studies. As the result, our framework achieved 70.4% accuracy, which was improved by 16.9% compared to a conventional framework