CMB Spectral μ\mu-Distortion of Multiple Inflation Scenario

In multiple inflation scenario having two inflations with an intermediate matter-dominated phase, the power spectrum is estimated to be enhanced on scales smaller than the horizon size at the beginning of the second inflation, $k > k_{\rm b}$. We require $k_{\rm b} > 10 {\rm Mpc}^{-1}$ to make sure that the enhanced power spectrum is consistent with large scale observation of cosmic microwave background (CMB). We consider the CMB spectral distortions generated by the dissipation of acoustic waves to constrain the power spectrum. The $\mu$-distortion value can be $10$ times larger than the expectation of the standard $\Lambda$CDM model ($\mu_{\Lambda\mathrm{CDM}} \simeq 2 \times 10^{-8}$) for $k_{\rm b} \lesssim 10^3 {\rm Mpc}^{-1}$, while the $y$-distortion is hardly affected by the enhancement of the power spectrum.Comment: 16 pages, 5 figure

Soil properties of cultivation sites for mountain-cultivated ginseng at local level

AbstractBackgroundIdentifying suitable site for growing mountain-cultivated ginseng is a concern for ginseng producers. This study was conducted to evaluate the soil properties of cultivation sites for mountain-cultivated ginseng in Hamyang-gun, which is one of the most well-known areas for mountain-cultivated ginseng in Korea.MethodsThe sampling plots from 30 sites were randomly selected on or near the center of the ginseng growing sites in July and August 2009. Soil samples for the soil properties analysis were collected from the top 20 cm at five randomly selected points.ResultsMountain-cultivated ginseng was grown in soils that varied greatly in soil properties on coniferous, mixed, and deciduous broad-leaved stand sites of elevations between > 200 m and < 1,000 m. The soil bulk density was higher in Pinus densiflora than in Larix leptolepis stand sites and higher in the < 700-m sites than in > 700-m sites. Soil pH was unaffected by the type of stand sites (pH 4.35–4.55), whereas the high-elevation sites of > 700 m were strongly acidified, with pH 4.19. The organic carbon and total nitrogen content were lower in the P. densiflora stand sites than in the deciduous broad-leaved stand sites. Available phosphorus was low in all of the stand sites. The exchangeable cation was generally higher in the mixed and low-elevation sites than in the P. densiflora and high-elevation sites, respectively.ConclusionThese results indicate that mountain-cultivated ginseng in Korea is able to grow in very acidic, nutrient-depleted forest soils

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

A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking

This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of high-level decision making. Existing works tackle these issues by heuristically assigning targets to each sensing agent and solving the split problem for each agent. However, such heuristic methods reduce the target estimation performance in the absence of considering the changes of target state estimation along time. In this work, we detour the task-assignment problem by reformulating the general non-myopic planning problem to a distributed optimization problem with respect to targets. By combining alternating direction method of multipliers (ADMM) and local trajectory optimization method, we solve the problem and induce consensus (i.e., high-level decisions) automatically among the targets. In addition, we propose a modified receding-horizon control (RHC) scheme and edge-cutting method for efficient real-time operation. The proposed algorithm is validated through simulations in various scenarios.Comment: Copyright 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

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

One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification

The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in employing them, which makes development and deployment difficult. Several studies have simply compressed SSL models through knowledge distillation (KD) without considering the target task. Consequently, these methods could not extract SV-tailored features. This paper suggests One-Step Knowledge Distillation and Fine-Tuning (OS-KDFT), which incorporates KD and fine-tuning (FT). We optimize a student model for SV during KD training to avert the distillation of inappropriate information for the SV. OS-KDFT could downsize Wav2Vec 2.0 based ECAPA-TDNN size by approximately 76.2%, and reduce the SSL model's inference time by 79% while presenting an EER of 0.98%. The proposed OS-KDFT is validated across VoxCeleb1 and VoxCeleb2 datasets and W2V2 and HuBERT SSL models. Experiments are available on our GitHub

Prevalence of sarcopenia and sarcopenic obesity in Korean adults: The Korean Sarcopenic Obesity Study (KSOS)

*Context:* Sarcopenic obesity (SO), a combination of excess weight and reduced muscle mass and/or strength, is suggested to be associated with an increased risk of adverse health outcomes. &#xd;&#xa;*Objectives:* To examine the prevalence and characteristics of Sarcopenic and SO defined by using different indices such as Appendicular Skeletal muscle Mass (ASM)/height^2^ and Skeletal Muscle Index (SMI (%): skeletal muscle mass (kg)/weight (kg) &#xd7; 100) for Korean adults. &#xd;&#xa;*Methods:* 591 participants were recruited from the Korean Sarcopenic Obesity Study (KSOS) which is an ongoing prospective observational cohort study. Analysis was conducted in 526 participants (328 women, 198 men) who had complete data on body composition using Dual X-ray absorptiometry and computed tomography. &#xd;&#xa;*Results:* The prevalence of sarcopenia and SO increases with aging. Using two or more standard deviations (SD) of ASM/height^2^ below reference values from young, healthy adults as a definition of sarcopenia, the prevalence of sarcopenia and SO was 6.3% and 1.3% in men and 4.1% and 1.7% in women over 60 years of age. However, using two or more SD of SMI, the prevalence of sarcopenia and SO was 5.1% and 5.1% respectively in men and 14.2% and 12.5% respectively in women. As defined by SMI, subjects with SO had 3 times the risk of metabolic syndrome (OR = 3.03, 95% confidence interval (CI) = 1.26-7.26) and subjects with non-sarcopenic obesity had approximately 2 times the risk of metabolic syndrome (OR = 1.89, 95% CI = 1.18-3.02) compared with normal subjects. &#xd;&#xa;*Conclusion:* Obese subjects with relative sarcopenia were associated with a greater likelihood for metabolic syndrome. As Koreans were more obese and aging, the prevalence of SO and its impact on health outcomes are estimated to be rapidly grow. Further research is requested to establish the definition, cause and consequences of SO.&#xd;&#xa

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

Blood neurofilament light chain as a biomarker for monitoring and predicting paclitaxel-induced peripheral neuropathy in patients with gynecological cancers

ObjectiveWe aimed to evaluate the potential of serum neurofilament light chain (sNfL) and serum brain-derived neurotrophic factor (sBDNF) as reliable biomarkers for paclitaxel-induced peripheral neuropathy (PIPN).MethodsForty-eight patients with gynecologic cancer scheduled to undergo six cycles of paclitaxel-based chemotherapy at the National Cancer Center of Korea between September 2020 and January 2022 were prospectively assessed during and after chemotherapy.ResultsAt the end of the chemotherapy, 12 (25%) patients were classified as having grade 3 PIPN according to the National Cancer Institute-Common Toxicity Criteria. The sNfL levels increased during paclitaxel treatment in all patients. After two, four, and six cycles, patients with grade 3 PIPN exhibited higher mean sNfL levels than those in the 0–2 grade range (p = 0.004, p = 001, and p &lt; 0.001, respectively). For sNfL levels ≥ 124 pg/mL, after two cycles of chemotherapy, the sensitivity and specificity for predicting grade 3 PIPN at the end of treatment were 80% and 79%, respectively. Over the course of paclitaxel-based treatment, sBDNF levels continued to decrease regardless of the severity of PIPN. At the end of treatment and six months after chemotherapy, patients with grade 3 PIPN had lower sBDNF levels than those within the 0–2 grade range (p =0.037 and 0.02, respectively), and the patients in the latter group had better clinical symptoms six months after the end of treatment.ConclusionsThe sNfL levels during paclitaxel-based chemotherapy reflect ongoing neuroaxonal injury and serve as reliable biomarkers of PIPN severity. The sNfL levels during early treatment with paclitaxel might be prognostic indicators for PIPN progression. Low sBDNF levels 6 months after chemotherapy might adversely affect PIPN recovery