H\"ormander type theorem for multilinear Pseudo-differential operators

We establish a H\"{o}rmander type theorem for the multilinear pseudo-differential operators, which is also a generalization of the results in \cite{MR4322619} to symbols depending on the spatial variable. Most known results for multilinear pseudo-differential operators were obtained by assuming their symbols satisfy pointwise derivative estimates(Mihlin-type condition), that is, their symbols belong to some symbol classes $n$-$\mathcal{S}^m_{\rho, \delta}(\mathbb{R}^d)$, $0 \le \delta \le \rho \le1$, $0 \le \delta<1$ for some $m \le 0$. In this paper, we shall consider multilinear pseudo-differential operators whose symbols have limited smoothness described in terms of function space and not in a pointwise form(H\"ormander type condition). Our conditions for symbols are weaker than the Mihlin-type conditions in two senses: the one is that we only assume the first-order derivative conditions in the spatial variable and lower-order derivative conditions in the frequency variable, and the other is that we make use of $L^2$-average condition rather than pointwise derivative conditions for the symbols. As an application, we obtain some mapping properties for the multilinear pseudo-differential operators associated with symbols belonging to the classes $n$-$\mathcal{S}^{m}_{\rho,\delta}(\mathbb{R}^{d})$, $0 \le \rho \le 1$, $0 \le \delta<1$, $m \le 0$. Moreover, it can be pointed out that our results can be applied to wider classes of symbols which do not belong to the traditional symbol classes $n$-$\mathcal{S}^{m}_{\rho,\delta}(\mathbb{R}^{d})$

Molecular-Beam Spectroscopy with an Infinite Interferometer: Spectroscopic Resolution and Accuracy

An interferometer with effectively infinite maximum optical path difference removes the dominant resolution limitation for interferometric spectroscopy. We present mass-correlated rotational Raman spectra that represent the world's highest resolution scanned interferometric data and discuss the current and expected future limitations in achievable spectroscopic performance.Comment: 8 pages, 12 figures, 1 table, submitted to the Journal of the Korean Physical Societ

The Effect of Phosphatidylcholine and Deoxycholate Compound Injections to the Localized Adipose Tissue: An Experimental Study with a Murine Model

BackgroundPhosphatidylcholine (PPC) and deoxycholate (DCA) compound has been recently used for the purpose of partial lipolysis and is valued for its efficacy and lower invasiveness compared to liposuction and dermolipectomy used previously. In this article, the authors discuss the efficacy of the PPC dissolved in DCA via an experimental rat study model, along with suggesting a useful animal experimental model for the study of adipose tissue and lipolysis.MethodsBilateral inguinal fat pads of an experimental rat were elevated with the deep inferior epigastric vessel as the sole vascular pedicle. Normal saline was injected on one side as a control group and a PPC and DCA compound was injected on the other side. After 4 days, the rats were euthanized for microscopic tissue examination. The pathology was scored by a semiquantitative system in 4 categories: normal fat amount, fat necrosis, inflammatory activity, and stage of fibrosis. A Wilcoxon signed-rank test powered by SPSS packet program was used for statistical analysis and to determine significance.ResultsMicroscopic examination was performed on the obtained samples, and the experimental data of all four categories showed significant histologic differences compared to the control group. All of the data also showed statistical significance by the Wilcoxon signedrank test (P<0.01).ConclusionsIn the inguinal fat pad rat model, the control group and the experimental group had a differed significantly in the amount of normal fat tissue, inflammation, necrosis, and fibrosis. We recommend the rat inguinal fat pad model used in this study, as it is likely to be useful in related research

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

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

Free Flap Coverage of Extensive Soft Tissue Defect in Cutaneous Aspergillosis: A Case Report

Isolated fungal soft-tissue infections are uncommon, but may cause severe morbidity or mortality. Aspergillosis infection is rare, but the frequency in increasing over the last two decades. Here, we present a patient with cutaneous aspergillosis of his right elbow with unusual clinical and radiological features suggestive of a malignant disease, which remained undiagnosed for an extended period of time. The patient presented with necrotic, black-colored skin ulcerations. We completely removed the skin ulcer with the surrounding erythematous skin lesion, and then we reconstructed the area with thoracodorsal perforator free flap. The biopsy specimen contained septate hyphae with dichotomous branching, which is morphologically consistent with a finding of Aspergillus. After surgery, we initiated antifungal medication therapy with amphotericin B and itraconazole. At the time of follow-up, the elbow with the reconstructed flap had fully healed, and no recurrent disease was found

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

Stereo visual-inertial odometry with an online calibration and its field testing

In this paper, we present a visual-inertial odometry (VIO) with an online calibration using a stereo camera in planetary rover localization. We augment the state vector with extrinsic (rigid body transformation) and temporal (time-offset) parameters of a camera-IMU system in a framework of an extended Kalman filter. This is motivated by the fact that when fusing independent systems, it is practically crucial to obtain precise extrinsic and temporal parameters. Unlike the conventional calibration procedures, this method estimates both navigation and calibration states from naturally occurred visual point features during operation. We describe mathematical formulations of the proposed method, and it is evaluated through the author-collected dataset which is recorded by the commercially available visual-inertial sensor installed on the testing rover in the environment lack of vegetation and artificial objects. Our experimental results showed that 3D return position error as 1.54m of total 173m traveled and 10ms of time-offset with the online calibration, while 6.52m of return position error without the online calibration