Efficient Differential Pixel Value Coding in CABAC for H.264/AVC Lossless Video Compression

Abstract Since context-based adaptive binary arithmetic coding (CABAC) as the entropy coding method in H.264/AVC was originally designed for lossy video compression, it is inappropriate for lossless video compression. Based on the fact that there are statistical differences of residual data between lossy and lossless video compression, we propose an efficient differential pixel value coding method in CABAC for H.264/AVC lossless video compression. Considering the observed statistical properties of the differential pixel value in lossless coding, we modified the CABAC encoding mechanism with the newly designed binarization table and the context-modeling method. Experimental results show that the proposed method achieves an approximately 12% bit saving, compared to the original CABAC method in the H.264/AVC standard

Solid tumors of the pancreas can put on a mask through cystic change

<p>Abstract</p> <p>Background</p> <p>Solid pancreatic tumors such as pancreatic ductal adenocarcinoma (PDAC), solid pseudopapillary tumor (SPT), and pancreatic endocrine tumor (PET) may occasionally manifest as cystic lesions. In this study, we have put together our accumulated experience with cystic manifestations of various solid tumors of the pancreas.</p> <p>Methods</p> <p>From 2000 to 2006, 376 patients with pancreatic solid tumor resections were reviewed. Ten (2.66%) of these tumors appeared on radiological imaging studies as cystic lesions. We performed a retrospective review of medical records and pathologic findings of these 10 cases.</p> <p>Results</p> <p>Of the ten cases in which solid tumors of the pancreas manifested as cystic lesions, six were PDAC with cystic degeneration, two were SPT undergone complete cystic change, one was cystic PET, and one was a cystic schwannoma. The mean tumor size of the cystic portion in PDAC was 7.3 cm, and three patients were diagnosed as 'pseudocyst' with or without cancer. Two SPT were found incidentally in young women and were diagnosed as other cystic neoplasms. One cystic endocrine tumor was preoperatively suspected as intraductal papillary mucinous neoplasm or mucinous cystic neoplasm.</p> <p>Conclusions</p> <p>Cystic changes of pancreas solid tumors are extremely rare. However, the possibility of cystic manifestation of pancreas solid tumors should be kept in mind.</p

Neuroprotective and anti-oxidant effects of caffeic acid isolated from Erigeron annuus leaf

<p>Abstract</p> <p>Background</p> <p>Since oxidative stress has been implicated in a neurodegenerative disease such as Alzheimer's disease (AD), natural antioxidants are promising candidates of chemopreventive agents. This study examines antioxidant and neuronal cell protective effects of various fractions of the methanolic extract of <it>Erigeron annuus </it>leaf and identifies active compounds of the extract.</p> <p>Methods</p> <p>Antioxidant activities of the fractions from <it>Erigeron annuus </it>leaf were examined with [2,2-azino-bis(3-ethylbenz thiazoline-6-sulfonic acid diammonium salt)] (ABTS) and ferric reducing antioxidant power (FRAP) assays. Neuroprotective effect of caffeic acid under oxidative stress induced by H₂O₂was investigated with [3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide] (MTT) and lactate dehydrogenase (LDH) assays.</p> <p>Results</p> <p>This study demonstrated that butanol fraction had the highest antioxidant activity among all solvent fractions from methanolic extract <it>E. annuus </it>leaf. Butanol fraction had the highest total phenolic contents (396.49 mg of GAE/g). Caffeic acid, an isolated active compound from butanol fraction, showed dose-dependent <it>in vitro </it>antioxidant activity. Moreover, neuronal cell protection against oxidative stress induced cytotoxicity was also demonstrated.</p> <p>Conclusion</p> <p><it>Erigeron annuus </it>leaf extracts containing caffeic acid as an active compound have antioxidative and neuroprotective effects on neuronal cells.</p

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

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

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