音源分離における音響モデリング(Acoustic modeling in audio source separation)Daichi Kitamura
?
北村大地, "音源分離における音響モデリング," 日本音響学会 サマーセミナー 招待講演, September 11th, 2017.
Daichi Kitamura, "Acoustic modeling in audio source separation," The Acoustical Society of Japan, Summer Seminar Invited Talk, September 11th, 2017.
独立低ランク行列分析に基づく音源分離とその発展(Audio source separation based on independent low-rank...Daichi Kitamura
?
北村大地, "独立低ランク行列分析に基づく音源分離とその発展," IEICE信号処理研究会, 2021年8月24日.
Daichi Kitamura, "Audio source separation based on independent low-rank matrix analysis and its extensions," IEICE Technical Group on Signal Processing, Aug. 24th, 2021.
http://d-kitamura.net
出典:Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
Facebook AI
公開URL : https://arxiv.org/abs/2005.12872
概要:Detection Transformer(DETRという)という新しいフレームワークによって,non-maximum-supressionやアンカー生成のような人手で設計する必要なく、End-to-Endで画像からぶった検出を行う手法を提案しています。物体検出を直接集合予測問題として解くためのtransformerアーキテクチャとハンガリアン法を用いて二部マッチングを行い正解と予測の組み合わせを探索しています。Attentionを物体検出に応用しただけでなく、競合手法であるFaster R-CNNと同等の精度を達成しています。
統計的独立性と低ランク行列分解理論に基づくブラインド音源分離 –独立低ランク行列分析– Blind source separation based on...Daichi Kitamura
?
北村大地, "統計的独立性と低ランク行列分解理論に基づくブラインド音源分離 –独立低ランク行列分析–," 筑波大学システム情報工学研究科マルチメディア研究室 招待講演, Ibaraki, September 26th, 2016.
Daichi Kitamura, "Blind source separation based on statistical independence and low-rank matrix decomposition –Independent low-rank matrix analysis–," University of Tsukuba, Graduate School of Systems and Information Engineering, Multimedia Laboratory, Invited Talk, Ibaraki, September 26th, 2016.
Presentation slide for AI seminar at Artificial Intelligence Research Center, The National Institute of Advanced Industrial Science and Technology, Japan.
URL (in Japanese): https://www.airc.aist.go.jp/seminar_detail/seminar_046.html
音源分離における音響モデリング(Acoustic modeling in audio source separation)Daichi Kitamura
?
北村大地, "音源分離における音響モデリング," 日本音響学会 サマーセミナー 招待講演, September 11th, 2017.
Daichi Kitamura, "Acoustic modeling in audio source separation," The Acoustical Society of Japan, Summer Seminar Invited Talk, September 11th, 2017.
独立低ランク行列分析に基づく音源分離とその発展(Audio source separation based on independent low-rank...Daichi Kitamura
?
北村大地, "独立低ランク行列分析に基づく音源分離とその発展," IEICE信号処理研究会, 2021年8月24日.
Daichi Kitamura, "Audio source separation based on independent low-rank matrix analysis and its extensions," IEICE Technical Group on Signal Processing, Aug. 24th, 2021.
http://d-kitamura.net
出典:Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
Facebook AI
公開URL : https://arxiv.org/abs/2005.12872
概要:Detection Transformer(DETRという)という新しいフレームワークによって,non-maximum-supressionやアンカー生成のような人手で設計する必要なく、End-to-Endで画像からぶった検出を行う手法を提案しています。物体検出を直接集合予測問題として解くためのtransformerアーキテクチャとハンガリアン法を用いて二部マッチングを行い正解と予測の組み合わせを探索しています。Attentionを物体検出に応用しただけでなく、競合手法であるFaster R-CNNと同等の精度を達成しています。
統計的独立性と低ランク行列分解理論に基づくブラインド音源分離 –独立低ランク行列分析– Blind source separation based on...Daichi Kitamura
?
北村大地, "統計的独立性と低ランク行列分解理論に基づくブラインド音源分離 –独立低ランク行列分析–," 筑波大学システム情報工学研究科マルチメディア研究室 招待講演, Ibaraki, September 26th, 2016.
Daichi Kitamura, "Blind source separation based on statistical independence and low-rank matrix decomposition –Independent low-rank matrix analysis–," University of Tsukuba, Graduate School of Systems and Information Engineering, Multimedia Laboratory, Invited Talk, Ibaraki, September 26th, 2016.
Presentation slide for AI seminar at Artificial Intelligence Research Center, The National Institute of Advanced Industrial Science and Technology, Japan.
URL (in Japanese): https://www.airc.aist.go.jp/seminar_detail/seminar_046.html
This document summarizes research using surface-enhanced Raman spectroscopy (SERS) to analyze the structural differences between amyloid beta 40, 42, and 42 fibril proteins involved in Alzheimer's disease. The study deposited amyloid beta solutions onto a graphene-coated gold nanoparticle substrate to obtain Raman spectra. Principal component analysis of the spectra showed the platform can differentiate amyloid beta 40, 42, and 42 fibril structures. This technique provides a method to fully characterize amyloid beta from the monomeric to fibril stages and could help understand Alzheimer's disease progression.
Alzheimer's disease is a progressive neurodegenerative disease that causes loss of neurons and synapses in the brain. The main pathological hallmarks are extracellular amyloid beta plaques and intraneuronal neurofibrillary tangles. Current treatments only temporarily improve cognitive symptoms but do not stop progression of the disease. New treatments are needed to both maintain cognitive abilities and halt the underlying disease process.
Alzheimer's disease is a progressive brain disorder that destroys memory and cognitive skills. Dr. Alois Alzheimer first described it in 1906 after examining a woman with dementia. The disease is characterized by beta-amyloid plaques and neurofibrillary tangles in the brain. Current treatments aim to improve symptoms but do not stop the underlying disease process. Researchers are exploring therapies targeting amyloid and tau proteins as well as other mechanisms to find a cure.