Chaos in two black holes with next-to-leading order spin-spin interactions

We take into account the dynamics of a complete third post-Newtonian conservative Hamiltonian of two spinning black holes, where the orbital part arrives at the third post-Newtonian precision level and the spin-spin part with the spin-orbit part includes the leading-order and next-to-leading-order contributions. It is shown through numerical simulations that the next-to-leading order spin-spin couplings play an important role in chaos. A dynamical sensitivity to the variation of single parameter is also investigated. In particular, there are a number of \textit{observable} orbits whose initial radii are large enough and which become chaotic before coalescence

FFTアクセラレーションを活用したRISC-Vベースの音声認識に関する研究

近年コンピュータ技術の進展や膨大な音声データの集積などに伴い、音声認識の実用化に向けた研究が加速している。音声認識では、認識をするための特徴量としてケプストラム領域の特徴量であるメル周波数ケプストラム係数(Mel-Frequency Cepstral Coefficients:MFCC)を用いるのが一般的である。しかし、現在組み込みシステムの音声MFCCを抽出するプロセスにおいて、大半の時間が高速 フーリエ変換(Fast Fourier Transform:FFT)に使用され、システムのリアルタイム性と消費電力に影響が大きいと考えられる。本研究では、RISC-Vベースのマイクロコントローラを使用し、Field Programmable Gate Array(以下 FPGA)に実装されるFFTアクセラレータを提案しする。アクセラレータの原理として、通常のFFTアルゴリズムで行う浮動小数点演算を整数に置き換え、ハードウェア回路に実装することで処理速度の向上が期待できる。RISC-V Rocketのマイクロコントローラを評価ボードFPGAに実装し、長さが異なる3種類の音声サンプルデータに対してアナログ・デジタル変換(ADC)、フィルタリング、窓関数処理、FFT、およびメル特徴抽出の流れで処理を行った。そして、FFTの処理において、浮動小数点演算FFT処理と整数演算FFT処理を実装したハードウェアFFTで実験を行い、実行時間とハードウェアリソース使用率を比較した。 結果として、整数演算FFTアクセラレータの実装により、処理速度が160倍以上に向上した。MFCC全体的な抽出パフォーマンスが20.7%向上した。ハードウェアリソース使用率として、マイクロプロセッサはSystem on Chip (SoC)の19%を占め、DDR3コントローラーとFFTアクセラレータは、それぞれ59%と16%のリソースを占めた。FFT1024、FFT512、FFT256はRISC-Vプロセッサのハードウェアリソース使 用率よりそれぞれ16%、57%、77%と少なった。 浮動小数点演算を整数演算に置き換え、そしてハードウェア化によって効率が大幅に向上することができた。今後の展望として、メル特徴抽出処理の整数演算の置き換え及びハードウェア化の課題は将来の最適化方向になると期待できる。電気通信大学202

Quantum reinforcement learning in continuous action space

Quantum mechanics has the potential to speedup machine learning algorithms, including reinforcement learning(RL). Previous works have shown that quantum algorithms can efficiently solve RL problems in discrete action space, but could become intractable in continuous domain, suffering notably from the curse of dimensionality due to discretization. In this work, we propose an alternative quantum circuit design that can solve RL problems in continuous action space without the dimensionality problem. Specifically, we propose a quantum version of the Deep Deterministic Policy Gradient method constructed from quantum neural networks, with the potential advantage of obtaining an exponential speedup in gate complexity for each iteration. As applications, we demonstrate that quantum control tasks, including the eigenvalue problem and quantum state generation, can be formulated as sequential decision problems and solved by our method.Comment: 9 pages, 8 figure

Static and dynamic crushing of novel porous crochet-sintered metal and its filled composite tube

© 2018 Elsevier Ltd A novel porous crochet-sintered metal (PCSM) is fabricated by rolling a crocheted porous cloth and subsequent vacuum sintering using a continual single super-fine soft 304 rope twisted by 49 fibers as raw material. This work investigates the quasi-static and dynamic axial crushing response of PCSMs and their filled composite tubes. The pore structures of PCSMs are formed by inter-crocheted and multiple inter-locked rope skeletons and metallurgical bonds. The PCSMs have almost no initial impact effects with a high crushing force efficiency. Filling the PCSMs changes the deformation model of 6063 tube, improves the static crashworthiness parameters of 6063 tube by 8–25% with almost no increasing initial impact effect, and doesn't always play a positive role in dynamic absorption. Porosity has obvious influence on the quasi-static and dynamic behavior and crashworthiness of PCSMs and their filled composite tube, and the effect of porosity on dynamic crashworthiness of composite tube is greater than that on quasi-static crashworthiness of composite tube. The PCSMs and their composite tubes show great potential for application in energy absorbers. The method of filling PCSM into bare tube is possible to improve the energy absorption ability of thin-walled tube with almost no increase in the initial peak force

A Retrospective Paired Study: Efficacy and Safety of Nimotuzumab Combined with Radiochemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma

Objective: To evaluate the efficacy and safety of nimotuzumab in combination with radiochemotherapy as the primary treatment in patients with locoregionally advanced nasopharyngeal carcinoma (NPC). Methods: We retrospectively reviewed patients with locoregionally advanced nasopharyngeal carcinoma from September 2012 to December 2016. 188 newly diagnosed patients with stage III–IVB nasopharyngeal carcinoma were treated with at least 1-2 cycles of chemotherapy concurrently with planned IMRT. 88 patients received nimotuzumab 200 mg/week. Acute and late radiation-related toxicities were graded according to the Acute and Late Radiation Morbidity Scoring Criteria of Radiation Therapy Oncology Group. Results: After 3 months of treatment, the complete response rates of nasopharyngeal tumors in the study group and the control group were 78.4% and 65.5%, respectively (?2=4.070, P=0.044). The total complete response rates of cervical lymph nodes in the study group and the control group were 80.7% and 67.6% respectively (?2=4.022, P=0.045).The median cycle for nimotuzumab addition was 6.3 weeks. With a median follow-up of 36.3 months (range, 12–72 months), the estimated 3-year progression failure-free survival and overall survival rates for the study group and the control group were 85.24% vs 81.97% and 96.67% vs 90.0%, respectively. The 3-year local recurrence-free survival rates for the study group and the control group were 96.67% vs 83.60%, respectively (P=0.047). Grade 3 radiation-induced mucositis accounted for 36.4% of treated patients. No skin rash and infusion reaction were observed, distinctly from what is reported in control patients. Conclusion: Nimotuzumab plus chemoradiotherapy in the treatment of locoregionally advanced nasopharyngeal carcinoma showed promising outcomes in terms of locoregional control, without increasing the incidence of radiation-related toxicities for patients

Audio-Based Classification of Respiratory Diseases using Advanced Signal Processing and Machine Learning for Assistive Diagnosis Support

In global healthcare, respiratory diseases are a leading cause of mortality, underscoring the need for rapid and accurate diagnostics. To advance rapid screening techniques via auscultation, our research focuses on employing one of the largest publicly available medical database of respiratory sounds to train multiple machine learning models able to classify different health conditions. Our method combines Empirical Mode Decomposition (EMD) and spectral analysis to extract physiologically relevant biosignals from acoustic data, closely tied to cardiovascular and respiratory patterns, making our approach apart in its departure from conventional audio feature extraction practices. We use Power Spectral Density analysis and filtering techniques to select Intrinsic Mode Functions (IMFs) strongly correlated with underlying physiological phenomena. These biosignals undergo a comprehensive feature extraction process for predictive modeling. Initially, we deploy a binary classification model that demonstrates a balanced accuracy of 87% in distinguishing between healthy and diseased individuals. Subsequently, we employ a six-class classification model that achieves a balanced accuracy of 72% in diagnosing specific respiratory conditions like pneumonia and chronic obstructive pulmonary disease (COPD). For the first time, we also introduce regression models that estimate age and body mass index (BMI) based solely on acoustic data, as well as a model for gender classification. Our findings underscore the potential of this approach to significantly enhance assistive and remote diagnostic capabilities.Comment: 5 pages, 2 figures, 3 tables, Conference pape