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37th International Cosmic Ray Conference, ICRC 2021

作者： Bergman, Douglas R. Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Dept. of Physics & Astronomy High Energy Astrophysics Inst. University of Utah Utah United States Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawashi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique ThÃ©orique UniversitÃ© Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Center

We present the result of fits of an astrophysical model of UHECR sources to the energy spectrum measured using the Telescope Array (TA) surface detectors and the composition data measured by TA fluorescence detectors in stereo mode. The astrophysical model consists of identical sources, uniformly distributed with a density which evolves with age, where groups of nuclei with varying fractions are accelerated producing a power law source with a rigidity-dependent cutoff. The model includes the propagation effects of photo-pion production, photo-nuclear disintegration and general energy loss from universal expansion. Comparison of model data with detector data in both the energy and the Xmax distributions is done via a forward-folding description of detector effects. A prediction of the cosmogenic neutrino flux at Earth resulting from the cosmic rays produced in the model with these fit values is also presented. The predicted flux is considerably higher than other similar fits to Auger data have previously predicted. © Copyright owned by the author(s).

关键词： Cosmic rays

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Cosmic Ray Composition between 2 PeV and 2 EeV measured by the TALE Fluorescence Detector 37

Cosmic Ray Composition between 2 PeV and 2 EeV measured by t...

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37th International Cosmic Ray Conference, ICRC 2021

作者： AbuZayyad, Tareq Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Loyola University Chicago ChicagoIL United States University of Utah Salt Lake CityUT United States Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawashi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Cente

The Telescope Array (TA) cosmic rays detector located in the State of Utah in the United States is the largest ultra high energy cosmic rays detector in the northern hemisphere. The Telescope Array Low Energy Extension (TALE) fluorescence detector (FD) was added to TA in order to lower the detector’s energy threshold, and has succeeded in measuring the cosmic rays energy spectrum down to PeV energies, by making use of the direct Cherenkov light produced by air showers. In this contribution we present the results of a measurement of the cosmic-ray composition using TALE FD data collected over a period of ∼ 7 years. TALE FD data is used to measure the Xmax distributions of showers seen in the energy range of 1015.3 - 1018.3 eV. The data distributions are fit to Monte Carlo distributions of H, He, N, Fe cosmic-ray primaries for energies up to 1018 eV. Mean Xmax values are measured for the full energy range. TALE observes a light composition at the "Knee", that gets gradually heavier as energy increases toward the "Second-Knee". An increase in the Xmax elongation rate is observed at energies just above 1017.3 eV indicating a change in the cosmic rays composition from a heavier to a lighter mix of primaries. © Copyright owned by the author(s).

关键词： Cosmic rays

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Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

arXiv

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arXiv 2022年

作者： Conde, Marcos V. Timofte, Radu Huang, Yibin Peng, Jingyang Chen, Chang Li, Cheng Pérez-Pellitero, Eduardo Song, Fenglong Bai, Furui Liu, Shuai Feng, Chaoyu Wang, Xiaotao Lei, Lei Zhu, Yu Li, Chenghua Jiang, Yingying Yong, A. Wang, Peisong Leng, Cong Cheng, Jian Liu, Xiaoyu Yin, Zhicun Zhang, Zhilu Li, Junyi Liu, Ming Zuo, Wangmeng Jiang, Jun Kim, Jinha Zhang, Yue Zou, Beiji Zong, Zhikai Liu, Xiaoxiao Vega, Juan Marín Sloth, Michael Schneider-Kamp, Peter Röttger, Richard Kınlı, Furkan Özcan, Barış Kıraç, Furkan Leyi, Li Nadim Uddin, S.M. Ghosh, Dipon Kumar Jung, Yong Ju Computer Vision Lab CAIDAS University of Würzburg Germany Huawei Noah's Ark Lab China Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China MAICRO AiRiA Harbin Institute of Technology China SenseBrain Technology United States School of Computer Science and Engineering Central South University Hunan Engineering Research Center of Machine Vision and Intelligent Medicine China China University of Southern Denmark Esoft Systems Denmark Özyeğin University Turkey Zhejiang University China Lab School of Computing Gachon University Korea Republic of

Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level vision tasks operate in the RAW domain (e.g. image denoising, white balance) due to its linear relationship with the scene irradiance, wide-range of information at 12bits, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public RGB datasets. This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation. The proposed methods and benchmark establish the state-of-the-art for this low-level vision inverse problem, and generating realistic raw sensor readings can potentially benefit other tasks such as denoising and super-resolution. © 2022, CC BY-SA.

关键词： Inverse problems

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Exact Differentiator with Lipschitz Continuous Output and Optimal Worst-Case Accuracy Under Bounded Noise

Exact Differentiator with Lipschitz Continuous Output and Op...

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IEEE Conference on Decision and Control

作者： Rodrigo Aldana-López Richard Seeber Hernan Haimovich David Gómez-Gutiérrez Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria Centro Internacional Franco-Argentino de Ciencias de la Información y de Sistemas (CIFASIS) CONICET-UNR Rosario Argentina Intel Tecnología de México Intel Labs Intelligent Systems Research Lab Jalisco Mexico Tecnológico Nacional de México Instituto Tecnológico José Mario Molina Pasquel y Henríquez Unidad Académica Zapopan Jalisco Mexico

The online differentiation of a signal contaminated with bounded noise is addressed. A differentiator is developed that generates a Lipschitz continuous output, is exact in the absence of noise, and provides the optimal worst-case accuracy among all possible exact differentiators when noise is present. This combination of features is not shared by any previously existing differentiator. Tuning of the developed differentiator is very simple, requiring only the knowledge of a bound for the second-order derivative of the signal. The approach consists in regularizing the possibly highly noisy output of a recently introduced linear adaptive robust exact differentiator and feeding it to a first-order sliding-mode filter designed to maintain optimal accuracy. The proposed regularization and filtering of this output allows trading the speed with which exactness is obtained for the feature of a Lipschitz continuous, hence less noisy, output. An illustrative example is provided to highlight the features of the developed differentiator.

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STJLA: A multi-context aware spatio-temporal joint linear attention network for traffic forecasting

arXiv

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arXiv 2021年

作者： Fang, Yuchen Qin, Yanjun Luo, Haiyong Zhao, Fang Wang, Chenxing The School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Traffic prediction has gradually attracted the attention of researchers because of the increase in traffic big data. Therefore, how to mine the complex spatio-temporal correlations in traffic data to predict traffic conditions more accurately become a difficult problem. Previous works combined graph convolution networks (GCNs) and self-attention mechanism with deep time series models (e.g. recurrent neural networks) to capture the spatio-temporal correlations separately, ignoring the relationships across time and space. Besides, GCNs are limited by over-smoothing issue and self-attention is limited by quadratic problem, result in GCNs lack global representation capabilities, and self-attention inefficiently capture the global spatial dependence. In this paper, we propose a novel deep learning model for traffic forecasting, named Multi-Context Aware Spatio-Temporal Joint Linear Attention (STJLA), which applies linear attention to the spatio-temporal joint graph to capture global dependence between all spatio-temporal nodes efficiently. More specifically, STJLA utilizes static structural context and dynamic semantic context to improve model performance. The static structure context based on node2vec and one-hot encoding enriches the spatio-temporal position information. Furthermore, the multi-head diffusion convolution network based dynamic spatial context enhances the local spatial perception ability, and the GRU based dynamic temporal context stabilizes sequence position information of the linear attention, respectively. Experiments on two real-world traffic datasets, England and PEMSD7, demonstrate that our STJLA can achieve up to 9.83% and 3.08% accuracy improvement in MAE measure over state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Scalable and efficient neural speech coding: A hybrid design

arXiv

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arXiv 2021年

作者： Zhen, Kai Sung, Jongmo Lee, Mi Suk Beack, Seungkwon Kim, Minje Department of Computer Science and Cognitive Science Program Indiana University BloomingtonIN47408 United States Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Dept. of Intelligent Systems Engineering Indiana University United States

We present a scalable and efficient neural waveform coding system for speech compression. We formulate the speech coding problem as an autoencoding task, where a convolutional neural network (CNN) performs encoding and decoding as a neural waveform codec (NWC) during its feedforward routine. The proposed NWC also defines quantization and entropy coding as a trainable module, so the coding artifacts and bitrate control are handled during the optimization process. We achieve efficiency by introducing compact model components to NWC, such as gated residual networks and depthwise separable convolution. Furthermore, the proposed models are with a scalable architecture, cross-module residual learning (CMRL), to cover a wide range of bitrates. To this end, we employ the residual coding concept to concatenate multiple NWC autoencoding modules, where each NWC module performs residual coding to restore any reconstruction loss that its preceding modules have created. CMRL can scale down to cover lower bitrates as well, for which it employs linear predictive coding (LPC) module as its first autoencoder. The hybrid design integrates LPC and NWC by redefining LPC’s quantization as a differentiable process, making the system training an end-to-end manner. The decoder of proposed system is with either one NWC (0.12 million parameters) in low to medium bitrate ranges (12 to 20 kbps) or two NWCs in the high bitrate (32 kbps). Although the decoding complexity is not yet as low as that of conventional speech codecs, it is significantly reduced from that of other neural speech coders, such as a WaveNet-based vocoder. For wide-band speech coding quality, our system yields comparable or superior performance to AMR-WB and Opus on TIMIT test utterances at low and medium bitrates. The proposed system can scale up to higher bitrates to achieve near transparent performance. © 2021, CC BY.

关键词： Convolution

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Control Theory Illustrates the Energy Efficiency in the Dynamic Reconfiguration of Functional Connectivity

arXiv

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arXiv 2022年

作者： Deng, Shikuang Li, Jingwei Yeo, B.T. Thomas Gu, Shi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Research Center Jülich Jülich Germany Institute for Systems Neuroscience Medical Faculty Heinrich-Heine University Düsseldorf Düsseldorf Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Centre for Sleep & Cognition Centre for Translational Magnetic Resonance Research Yong Loo Lin School of Medicine Singapore N.1 Institute for Health Institute for Digital Medicine National University of Singapore Singapore National University of Singapore Singapore Singapore Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA02129 United States

The brain’s functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores. Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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The boundary of Kirkwood-Dirac quasiprobability

arXiv

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arXiv 2025年

作者： Liu, Lijun Cheng, Shuming Department of Mathematics and Computer Science Shanxi Normal University Taiyuan030006 China College of Electronic and Information Engineering Tongji University Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201203 China

The Kirkwood-Dirac (KD) quasiprobability describes measurement statistics of joint quantum observables, and has generated great interest as prominent indicators of non-classical features in various quantum information processing tasks. It relaxes the Kolmogorov axioms of probability by allowing for negative and even imaginary probabilities, and thus incorporates the classical probability theory as its inner boundary. In this work, we introduce the postquantum quasiprobability under mild assumptions to provide an outer boundary for KD quasiprobability. Specifically, we present qualitative and quantitative evidence to show that the classical, KD, and postquantum quasiprobabilities form a strict hierarchy, in the sense that joint probability distributions are a strict subset of KD quasiprobability distributions that are a strict subset of postquantum ones. Surprisingly, we are able to derive some nontrivial bounds valid for both classical probability and KD quasiprobability, and even valid for the KD quasiprobability generated by an arbitrary number of measurements. Finally, other interesting bounds are obtained, and their implications are noted. Our work solves the fundamental problems of what and how to bound the KD quasiprobability, and hence provides a deeper understanding of utilizing it in quantum information processing. Copyright © 2025, The Authors. All rights reserved.

关键词： Distribution functions

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Structural Investigation and Enriched Catalysis of Cu-complex Encapsulated Microporous Catalyst with Pragmatic Modelling for Prediction of Activity by Using Machine Learning

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Chemphyschem : a European journal of chemical physics and physical chemistry 2025年 e202400950页

作者： Rohit Prajapati Jetal Chaudhari Parikshit Paredi Nao Tsunoji Daksh Vyawhare Rayan Bandyopadhyay Krupa Shah Rajib Bandyopadhyay Mahuya Bandyopadhyay Pandit Deendayal Energy University Chemistry INDIA. Institute of Infrastructure Technology Research and Management Electrical and Computer Science Engineering INDIA. Institute of Infrastructure Technology Research and Management Basic Sciences INDIA. Hiroshima University Graduate School of Advanced Science and Engineering Applied Chemistry JAPAN. Campus Kleve: Hochschule Rhein-Waal Mechatronic Systems Engineering GERMANY. Institute of Infrastructure Technology Research and Management Chemistry Khokhara Circle Maninagar (East) 380008 Ahmedabad INDIA.

A facile and efficient post-synthetic method has been systematically employed to prepare a hybrid silicoaluminophosphate by immobilizing a mixed ligand copper (׀׀) complex comprising 2,9-dimethyl-1,10-phenanthroline and copper nitrate on base-functionalized material. The effective activity of this catalyst in ring opening reaction of epoxide was achieved when this complex was anchored on amine functionalized SAPO materials. Structure, phase integrity, thermal stability and the existence of functional groups are identified by using different analytical techniques like powder XRD, N2 adsorption-desorption, FT-IR, nuclear magnetic resolution, etc. 90% and 88% conversion were achieved using copper complex immobilized SAPO-34, and SAPO-5 materials, respectively which demonstrate the effectiveness of the materials for this reaction. Machine learnings are employed to predict product conversion and selectivity for scaling up the reaction for further industrial applications. Linear regression (LR), support vector machine (SVM), and k-nearest neighbors (kNN), have been utilized in this study, whereas SVM and kNN both exhibited good performance in predicting catalyst conversion and selectivity, by analysing mean squared error (MSE), mean absolute percentage error (MAPE), and R score. This study showcases the innovative synthesis and performance of the hybrid silicoaluminophosphate catalyst, illustrating the accurate predictive machine learning algorithms to find quality of catalyst for specific reactions.

关键词： data augmentation hybrid porous materials k-nearest neighbors linear regression ring-opening reactions of epoxide

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Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO

arXiv

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arXiv 2021年

作者： Das, Proloy Babadi, Behtash The Department of Anesthesia Critical Care and Pain Medicine Massachusetts General Hospital BostonMA02114 United States The Department of Electrical and Computer Engineering the Institute for Systems Research University of Maryland College ParkMD20742 United States

Granger causality is among the widely used data-driven approaches for causal analysis of time series data with applications in various areas including economics, molecular biology, and neuroscience. Two of the main challenges of this methodology are: 1) over-fitting as a result of limited data duration, and 2) correlated process noise as a confounding factor, both leading to errors in identifying the causal influences. Sparse estimation via the LASSO has successfully addressed these challenges for parameter estimation. However, the classical statistical tests for Granger causality resort to asymptotic analysis of ordinary least squares, which require long data duration to be useful and are not immune to confounding effects. In this work, we address this disconnect by introducing a LASSO-based statistic and studying its non-asymptotic properties under the assumption that the true models admit sparse autoregressive representations. We establish fundamental limits for reliable identification of Granger causal influences using the proposed LASSO-based statistic. We further characterize the false positive error probability and test power of a simple thresholding rule for identifying Granger causal effects and provide two methods to set the threshold in a data-driven fashion. We present simulation studies and application to real data to compare the performance of our proposed method to ordinary least squares and existing LASSO-based methods in detecting Granger causal influences, which corroborate our theoretical results. © 2021, CC BY.

关键词： Statistical tests

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