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Exchange rates forecasting using nonlinear autoregressive

AIP Conference Proceedings 2016年第1期1755卷

作者： Agus Sihabuddin Sri Hartati 1Computer Science Study Program Faculty of Mathematics and Natural Science Gadjah Mada University Yogyakarta - Indonesia

In this paper, an attempt has been made to forecast two major exchange rates USDAUD (US Dolar versus Australian Dolar currency) and USDJPY (US Dolar versus Japanese Yen currency) using Non-Linear Autoregressive (NAR) with monthly period data from January 1975 to April 2014. The data is collected from Bank of England. The ecchange rates forecasting use 3 layer models with input, hidden and output layer. The number of hidden nodes are 2, and 3 for USDAUD and USDJPY respectively. Levernberg-Marquardt (LM) learning algorithm is used to do training. The accuracy result for the experiments showed that Dstat parameter for USDAUD (60.56%) is a bit higher than industry standard accuracy but lower than industry standard accuracy for USDJPY (52.11%). The MSE result is 0.3239 for USDAUD and 7.518 for USDJPY.

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Roadmap on Nonlocality in Photonic Materials and Metamaterials

arXiv

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arXiv 2025年

作者： Monticone, Francesco Mortensen, N. Asger Fernández-Domínguez, Antonio I. Luo, Yu Tserkezis, Christos Khurgin, Jacob B. Shahbazyan, Tigran V. Chaves, André J. Peres, Nuno M.R. Wegner, Gino Busch, Kurt Hu, Huatian della Sala, Fabio Zhang, Pu Ciracì, Cristian Aizpurua, Javier Babaze, Antton Borisov, Andrei G. Chen, Xue-Wen Christensen, Thomas Yan, Wei Yang, Yi Hohenester, Ulrich Huber, Lorenz Wubs, Martijn de Liberato, Simone Gonçalves, P.A.D. de Abajo, F. Javier García Hess, Ortwin Tarasenko, Illya Cox, Joel D. Jelver, Line Dias, Eduardo J.C. Sánchez, Miguel Sánchez Margetis, Dionisios Gómez-Santos, Guillermo Stauber, Tobias Tretyakov, Sergei Simovski, Constantin Pakniyat, Samaneh Gómez-Díaz, J. Sebastián Bondarev, Igor V. Biehs, Svend-Age Boltasseva, Alexandra Shalaev, Vladimir M. Krasavin, Alexey V. Zayats, Anatoly V. Alù, Andrea Song, Jung-Hwan Brongersma, Mark L. Levy, Uriel Long, Olivia Y. Guo, Cheng Fan, Shanhui Bozhevolnyi, Sergey I. Overvig, Adam Prudêncio, Filipa R. Silveirinha, Mário G. Gangaraj, S. Ali Hassani Argyropoulos, Christos Huidobro, Paloma A. Galiffi, Emanuele Yang, Fan Pendry, John B. Miller, David A.B. School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States POLIMA Center for Polariton-driven Light–Matter Interactions University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid MadridE-28049 Spain Universidad Autónoma de Madrid MadridE-28049 Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Physics Jackson State University JacksonMS39217 United States Department of Physics Aeronautics Institute of Technology SP So Jos dos Campos12228-900 Brazil Departamento de Física Universidade do Minho BragaP-4710-057 Portugal Av Mestre José Veiga Braga4715-330 Portugal Max-Born-Institut Berlin12489 Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik Berlin12489 Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 Arnesano73010 Italy Via Monteroni Campus Unisalento Lecce73100 Italy School of physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Donostia International Physics Center DIPC Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Electricity and Electronics University of the Basque Country Leioa48940 Spain Department of Applied Physics University of the Basque Country Donostia20018 Spain Université Paris-Saclay CNRS Institut des Scienc

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces–can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward–toward new scientific discoveries and technological advancements. Copyright © 2025, The Authors. All rights reserved.

关键词： Nanocrystals

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Course-based online learning video retrieval system

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AIP Conference Proceedings 2023年第1期2654卷

作者： Faisal Ramadhan Aina Musdholifah 1Computer Science Program Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta Indonesia 2Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta Indonesia

Audio-visual learning media, such as learning videos on YouTube, is an alternative representation of the learning media commonly used during the COVID-19 pandemic. There are many learning videos available on YouTube; thus, selecting the appropriate learning videos is a challenge in the research field of information retrieval. This study focuses on building an information retrieval system that can recommend online learning videos based on the user requirements, namely the course name and its syllabus. The proposed information retrieval system works through cosine similarity measurement to calculate the closeness between the course and its syllabus with video annotations. The video annotation consists of the title and description metadata of the video captured in real-time from YouTube using the YouTube API. The proposed information retrieval system provides the five most recommended learning videos based on the selected courses and their syllabus. The experimental results involving 40 respondents show that the online video retrieval system has a good performance proven by an accurate level of 80.5% that in line with the relevance score of 84. Furthermore, another three performance indicators also great, i.e., the values of novelty, serendipity, and increasing recommendation diversity are 78%, 77%, and 85.5, respectively.

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The complexity calculation for group decision making using TOPSIS algorithm

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AIP Conference Proceedings 2016年第1期1755卷

作者： Hamdani Retantyo Wardoyo 1Computer Sciences Doctoral Program Departement of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia 2Departement of Computer Sciences Universitas Mulawarman Indonesia 3Departement of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

The complexity calculation is one of the things to be considered in choosing an algorithm to be applied to a decision support system (DSS). This study analyzes the complexity of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This algorithm is applied to the ranking process to resolve the problems associated with Multi-Criteria Decision Making (MCDM). MCDM is used for Group Decision Making (GDM) problems in making a decision. The end output of this study is making DM ranking resulted from decision-making. This problem also produces TOPSIS algorithm complexity used in group decision-making. This algorithm complexity is in the part of attribute normalization and weighting which results in O(n2). Whereas the complexity of a positive-negative ideal solution and V distance to A1, A2, A3, and A4 is O(n) and the algorithm complexity ranking results in O(1).

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Galaxy clusters at z ∼ 1 imaged by ALMA with the Sunyaev–Zel’dovich effect

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Publications of the Astronomical Society of Japan 2023年第2期75卷 311-337页

作者： Kitayama, Tetsu Ueda, Shutaro Okabe, Nobuhiro Akahori, Takuya Hilton, Matt Hughes, John P. Ichinohe, Yuto Kohno, Kotaro Komatsu, Eiichiro Lin, Yen-Ting Miyatake, Hironao Oguri, Masamune Sifón, Cristóbal Takakuwa, Shigehisa Takizawa, Motokazu Tsutsumi, Takahiro van Marrewijk, Joshiwa Wollack, Edward J. Department of Physics Toho University 2-2-1 Miyama Chiba Funabashi274-8510 Japan No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Institute of Astronomy National Tsing Hua University Hsinchu30013 Taiwan Department of Physics Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Core Research for Energetic Universe Department of Physics Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Physics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan Mizusawa VLBI observatory National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Operation Division SKA Observatory Jodrell Bank Lower Withington MacclesfieldSK11 9DL United Kingdom Astrophysics Research Centre University of KwaZulu-Natal Westville Campus Durban4041 South Africa School of Mathematics Statistics & Computer Science University of KwaZulu-Natal Westville Campus Durban4041 South Africa Wits Centre for Astrophysics School of Physics University of the Witwatersrand Private Bag 3 Johannesburg2050 South Africa Department of Physics and Astronomy Rutgers The State University of New Jersey PiscatawayNJ08854-8019 United States Department of Physics Rikkyo University 3-34-1 Nishi-Ikebukuro Toshima-ku Tokyo171-8501 Japan Institute of Astronomy School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Research Center for the Early Universe School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Max-Planck-Institut für Astrophysik Karl-Schwarzschild Str. 1 GarchingD-85741 Germany The University of Tokyo Institutes for Advanced Study The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8583 Japan Nagoya University Furo-cho Chikusa-

We present high angular resolution measurements of the thermal Sunyaev–Zel’dovich effect (SZE) toward two galaxy clusters, RCS J2319+0038 at z = 0.9 and HSC J0947−0119 at z = 1.1, by the Atacama Large Millimeter/submillimeter Array (ALMA) in Band 3. They are supplemented with available Chandra X-ray data, optical data taken by Hyper Suprime-Cam on Subaru, and millimeter-wave SZE data from the Atacama Cosmology Telescope. Taking into account departures from spherical symmetry, we have reconstructed non-parametrically the inner pressure profile of two clusters as well as electron temperature and density profiles for RCS J2319+0038. This is one of the first such measurements for an individual cluster at z 0.9. We find that the inner pressure profile of both clusters is much shallower than that of local cool-core clusters. Our results consistently suggest that RCS J2319+0038 hosts a weak cool core, where radiative cooling is less significant than in local cool cores. On the other hand, HSC J0947−0119 exhibits an even shallower pressure profile than RCS J2319+0038 and is more likely to be a non-cool-core cluster. The SZE centroid position is offset by more than 140 h−170 kpc from the peaks of galaxy distribution in HSC J0947−0119, suggesting a stronger influence of mergers in this cluster. We conclude that these distant clusters are at a very early stage of developing the cool cores typically found in clusters at lower redshifts. © The Author(s) 2023. Published by Oxford University Press on behalf of the Astronomical Society of Japan.

关键词： Millimeter waves

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Computational Methods for Single-Cell RNA Sequencing

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Biomedical Data science 1000年第1期3卷 339-364页

作者： Brian Hie Joshua Peters Sarah K. Nyquist Alex K. Shalek Bonnie Berger Bryan D. Bryson 1Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA email: bab@mit.edu 3Ragon Institute of MGH MIT and Harvard Cambridge Massachusetts 02139 USA 2Department of Biological Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA email: bryand@mit.edu 4Program in Computational and Systems Biology Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 5Department of Chemistry Institute for Medical Engineering & Science (IMES) and Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA 6Department of Mathematics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

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Combining rating scale and FANP to determine the important weight of learning competency for students’ assessment

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AIP Conference Proceedings 2016年第1期1755卷

作者： Sri Andayani Sri Hartati Retantyo Wardoyo Djemari Mardapi 1Doctoral program of Computer Science Universitas Gadjah Mada Yogyakarta Indonesia 2Department of Computer Science and Electrical Universitas Gadjah Mada Yogyakarta Indonesia 3Study Program of Educational Research and Evaluation Graduate School Universitas Negeri Yogyakarta Indonesia

Determining the important weight of learning competency is an important problem for students’ assessment in credentialing and certification decision-making. Learning competencies should have importance weight that definitely affect the final decision, and can represent strengths and weaknesses of student achievement of certain competencies. The weight of learning competency is determined based on its difficulty level, which is identified by the depth, scope and complexity of material involved in it. The dependency among learning competencies must also be considered in determining the weight. Therefore, this study aims to develop a weighting method based on combining rating scale and fuzzy analytic network process (FANP) to determine the weight of learning competency. Using the proposed method, the weight of learning competency is obtained and then can be used in calculating the final score for students’ assessment.

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The development of AR book for computer learning

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AIP Conference Proceedings 2017年第1期1868卷

作者： Muneeroh Phadung Najela Wani Nur-aiynee Tongmnee 1Computer Education Program Faculty of Science Technology and Agriculture Yala Rajabhat University 133 Tessaban 3 Rd A.Muang Yala Thailand 2Computer Education Program Faculty of Science Technology and Agriculture Yala Rajabhat University 133 Tessaban 3 Rd A.Muang Yala Thailand 3Computer Education Program Faculty of Science Technology and Agriculture Yala Rajabhat University 133 Tessaban 3 Rd A.Muang Yala Thailand

Educators need to provide the alternative educational tools to foster learning outcomes of students. By using AR technology to create exciting edutainment experiences, this paper presents how augmented reality (AR) can be applied in the education. This study aims to develop the AR book for tenth grade students (age 15-16) and evaluate its quality. The AR book was developed based on ADDIE framework processes to provide computer learning on software computer knowledge. The content was accorded with the current Thai education curriculum. The AR book had 10 pages in three topics (the first was “Introduction,” the second was “System Software” and the third was “Application Software”). Each page contained markers that placed virtual objects (2D animation and video clip). The obtained data were analyzed in terms of average and standard deviation. The validity of multimedia design of the AR book was assessed by three experts in multimedia design. A five-point Likert scale was used and the values were X¯=4.84 , S.D. = 1.27 which referred to very high. Moreover, three content experts, who specialize in computer teaching, evaluated the AR book’s validity. The values determined by the experts were X¯=4.69 , S.D. = 0.29 which referred to very high. Implications for future study and education are discussed.

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Graph Artificial Intelligence in Medicine

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Biomedical Data science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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Improving students’ mathematical problem-solving ability using differentiated instruction approach

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AIP Conference Proceedings 2022年第1期2468卷

作者： Ade Evi Fatimah Muhammad Daut Siagian Suwanto Fitry Wahyuni Azrina Purba 1Computer Informatics Engineering Education Study Program STKIP Al-Maksum Langkat Jl. Sei Batang Serangan Kwala Bingai Kec. Stabat Kabupaten Langkat Sumatera Utara 20811 Indonesia 2Mathematics Education Study Program Universitas Islam Sumatera Utara Jl. Sisingamangaraja Teladan- Medan Sumatera Utara 20217 Indonesia 3Mathematics Education Study Program STKIP Asy-Syafi’iyah Internasional Medan Jl. Karya Tani No. 1 Pangkalan Masyhur Kec. Medan Johor Kota Medan Sumatera Utara 20146 Indonesia 4Corporate Management Study Program Akademi Perniagaan dan Perusahaan APIPSU Medan Jalan Rasmi No. 28 Medan Sumatera Utara 20123 Indonesia 5Science Education Study Program STKIP Al-Maksum Langkat Jl. Sei Batang Serangan Kwala Bingai Kec. Stabat Kabupaten Langkat Sumatera Utara 20811 Indonesia

Problem-solving abilities are very important in mathematics, not only for those who will study mathematics in the future but also for those who will apply it in other fields of study and in everyday life because in the process of learning and solving it, students can gain experience using knowledge and skills already possessed to be applied in solving non-routine problems. This study aims to analyze and describe students' mathematical problem-solving abilities through the application of the Differentiated Instruction approach. This research is quantitative research with a quasi-experimental method. This research was conducted in one of the Vocational High Schools in Deli Serdang Regency, North Sumatra, for grade 10 students consisting of two classes, namely the control class and the experimental class, where each class consisted of 24 students. The sampling technique with cluster random sampling was used to collect data. Data collection is done by giving a test on trigonometry material. Data analysis was performed using a two-way ANOVA test. The results of this study indicate that the increase in mathematical problem-solving abilities of students who receive learning using the Differentiated Instruction approach is better than students who receive direct learning.

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