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arXiv 2019年

作者： Damani, Farhan Sresht, Vishnu Ra, Stephen Dept. of Computer Science Princeton University Simulation and Modeling Sciences Pfizer Research and Development

Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery. We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties. Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step. We show that this finding is invariant to the choice of translation model, making this a "black box" algorithm. We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization. This simple, powerful technique operates strictly on the inputs and outputs of any translation model. We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models. Our method provides a significant boost in performance relative to its non-recursive peers with just a simple "for" loop. Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points. Copyright © 2019, The Authors. All rights reserved.

关键词： Iterative methods

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Investigation of the Electronic, Magnetic, Exchange Interactions, and Magnetocaloric Properties in Orthotitanate Oxides Rtio3 (R = Er, Ho)

SSRN

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SSRN 2023年

作者： Jmal, Sohail Ait Tamerd, Mohamed Ait Mounkachi, Omar Salvestrini, Jean Paul Balli, Mohamed AMEEC Team LERMA International University of Rabat Parc Technopolis Rocade de Rabat- Salé 11100 Morocco Laboratory of Condensed Matter and Interdisciplinary Sciences Department of Physics Faculty of Sciences Mohammed V University in Rabat PO Box 1014 Rabat Morocco Modeling Simulation & Data Analysis Program Mohammed VI Polytechnic University Ben Guerir43150 Morocco Institute of Applied Physics Mohammed VI Polytechnic University Lot 660 Hay Moulay Rachid BP 43150 Ben Guerir Morocco CNRS IRL 2958 Georgia Tech CNRS 2 rue Marconi Metz57070 France Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332-0250 United States

Strong correlated materials are emerging materials in low magnetocaloric cooling applications. The development of these fields necessitates materials with appealing magnetic, and magnetocaloric features. In this context, Density Functional Theory (DFT) combined with Monte Carlo simulations (MCs) are performed to comprehensively examine various physical, and magnetic features of the orthorhombic perovskite-type RTiO3. A metallic behavior of (Ho, Er)TiO3 has been found via the density of state using GGA+U approach. X-ray magnetic circular dichroism (XMCD) was carried out to identify the magnetic contributions of Ho, Er, and Ti atoms in HoTiO3, and ErTiO3 compounds. The spin-orbit coupling was added in our calculations to study and evaluate the magnetocrystalline anisotropies of RTiO3 with (R= Ho, Er). We noticed an easy magnetization axis along the b-direction for HoTiO3, and along the c-direction for ErTiO3. Low magnetocrystalline anisotropy energies were found compared to other perovskite oxides such as RVO3, and RMnO3indicating that a weak rotating magnetocaloric effect (RMCE) is expected in (Ho, Er)TiO3. Moreover, the exchange coupling interactions were calculated using Ising model, the (Ti-Ti) interaction coupling is ferromagnetic, and larger than the other exchange interactions in (Ho, Er)TiO3. Furthermore, these exchange coupling interactions were utilized within the Heisenberg model for MCs, allowing us to understand the driving mechanisms underlying magnetocaloric and magnetic behaviors in (Ho, Er)TiO3. © 2023, The Authors. All rights reserved.

关键词： Perovskite

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TMBO-AOD: Transparent Mask Background Optimization for Accurate Object Detection in Large-Scale Remote-Sensing Images

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Remote Sensing 2025年第10期17卷 1762-1762页

作者： Tianyi Fu Hongbin Dong Benyi Yang Baosong Deng National Engineering Laboratory for E-Government Modeling and Simulation Harbin Engineering University Nantong Street Harbin 150001 China College of Computer Science and Technology Harbin Engineering University Nantong Street Harbin 150001 China Defense Innovation Institute Chinese Academy of Military Science Beijing 100071 China Intelligent Game and Decision Laboratory Chinese Academy of Military Science Beijing 100071 China

Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. To address this, we propose a novel framework for accurate object detection, termed transparent mask background optimization for accurate object detection (TMBO-AOD), which incorporates a clear focus module and an adaptive filtering framework. The clear focus module constructs an empirical background pool using a Gaussian distribution and introduces transparent masks to prepare for subsequent optimization stages. The adaptive filtering framework can be applied to anchor-based or anchor-free models. It dynamically adjusts the number of candidates generated based on background flags, thereby optimizing the label assignment process. This approach not only alleviates the imbalance between positive and negative samples but also enhances the efficiency of candidate generation. Furthermore, we introduce a novel separated loss function that strengthens both foreground and background consistencies. Specifically, it focuses the model’s attention on foreground objects while enabling it to learn the consistency of background features, thus improving its ability to distinguish objects from the background. We employ YOLOv8 combined with our proposed optimizations to evaluate our model in many datasets, demonstrating improvements in both accuracy and efficiency. Additionally, we validate the effectiveness of our adaptive filtering framework in both anchor-based and anchor-free methods. When implemented with YOLOv5 (anchor based), the framework reduces the candidate generation time by 48.36%, while the YOLOv8 (anchor-free) implementation achieves a 46.81% reduction, both with

关键词： convolutional neural networks remote sensing diversity feature extraction object detection

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A preliminary model for recycling facility location-allocation problem

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AIP Conference Proceedings 2024年第1期2895卷

作者： Zati Aqmar Zaharudin Adibah Shuib Rieske Hadianti Zahari Md Rodzi Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Negeri Sembilan Kampus Seremban 73000 Seremban Negeri Sembilan Malaysia Malaysia Institute of Transport (MITRANS) Universiti Teknologi MARA (UiTM) 40450 Shah Alam Selangor Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA (UiTM) 40450 Shah Alam Selangor Malaysia Center for Mathematical Modeling and Simulation Institut Teknologi Bandung (ITB) Jl. Ganesa 10 Bandung 40132 Indonesia

Optimal location of a facility is important for public or private organizations. Spatial distribution of demand, reachability of demand to the nearest facility, and availability of service provided by the facility are some of the factors that must be considered in locating the facilities. Facility location model with covering problem is an approach that is capable of locating a facility at an optimal location while ensuring demand is served within a defined travel distance. The recycling facility is one of the services provided by the local authorities to encourage households to practice separation at source activity. Within the scope of study, we present a set covering model for solving the recycling facility location problem. The proposed model can determine the optimal location for a recycling facility as well as the ideal allocation of bins to satisfy facility capacity and demand. We evaluate the proposed model's performance using a data set generated at random. Results showed that the proposed model was effective in locating demand at the closest facility while allocating the optimal number of bins to guarantee service accessibility and availability.

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Hybrid nanofluid flow towards an elastic surface with tantalum and nickel nanoparticles, under the influence of an induced magnetic field

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The European Physical Journal Special Topics 2021年第3期231卷 521-533页

作者： Zhang, Lijun Bhatti, M. M. Michaelides, Efstathios E. Marin, M. Ellahi, R. College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China International Institute for Symmetry Analysis and Mathematical Modelling Department of Mathematical Sciences North-West University Mmabatho South Africa Department of Engineering TCU Fort Worth USA Department of Mathematics and Computer Science Transilvania University of Brasov Brasov Romania Department of Mathematics and Statistics International Islamic University Islamabad Pakistan Fulbright Fellow Department of Mechanical Engineering University of California Riverside Riverside USA Center for Modeling and Computer Simulation Research Institute King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

The nanofluid, composed of kerosene and tantalum and nickel nanoparticles, is propagating through a porous, elastic surface. The kerosene base fluid is incompressible and electrically conducting. The energy equation for this nanofluid is formulated taking into account the viscous dissipation. The mathematical modeling is performed with the help of a similarity transformation. The developed governing equations are numerically solved using the shooting technique and the Matlab software. The physical behavior of different parameters in the model is discussed through tabular and graphical forms. The present results are also compared to past results. The results indicate that the flow propagates faster for higher values of Darcy number and Tantalum nanoparticles and that the magnetic field opposes the fluid motion. Also that the thermal boundary layer decreases in the presence of Tantalum and Nickel nanoparticles.

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Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods

arXiv

引用

arXiv 2025年

作者： Hooshyar, Danial Kikas, Eve Yang, Yeongwook Šír, Gustav Hämäläinen, Raija Kärkkäinen, Tommi Azevedo, Roger School of digital Technologies Tallinn University Tallinn Estonia Department of Education University of Jyväskylä Jyväskylä Finland School of Natural Sciences and Health Tallinn University Tallinn Estonia Department of Computer Science and Engineering Gangneung-Wonju National University Wonju Korea Republic of Department of Computer Science Czech Technical University Prague Czech Republic Faculty of Information Technology University of Jyväskylä Jyväskylä Finland School of Modeling Simulation and Training University of Central Florida Orlando United States

Given the demand for responsible and trustworthy AI for education, this study evaluates symbolic, sub-symbolic, and neural-symbolic AI (NSAI) in terms of generalizability and interpretability. Our extensive experiments on balanced and imbalanced self-regulated learning datasets of Estonian primary school students predicting 7th-grade mathematics national test performance showed that symbolic and sub-symbolic methods performed well on balanced data but struggled to identify low performers in imbalanced datasets. Interestingly, symbolic and sub-symbolic methods emphasized different factors in their decision-making: symbolic approaches primarily relied on cognitive and motivational factors, while sub-symbolic methods focused more on cognitive aspects, learnt knowledge, and the demographic variable of gender—yet both largely overlooked metacognitive factors. The NSAI method, on the other hand, showed advantages by: (i) being more generalizable across both classes—even in imbalanced datasets—as its symbolic knowledge component compensated for the underrepresented class;and (ii) relying on a more integrated set of factors in its decision-making, including motivation, (meta)cognition, and learnt knowledge, thus offering a comprehensive and theoretically grounded interpretability framework. These contrasting findings highlight the need for a holistic comparison of AI methods before drawing conclusions based solely on predictive performance. They also underscore the potential of hybrid, human-centred NSAI methods to address the limitations of other AI families and move us closer to responsible AI for education. Specifically, by enabling stakeholders to contribute to AI design, NSAI aligns learned patterns with theoretical constructs, incorporates factors like motivation and metacognition, and strengthens the trustworthiness and responsibility of educational data mining. Copyright © 2025, The Authors. All rights reserved.

关键词： Students

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Glioblastoma multiforme classification on high resolution histology image using deep spatial fusion network ? 5

Glioblastoma multiforme classification on high resolution hi...

引用

5th International Workshop on Experimental Economics and Machine Learning, EEML 2019

作者： Sumi, P. Sobana Delhibabu, Radhakrishnan School of Computer Science and Engg. Vellore Institute of Technology Vellore India Modeling Evolutionary Algorithms Simulation and Artificial Intelligence Faculty of Electrical and Electronics Engineering Ton Due Thang University Ho Chi Minh City Viet Nam

Brain tumor is a growth of abnormal cells in brain, which can be cancerous or non-cancerous. The Brain tumor have scarce symptoms so it is very difficult to classify. Diagnosing brain tumor with histology images will efficiently helps us to classify brain tumor types. Sometimes, histology based image analysis is not accepted due to its variations in morphological features. Deep learning CNN models helps to overcome this problem by feature extraction and classification. Here proposed a method to classify high resolution histology image. InceptionResNetV2 is an CNN model, which is adopted to extract hierarchical features without any loss of data. Next generated deep spatial fusion network to extract spatial features found in between patches and to predict correct features from unpredictable discriminative features. 10-fold cross-validation is performed on the histology image. This achieves 95.6 percent accuracy on 4-class classification (benign, malignant, Glioblastoma, Oligodendroglioma). Also obtained 99.1 percent accuracy and 99.6 percent AUC on 2-way classification (necrosis and non-necrosis). Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Histology

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Optimizing Multi-Hop Quantum Communication using Bidirectional Quantum Teleportation Protocol

arXiv

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arXiv 2025年

作者： Ikken, N. Kumar, P. Slaoui, A. Kar, B. Laamara, R. Ahl Almousa, M. El-Latif, A.A. Abd LPHE-Modeling and Simulation Faculty of Sciences Mohammed V University Rabat Morocco Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Centre of Physics and Mathematics CPM Faculty of Sciences Mohammed V University in Rabat Rabat Morocco EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

In this paper, we introduce a new method for Bidirectional Quantum Teleportation called Bidirectional Quantum Teleportation using the Modified Dijkstra Algorithm and Quantum Walk (BQT-MDQW). This method uses different types of entangled states, such as the GHZ-Bell state, W-Bell state, and Cluster-Bell state, to improve quantum communication in multi-hop quantum wireless networks. We focus on the W-Bell state and compare the quantum Dijkstra algorithm with the classical Dijkstra method to see which one works better. We apply both versions to quantum and classical simulators, measuring their performance through fidelity, memory utilization, and throughput calculations. Our results show that the shortest path problem may be solved with significantly reduced computer complexity using the quantum Dijkstra algorithm based on quantum walks. The introduction of a quantum walk, which permits dynamic transitions between quantum channels and the effective exploration of quantum network states, is an important part of the protocol. Using the capacity of the quantum walk to adjust to changing quantum states, we also introduce a method for successfully identifying unitary matrices under varying quantum channels. The bidirectional teleportation structure of the protocol is designed to solve the multi-hop teleportation problem in quantum wireless networks. In addition, we present quantum Dijkstra’s algorithm, which uses quantum gates to significantly decrease computational complexity and solve the networking problem by building on the quantum walk framework. This method shows how quantum computing may be used to solve arbitrary optimization issues such as the shortest path problem. Finally, we present a novel multi-hop quantum teleportation system encompassing both unidirectional and bidirectional communication, as introduced in the quantum Dijkstra algorithm system. This system significantly enhances communication in quantum networks by enabling efficient and reliable information

关键词： Quantum channel

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PARDA: A dataset for scholarly PDF document metadata extraction evaluation 14th

PARDA: A dataset for scholarly PDF document metadata extract...

引用

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Fan, Tiantian Liu, Junming Qiu, Yeliang Jiang, Congfeng Zhang, Jilin Zhang, Wei Wan, Jian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9783030129804

Metadata extraction from scholarly PDF documents is the fundamental work of publishing, archiving, digital library construction, bibliometrics, and scientific competitiveness analysis and evaluations. However, different scholarly PDF documents have different layout and document elements, which make it impossible to compare different extract approaches since testers use different source of test documents even if the documents are from the same journal or conference. Therefore, standard datasets based performance evaluation of various extraction approaches can setup a fair and reproducible comparison. In this paper we present a dataset, namely, PARDA(Pdf Analysis and Recognition DAtaset), for performance evaluation and analysis of scholarly documents, especially on metadata extraction, such as title, authors, affiliation, author-affiliation-email matching, year, date, etc. The dataset covers computer science, physics, life science, management, mathematics, and humanities from various publishers including ACM, IEEE, Springer, Elsevier, arXiv, etc. And each document has distinct layouts and appearance in terms of formatting of metadata. We also construct the ground truth metadata in Dublin Core XML format and BibTex format file associated this dataset. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Metadata

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Energy aware edge computing: a survey 2nd

Energy aware edge computing: a survey

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2nd International Workshop on High Performance Computing for Advanced modeling and simulation in Nuclear Energy and Environmental Science, HPCMS 2018, and 1st International Workshop on HPC Supported Data Analytics for Edge Computing, HiDEC 2018 held at the 32nd ACM International Conference on Supercomputing, ACM ICS 2018

作者： Fan, Tiantian Qiu, Yeliang Jiang, Congfeng Wan, Jian Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789813299863

Edge computing is an emerging paradigm to meet the ever-increasing computation demands from pervasive devices such as sensors, actuators, and smart things. Though the edge devices can execute complex applications, it is necessary for some applications to migrate to centralized servers. By offloading the computation from the edge nodes to the edge servers or cloud servers, the quality of computation experience could be greatly improved. However, it may cause delay and increase network overheads, and energy consumption eventually. Therefore, an optimal offloading strategy should take into account what task should be offloaded, when to offload and where to offload to avoid the overheads. Thus, it is important to tradeoff between energy consumption, computation delay and throughput when the system makes the computation offloading to achieve high energy efficiency. In this paper, we conduct a survey of energy aware edge computing, including the existing work on computation offloading frameworks and strategies in edge computing. Specifically, we describe the strategies from the perspective of energy aware offloading, energy optimization offloading and offloading algorithms. © Springer Nature Singapore Pte Ltd 2019.

关键词： Energy efficiency

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