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Frontiers in Education (FIE) Conference

作者： Joel Alejandro Mejia Sofia Hernandez Neethu Paul Dept. of Biomedical & Chemical Eng. University of Texas at San Antonio San Antonio TX USA Dept. of Computer Science University of Texas at San Antonio San Antonio TX USA Dept. of Int. Learning & Teaching University of Texas at San Antonio San Antonio United States

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This special session explores the application of Chicana Feminist Epistemology methodologies in engineering education research with a focus on Latino/a/x students at Hispanic Serving Institutions. By utilizing Platicas and Testimonios as forms of data and data collection approaches in engineering education research, this session aims to foster a more inclusive and empowering research approach while challenging traditional Westernized paradigms. Through the analysis of Animaker videos created from vignettes emerging from these Platicas and Testimonios, participants will have a better understanding of the experiences of Latino/a/x engineering students, emphasizing asset-based methodologies and the creation of multimodal artifacts for research purposes. The research approaches presented in this session seek to amplify marginalized voices, promote critical consciousness, and challenge traditional ways of knowing and doing in engineering education research, encouraging attendees to integrate these methodologies into their research and teaching practices effectively to support diverse student populations.

关键词： Data collection Animation Engineering students Videos Gender issues

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Double Transformed Tubal Nuclear Norm Minimization for Tensor Completion

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Transactions of Nanjing University of Aeronautics and Astronautics 2022年第S01期39卷 166-174页

作者： TIAN Jialue ZHU Yulian LIU Jiahui College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China Fundamental Experimental Teaching Department Nanjing University of Aeronautics and Astronautics

Non-convex methods play a critical role in low-rank tensor completion for their approximation to tensor rank is tighter than that of convex *** they usually cost much more time for calculating singular values of large *** this paper,we propose a double transformed tubal nuclear norm(DTTNN)to replace the rank norm penalty in low rank tensor completion(LRTC)*** turns the original non-convex penalty of a large tensor into two convex penalties of much smaller tensors,and it is shown to be an equivalent ***,DTTNN could take advantage of non-convex envelopes while saving *** results on color image and video inpainting tasks verify the effectiveness of DTTNN compared with state-of-the-art methods.

关键词： double transformed tubal nuclear norm low tubal-rank non-convex optimization tensor factorization tensor completion

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Digital Twins for Energy-Efficient Manufacturing

Digital Twins for Energy-Efficient Manufacturing

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Annual IEEE Systems Conference

作者： Nader Mohamed Sanja Lazarova-Molnar Jameela Al-Jaroodi Department of Computer Science and Information Systems Pennsylvania Western University California PA USA Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany Faculty of Engineering University of Southern Denmark Odense Denmark Department of Engineering Robert Morris University Pittsburgh PA USA

Some manufacturing industries consume massive energy for their manufacturing and logistics processes. The costs of manufacturing can increase significantly as the energy cost increases. Such increase amplifies the final cost of the created products on the consumers. This makes finding different approaches that utilize innovative technologies to improve energy efficiency in manufacturing extremely important. This paper investigates how digital twins can be utilized to enable improved energy efficiency in manufacturing. Different applications of digital twins for energy-efficient manufacturing are investigated. In addition, the paper discusses some challenges of utilizing digital twins for this purpose. A framework for utilizing digital twins for energy-efficient manufacturing is also discussed.

关键词： Manufacturing industries Costs Manufacturing processes Buildings Energy efficiency Raw materials Manufacturing

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RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer 10th

RAST: A Reward Augmented Model for Fine-Grained Sentiment Tr...

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10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Hu, Xiaoxuan Zhang, Hengtong Zhao, Wayne Xin Li, Yaliang Gao, Jing Wen, Ji-Rong School of Information Renmin University of China Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette United States Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Engineering University at Buffalo Buffalo United States Alibaba Group Hangzhou China

ISBN: (纸本)9783030884796

In this paper, we propose a novel model RAST (Reward Augmented Sentiment Transfer) for fine-grained sentiment transfer. Existing methods usually suffer from two major drawbacks, i.e., blurre d sentiment distinction and unsatisfactory content preservation. Considering the above issues, we design two kinds of rewards to better control sentiment and content. Specially, we develop a pairwise comparative discriminator that enforces to generate sentences with clear distinctions for different sentiment intensities. Moreover, we utilize an effective sampling strategy to obtain pseudo-parallel sentences with minor changes on the input sentence to enhance content preservation. Experiments on a benchmark dataset show that the proposed model outperforms several competitive approaches. © 2021, Springer Nature Switzerland AG.

关键词： computers

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Intelligent Facility Layout Planning for Emergency Department: A Multi-Agent System and Particle Swarm Optimization Approach

Intelligent Facility Layout Planning for Emergency Departmen...

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International Conference on Intelligent Systems: Theories and Applications (SITA)

作者： Khalil Bouramtane Said Kharraja Jamal Riffi Omar Elbeqqali Saïd Boujraf Laboratory of Signal and Industrial Process Analysis (LASPI) University of Saint-Etienne Roanne France Faculty of Science Dhar El Mahraz Laboratory of Computer Science Signals Automation and Cognitivism (LISAC) University Sidi Mohamed Ben Abdellah Fez Morocco Dept. Biophysics & Clinical MRI Methods Faculty of Medicine Clinical Neuroscience Laboratory University of Sidi Mohamed Ben Abdellah Fez Morocco

The efficient design of facility layouts for emergency services represents a major challenge due to the dynamic and complex nature of their operations. Within this paper, a fresh approach is introduced to tackle this particular challenge, drawing upon the collaborative power of multi-agent systems along with the principles of particle swarm optimization. Moreover, the utilization of simulation technique is incorporated to enrich the comprehension and depiction of the analyzed *** the proposed framework, agents representing different entities within the emergency department collaborate through a particle swarm optimization algorithm, working collectively towards an optimal facility layout. The main aim is to minimize travel costs and improve workflow efficiency. The results highlight the superiority of the multi-agent particle swarm optimization approach in the realization of intelligent layouts for emergency services. This research contributes to the advancement of facility planning by providing an innovative and efficient solution applicable to dynamic operational environments.

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Learning superconductivity from ordered and disordered material structures 24

Learning superconductivity from ordered and disordered mater...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Pin Chen Luoxuan Peng Rui Jiao Qing Mo Zhen Wang Wenbing Huang Yang Liu Yutong Lu National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University and Institute for AIR Tsinghua University Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9798331314385

Superconductivity is a fascinating phenomenon observed in certain materials under certain conditions. However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking. Hence, we present a new dataset for data-driven approaches, namely SuperCon3D, containing both 3D crystal structures and experimental superconducting transition temperature (Tc) for the first time. Based on SuperCon3D, we propose two deep learning methods for designing high Tc superconductors. The first is SODNet, a novel equivariant graph attention model for screening known structures, which differs from existing models in incorporating both ordered and disordered geometric content. The second is a diffusion generative model DiffCSP-SC for creating new structures, which enables high Tc-targeted generation. Extensive experiments demonstrate that both our proposed dataset and models are advantageous for designing new high Tc superconducting candidates.

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PARTITIONS AND ELEMENTARY SYMMETRIC POLYNOMIALS - AN EXPERIMENTAL APPROACH

arXiv

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arXiv 2024年

作者： Ballantine, Cristina Beck, George Merca, Mircea Department of Mathematics and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Mathematics and Statistics Dalhousie University HalifaxNSB3H 4R2 Canada Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center University Politehnica of Bucharest BucharestRO-060042 Romania

Given a partition λ, we write ej(λ) for the jth elementary symmetric polynomial ej evaluated at the parts of λ and ejpA(n) for the sum of ej(λ) as λ ranges over the set of partitions of n with parts in A. For ejpA(n), we prove analogs of the classical formula for the partition function, (formula presented), where σ1 is the sum of divisors function. We prove several congruences for e2p4(n), the sum of e2 over the set of partitions of n into four parts. Define the function prej(λ) to be the multiset of monomials in ej(λ), which is itself a partition. If A is a set of partitions, we define prej(A) to be the set of partitions prej(λ) as λ ranges over A. If P(n) is the set of all partitions of n, we conjecture that the number of odd partitions in pre2(P(n)) is at least the number of distinct partitions. We prove some results about pre2(B(n)), where B(n) is the set of binary partitions of n. We conclude with conjectures on the log-concavity of functions related to ejp(n), the sum of ej(λ) for all λ ∈ P(n).MSC Codes 05E05, 11P81 © 2024, CC BY.

关键词： Polynomials

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Continuous Prediction of Leg Kinematics during Walking using Inertial Sensors, Smart Glasses, and Embedded Computing

Continuous Prediction of Leg Kinematics during Walking using...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Oleksii Tsepa Roman Burakov Brokoslaw Laschowski Alex Mihailidis Department of Computer Science AGE-WELL Networks of Centres of Excellence program University of Toronto and the Vector Institute for Artificial Intelligence Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto ON Canada Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mechanical and Industrial Engineering University of Toronto Toronto ON Canada Toronto Rehabilitation Institute University Health Network Toronto ON Canada Institute of Biomedical Engineering University of Toronto Toronto ON Canada

Unlike traditional hierarchical controllers for robotic leg prostheses and exoskeletons, continuous systems could allow persons with mobility impairments to walk more naturally in real-world environments without requiring high-level switching between locomotion modes. To support these next-generation controllers, we developed a new system called KIFNet (Kinematics and Image Fusing Network) that uses lightweight and efficient deep learning models to continuously predict the leg kinematics during walking. We tested different sensor fusion methods to combine kinematics data from inertial sensors and computer vision data from smart glasses and found that adaptive instance normalization achieved the lowest RMSE predictions for knee and ankle joint kinematics. We also deployed our model on an embedded device. Without inference optimization, our model was 20 times faster than the previous state-of-the-art and achieved 20% higher prediction accuracies, and during some locomotor activities like stair descent, decreased RMSE up to 300%. With inference optimization, our best model achieved 125 FPS on an NVIDIA Jetson Nano. These results demonstrate the potential to build fast and accurate deep learning models for continuous prediction of leg kinematics during walking based on sensor fusion and embedded computing, therein providing a foundation for real-time continuous controllers for robotic leg prostheses and exoskeletons.

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Mural image restoration with spatial geometric perception and progressive context refinement

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computers & Graphics 2025年 130卷

作者： Yumeng Zhou Min Guo Miao Ma Key Laboratory of Modern Teaching Technology Ministry of Education School of Computer Science Shaanxi Normal University Xi’an 710119 China

Ancient murals, as invaluable cultural heritage, have long been a focal point and significant challenge in the field of cultural heritage preservation. Traditional restoration methods typically address texture and structural features separately, leading to inconsistencies between local details and the overall structure. This approach is insufficient to meet the complex demands of texture and structural restoration for ancient murals. To address this issue, this paper proposes a collaborative encoder–decoder architecture (MIR-SGPR) that achieves simultaneous restoration of texture and structural features in ancient mural images. The generator extracts shallow texture features and deep structural features through the encoder and, in conjunction with the Spatial Geometric Awareness (SGA) module, achieves precise modeling of the spatial location and directional information of damaged areas. To resolve the imbalance between local details and global semantics, this paper introduces the Progressive Contextual Refinement (PCR) network, which progressively optimizes multi-scale features and effectively integrates texture and structural information, thereby enhancing the collaborative modeling capability of local details and global structure. Furthermore, this paper proposes the Mask Reverse-Focus Mechanism (MRF), which leverages mask information to eliminate feature interference from undamaged areas, significantly improving the efficiency and accuracy of restoration. Ultimately, the generated images are optimized through both the global and local discriminators. Experimental results demonstrate that this method significantly outperforms existing state-of-the-art approaches across multiple evaluation metrics. The generated restored images exhibit superior visual consistency, detail authenticity, and overall structural recovery, providing an efficient and reliable solution for the digital preservation of ancient murals.

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INFORMED BAYESIAN SURVIVAL ANALYSIS

arXiv

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arXiv 2021年

作者： Bartoš, František Aust, Frederik Haaf, Julia M. Department of Psychological Methods University of Amsterdam Noord-Holland Netherlands Institute of Computer Science Czech Academy of Sciences Prague Czech Republic

Background: We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process. methods: We illustrate the application of the outlined Bayesian approaches on an example data set from a colon cancer trial. We assess the performance of Bayesian parametric survival analysis and maximum likelihood survival models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study. Results: In the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect of adding Cetuximab to FOLFOX6 regimen on disease-free survival in patients with resected stage III colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We found no noticeable differences for survival predictions. We have made the analytic approach readily available to other researchers in the RoBSA R package. Conclusions: The outlined Bayesian framework provides several benefits when applied to parametric survival ana

关键词： Bayesian networks

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