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55th IEEE Conference on Decision and Control, CDC 2016

作者： Wang, Chu Li, Qianxiao Weinan, E. Chazelle, Bernard Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Nokia Bell Labs 600 Mountain Avenue Murray HillNJ07974 United States Department of Mathematics Program in Applied and Computational Mathematics Princeton University NJ08540 United States Department of Computer Science Princeton University PrincetonNJ08540 United States

ISBN: (纸本)9781509018376

The classic Hegselmann-Krause (HK) model for opinion dynamics consists of a set of agents on the real line, each one instructed to move, at every time step, to the mass center of the agents within a fixed distance R. In this work, we investigate the effects of noise in the continuous-time version of the model as described by its mean-field Fokker-Planck equation. In the presence of a finite number of agents, the system exhibits a phase transition from order to disorder as the noise increases. We introduce an order parameter to track the phase transition and resolve the corresponding phase diagram. The system undergoes a phase transition for small R but none for larger R. Based on the stability analysis of the mean-field equation, we derive the existence of a forbidden zone for the disordered phase to emerge. We also provide a theoretical explanation for the well-known 2R conjecture, which states that, for a random initial distribution in a fixed interval, the final configuration consists of clusters separated by a distance of roughly 2R. Our theoretical analysis confirms previous simulations and predicts properties of the noisy HK model in higher dimension. © 2016 IEEE.

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Manifold learning-based methods for analyzing single-cell RNA-sequencing data

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Current Opinion in Systems Biology 2018年 7卷 36-46页

作者： Moon, Kevin R. Stanley, Jay S. Burkhardt, Daniel van Dijk, David Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New Haven CT United States Applied Mathematics Program Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States

Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression for thousands of cells in a single experiment. While these technologies hold great potential for improving our understanding of cellular states and progression, they also pose new challenges and require advanced mathematical and algorithmic tools to extract underlying biological signals. In this review, we cover one of the most promising avenues of research into unlocking the potential of scRNA-seq data: the field of manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to process the data, extract its dynamics, and infer patterns in it. In particular, we cover manifold learning-based methods for denoising the data, revealing gene interactions, extracting pseudotime progressions with model fitting, visualizing the cellular state space via dimensionality reduction, and clustering the data. © 2017 Published by Elsevier Ltd.

关键词： Data mining Gene regulatory networks Imputation Manifold learning Pseudotime trends Single cell RNA sequencing

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Doctrine based multi-resolution HLA distributed simulation 17

Doctrine based multi-resolution HLA distributed simulation

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32nd Annual ACM Symposium on applied Computing, SAC 2017

作者： Paul, Ricardo L. Nunes, Raul C. Oliveira, Vanderlan D. Kunde, Diogo Computer Science Graduate Program Fed. Univ. of Santa Maria Santa Maria RS Brazil Applied Computing Dpt. Fed. Univ. of Santa Maria Santa Maria RS Brazil

ISBN: (纸本)9781450344869

In distributed simulation, the purpose of multi-resolution methods is to allow simulation integration getting consis- tent views of different resolutions. In these methods, chal- lenges in the treatment of aggregation and disaggregation can be found, such as synchronization of events and objects, and data reliability when occurs a change of resolu- tion. Doctrines are the basis for operations involving different resources (human and material) with different roles to succeed in operation, and multi-resolution in distributed simulation can take advantage of this factor to ensure the reliability of aggregation and disaggregation processes. The aim of this paper is to propose a new strategy for treatment of multi-resolution, which builds converting rules from doctrine dynamic rules. The multi-resolution converter uses dynamic rules to describe aspects of the doctrine in the ag- gregation and disaggregation processes, increasing the reliability of multi-resolution distributed simulations. This strat- egy was implemented on a High Level Architecture (HLA) federation with virtual and constructive Commercial Off- The-Shelf (COTS) simulators. The experiments showed the exibility of this strategy on managing different doctrines. © 2017 ACM.

关键词： Reliability

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Applying multi-criteria decision analysis to global software development with scrum project planning

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6th International Conference on Rough Sets and Knowledge Technology, RSKT 2011

作者： Almeida, Luis Henrique Pinheiro, Plácido Rogério Albuquerque, Adriano Bessa CPQi IT Offshore Solutions Av. Washington Soares 909 60.811-341 Fortaleza Brazil University of Fortaleza Graduate Program in Applied Computer Science Av. Washington Soares 1321 60811-905 Fortaleza Brazil

ISBN: (纸本)9783642244247

Distributed Software Development (DSD) projects have become a common reality for many organizations. Scrum is a consolidated Agile methodology and has been increasingly used in a distributed fashion. As distance makes difficult to interact and to cooperate effectively, it is paramount to use methodologies like Scrum that emphasizes communication, reduces coordination and control overhead. Successfully planning and managing the combined use of DSD and Scrum is a complex task and requires carefully planning. Despite the importance and complexity of this type of problem, there seems to be a lack of reports, in the literature, of models that could support managers dealing with such decision context. This paper applies a multi-criteria decision model on the choice of DSD Scrum project plans that have a better chance of success. The model, presented in [1], was developed using cognitive mapping and MACBETH[2]. The application of the model is demonstrated, followed by conclusion and future work. © 2011 Springer-Verlag.

关键词： Project management

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A Multi-criteria Decision Model for Planning Inter-organizational Global Agile Software Development Projects

A Multi-criteria Decision Model for Planning Inter-organizat...

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作者： Almeida, Luis Henrique Albuquerque, Adriano Pinheiro, Plácido Rogério CPQi IT Offshore Solutions R. Chico Lemos 946 60822-780 Fortaleza Brazil Graduate Program in Applied Computer Science University of Fortaleza Av. Washington Soares 1321 60811-905 Fortaleza Brazil

ISBN: (纸本)9783642358784

Inter-organizational Global Software Development (GSD) has become a common reality for many projects. It is well established that distance makes difficult to interact and to cooperate effectively. Scrum, a consolidated Agile methodology, emphasizes communication, reduces coordination and control overhead and has been increasingly used in a distributed fashion. Successfully planning and managing the combined use of GSD and Scrum is a complex task and requires carefully planning. Despite the importance and complexity of this type of problem, there seems to be a lack of reports, in the literature, of models that could support managers dealing with these decision contexts. This paper presents a multi-criteria decision model for planning and fine-tuning such project plans. This model was developed using cognitive mapping and MACBETH [1]. The application of the model is demonstrated, followed by conclusion and future work. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Project management

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Convolutional neural networks with low-rank regularization 4

Convolutional neural networks with low-rank regularization

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4th International Conference on Learning Representations, ICLR 2016

作者： Tai, Cheng Xiao, Tong Zhang, Yi Wang, Xiaogang Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Department of Electronic Engineering Chinese University of Hong Kong Hong Kong Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

Large CNNs have delivered impressive performance in various computer vision applications. But the storage and computation requirements make it problematic for deploying these models on mobile devices. Recently, tensor decompositions have been used for speeding up CNNs. In this paper, we further develop the tensor decomposition technique. We propose a new algorithm for computing the low-rank tensor decomposition for removing the redundancy in the convolution kernels. The algorithm finds the exact global optimizer of the decomposition and is more effective than iterative methods. Based on the decomposition, we further propose a new method for training low-rank constrained CNNs from scratch. Interestingly, while achieving a significant speedup, sometimes the low-rank constrained CNNs delivers significantly better performance than their non-constrained counterparts. On the CIFAR-10 dataset, the proposed low-rank NIN model achieves 91.31% accuracy (without data augmentation), which also improves upon state-of-the-art result. We evaluated the proposed method on CIFAR-10 and ILSVRC12 datasets for a variety of modern CNNs, including AlexNet, NIN, VGG and GoogleNet with success. For example, the forward time of VGG-16 is reduced by half while the performance is still comparable. Empirical success suggests that low-rank tensor decompositions can be a very useful tool for speeding up large CNNs. © ICLR 2016: San Juan, Puerto Rico. All Rights Reserved.

关键词： Iterative methods

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Meal Image Recognition and Healthy Meal Combination Recommendation System Integrated with Generative Artificial Intelligence 6

Meal Image Recognition and Healthy Meal Combination Recommen...

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6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Chang, Chin-Chih Wei, Chi-Hung Hsiao, Sean Yang, Chyuan-Huei Thomas Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan School of Information Engineering Shandong Vocational and Technical University of International Studies Shandong China

ISBN: (纸本)9798350396133

People increasingly prioritize a balanced diet to enhance well-being, yet making informed dietary choices remains challenging amidst the abundance of options. To address this, we developed a meal image recognition and healthy meal combination recommender system, integrating generative artificial intelligence (AI). Convolutional neural networks were used for precise meal image recognition to identify diverse food items. The generative AI-augmented recommendation engine offers personalized meal suggestions aligned with nutritional goals and dietary preferences, utilizing a nutrition knowledge base to ensure overall well-being. The system's feasibility was validated, illustrating its excellence in meal recognition accuracy, recommendation diversity, and user engagement. By integrating generative AI, the system shows its potential to enhance dietary recommendations and public health. © 2024 IEEE.

关键词： Generative adversarial networks

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A lower bound on the complexity of approximate nearest-neighbor searching on the Hamming cube 99

A lower bound on the complexity of approximate nearest-neigh...

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Proceedings of the thirty-first annual ACM symposium on Theory of Computing

作者： Amit Chakrabarti Bernard Chazelle Benjamin Gum Alexey Lvov Department of Computer Science Princeton University Department of Computer Science Princeton University and NEC Research Institute Program in Applied and Computational Mathematics Princeton University

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The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

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Neuron 2025年

作者： Wolcott, Nora S. Redman, William T. Karpinska, Marie Jacobs, Emily G. Goard, Michael J. Department of Molecular Cellular and Developmental Biology University of California Santa Barbara Santa Barbara 93106 CA United States Interdepartmental Graduate Program in Dynamical Neuroscience University of California Santa Barbara Santa Barbara 93106 CA United States Intelligent Systems Center Johns Hopkins University Applied Physics Laboratory Laurel 20723 MD United States Department of Computer Science University of California Santa Barbara Santa Barbara 93106 CA United States Department of Psychological & Brain Sciences University of California Santa Barbara Santa Barbara 93106 CA United States Ann S. Bowers Women's Brain Health Initiative University of California Santa Barbara Santa Barbara 93106 CA United States Neuroscience Research Institute University of California Santa Barbara Santa Barbara 93106 CA United States

Histological evidence suggests that the estrous cycle exerts a powerful influence on CA1 neurons in the mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice across multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with spine density peaking during proestrus when estradiol levels are highest. These morphological changes coincided with greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed greater place field stability during proestrus, evident at both the single-cell and population levels. These findings demonstrate that the estrous cycle drives large-scale structural and functional plasticity in hippocampal neurons essential for learning and memory. © 2025 The Author(s)

关键词： dendritic integration estradiol estrous cycle hippocampus multiphoton imaging neuroendocrine place cells spine morphology

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Multiagent-Based Model Integration

Multiagent-Based Model Integration

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Web Intelligence and Intelligent Agent Technology Workshops (WI-IAT)

作者： Ana Carolina M. Pilatti De Paula Braulio C. Avila Edson Scalabrin Fabricio Enembreck Graduate Program in Applied Computer Science-PPGIA Pontifical Catholic University of Paraná Curitiba Parana Brazil

This paper presents a distributed data mining technique based on a multiagent environment, called SMAMDD (multiagent system for distributed data mining), which uses model integration. Model integration consists in the amalgamation of local models into a global, consistent one. In each subset, agents perform mining tasks locally and, afterwards, results are merged into a global model. In order to achieve that, agents cooperate by exchanging messages, aiming to improve the process of knowledge discover generating accurate results. The multiagent system for distributed data mining proposed in this paper has been compared with classical machine learning algorithms which are based on model integration as well, simulating a distributed environment. The results obtained show that SMAMDD can produce highly accurate data models

关键词： Distributed decision making Data mining Multiagent systems Machine learning algorithms Internet Distributed databases Intelligent agent computer science Computational modeling Data models

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