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On the inevitability of online echo chambers

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arXiv 2019年

作者： Sasahara, Kazutoshi Chen, Wen Peng, Hao Ciampaglia, Giovanni Luca Flammini, Alessandro Menczer, Filippo Department of Complex Systems Science Graduate School of Informatics Nagoya University Japan JST PRESTO Japan Center for Complex Networks and Systems Research Indiana University Bloomington United States School of Informatics Computing and Engineering Indiana University Bloomington United States School of Information University of Michigan United States Network Science Institute Indiana University United States Department of Computer Science and Engineering University of South Florida United States

While social media make it easy to connect with and access information from anyone, they also facilitate basic inuence and unfriending mechanisms that may lead to segregated and polarized clusters known as "echo chambers." Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of inuence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of inuence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

Physical Layer Security Protocol for Poisson Channels for Passive Man-in-the-middle Attack

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arXiv 2019年

作者： Hayashi, Masahito Vázquez-Castro, Ángeles Graduate School of Mathematics Nagoya University Nagoya Japan Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen China Center for Quantum Computing Peng Cheng Laboratory Shenzhen China Centre for Quantum Technologies National University of Singapore Singapore Singapore Department of Telecommunications and Systems Engineering Autonomous University of Barcelona Barcelona Spain

In this work, we focus on the classical optical channel having Poissonian statistical behavior and propose a novel secrecy coding-based physical layer protocol. Our protocol is different but complementary to both (computationally secure) quantum immune cryptographic protocols and (information theoretically secure) quantum cryptographic protocols. Specifically, our (information theoretical) secrecy coding protocol secures classical digital information bits at photonic level exploiting the random nature of the Poisson channel. It is known that secrecy coding techniques for the Poisson channel based on the classical one-way wiretap channel (introduced by Wyner in 1975) ensure secret communication only if the mutual information to the eavesdropper is smaller than that to the legitimate receiver. In order to overcome such a strong limitation, we introduce a two-way protocol that always ensures secret communication independently of the conditions of legitimate and eavesdropper channels. We prove this claim showing rigorous comparative derivation and analysis of the information theoretical secrecy capacity of the classical one-way and of the proposed two-way protocols. We also show numerical calculations that prove drastic gains and strong practical potential of our proposed two-way protocol to secure information transmission over optical channels. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum cryptography

Deep convolutional neural network approach for forehead tissue thickness estimation

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Deep convolutional neural network approach for forehead tiss...

作者： Manit, Jirapong Schweikard, Achim Ernst, Floris Institute for Robotics and Cognitive Systems Graduate School for Computing in Medicine and Life Sciences University of Lübeck Germany

In this paper, we presented a deep convolutional neural network (CNN) approach for forehead tissue thickness estimation. We use down sampled NIR laser backscattering images acquired from a novel marker-less near-infrared laser-based head tracking system, combined with the beam’s incident angle parameter. These two-channel augmented images were constructed for the CNN input, while a single node output layer represents the estimated value of the forehead tissue thickness. The models were - separately for each subject - trained and tested on datasets acquired from 30 subjects (high resolution MRI data is used as ground truth). To speed up training, we used a pre-trained network from the first subject to bootstrap training for each of the other subjects. We could show a clear improvement for the tissue thickness estimation (mean RMSE of 0.096 mm). This proposed CNN model outperformed previous support vector regression (mean RMSE of 0.155 mm) or Gaussian processes learning approaches (mean RMSE of 0.114 mm) and eliminated their restrictions for future research. © 2017 Jirapong Manit et al., published by De Gruyter.

关键词： Tissue

Optimizing the human learnability of abstract network representations

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arXiv 2021年

作者： Qian, William Lynn, Christopher W. Klishin, Andrei A. Stiso, Jennifer Christianson, Nicolas H. Bassett, Dani S. Department of Physics & Astronomy College of Arts & Sciences University of Pennsylvania PhiladelphiaPA19104 United States Initiative for the Theoretical Sciences Graduate Center City University of New York New YorkNY10016 United States Joseph Henry Laboratories of Physics Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ08544 United States Department of Bioengineering School of Engineering & Applied Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Computing and Mathematical Sciences Caltech PasadenaCA91125 United States Department of Electrical & Systems Engineering School of Engineering & Applied Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Neurology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Psychiatry Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Santa Fe Institute Santa FeNM87501 United States

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To answer these questions, we study the optimization of network learnability. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core-periphery structure we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information. Copyright © 2021, The Authors. All rights reserved.

关键词： Music

Using IoT technology to improve online education through data mining

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Using IoT technology to improve online education through dat...

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Njeru, Alexander Muriuki Omar, Mwana Said Yi, Sun Paracha, Samiullah Wannous, Muhammad Department of Information Systems Graduate School of Information Technology Kobe Institute of Computing Japan United Nations University China

ISBN: (纸本)9781509048977

Online learning is rapidly expanding in both the number of programs and users. Concerns are arising on how to improve the quality of materials and delivery methods. This research intends to explore the potential use of Internet of Things (IoT) to collect data, which will be analyzed to get information useful for decision making to improve the online programmes in Higher Education Institutions (HEIs) © 2017 IEEE.

关键词： Decision making

The Quantum Internet (Technical Version)

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

Proposal of IoT based learning material and its management system for primary/secondary education 3

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Proposal of IoT based learning material and its management s...

3rd International Conference on Digital Technology in Education, ICDTE 2019

作者： Todoriki, Takafumi Kayama, Mizue Tachi, Nobuyuki Nagai, Takashi Futagami, Takao Asuke, Takehiko Graduate School of Science and Technology Shinshu University Wakasato 4-17-1 Nagano Nagano380-0928 Japan Shinshu University Faculty of Engineering Wakasato 4-17-1 Nagano Nagano380-0928 Japan Nagoya University Center for Embedded Computing Systems Furotyou NIC508 Nagoya Aichi464-8601 Japan Institute of Technologists Department of Mechanical and Production Engineering Maeya 333 Gyoda Saitama361-0038 Japan TOYO Corporation Yaesu 1-1-6 Chuo-ku Tokyo103-8284 Japan Ina City Takato Junior Highschool Takato Machi Higashi Takato 232 Ina Nagano396-0213 Japan

ISBN: (纸本)9781450372206

The purpose of this study was lesson support by building a system which IoT based learning material and using it. This system is assumed to use the regular curriculum of primary and secondary education in Japan. We previously examined that apply IoT technology to learning material. That learning material use when experiment with a measurement. And, we designed its management system. Here we describe the outline of IoT based learning material and its management system. And we report about concrete IoT based learning material. © 2019 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Learning systems

Independent components of human brain morphology

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arXiv 2020年

作者： Wang, Yujiang Ludwig, Tobias Little, Bethany Necus, Joe H. Winston, Gavin Vos, Sjoerd B. de Tisi, Jane Duncan, John S. Taylor, Peter N. Mota, Bruno Interdisciplinary Complex Systems Group School of Computing Newcastle University Newcastle upon Tyne United Kingdom Faculty of Medical Sciences Newcastle University Newcastle upon Tyne United Kingdom UCL Queen Square Institute of Neurology London United Kingdom Graduate Training Center of Neuroscience University of Tübingen Tübingen Germany University College London London United Kingdom Department of Clinical and Experimental Epilepsy University College London London United Kingdom Department of Medicine Division of Neurology Queens University Kingston Canada Epilepsy Society MRI Unit Chalfont St Peter United Kingdom Institute of Physics Federal University of Rio de Janeiro Brazil

Quantification of brain morphology has become an important cornerstone in understanding brain structure. Measures of cortical morphology such as thickness and surface area are frequently used to compare groups of subjects or characterise longitudinal changes. However, such measures are often treated as independent from each other. A recently described scaling law, derived from a statistical physics model of cortical folding, demonstrates that there is a tight covariance between three commonly used cortical morphology measures: cortical thickness, total surface area, and exposed surface area. We show that assuming the independence of cortical morphology measures can hide features and potentially lead to misinterpretations. Using the scaling law, we account for the covariance between cortical morphology measures and derive novel independent measures of cortical morphology. By applying these new measures, we show that new information can be gained;in our example we show that distinct morphological alterations underlie healthy ageing compared to temporal lobe epilepsy, even on the coarse level of a whole hemisphere. We thus provide a conceptual framework for characterising cortical morphology in a statistically valid and interpretable manner, based on theoretical reasoning about the shape of the cortex. Copyright © 2020, The Authors. All rights reserved.

关键词： Morphology

Learning performance in inverse Ising problems with sparse teacher couplings

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arXiv 2019年

作者： Abbara, Alia Kabashima, Yoshiyuki Obuchi, Tomoyuki Xu, Yingying Laboratoire de Physique de l'Ecole Normale Supérieure 24 rue Lhomond Paris75005 France Department of Mathematical and Computing Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo Japan Department of Systems Science Graduate School of Informatics Kyoto University Yoshida Hon-machi Sakyo-ku Kyoto-shi Kyoto606-8501 Japan

We investigate the learning performance of the pseudolikelihood maximization method for inverse Ising problems. In the teacher-student scenario under the assumption that the teacher's couplings are sparse and the student does not know the graphical structure, the learning curve and order parameters are assessed in the typical case using the replica and cavity methods from statistical mechanics. Our formulation is also applicable to a certain class of cost functions having locality;the standard likelihood does not belong to that class. The derived analytical formulas indicate that the perfect inference of the presence/absence of the teacher's couplings is possible in the thermodynamic limit taking the number of spins N as infinity while keeping the dataset size M proportional to N, as long as α = M/N > 2. Meanwhile, the formulas also show that the estimated coupling values corresponding to the truly existing ones in the teacher tend to be overestimated in the absolute value, manifesting the presence of estimation bias. These results are considered to be exact in the thermodynamic limit on locally tree-like networks, such as the regular random or Erdős-Rényi graphs. Numerical simulation results fully support the theoretical predictions. Additional biases in the estimators on loopy graphs are also discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Couplings