🔖 The advantages of interdisciplinarity in modern science
Moreno Bonaventura, Vito Latora, Vincenzo Nicosia, Pietro Panzarasa
🔗 arxiv.org/pdf/1712.07910.pdf
📌 ABSTRACT
As the increasing complexity of large-scale research requires the combined efforts of scientists with expertise in different fields, the advantages and costs of interdisciplinary scholarship have taken center stage in current debates on scientific production. Here we conduct a comparative assessment of the scientific success of specialized and interdisciplinary researchers in modern science. Drawing on comprehensive data sets on scientific production, we propose a two-pronged approach to interdisciplinarity. For each scientist, we distinguish between background interdisciplinarity, rooted in knowledge accumulated over time, and social interdisciplinarity, stemming from exposure to collaborators' knowledge. We find that, while abandoning specialization in favor of moderate degrees of background interdisciplinarity deteriorates performance, very interdisciplinary scientists outperform specialized ones, at all career stages. Moreover, successful scientists tend to intensify the heterogeneity of collaborators and to match the diversity of their network with the diversity of their background. Collaboration sustains performance by facilitating knowledge diffusion, acquisition and creation. Successful scientists tend to absorb a larger fraction of their collaborators' knowledge, and at a faster pace, than less successful ones. Collaboration also provides successful scientists with opportunities for the cross-fertilization of ideas and the synergistic creation of new knowledge. These results can inspire scientists to shape successful careers, research institutions to develop effective recruitment policies, and funding agencies to award grants of enhanced impact.
Moreno Bonaventura, Vito Latora, Vincenzo Nicosia, Pietro Panzarasa
🔗 arxiv.org/pdf/1712.07910.pdf
📌 ABSTRACT
As the increasing complexity of large-scale research requires the combined efforts of scientists with expertise in different fields, the advantages and costs of interdisciplinary scholarship have taken center stage in current debates on scientific production. Here we conduct a comparative assessment of the scientific success of specialized and interdisciplinary researchers in modern science. Drawing on comprehensive data sets on scientific production, we propose a two-pronged approach to interdisciplinarity. For each scientist, we distinguish between background interdisciplinarity, rooted in knowledge accumulated over time, and social interdisciplinarity, stemming from exposure to collaborators' knowledge. We find that, while abandoning specialization in favor of moderate degrees of background interdisciplinarity deteriorates performance, very interdisciplinary scientists outperform specialized ones, at all career stages. Moreover, successful scientists tend to intensify the heterogeneity of collaborators and to match the diversity of their network with the diversity of their background. Collaboration sustains performance by facilitating knowledge diffusion, acquisition and creation. Successful scientists tend to absorb a larger fraction of their collaborators' knowledge, and at a faster pace, than less successful ones. Collaboration also provides successful scientists with opportunities for the cross-fertilization of ideas and the synergistic creation of new knowledge. These results can inspire scientists to shape successful careers, research institutions to develop effective recruitment policies, and funding agencies to award grants of enhanced impact.
Forwarded from رادیوفیزیک 📣
🎞 در جشنواره روز فیزیک دانشگاه شهید بهشتی، عباس کریمی درباره اینکه به طور کلی فیزیکدانان به دنبال چه هستند صحبت می کند. سپس به مثالهایی اشاره میکند که دانشمندان پیچیدگی به بررسی آن ها می پردازند. مخاطب این سخرانی غیرمتخصصان است.
https://www.aparat.com/v/ul8kh
https://www.aparat.com/v/ul8kh
آپارات - سرویس اشتراک ویدیو
در جستجوی الگوها؛ کار فیزیکدانان و دانشمندان پیچیدگی
در جشنواره روز فیزیک دانشگاه شهید بهشتی، عباس کریمی درباره اینکه به طور کلی فیزیکدانان به دنبال چه هستند صحبت می کند. سپس به مثال هایی اشاره می کند که دانشمندان پیچیدگی به بررسی آن ها می پردازند. مخاطب این سخرانی غیرمتخصصان است.
Watch this short video to learn how to classify images at the pixel level and analyze them with MATLAB
https://t.co/qr6kTl5Wtj
https://t.co/qr6kTl5Wtj
Complex Systems Studies
Old theory: Long-term memories form in the brain as short-term ones expire. New discovery: Both types of memory form at the same time — but we only get to experience one.
Quanta Magazine
Light-Triggered Genes Reveal the Hidden Workings of Memory
Nobel laureate Susumu Tonegawa’s lab is overturning old assumptions about how memories form, how recall works and whether lost memories might be restored from "silent engrams."
🔅 Lyapunov exponent as a metric for assessing the dynamic content and predictability of large-eddy simulations
https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.2.094606
https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.2.094606
Physical Review Fluids
Lyapunov exponent as a metric for assessing the dynamic content and predictability of large-eddy simulations
Temporal evolution of the solution separation of two initially infinitesimally perturbed simulations for a turbulent flow. This metric provides assessment of the Lyapunov exponent as a measure for the predictability time and dynamic content of turbulent flow…
🔖 Change points, memory and epidemic spreading in temporal networks
Tiago P. Peixoto, Laetitia Gauvin
🔗 https://arxiv.org/pdf/1712.08948
📌 ABSTRACT
Dynamic networks exhibit temporal patterns that vary across different time scales, all of which can potentially affect processes that take place on the network. However, most data-driven approaches used to model time-varying networks attempt to capture only a single characteristic time scale in isolation --- typically associated with the short-time memory of a Markov chain or with long-time abrupt changes caused by external or systemic events. Here we propose a unified approach to model both aspects simultaneously, detecting short and long-time behaviors of temporal networks. We do so by developing an arbitrary-order mixed Markov model with change points, and using a nonparametric Bayesian formulation that allows the Markov order and the position of change points to be determined from data without overfitting. In addition, we evaluate the quality of the multiscale model in its capacity to reproduce the spreading of epidemics on the temporal network, and we show that describing multiple time scales simultaneously has a synergistic effect, where statistically significant features are uncovered that otherwise would remain hidden by treating each time scale independently.
Tiago P. Peixoto, Laetitia Gauvin
🔗 https://arxiv.org/pdf/1712.08948
📌 ABSTRACT
Dynamic networks exhibit temporal patterns that vary across different time scales, all of which can potentially affect processes that take place on the network. However, most data-driven approaches used to model time-varying networks attempt to capture only a single characteristic time scale in isolation --- typically associated with the short-time memory of a Markov chain or with long-time abrupt changes caused by external or systemic events. Here we propose a unified approach to model both aspects simultaneously, detecting short and long-time behaviors of temporal networks. We do so by developing an arbitrary-order mixed Markov model with change points, and using a nonparametric Bayesian formulation that allows the Markov order and the position of change points to be determined from data without overfitting. In addition, we evaluate the quality of the multiscale model in its capacity to reproduce the spreading of epidemics on the temporal network, and we show that describing multiple time scales simultaneously has a synergistic effect, where statistically significant features are uncovered that otherwise would remain hidden by treating each time scale independently.
🔖 Big Data, Data Science, and Civil Rights
Solon Barocas, Elizabeth Bradley, Vasant Honavar, Foster Provost
🔗 https://arxiv.org/pdf/1706.03102
📌 ABSTRACT
Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how colleges and universities make admissions and financial aid decisions, and much more. As data-driven decisions increasingly affect every corner of our lives, there is an urgent need to ensure they do not become instruments of discrimination, barriers to equality, threats to social justice, and sources of unfairness. In this paper, we argue for a concrete research agenda aimed at addressing these concerns, comprising five areas of emphasis: (i) Determining if models and modeling procedures exhibit objectionable bias; (ii) Building awareness of fairness into machine learning methods; (iii) Improving the transparency and control of data- and model-driven decision making; (iv) Looking beyond the algorithm(s) for sources of bias and unfairness-in the myriad human decisions made during the problem formulation and modeling process; and (v) Supporting the cross-disciplinary scholarship necessary to do all of that well.
Solon Barocas, Elizabeth Bradley, Vasant Honavar, Foster Provost
🔗 https://arxiv.org/pdf/1706.03102
📌 ABSTRACT
Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how colleges and universities make admissions and financial aid decisions, and much more. As data-driven decisions increasingly affect every corner of our lives, there is an urgent need to ensure they do not become instruments of discrimination, barriers to equality, threats to social justice, and sources of unfairness. In this paper, we argue for a concrete research agenda aimed at addressing these concerns, comprising five areas of emphasis: (i) Determining if models and modeling procedures exhibit objectionable bias; (ii) Building awareness of fairness into machine learning methods; (iii) Improving the transparency and control of data- and model-driven decision making; (iv) Looking beyond the algorithm(s) for sources of bias and unfairness-in the myriad human decisions made during the problem formulation and modeling process; and (v) Supporting the cross-disciplinary scholarship necessary to do all of that well.