Gravity model explained by the radiation model on a population landscape

Understanding the mechanisms behind human mobility patterns is crucial to improve our ability to optimize and predict traffic flows. Two representative mobility models, i.e., radiation and gravity models, have been extensively compared to each other against various empirical data sets, while their fundamental relation is far from being fully understood. In order to study such a relation, we first model the heterogeneous population landscape by generating a fractal geometry of sites and then by assigning to each site a population independently drawn from a power-law distribution. Then the radiation model on this population landscape, which we call the radiation-on-landscape (RoL) model, is compared to the gravity model to derive the distance exponent in the gravity model in terms of the properties of the population landscape, which is confirmed by the numerical simulations. Consequently, we provide a possible explanation for the origin of the distance exponent in terms of the properties of the heterogeneous population landscape, enabling us to better understand mobility patterns constrained by the travel distance.Comment: 14 pages, 4 figure

A common trajectory recapitulated by urban economies

Is there a general economic pathway recapitulated by individual cities over and over? Identifying such evolution structure, if any, would inform models for the assessment, maintenance, and forecasting of urban sustainability and economic success as a quantitative baseline. This premise seems to contradict the existing body of empirical evidences for path-dependent growth shaping the unique history of individual cities. And yet, recent empirical evidences and theoretical models have amounted to the universal patterns, mostly size-dependent, thereby expressing many of urban quantities as a set of simple scaling laws. Here, we provide a mathematical framework to integrate repeated cross-sectional data, each of which freezes in time dimension, into a frame of reference for longitudinal evolution of individual cities in time. Using data of over 100 millions employment in thousand business categories between 1998 and 2013, we decompose each city's evolution into a pre-factor and relative changes to eliminate national and global effects. In this way, we show the longitudinal dynamics of individual cities recapitulate the observed cross-sectional regularity. Larger cities are not only scaled-up versions of their smaller peers but also of their past. In addition, our model shows that both specialization and diversification are attributed to the distribution of industry's scaling exponents, resulting a critical population of 1.2 million at which a city makes an industrial transition into innovative economies

Measuring national capability over big science's multidisciplinarity: A case study of nuclear fusion research

In the era of big science, countries allocate big research and development budgets to large scientific facilities that boost collaboration and research capability. A nuclear fusion device called the "tokamak" is a source of great interest for many countries because it ideally generates sustainable energy expected to solve the energy crisis in the future. Here, to explore the scientific effects of tokamaks, we map a country's research capability in nuclear fusion research with normalized revealed comparative advantage on five topical clusters-material, plasma, device, diagnostics, and simulation-detected through a dynamic topic model. Our approach captures not only the growth of China, India, and the Republic of Korea but also the decline of Canada, Japan, Sweden, and the Netherlands. Time points of their rise and fall are related to tokamak operation, highlighting the importance of large facilities in big science. The gravity model points out that two countries collaborate less in device, diagnostics, and plasma research if they have comparative advantages in different topics. This relation is a unique feature of nuclear fusion compared to other science fields. Our results can be used and extended when building national policies for big science.11Yscopu

Wave Run-Up Phenomenon on Offshore Platforms: Part 1. Tension Leg Platform

This study reports on an extensive experimental campaign carried out to evaluate non-linear waves applied to offshore structures in extreme marine environments. An offshore tension leg platform (TLP) model was used to observe the waves around a fixed-type offshore structure. The wave amplitude measured in the experiments of this study was indicated as a wave run-up ratio. Both the first-order analysis and the analysis of the entire wave amplitude were described. The experimental results were compared with the calculations from a potential-based code in order to verify the effectiveness of the developed technology

Microscopic Theory of Rashba Interaction in Magnetic Metal

Theory of Rashba spin-orbit coupling in magnetic metals is worked out from microscopic Hamiltonian describing d-orbitals. When structural inversion symmetry is broken, electron hopping between $d$-orbitals generates chiral ordering of orbital angular momentum, which combines with atomic spin-orbit coupling to result in the Rashba interaction. Rashba parameter characterizing the interaction is band-specific, even reversing its sign from band to band. Large enhancement of the Rashba parameter found in recent experiments is attributed to the orbital mixing of 3d magnetic atoms with non-magnetic heavy elements as we demonstrate by first-principles and tight-binding calculations.Comment: 5 pages, 2 figure

Analysis of the Rainbow Tradeoff Algorithm Used in Practice

Cryptanalytic time memory tradeoff is a tool for inverting one-way functions, and the rainbow table method, the best-known tradeoff algorithm, is widely used to recover passwords. Even though extensive research has been performed on the rainbow tradeoff, the algorithm actually used in practice differs from the well-studied original algorithm. This work provides a full analysis of the rainbow tradeoff algorithm that is used in practice. Unlike existing works on the rainbow tradeoff, the analysis is done in the external memory model, so that the practically important issue of table loading time is taken into account. As a result, we are able to provide tradeoff parameters that optimize the wall-clock time

KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application

Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data. This poses a critical risk when deploying LLM-based applications. Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups. This limitation requires localized social bias datasets to ensure the safe and effective deployment of LLMs. To this end, we present KO SB I, a new social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories. We find that through filtering-based moderation, social biases in generated content can be reduced by 16.47%p on average for HyperCLOVA (30B and 82B), and GPT-3.Comment: 17 pages, 8 figures, 12 tables, ACL 202