Unsupervised embedding of trajectories captures the latent structure of scientific migration.

Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout the history, advances, and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a "digital double" that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science.

COVID-19 confines recreational gatherings in Seoul to familiar, less crowded, and neighboring urban areas.

Recreational gatherings are sources of the spread of infectious diseases. Understanding the dynamics of recreational gatherings is essential to building effective public health policies but challenging as the interaction between people and recreational places is complex. Recreational activities are concentrated in a set of urban areas and establish a recreational hierarchy. In this hierarchy, higher-level regions attract more people than lower-level regions for recreational purposes. Here, using customers' motel booking records which are highly associated with recreational activities in Korea, we identify that recreational hierarchy, geographical distance, and attachment to a location are crucial factors of recreational gatherings in Seoul, Republic of Korea. Our analyses show that after the COVID-19 outbreak, people are more likely to visit familiar recreational places, avoid the highest level of the recreational hierarchy, and travel close distances. Interestingly, the recreational visitations were reduced not only in the highest but also in low-level regions. Urban areas at low levels of the recreational hierarchy were more severely affected by COVID-19 than urban areas at high and middle levels of the recreational hierarchy.

The latent structure of global scientific development.

Science is essential to innovation and economic prosperity. Although studies have shown that national scientific development is affected by geographic, historic and economic factors, it remains unclear whether there are universal structures and trajectories of national scientific development that can inform forecasting and policy-making. Here, by examining the scientific 'exports'-publications that are indexed in international databases-of countries, we reveal a three-cluster structure in the relatedness network of disciplines that underpin national scientific development and the organization of global science. Tracing the evolution of national research portfolios reveals that while nations are proceeding to more diverse research profiles individually, scientific production is increasingly specialized in global science over the past decades. By uncovering the underlying structure of scientific development and connecting it with economic development, our results may offer a new perspective on the evolution of global science.

Design and Implementation of a Multi-Hop Real-Time LoRa Protocol for Dynamic LoRa Networks.

Recently, LoRa (Long Range) technology has been drawing attention in various applications due to its long communication range and high link reliability. However, in industrial environments, these advantages are often compromised by factors such as node mobility, signal attenuation due to various obstacles, and link instability due to external signal interference. In this paper, we propose a new multi-hop LoRa protocol that can provide high reliability for data transmission by overcoming those factors in dynamic LoRa networks. This study extends the previously proposed two-hop real-time LoRa (Two-Hop RT-LoRa) protocol to address technical aspects of dynamic multi-hop networks, such as automatic configuration of multi-hop LoRa networks, dynamic topology management, and updating of real-time slot schedules. It is shown by simulation that the proposed protocol achieves high reliability of over 97% for mobile nodes and generates low control overhead in topology management and schedule updates. The protocol was also evaluated in various campus deployment scenarios. According to experiments, it could achieve high packet delivery rates of over 97% and 95%, respectively, for 1-hop nodes and 2-hop nodes against node mobility.

Urban green space and happiness in developed countries.

Urban green space is thought to contribute to citizen happiness by promoting physical and mental health. Nevertheless, how urban green space and happiness are related across many countries with different socioeconomic conditions has not been explored. By measuring the urban green space score (UGS) from high-resolution satellite imagery of 90 global cities covering 179,168 km2 and 230 million people in 60 developed countries, we find that the amount of urban green space and GDP are correlated with a nation's happiness level. More specifically, urban green space and GDP are each individually associated with happiness. Yet, only urban green space is related to happiness in the 30 wealthiest countries, whereas GDP alone can explain happiness in the subsequent 30 countries in terms of wealth. We further show that the relationship between urban green space and happiness is mediated by social support and that GDP moderates this relationship. These findings corroborate the importance of maintaining urban green space as a place for social cohesion to support people's happiness. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1140/epjds/s13688-021-00278-7.

Persona2vec: a flexible multi-role representations learning framework for graphs.

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.

Missed Diagnosis of Anaphylaxis in Patients With Pediatric Urticaria in the Emergency Department.

OBJECTIVES: This study was to determine the characteristics of missed diagnosis of pediatric anaphylaxis that were registered as urticaria only at the emergency department (ED) by comparing those who had only urticaria symptoms with those who had both anaphylaxis and urticaria symptoms. METHODS: Subjects were classified into missed anaphylaxis and urticaria group according to whether satisfied anaphylaxis diagnostic criteria or not. Anaphylaxis group, those who were initially registered as anaphylaxis with urticaria and anaphylaxis symptoms simultaneously, were further investigated. RESULTS: The missed anaphylaxis group included 37 patients of 1051 pediatric urticaria patients. The anaphylaxis group included 11 patients. The time from symptom onset to ED arrival in the missed anaphylaxis group was shorter than the urticaria group. More patients in the missed anaphylaxis group had a history of past food allergy. Seafood, egg, and milk were more common causes of allergy in the missed anaphylaxis group; however, idiopathic causes were more common in the urticaria group. Symptom was more severe in the missed anaphylaxis group than the urticaria group. More treatments except antihistamine were performed at ED in the missed anaphylaxis group. Cardiovascular symptoms were more common in the anaphylaxis group than the missed anaphylaxis group. CONCLUSIONS: Of all pediatric urticaria patients, 3.5% of patients were not registered as anaphylaxis although they had anaphylaxis symptoms. Missed diagnosis of anaphylaxis in pediatric urticaria patients at ED was associated with a history of past food allergy, milk, egg, and seafood as causes of allergy, treated with fluid administration, steroid, and epinephrine.

The universal pathway to innovative urban economies.

Is there a universal economic pathway individual cities recapitulate over and over? This evolutionary structure-if any-would inform a reference model for fairer assessment, better maintenance, and improved forecasting of urban development. Using employment data including more than 100 million U.S. workers in all industries between 1998 and 2013, we empirically show that individual cities indeed recapitulate a common pathway where a transition to innovative economies is observed at the population of 1.2 million. This critical population is analytically derived by expressing the urban industrial structure as a function of scaling relations such that cities are divided into two economic categories: small city economies with sublinear industries and large city economies with superlinear industries. Last, we define a recapitulation score as an agreement between the longitudinal and the cross-sectional scaling exponents and find that nontradeable industries tend to adhere to the universal pathway more than the tradeable.

The dynamics of the aggressive order during a crisis.

We investigate the dynamics of aggressive order in the financial market to further understand volatility. To analyze aggressive order, market orders in the order book are scrutinized. The market orders have different degrees of aggressiveness; therefore, we categorize market orders into four types: types Zero, One, A, and B, of which type B is the most aggressive. To examine the dynamics and impacts of each type of order, we use both macro- and micro-level approaches. From the macroscopic perspective, the burstiness and memory of type B is highly correlated with volatility. When traders face a financial crisis, they place bursty aggressive orders, and the orders are more predictable than usual. From the microscopic perspective, we additionally focus on the influence of the orders, particularly the price impact and resilience. The aggressive order has a greater impact than others, even when the price change of the aggressive order is smaller. Moreover, the aggressive order delivers more information on price because the aggressive order has a higher price impact than the execution cost.

Copula-based algorithm for generating bursty time series.

Dynamical processes in various natural and social phenomena have been described by a series of events or event sequences showing non-Poissonian, bursty temporal patterns. Temporal correlations in such bursty time series can be understood not only by heterogeneous interevent times (IETs) but also by correlations between IETs. Modeling and simulating various dynamical processes requires us to generate event sequences with a heavy-tailed IET distribution and memory effects between IETs. For this, we propose a Farlie-Gumbel-Morgenstern copula-based algorithm for generating event sequences with correlated IETs when the IET distribution and the memory coefficient between two consecutive IETs are given. We successfully apply our algorithm to the cases with heavy-tailed IET distributions. We also compare our algorithm to the existing shuffling method to find that our algorithm outperforms the shuffling method for some cases. Our copula-based algorithm is expected to be used for more realistic modeling of various dynamical processes.

Lightweight Driver Monitoring System Based on Multi-Task Mobilenets.

Research on driver status recognition has been actively conducted to reduce fatal crashes caused by the driver's distraction and drowsiness. As in many other research areas, deep-learning-based algorithms are showing excellent performance for driver status recognition. However, despite decades of research in the driver status recognition area, the visual image-based driver monitoring system has not been widely used in the automobile industry. This is because the system requires high-performance processors, as well as has a hierarchical structure in which each procedure is affected by an inaccuracy from the previous procedure. To avoid using a hierarchical structure, we propose a method using Mobilenets without the functions of face detection and tracking and show this method is enabled to recognize facial behaviors that indicate the driver's distraction. However, frames per second processed by Mobilenets with a Raspberry pi, one of the single-board computers, is not enough to recognize the driver status. To alleviate this problem, we propose a lightweight driver monitoring system using a resource sharing device in a vehicle (e.g., a driver's mobile phone). The proposed system is based on Multi-Task Mobilenets (MT-Mobilenets), which consists of the Mobilenets' base and multi-task classifier. The three Softmax regressions of the multi-task classifier help one Mobilenets base recognize facial behaviors related to the driver status, such as distraction, fatigue, and drowsiness. The proposed system based on MT-Mobilenets improved the accuracy of the driver status recognition with Raspberry Pi by using one additional device.

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.

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.

A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks.

Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones' tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.

Hierarchical burst model for complex bursty dynamics.

Temporal inhomogeneities observed in various natural and social phenomena have often been characterized in terms of scaling behaviors in the autocorrelation function with a decaying exponent γ, the interevent time distribution with a power-law exponent α, and the burst size distributions. Here the interevent time is defined as a time interval between two consecutive events in the event sequence, and the burst size denotes the number of events in a bursty train detected for a given time window. To understand such temporal scaling behaviors implying a hierarchical temporal structure, we devise a hierarchical burst model by assuming that each observed event might be a consequence of the multilevel causal or decision-making process. By studying our model analytically and numerically, we confirm the scaling relation α+γ=2, established for the uncorrelated interevent times, despite of the existence of correlations between interevent times. Such correlations between interevent times are supported by the stretched exponential burst size distributions, for which we provide an analytic argument. In addition, by imposing conditions for the ordering of events, we observe an additional feature of log-periodic behavior in the autocorrelation function. Our modeling approach for the hierarchical temporal structure can help us better understand the underlying mechanisms behind complex bursty dynamics showing temporal scaling behaviors.

Diversity of functional connectivity patterns is reduced in propofol-induced unconsciousness.

INTRODUCTION: Recent evidence suggests that the conscious brain is characterized by a diverse repertoire of functional connectivity patterns while the anesthetized brain shows stereotyped activity. However, classical time-averaged methods of connectivity dismiss dynamic and temporal characteristics of functional configurations. Here we demonstrate a new approach which characterizes time-varying patterns of functional connectivity at the subsecond time scale. METHODS: We introduce phase-lag entropy (PLE), a measure of the diversity of temporal patterns in the phase relationships between two signals. The proposed measure was applied to multichannel electroencephalogram (EEG), which were recorded from two distinct experimental settings: (1) propofol was administrated at a constant infusion rate for 60 min (n = 96); (2) administration of propofol by a target effect-site concentration-controlled infusion with simultaneous assessment of the level of consciousness (n = 10). RESULTS: From the first dataset, two substantial changes of the phase relationship during anesthesia was found: (1) the dynamics of the phase relationship between frontal channels became progressively less diverse and more stereotyped during unconsciousness, quantified as a reduction in PLE; and (2) the reduction in PLE was consistent across subjects. Furthermore, PLE provided better performance in the classification of states of consciousness than did phase-lag index, a classical time-averaged connectivity method. From the second dataset, PLE showed the highest agreement with the level of consciousness, compared to existing anesthetic depth indicators. CONCLUSIONS: This study suggests that a scarcity of functional configurations is closely associated with anesthetically induced unconsciousness, and shows promise as a basis for a new consciousness monitoring system during general anesthesia. Hum Brain Mapp 38:4980-4995, 2017. © 2017 Wiley Periodicals, Inc.

Suppressed neural complexity during ketamine- and propofol-induced unconsciousness.

Ketamine and propofol have distinctively different molecular mechanisms of action and neurophysiological features, although both induce loss of consciousness. Therefore, identifying a common feature of ketamine- and propofol-induced unconsciousness would provide insight into the underlying mechanism of losing consciousness. In this study we search for a common feature by applying the concept of type-II complexity, and argue that neural complexity is essential for a brain to maintain consciousness. To test this hypothesis, we show that complexity is suppressed during loss of consciousness induced by ketamine or propofol. We analyzed the randomness (type-I complexity) and complexity (type-II complexity) of electroencephalogram (EEG) signals before and after bolus injection of ketamine or propofol. For the analysis, we use Mean Information Gain (MIG) and Fluctuation Complexity (FC), which are information-theory-based measures that quantify disorder and complexity of dynamics respectively. Both ketamine and propofol reduced the complexity of the EEG signal, but ketamine increased the randomness of the signal and propofol decreased it. The finding supports our claim and suggests EEG complexity as a candidate for a consciousness indicator.

A Case of endoscopic retrieval of a long bamboo stick from a Humboldt penguin (Spheniscus humboldti).

An eighteen-month-old female Humboldt penguin (Spheniscus humboldti) that was 50 cm in length and 4.5 kg in weight was presented with anorexia and vomiting. The hematological and blood biochemical profiles revealed no remarkable findings, and no Salmonella, Shigella or Vibrio spp. were isolated from the fecal culture. However, radiographic imaging revealed a long linear foreign body presenting from the lower esophagus to the stomach. To retrieve this foreign body, flexible endoscopic extraction was performed using flexible rat tooth grasping forceps. A long bamboo stick (29 × 1 cm) was removed from the stomach, and the penguin fully recovered.

Chaos and unpredictability in evolution of cooperation in continuous time.

Cooperators benefit others with paying costs. Evolution of cooperation crucially depends on the cost-benefit ratio of cooperation, denoted as c. In this work, we investigate the infinitely repeated prisoner's dilemma for various values of c with four of the representative memory-one strategies, i.e., unconditional cooperation, unconditional defection, tit-for-tat, and win-stay-lose-shift. We consider replicator dynamics which deterministically describes how the fraction of each strategy evolves over time in an infinite-sized well-mixed population in the presence of implementation error and mutation among the four strategies. Our finding is that this three-dimensional continuous-time dynamics exhibits chaos through a bifurcation sequence similar to that of a logistic map as c varies. If mutation occurs with rate µâ‰ª1, the position of the bifurcation sequence on the c axis is numerically found to scale as µ^{0.1}, and such sensitivity to µ suggests that mutation may have nonperturbative effects on evolutionary paths. It demonstrates how the microscopic randomness of the mutation process can be amplified to macroscopic unpredictability by evolutionary dynamics.

Structural efficiency to manipulate public research institution networks.

With the rising use of network analysis in the public sector, researchers have recently begun paying more attention to the management of entities from a network perspective. However, guiding elements in a network is difficult because of their complex and dynamic states. In a bid to address the issues involved in achieving network-wide outcomes, our work here sheds new light on quantifying structural efficiency to control inter-organizational networks maintained by public research institutions. In doing so, we draw attention to the set of subordinates suitable as change initiators to influence the entire research profiles of subordinates from three major public research institutions: the Government-funded Research Institutes (GRIs) in Korea, the Max-Planck-Gesellschaft (MPG) in Germany, and the National Laboratories (NLs) in the United States. Building networks on research similarities in portfolios, we investigate these networks with respect to their structural efficiency and topological properties. According to our estimation, only less than 30% of nodes are sufficient to initiate a cascade of changes throughout the network across institutions. The subunits that drive the network exhibit an inclination neither toward retaining a large number of connections nor toward having a long academic history. Our findings suggest that this structural efficiency indicator helps assess structural development or improvement plans for networks inside a multiunit public research institution.