Emergence of communities and diversity in social networks

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.This work was supported by the National Nature Science Foundation of China under Grants 61573064, 71631002, 71401037, and 11301032; the Fundamental Research Funds for the Central Universities and Beijing Nova Programme; and the Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant). The Boston University work was supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE- 1213217, and by Defense Threat Reduction Agency Grant HDTRA1-14-1-0017, and Department of Energy Contract DE-AC07-05Id14517. (61573064 - National Nature Science Foundation of China; 71631002 - National Nature Science Foundation of China; 71401037 - National Nature Science Foundation of China; 11301032 - National Nature Science Foundation of China; Fundamental Research Funds for the Central Universities and Beijing Nova Programme; Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant); PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - Defense Threat Reduction Agency; DE-AC07-05Id14517 - Department of Energy)Published versio

Analyzing Journal Category Assignment Using Paper-level Classification System: Multidisciplinary Sciences Journals

<p>In the field of scientometrics, the subject classification system of academic journals holds great importance. Accurate identification and classification of "multidisciplinary" journals are crucial in revealing the scientific structure and evaluating journals. Based on data from the Web of Science database from 2016 to 2020, we calculated the disciplinary diversity of journals using the paper-level subject classification system, then conducted a systematic analysis of JCR multidisciplinary journals. Studies showed that most multidisciplinary journals have high disciplinary diversity, while non-multidisciplinary journals tend to have relatively lower diversity. Some multidisciplinary journals with low disciplinary diversities may misclassify disciplines. In addition, there are inconsistencies in the diversity of journal disciplines at different granularities. Our study also visually analyzed the four types of diversity distribution tendencies of multidisciplinary journals. Moreover, ten potential multidisciplinary journals were found in non-multidisciplinary categories.</p&gt

Under-representativeness of Physical Chemistry Journals

In the contemporary landscape of scientific publishing, the categorization and ranking of journals significantly influence academic research and scholarly careers [1-2]. Particularly in chemistry with numerous research fields, the issue of misclassification of journals has emerged as a notable concern. In this comment, we aim to examine the misclassification of physical chemistry journals in the Web of Science (WOS) and how this impacts their ranking in the Journal Citation Reports (JCR). We highlight that due to the erroneous categorization of physical chemistry journals within other fields, they are forced to compete with journals possessing much higher impact factors, adversely affecting their standings in the JCR rankings. This not only undermines the reputation of physical chemistry journals but also negatively impacts the academic research and scholars within the field. Through our analysis, we seek to illuminate the severity of this issue and propose recommendations for a more equitable and accurate evaluation of physical chemistry journals

Robust Lead‐Free Perovskite Nanowire Array‐Based Artificial Synapses Exemplifying Gestalt Principle of Closure via a Letter Recognition Scheme

The Gestalt principles of perceptual learning elucidate how the human brain categorizes and comprehends a set of visual elements grouped together. One of the principles of Gestalt perceptual learning is the law of closure which propounds that human perception has the proclivity to visualize a fragmented object as a preknown whole by bridging the missing gaps. Herein, a letter recognition scheme emulating the Gestalt closure principle is demonstrated, utilizing artificial synapses made of 3D integrated MA3Bi2I9 (MBI) perovskite nanowire (NW) array. The artificial synapses exhibit short‐term plasticity (STP) and long‐term potentiation (LTP) and a transition from STP to LTP with increasing number of input electrical pulses. Initiatory ab initio molecular dynamics (AIMD) simulations attribute the conductance change in the MBI NW artificial synapses to the rotation of MA+ clusters, culminating in charge exchange between MA+ and Bi2I93−. Each device yields 40 conductance states with excellent retention >105 s, minimal variation (2σ/mean) <10%, and endurance of ≈105 cycles. MBI NW‐based artificial neural network (ANN) is constructed to recognize fragmented letters alike their distinction in unabridged form and also the gradual withering of synaptic connectivity with engendered missing fragments is demonstrated, thereby successfully implementing Gestalt closure principle

Geometric Shape Recognition with an Ultra-High Density Perovskite Nanowire Array-Based Artificial Vision System

Artificial vision systems (AVS) have potential applications in visual prosthetics and artificially intelligent robotics, and they require a preprocessor and a processor to mimic human vision. Halide perovskite (HP) is a promising preprocessor and processor due to its excellent photoresponse, ubiquitous charge migration pathways, and innate hysteresis. However, the material instability associated with HP thin films hinders their utilization in physical AVSs. Herein, we have developed ultrahigh-density arrays of robust HP nanowires (NWs) rooted in a porous alumina membrane (PAM) as the active layer for an AVS. The NW devices exhibit gradual photocurrent change, responding to changes in light pulse duration, intensity, and number, and allow contrast enhancement of visual inputs with a device lifetime of over 5 months. The NW-based processor possesses temporally stable conductance states with retention >105 s and jitter <10%. The physical AVS demonstrated 100% accuracy in recognizing different shapes, establishing HP as a reliable material for neuromorphic vision systems

Emergence of communities and diversity in social networks

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics

Genome sequence and genetic diversity of European ash trees

Eurofins MWG provided a discounted service for Illumina and 454 sequencing of the reference genome, funded by Natural Environment Research Council (NERC) Urgency Grant NE/K01112X/1 to R.J.A.B. The associative transcriptomic and metabolomic work was part of the ‘Nornex’ project led by J.A.D. funded jointly by the UK Biotechnology and Biological Sciences Research Council (BBSRC) (BBS/E/J/000CA5323) and the Department for Environment, Food & Rural Affairs. The Earlham Institute, Norwich, UK, sequenced ‘Tree 35’ funded by ‘Nornex’ and the European Diversity Panel funded by the Earlham Institute National Capability in Genomics (BB/J010375/1) grant. W. Crowther assisted with DNA extractions for the KASP assay; The John Innes Centre contributed KASP analyses. J. F. Miranda assisted with RNA extractions and quantitative PCR with reverse transcription (qRT–PCR) at the University of York. H. V. Florance, N. Smirnoff and the Exeter Metabolomics Facility developed metabolomic methods and ran samples, and T. P. Howard helped with statistics. L.J.K. and R.J.A.B. were partly funded by Living with Environmental Change (LWEC) Tree Health and Plant Biosecurity Initiative - Phase 2 grant BB/L012162/1 to R.J.A.B., S.L. and P. Jepson funded jointly by a grant from the BBSRC, Defra, Economic and Social Research Council, the Forestry Commission, NERC and the Scottish Government, under the Tree Health and Plant Biosecurity Initiative. G.W. was funded by Teagasc Walsh Fellowship 2014001 to R.J.A.B. and G.C.D. E.D.C. was funded by a Marie Skłodowska-Curie Individual Fellowship ‘FraxiFam’ (grant agreement 660003) to E.D.C. and R.J.A.B. E.S.A.S. and J.Z. were funded by the Marie Skłodowska-Curie Initial Training Network INTERCROSSING. J.A.D. received a John Innes Foundation fellowship. We thank A. Joecker for supervising E.S.A.S. at Qiagen and for helpful discussions. R.H.R.G. is supported by a Norwich Research Park PhD Studentship and Earlham Institute Funding and Maintenance Grant. This research used Queen Mary’s MidPlus computational facilities, supported by QMUL Research-IT and funded by Engineering and Physical Sciences Research Council grant EP/K000128/1 and NERC EOS Cloud. D.J.S. acknowledges the support of BBSRC grant BB/N021452/1, which partly supported M.G., C.M.S. and D.J.S. during this work