In our study published today in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries. We’ve built a dedicated, interdisciplinary team in hopes of using AI to push basic research forward: bringing together experts from the fields of structural biology, physics, and machine learning to apply cutting-edge techniques to predict the 3D structure of a protein based solely on its genetic sequence.

Source: deepmind.com

Edmond Awad, Sohan Dsouza, Azim Shariff, Iyad Rahwan, and Jean-François Bonnefon
PNAS

 

We report the largest cross-cultural study of moral preferences in sacrificial dilemmas, that is, the circumstances under which people find it acceptable to sacrifice one life to save several. On the basis of 70,000 responses to three dilemmas, collected in 10 languages and 42 countries, we document a universal qualitative pattern of preferences together with substantial country-level variations in the strength of these preferences. In particular, we document a strong association between low relational mobility (where people are more cautious about not alienating their current social partners) and the tendency to reject sacrifices for the greater good—which may be explained by the positive social signal sent by such a rejection. We make our dataset publicly available for researchers.

 

 

Source: www.pnas.org

Pierre-Alexandre Balland, Cristian Jara-Figueroa, Sergio G. Petralia, Mathieu P. A. Steijn, David L. Rigby & César A. Hidalgo 
Nature Human Behaviour (2020)

 

Human activities, such as research, innovation and industry, concentrate disproportionately in large cities. The ten most innovative cities in the United States account for 23% of the national population, but for 48% of its patents and 33% of its gross domestic product. But why has human activity become increasingly concentrated? Here we use data on scientific papers, patents, employment and gross domestic product, for 353 metropolitan areas in the United States, to show that the spatial concentration of productive activities increases with their complexity. Complex economic activities, such as biotechnology, neurobiology and semiconductors, concentrate disproportionately in a few large cities compared to less–complex activities, such as apparel or paper manufacturing. We use multiple proxies to measure the complexity of activities, finding that complexity explains from 40% to 80% of the variance in urban concentration of occupations, industries, scientific fields and technologies. Using historical patent data, we show that the spatial concentration of cutting-edge technologies has increased since 1850, suggesting a reinforcing cycle between the increase in the complexity of activities and urbanization. These findings suggest that the growth of spatial inequality may be connected to the increasing complexity of the economy.

Source: www.nature.com

Lana Sinapayen, Atsushi Masumori, and Takashi Ikegami

Front. Comput. Neurosci., 22 January 2020

 

Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of new stimuli, and act on these predictions. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior. Based on earlier in-vitro and in-silico experiments, we define the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior. Our results support the existence of specific evolutionary steps and four conditions necessary for embodied neural networks to evolve predictive and inductive abilities from an initial reactive strategy.

Source: www.frontiersin.org

Lorenzo Zino, Alessandro . Rizzo, Maurizio Porfiri

European Journal of Control

 

The complexity of interaction patterns among individuals in social systems plays a fundamental role on the inception and spreading of epidemic outbreaks. Empirical evidence has shown that the network of social interactions may co-evolve with the spread of the disease at comparable time-scales. Time-varying features have also been documented in the study of the propensity of individuals toward social activity, leading to the emergence of burstiness and temporal clustering. These temporal network dynamics are not independent of the disease evolution, whereby infected individuals could experience changes in their tendency to form connections, spontaneously or due to exogenous control policies. Neglecting these phenomena in modeling epidemics could lead to dangerous mispredictions of an outbreak and ineffective control interventions. In this paper, we propose a mathematically tractable modeling framework that relies on a limited number of parameters and encapsulates all these instances of complex phenomena through the lens of activity driven networks. Hawkes processes, Markov chains, and stability theory are leveraged to assist in the analysis of the framework and the formulation of theory-based control interventions. Our mathematical findings confirm the intuition that bursty activity patterns, typical of humans, facilitate epidemic spreading, while behavioral changes aiming at individual isolation could accelerate the eradication of epidemics. The proposed tools are demonstrated on a real-world case of influenza spreading in Italy. Overall, this work contributes new insight into the theory of temporal networks, laying the foundations for the analysis and control of spreading processes over networks with complex interaction patterns.

Source: www.sciencedirect.com