Ornob Alam

Population genomic analyses of ancient and modern individuals are providing unprecedented resolution in our view of past human demography and adaptive episodes. Here, I highlight three recent studies in the field that investigated ancient human encounters with major pathogens.

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Mustafa Kemal Taş, Kamer Kaya, Hüsnü Yenigün

Information Sciences
Volume 574, October 2021, Pages 162-175

• Existing synchronization heuristics do not scale due to quadratic space complexity.
• We propose a simple approach to avoid memory usage thanks to massive parallelism.
• We use different parallelization approaches on CPUs and GPUs, in a hybrid way.
• A different treatment of parallelism is useful at different phases of the algorithm.
• Our algorithms can synchronize a billion-state automaton in around 4 mins.

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Zeinab Nezami; Kamran Zamanifar; Karim Djemame; Evangelos Pournaras

IEEE Access ( Volume: 9)

The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. On the other hand, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and a balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.

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Joseph B. Bak-Coleman, et al.

PNAS July 6, 2021 118 (27) e2025764118

Collective behavior provides a framework for understanding how the actions and properties of groups emerge from the way individuals generate and share information. In humans, information flows were initially shaped by natural selection yet are increasingly structured by emerging communication technologies. Our larger, more complex social networks now transfer high-fidelity information over vast distances at low cost. The digital age and the rise of social media have accelerated changes to our social systems, with poorly understood functional consequences. This gap in our knowledge represents a principal challenge to scientific progress, democracy, and actions to address global crises. We argue that the study of collective behavior must rise to a “crisis discipline” just as medicine, conservation, and climate science have, with a focus on providing actionable insight to policymakers and regulators for the stewardship of social systems.

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Marco Dorigo; Guy Theraulaz; Vito Trianni

Proceedings of the IEEE ( Volume: 109, Issue: 7, July 2021)

Swarm robotics deals with the design, construction, and deployment of large groups of robots that coordinate and cooperatively solve a problem or perform a task. It takes inspiration from natural self-organizing systems, such as social insects, fish schools, or bird flocks, characterized by emergent collective behavior based on simple local interaction rules [1] , [2] . Typically, swarm robotics extracts engineering principles from the study of those natural systems in order to provide multirobot systems with comparable abilities. This way, it aims to build systems that are more robust, fault-tolerant, and flexible than single robots and that can better adapt their behavior to changes in the environment.

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