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1. Long-range interactions of kinks

   https://doi.org/10.1103/PhysRevD.99.016010  

   Christov, Ivan C.; Decker, Robert J.; Demirkaya, A.; Gani, Vakhid A.; Kevrekidis, P. G.; Radomskiy, R. V. (January 2019, Physical Review D)
2. How range residency and long-range perception change encounter rates

   https://doi.org/10.1016/j.jtbi.2020.110267  

   Martinez-Garcia, Ricardo; Fleming, Christen H.; Seppelt, Ralf; Fagan, William F.; Calabrese, Justin M. (August 2020, Journal of Theoretical Biology)
3. LongShoT: Long-range Synchronization of Time

   https://doi.org/10.1145/3302506.3310408  

   Ramirez, Ceferino Gabriel; Sergeyev, Anton; Dyussenova, Assya; Iannucci, Bob (April 2019, 2019 18th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN))

   Low-Power Wide Area Networks, such as LoRaWAN, are rapidly gaining popularity in the field of wireless sensing and actuation. While LoRaWan is heavily studied in applications and performance, the concept of time has rarely been characterized in such networks. Many applications will require synchronized local clocks with varying levels of precision in order to maintain consistency and coordination in the network. Traditional time synchronization protocols however do not fit LoRaWAN's delay-inherent, low duty cycle, network model and wide-area deployment topology. Meanwhile, relying on GPS for time is not an option for low-power applications. In this paper, we present LongShoT, a time synchronization scheme built on LoRaWan capable of synchronizing device clocks to within 10μs of a reference clock with a single network request. This is achieved by utilizing the deterministic properties of Lo-Ra Wan networks along with hardware- and MAC-level timestamping of packets. LongShoT was implemented on consumer off-the-shelf hardware and evaluated over physically distributed devices using GPS 1PPS as a reference. Our results show that LongShoT achieves an average synchronization error of less than 2μs and compensates oscillator drift to less than 0.1ppm with devices distributed within 4km of a gateway. 

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4. Eco‐evolutionary dynamics of range expansion

   https://doi.org/10.1002/ecy.3139  

   Miller, Tom_E_X; Angert, Amy_L; Brown, Carissa_D; Lee‐Yaw, Julie_A; Lewis, Mark; Lutscher, Frithjof; Marculis, Nathan_G; Melbourne, Brett_A; Shaw, Allison_K; Szűcs, Marianna; et al (September 2020, Ecology)

   Abstract Understanding the movement of species’ ranges is a classic ecological problem that takes on urgency in this era of global change. Historically treated as a purely ecological process, range expansion is now understood to involve eco‐evolutionary feedbacks due to spatial genetic structure that emerges as populations spread. We synthesize empirical and theoretical work on the eco‐evolutionary dynamics of range expansion, with emphasis on bridging directional, deterministic processes that favor evolved increases in dispersal and demographic traits with stochastic processes that lead to the random fixation of alleles and traits. We develop a framework for understanding the joint influence of these processes in changing the mean and variance of expansion speed and its underlying traits. Our synthesis of recent laboratory experiments supports the consistent role of evolution in accelerating expansion speed on average, and highlights unexpected diversity in how evolution can influence variability in speed: results not well predicted by current theory. We discuss and evaluate support for three classes of modifiers of eco‐evolutionary range dynamics (landscape context, trait genetics, and biotic interactions), identify emerging themes, and suggest new directions for future work in a field that stands to increase in relevance as populations move in response to global change. 

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5. Algorithms and complexity of range clustering

   https://doi.org/10.1002/net.21854  

   Hochbaum, Dorit S. (September 2018, Networks)

   We introduce a novel criterion in clustering that seeks clusters with limitedrangeof values associated with each cluster's elements. In clustering or classification the objective is to partition a set of objects into subsets, called clusters or classes, consisting of similar objects so that different clusters are as dissimilar as possible. We propose a number of objective functions that employ the range of the clusters as part of the objective function. Several of the proposed objectives mimic objectives based on sums of similarities. These objective functions are motivated by image segmentation problems, where the diameter, or range of values associated with objects in each cluster, should be small. It is demonstrated that range‐based problems are in general easier, in terms of their complexity, than the analogous similarity‐sum problems. Several of the problems we present could therefore be viable alternatives to existing clustering problems which are NP‐hard, offering the advantage of efficient algorithms. 

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