Wildlife biology applications of unmanned aerial systems (UAS) are extensive. Survey, identification, and measurement using UAS equipped with appropriate sensors can now be added to the suite of techniques available for monitoring animals -here we detail our experiences in using UAS to obtain detailed information from groups of seals, which can be difficult to observe from land. Trial flights to survey gray and harbor seals using a range of different platforms and imaging systems have been carried out with varying success at a number of sites in Scotland over the last two years. The best performing UAS system was determined by site, field situation, and the data required. Our systems routinely allow relative abundance, species, age-class, and individual identity to be obtained from images currently, with measures of body size also obtainable but open to refinement. However, the impacts of UAS on target species can also be variable and should be monitored closely. We found variable responses to UAS flights, possibly related to the animals' experience of previous disturbance.Key words: UAS, wildlife, seals, photo-ID, photogrammetry, behaviour.Résumé : L'utilisation des systèmes aériens sans pilote (UAS) en biologie faunique ne cesse d'augmenter. Les levés, l'identification et les mesures utilisant les UAS munis de capteurs appropriés font maintenant partie de l'ensemble des techniques disponibles pour surveiller les animaux -ici, nous exposons en détail nos expériences d'utilisation des UAS pour obtenir des informations détaillées sur des groupes de phoques, qui peuvent être difficiles à observer à partir de la terre. Des vols d'essais pour faire le levé des phoques gris et communs utilisant une gamme de plateformes et de systèmes d'imagerie ont été réalisés avec un niveau de succès varié à un nombre de sites en Écosse au cours de deux dernières années. On a déterminé l'UAS le plus performant selon le site, la situation sur le terrain et les données requises. Nos systèmes permettent de couramment obtenir l'abondance relative, les espèces, la classe d'âge et l'identité particulière à partir d'images, y compris les mesures de la masse corporelle mais pouvant être perfectionnées. Cependant, les effets des UAS sur les espèces ciblées peuvent varier et doivent être surveillés de près. Nous avons trouvé des réactions variées aux vols d'UAS, probablement lié à l'expérience des animaux suite à des perturbations antérieures.Mots-clés : système aérien sans pilote (UAS), faune, phoques, photo-identificateur, photogrammétrie, comportement.

The Massively Parallel Computation (MPC) model is an emerging model that distills core aspects of distributed and parallel computation, developed as a tool to solve combinatorial (typically graph) problems in systems of many machines with limited space. Recent work has focused on the regime in which machines have sublinear (in n , the number of nodes in the input graph) space, with randomized algorithms presented for the fundamental problems of Maximal Matching and Maximal Independent Set. However, there have been no prior corresponding deterministic algorithms. A major challenge underlying the sublinear space setting is that the local space of each machine might be too small to store all edges incident to a single node. This poses a considerable obstacle compared to classical models in which each node is assumed to know and have easy access to its incident edges. To overcome this barrier, we introduce a new graph sparsification technique that deterministically computes a low-degree subgraph, with the additional property that solving the problem on this subgraph provides significant progress towards solving the problem for the original input graph. Using this framework to derandomize the well-known algorithm of Luby [SICOMP’86], we obtain O (log Δ + log log  n )-round deterministic MPC algorithms for solving the problems of Maximal Matching and Maximal Independent Set with O ( n ɛ ) space on each machine for any constant ɛ > 0. These algorithms also run in O (log Δ) rounds in the closely related model of CONGESTED CLIQUE, improving upon the state-of-the-art bound of O (log  2 Δ) rounds by Censor-Hillel et al. [DISC’17].

In this paper, we study the power and limitations of componentstable algorithms in the low-space model of Massively Parallel Computation (MPC). Recently Ghaffari, Kuhn and Uitto (FOCS 2019) introduced the class of component-stable low-space MPC algorithms, which are, informally, defined as algorithms for which the outputs reported by the nodes in different connected components are required to be independent. This very natural notion was introduced to capture most (if not all) of the known efficient MPC algorithms to date, and it was the first general class of MPC algorithms for which one can show non-trivial conditional lower bounds. In this paper we enhance the framework of component-stable algorithms and investigate its effect on the complexity of randomized and deterministic low-space MPC. Our key contributions include:• We revise and formalize the lifting approach of Ghaffari, Kuhn and Uitto. This requires a very delicate amendment of the notion of component stability, which allows us to fill in gaps in the earlier arguments. • We also extend the framework to obtain conditional lower bounds for deterministic algorithms and fine-grained lower bounds that depend on the maximum degree Δ. • We demonstrate a collection of natural graph problems for which non-component-stable algorithms break the conditional lower bound obtained for component-stable algorithms. This implies that, for both deterministic and randomized algorithms, component-stable algorithms are conditionally weaker than the non-component-stable ones.Altogether our results imply that component-stability might limit the computational power of the low-space MPC model, paving the way for improved upper bounds that escape the conditional lower bound setting of Ghaffari, Kuhn, and Uitto. CCS CONCEPTS• Computing methodologies → Distributed algorithms; • Mathematics of computing → Graph algorithms; • Theory of computation → Pseudorandomness and derandomization.

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We present a deterministic (log log log )-round low-space Massively Parallel Computation (MPC) algorithm for the classical problem of (Δ + 1)-coloring on -vertex graphs. In this model, every machine has sublinear local space of size for any arbitrary constant ∈ (0, 1). Our algorithm works under the relaxed setting where each machine is allowed to perform exponential local computations, while respecting the space and bandwidth limitations. Our key technical contribution is a novel derandomization of the ingenious (Δ + 1)-coloring local algorithm by Chang-Li-Pettie (STOC 2018, SIAM J. Comput. 2020). The Chang-Li-Pettie algorithm runs in = (log log ) rounds, which sets the state-ofthe-art randomized round complexity for the problem in the local model. Our derandomization employs a combination of tools, notably pseudorandom generators (PRG) and bounded-independence hash functions.The achieved round complexity of (log log log ) rounds matches the bound of log(), which currently serves an upper bound barrier for all known randomized algorithms for locally-checkable problems in this model. Furthermore, no deterministic sublogarithmic low-space MPC algorithms for the (Δ + 1)-coloring problem have been known before. CCS CONCEPTS• Computing methodologies → Distributed algorithms; • Mathematics of computing → Graph algorithms; • Theory of computation → Pseudorandomness and derandomization.