Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?

Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophisticated cognitive tools such as spatial maps, episodic memory and travel cost discounting. These properties call for other modeling approaches of mobility patterns. We propose a foraging framework where a learning mobile agent uses a combination of memory-based and random steps. We investigate how advantageous it is to use memory for exploiting resources in heterogeneous and changing environments. An adequate balance of determinism and random exploration is found to maximize the foraging efficiency and to generate trajectories with an intricate spatio-temporal order. Based on this approach, we propose some tools for analysing the non-random nature of mobility patterns in general.Comment: 14 pages, 4 figures, improved discussio

READYS: A Reinforcement Learning Based Strategy for Heterogeneous Dynamic Scheduling

International audienceIn this paper, we propose READYS, a reinforcement learning algorithm for the dynamic scheduling of computations modeled as a Directed Acyclic Graph (DAGs). Our goal is to develop a scheduling algorithm in which allocation and scheduling decisions are made at runtime, based on the state of the system, as performed in runtime systems such as StarPU or ParSEC. Reinforcement Learning is a natural candidate to achieve this task, since its general principle is to build step by step a strategy that, given the state of the system (the state of the resources and a view of the ready tasks and their successors in our case), makes a decision to optimize a global criterion. Moreover, the use of Reinforcement Learning is natural in a context where the duration of tasks (and communications) is stochastic. We propose READYS that combines Graph Convolutional Networks (GCN) with an Actor-Critic Algorithm (A2C): it builds an adaptive representation of the scheduling problem on the fly and learns a scheduling strategy, aiming at minimizing the makespan. A crucial point is that READYS builds a general scheduling strategy which is neither limited to only one specific application or task graph nor one particular problem size, and that can be used to schedule any DAG. We focus on different types of task graphs originating from linear algebra factorization kernels (CHOLESKY, LU, QR) and we consider heterogeneous platforms made of a few CPUs and GPUs. We first propose to analyze the performance of READYS when learning is performed on a given (platform, kernel, problem size) combination. Using simulations, we show that the scheduling agent obtains performances very similar or even superior to algorithms from the literature, and that it is especially powerful when the scheduling environment contains a lot of uncertainty. We additionally demonstrate that our agent exhibits very promising generalization capabilities. To the best of our knowledge, this is the first paper which shows that reinforcement learning can really be used for dynamic DAG scheduling on heterogeneous resources

Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling

International audienceIn practice, it is quite common to face combinatorial optimization problems which contain uncertainty along with non-determinism and dynamicity. These three properties call for appropriate algorithms; reinforcement learning (RL) is dealing with them in a very natural way. Today, despite some efforts, most real-life combinatorial optimization problems remain out of the reach of reinforcement learning algorithms. In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization. On the contrary to static scheduling, where tasks are assigned to processors in a predetermined ordering before the beginning of the parallel execution, our method is dynamic: task allocations and their execution ordering are decided at runtime, based on the system state and unexpected events, which allows much more flexibility. To do so, our algorithm uses graph neural networks in combination with an actor-critic algorithm (A2C) to build an adaptive representation of the problem on the fly. We show that this approach is competitive with state-of-the-art heuristics used in high-performance computing runtime systems. Moreover, our algorithm does not require an explicit model of the environment, but we demonstrate that extra knowledge can easily be incorporated and improves performance. We also exhibit key properties provided by this RL approach, and study its transfer abilities to other instances

p-SMAD2/3 and DICER promote pre-miR-21 processing during pressure overload-associated myocardial remodeling

AbstractTransforming growth factor-β (TGF-β) induces miR-21 expression which contributes to fibrotic events in the left ventricle (LV) under pressure overload. SMAD effectors of TGF-β signaling interact with DROSHA to promote primary miR-21 processing into precursor miR-21 (pre-miR-21). We hypothesize that p-SMAD-2 and -3 also interact with DICER1 to regulate the processing of pre-miR-21 to mature miR-21 in cardiac fibroblasts under experimental and clinical pressure overload. The subjects of the study were mice undergoing transverse aortic constriction (TAC) and patients with aortic stenosis (AS). In vitro, NIH-3T3 fibroblasts transfected with pre-miR-21 responded to TGF-β1 stimulation by overexpressing miR-21. Overexpression and silencing of SMAD2/3 resulted in higher and lower production of mature miR-21, respectively. DICER1 co-precipitated along with SMAD2/3 and both proteins were up-regulated in the LV from TAC-mice. Pre-miR-21 was isolated bound to the DICER1 maturation complex. Immunofluorescence analysis revealed co-localization of p-SMAD2/3 and DICER1 in NIH-3T3 and mouse cardiac fibroblasts. DICER1-p-SMAD2/3 protein–protein interaction was confirmed by in situ proximity ligation assay. Myocardial up-regulation of DICER1 constituted a response to pressure overload in TAC-mice. DICER mRNA levels correlated directly with those of TGF-β1, SMAD2 and SMAD3. In the LV from AS patients, DICER mRNA was up-regulated and its transcript levels correlated directly with TGF-β1, SMAD2, and SMAD3. Our results support that p-SMAD2/3 interacts with DICER1 to promote pre-miR-21 processing to mature miR-21. This new TGFβ-dependent regulatory mechanism is involved in miR-21 overexpression in cultured fibroblasts, and in the pressure overloaded LV of mice and human patients

EARTH SCIENCE MARKUP LANGUAGE

The Earth Science community is the processing and analyzing large amount and variety of data from space and ground-based observations and from models. These data are generally stored in physical media with different data formats. This large variety of data formats forces the scientists to spend significant amount of time in writing specialized data format specific, readers before their analysis can even begin. Formats for Earth Science data can be as simple as ASCII and binary formats or be as complex as Hierarchical Data Format (HDF) and HDF Earth Observing System (HDF-EOS) formats. In this paper, we introduce the Earth Science Markup Language (ESML), being currently developed at the Information Technology and Systems Center at the University of Alabama in Huntsville. ESML would make applications independent of data formats and facilitate easier searches for data via internet search engine. Primary purpose of this paper is to bring ESML to the attention of data consumers and producers, and invite comments and suggestions

EARTH SCIENCE MARKUP LANGUAGE

The Earth Science community is the processing and analyzing large amount and variety of data from space and ground-based observations and from models. These data are generally stored in physical media with different data formats. This large variety of data formats forces the scientists to spend significant amount of time in writing specialized data format specific, readers before their analysis can even begin. Formats for Earth Science data can be as simple as ASCII and binary formats or be as complex as Hierarchical Data Format (HDF) and HDF Earth Observing System (HDF-EOS) formats. In this paper, we introduce the Earth Science Markup Language (ESML), being currently developed at the Information Technology and Systems Center at the University of Alabama in Huntsville. ESML would make applications independent of data formats and facilitate easier searches for data via internet search engine. Primary purpose of this paper is to bring ESML to the attention of data consumers and producers, and invite comments and suggestions

Active megadetachment beneath the western United States

Geodetic data, interpreted in light of seismic imaging, seismicity, xenolith studies, and the late Quaternary geologic history of the northern Great Basin, suggest that a subcontinental-scale extensional detachment is localized near the Moho. To first order, seismic yielding in the upper crust at any given latitude in this region occurs via an M7 earthquake every 100 years. Here we develop the hypothesis that since 1996, the region has undergone a cycle of strain accumulation and release similar to “slow slip events” observed on subduction megathrusts, but yielding occurred on a subhorizontal surface 5–10 times larger in the slip direction, and at temperatures >800°C. Net slip was variable, ranging from 5 to 10 mm over most of the region. Strain energy with moment magnitude equivalent to an M7 earthquake was released along this “megadetachment,” primarily between 2000.0 and 2005.5. Slip initiated in late 1998 to mid-1999 in northeastern Nevada and is best expressed in late 2003 during a magma injection event at Moho depth beneath the Sierra Nevada, accompanied by more rapid eastward relative displacement across the entire region. The event ended in the east at 2004.0 and in the remainder of the network at about 2005.5. Strain energy thus appears to have been transmitted from the Cordilleran interior toward the plate boundary, from high gravitational potential to low, via yielding on the megadetachment. The size and kinematic function of the proposed structure, in light of various proxies for lithospheric thickness, imply that the subcrustal lithosphere beneath Nevada is a strong, thin plate, even though it resides in a high heat flow tectonic regime. A strong lowermost crust and upper mantle is consistent with patterns of postseismic relaxation in the southern Great Basin, deformation microstructures and low water content in dunite xenoliths in young lavas in central Nevada, and high-temperature microstructures in analog surface exposures of deformed lower crust. Large-scale decoupling between crust and upper mantle is consistent with the broad distribution of strain in the upper crust versus the more localized distribution in the subcrustal lithosphere, as inferred by such proxies as low P wave velocity and mafic magmatism

A synthesis of empirical plant dispersal kernels

1. Dispersal is fundamental to ecological processes at all scales and levels of organisation but progress is limited by a lack of information about the general shape and form of plant dispersal kernels. We addressed this gap by synthesising empirical data describing seed dispersal and fitting general dispersal kernels representing major plant types and dispersal modes. 2. A comprehensive literature search resulted in 107 papers describing 168 dispersal kernels for 144 vascular plant species. The data covered 63 families, all the continents except Antarctica, and the broad vegetation types of forest, grassland, shrubland, and more open habitats (e.g. deserts). We classified kernels in terms of dispersal mode (ant, ballistic, rodent, vertebrates other than rodents, vehicle or wind), plant growth form (climber, graminoid, herb, shrub or tree), seed mass and plant height. 3. We fitted 11 widely-used probability density functions to each of the 168 datasets to provide a statistical description of the dispersal kernel. The Exponential Power (ExP) and Log-sech (LogS) functions performed best. Other 2-parameter functions varied in performance. For example, the Lognormal and Weibull performed poorly, while the 2Dt and Power law performed moderately well. Of the single-parameter functions, the Gaussian performed very poorly, while the Exponential performed better. No function was among the best-fitting for all datasets. 4. For 10 plant growth form/dispersal mode combinations for which we had >3 datasets, we fitted ExP and LogS functions across multiple datasets to provide generalised dispersal kernels. We also fitted these functions to sub-divisions of these growth form/dispersal mode combinations in terms of seed mass (for animal-dispersed seeds) or plant height (wind-dispersed) classes. These functions provided generally good fits to the grouped datasets, despite variation in empirical methods, local conditions, vegetation type and the exact dispersal process. 5. Synthesis. We synthesise the rich empirical information on seed dispersal distances to provide standardised dispersal kernels for 168 case studies and generalised kernels for plant growth form/dispersal mode combinations. Potential uses include: a) choosing appropriate dispersal functions in mathematical models; b) selecting informative dispersal kernels for one’s empirical study system; and c) using representative dispersal kernels in cross-taxon comparative studies

Forty-thousand years of maritime subsistence near a changing shoreline on Alor Island (Indonesia)

We report archaeological findings from a significant new cave site on Alor Island, Indonesia, with an in situ basal date of 40,208-38,454 cal BP. Twenty thousand years older than the earliest Pleistocene site previously known from this island, Makpan retains dense midden deposits of marine shell, fish bone, urchin and crab remains, but few terrestrial species; demonstrating that protein requirements over this time were met almost exclusively from the sea. The dates for initial occupation at Makpan indicate that once Homo sapiens moved into southern Wallacea, settlement of the larger islands in the archipelago occurred rapidly. However, the Makpan sequence also suggests that the use of the cave following initial human arrival was sporadic prior to the terminal Pleistocene about 14,000 years ago, when occupation became intensive, culminating in the formation of a midden. Like the coastal sites on the larger neighbouring island of Timor, the Makpan assemblage shows that maritime technology in the Pleistocene was highly developed in this region. The Makpan assemblage also contains a range of distinctive personal ornaments made on Nautilus shell, which are shared with sites located on Timor and Kisar supporting connectivity between islands from at least the terminal Pleistocene. Makpan's early inhabitants responded to sea-level change by altering the way they used both the site and local resources. Marine food exploitation shows an initial emphasis on sea-urchins, followed by a subsistence switch to molluscs, barnacles, and fish in the dense middle part of the sequence, with crabs well represented in the later occupation. This new record provides further insights into early modern human movements and patterns of occupation between the islands of eastern Nusa Tenggara from ca. 40 ka.The fieldwork and dating for this project was funded by an Australian Research Council Laureate Fellowship to O’Connor (FL120100156) and analysis by the ARC Centre of Excellence for Australian Biodiversity and Heritage (CE170100015)