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2,774 results for “Agent”
Supporting data and code for: Thiophanate-methyl and carbendazim resistance in Fusicoccum amygdali, the causal agent of constriction canker of peach and almond
<p>This is the first release of the final data and code for the article accepted for publication in Plant Pathology journal. It contains all the necessary scripts to perform the data analysis and to produce the Figures. All the necessary data can be found in the 'data' folder.</p>
Dataset used in the article: Evaluation of goal recognition systems on unreliable data and uninspectable agents
<p>Goal or intent recognition, where one agent recognizes the goals or intentions of another, can be a powerful tool for effective teamwork and improving interaction between agents. Such reasoning can be challenging to perform, however, because observations of an agent can be unreliable and, often, an agent does not have access to the reasoning processes and mental models of the other agent. Despite this difficulty, recent work has made great strides in addressing these challenges.</p> <p>In particular, two Artificial Intelligence (AI)-based approaches to goal recognition have recently been shown to perform well: goal recognition as planning, which reduces a goal recognition problem to the problem of plan generation; and Combinatory Categorical Grammars (CCGs), which treat goal recognition as a parsing problem. Additionally, new advances in cognitive science with respect to Theory of Mind reasoning have yielded an approach to goal recognition that leverages analogy in its decision making.</p> <p>However, there is still much unknown about the potential and limitations of these approaches, especially with respect to one another. Here, we explore this space and compare three state-of-the-art approaches to goal recognition along two different axes: reliability of observations and inspectability of the other agent's mental model. Overall, we show that no approach dominates across all cases and discuss the relative strengths and weaknesses of these approaches. Scientists interested in goal recognition problems can use this knowledge as a guide to select the correct starting point for their specific domains and tasks.</p> <p> Research Topic: Advances in Goal, Plan and Activity Recognition</p>
20210601-DOTG: Knowledge is transmitted between agents and across generations
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br> <br> Experiment Label: 20210601-DOTG<br> <br> Experiment design: Knowledge is transmitted between agents and across generations<br> <br> Experiment setting: Agents start with random ontologies; agents adapt their knowledge to interaction failures with other agents (intra-generation knowledge transmission); agents provide training samples to their children to learn ontologies and interact with them (inter-generation knowledge transmission)<br> <br> Hypotheses: Adding inter-generation transmission leads to higher accuracy than intra-generation transmission alone<br> <br> Detailed information can be found in index.html or notebook.ipynb.<br> <br> [1] <a href="https://sake.re/20210601-DOTG">https://sake.re/20210601-DOTG</a><br> [2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br> </p>
20210927-DOTG: Knowledge is transmitted between agents and across generations with different parent Selection methods
This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20210927-DOTG<br><br>Experiment design: Knowledge is transmitted between agents and across generations with different parent Selection methods<br><br>Experiment setting: Agents start with random ontologies which are used to interact with the environment and other agents; agents adapt their knowledge to interaction failures (intra-generation knowledge transmission); agents provide training samples to their children to learn ontologies (inter-generation knowledge transmission)<br><br>Hypotheses: Intra-generation transmission can compensate to the absence of parent selection<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20210927-DOTG">https://sake.re/20210927-DOTG</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br>
No impact of biocontrol agent's predation cues on development time or size of surviving Aedes albopictus under optimal nutritional availability
<p>Cyclopoid copepods have been applied successfully to limit populations of highly invasive <em>Aedes albopictus</em> mosquitoes that can transmit diseases of public health importance. However, there is concern that changes in certain mosquito traits, induced by exposure to copepod predation, might increase the risk of disease transmission. In this study, third instar <em>Ae. albopictus</em> larvae, hereafter referred to as "focal individuals", were exposed to <em>Megacyclops viridis</em> predator cues associated with both consumption of newly-hatched mosquito larvae and attacks on focal individuals. The number of newly-hatched larvae surrounding each focal larva was held constant to control for density effects on size, and the focal individual's day of pupation and wing length were recorded for each replicate. Exposing late instar <em>Ae. albopictus</em> to predation decreased their chances of surviving to adulthood, and three focal larvae that died in the predator treatment showed signs of melanization, indicative of wounding. Among surviving focal <em>Ae. albopictus</em>, no significant difference in either pupation day or wing length was observed due to copepod predation. The absence of a significant sublethal impact from <em>M. viridis</em> copepod predation on surviving later-stage larvae in this analysis supports the use of <em>M. viridis</em> as a biocontrol agent against <em>Ae. albopictus</em>.</p>
An innovative approach combining metabarcoding and ecological interaction networks for selecting candidate biological control agents
<p>Classical biological control (CBC) can be used to decrease the density of invasive species to below an acceptable ecological and economic threshold. Natural enemies specific to the invasive species are selected from its native range and released into the invaded range. This approach has drawbacks, despite the performance of specificity tests to ensure its safety, because the fundamental host range defined under controlled conditions does not represent the actual host range <em>in natura, </em>and these tests omit indirect interactions within community.</p> <p>We focus on <em>Sonchus oleraceus</em> (Asteraceae), a weed species originating from Western Palearctic that is invasive in many countries and notably in Australia. We explore how analyses of interaction network within its native range can be used to 1) inventory herbivores associated to the target plant, 2) characterize their ecological host ranges, and 3) guide the selection of candidate biocontrol agents considering interactions with species from higher trophic levels. Arthropods were collected from plant community sympatric to <em>S. oleraceus</em>, in three bioclimatic regions, and interactions were inferred by a combination of molecular and morphological approaches.</p> <p>The networks reconstructed were structured in several trophic levels from basal species (plant community), to intermediate and top species (herbivorous arthropods and their natural enemies). The subnetwork centered on <em>S. oleraceus</em> related interactions contained 116 taxa and 213 interactions. We identified 47 herbivores feeding on <em>S. oleraceus</em>, 15 of which were specific to the target species. Some discrepancies with respect to published findings or conventional specificity tests suggested possible insufficient sampling effort for the recording of interactions or the existence of cryptic species. Among potential candidate agents, 6 exhibited interactions with natural enemies.</p> <p>Synthesis and applications: Adopting a network approach as prerequisite step of the classical biological control program can provide a rapid screening of potential agents to be tested in priority. Once ecological host range defined, we suggest that priority should be given to agent used by a minimum species, and, when they exist, to agents that possess enemies from the most distant taxonomical group from those occurring in the range of introduction.</p>
Effectiveness of Softening Agents for Enhancing Properties of Asphalt Mixes with High RAP Contents
<p>A high percentage of reclaimed asphalt pavement (RAP) in new asphalt concrete can lead to developing premature failure of asphalt pavements due to fatigue or low-temperature cracking. The use of softening agents in asphalt binders can resolve these problems. The aim of this study is to evaluate the effectiveness of softening agents for enhancing the properties of asphalt mixes with high RAP contents. Two waste products, namely, waste cooking oil (WCO), and engine bottom oil (EBO) along with a commercially produced rejuvenator were investigated in this study. The following three types of Performance Grade (PG) binders, each collected from two different sources, were considered in this study: PG 64-22, PG 70-22, and PG 76-22. These PG binders blended with different amounts (0, 15, 25, 40, and 60%) of RAP binders were rejuvenated with different dosages (0, 10, 15, and 20%) of the selected softening agents. Empirical tests (e.g., penetration and pH), Superpave tests, Atomic Force Microscope (AFM), Scanning Electron Microscope (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and limited laboratory and field performance of asphalt mixture samples were also evaluated. It was found that the rejuvenators reduced the viscosity of the binder samples. The results showed that the rejuvenated binders reduce the production temperatures as well as the brittleness of the hard binders. The AFM results showed that modulus and deformation values of rejuvenated binders were significantly less than those of their unrejuvenated counterparts. Similarly, distinct peaks were observed in the FTIR peaks due to the rejuvenation. The findings of this study will help pavement professionals in selecting suitable rejuvenators in the construction of pavements with high RAP contents.</p>
Data from: Delegation to artificial agents fosters prosocial behaviors in the collective risk dilemma
<p>Home assistant chat-bots, self-driving cars, drones or automated negotiation systems are some of the several examples of autonomous (artificial) agents that have pervaded our society. These agents enable the automation of multiple tasks, saving time and (human) effort. However, their presence in social settings raises the need for a better understanding of their effect on social interactions and how they may be used to enhance cooperation towards the public good, instead of hindering it. To this end, we present an experimental study of human delegation to autonomous agents and hybrid human-agent interactions centered on a non-linear public goods dilemma with uncertain returns in which participants face a collective risk. Our aim is to understand experimentally whether the presence of autonomous agents has a positive or negative impact on social behaviour, equality and cooperation in such a dilemma. Our results show that cooperation and group success increases when participants delegate their actions to an artificial agent that plays on their behalf. Yet, this positive effect is less pronounced when humans interact in hybrid human-agent groups, where we mostly observe that humans in successful hybrid groups make higher contributions earlier in the game. Also, we show that participants wrongly believe that artificial agents will contribute less to the collective effort. In general, our results suggest that delegation to autonomous agents has the potential to work as commitment devices, which prevent both the temptation to deviate to an alternate (less collectively good) course of action, as well as limiting responses based on betrayal aversion.</p>
Modelling the potential global distribution of suitable habitat for the biological control agent Heterorhabditis indica
<p class="MsoNoSpacing">Entomopathogenic nematode (EPN) <em>Heterorhabditis indica</em> is a promising biocontrol candidate. Despite the acknowledged importance of EPN in pest control, no extensive data sets or maps have been developed on their distribution at global level. This study is the first attempt to generate Ecological Niche Models (ENM) for <em>H. indica</em> and its global Habitat Suitability Map (HSM) to generate biogeographical information and predicts its global geographical range of prospective areas for its exploration and to help identify the suitable release areas for biocontrol purpose. The aim of the modelling exercise was to access the influence of temperature and soil moisture on the biogeographical patterns of <em>H. indica</em> at the global level. CLIMEX software was used to model the distribution of <em>H. indica</em> and access to the influence of environmental variable on its global distribution. In total, 162 records of <em>H. indica</em> occurrence from 27 countries over 25 years was combined to generate the known distribution data. The model was further fine-tuned using the direct experimental observations of the <em>H. indica</em>'s growth response to temperature and soil moisture. Model predicts much of the tropics and subtropics has suitable climatic conditions for <em>H. indica</em>. It further predicts that <em>H. indica</em> distribution can extends into warmer temperate climates. Examination of the model output, predictions maps at a global level indicate that <em>H. indica</em> distribution may be limited by cold stress, heat stress and dry stresses in different areas. However, cold stress appears to be the major limiting factor. This study, highlighted an efficient way to construct HSM for EPN potentially useful in the search/release of target species in new locations. The study showed that <em>H. indica</em> which is known as warm adapted EPN generally found in tropics and subtropics can potentially establish itself in warmer temperate climates as well. The model can also be used to decide the release timing of EPN by adjusting with season for maximum growth. The model developed in the current study clearly identified the value and potential of Habitat Suitability Map (HSM) in planning of future surveys and application of <em>H. indica.</em></p>
Effect of using mycotoxin-detoxifying agents in dairy cattle feed on natural whey starter biodiversity
<p><strong>Supplemental Figure S1</strong>: p-values for alpha diversity index in the milk whey microbiota between treatments and controls. The control group for BD1 (ctr_BD1) was taken as reference class in the model. </p>
Supplementary data - "Learning Reduced Models for Large-Scale Agent-Based Systems"
<p>This repository contains supplementary data on my PhD thesis "Learning Reduced Models for Large-Scale Agent-Based Systems". Chapters 1-3, 7 and A do not have supplementary data.</p> <p><strong>Chapter 4</strong></p> <ul> <li><em>Large_deviation_example.zip</em> contains the trajectory for Figure 4.8.</li> <li><em>mean_exit_time*</em> contains the raw data to compute the mean exit time and standard deviation for the ABM process (JP) and SDE process (CLE). It contains additionally a precomputed mean and standard deviation as well as the corresponding numbers of agents.</li> <li><em>transition_matrix*</em> contain the computed box discretizations as MATLAB and Numpy files as used for Figures 4.2-4.4, 4.6 and Tables 4.1 and 4.2.</li> </ul> <p><strong>Chapter 5</strong></p> <ul> <li><em>CVM_2021-07-09-15-53_training_data.npz</em> contains the training data for Figure 5.7 a and b.</li> <li><em>CVM_2021-09-29-07-13_distribution.npz </em>contains the raw data for Figure 5.7 c.</li> <li>The remaining data for Chapter 5 can be found in the related dataset <a href="https://doi.org/10.5281/zenodo.4522119">doi.org/10.5281/zenodo.4522119</a>.</li> </ul> <p><strong>Chapter 6</strong></p> <ul> <li><em>CVM_pareto_estimate</em> contains trajectory data required for Figure 6.6 b to estimate points in the Pareto Front using the civil violence model. </li> <li><em>CVM_training_data</em> contains the training data to construct the surrogate model. Each data set consists of <em>CVM_*_cops_train.npz</em> as training set, <em>CVM_*_cops_trajectory.npz</em> as sample trajectory and <em>CVM_*_cops.pkl</em> to compute the training data.</li> <li><em>CVM_covering_iterations_8.mat</em> Pareto set covering after 8 iterations for the civil violence model. Required for Figure 6.6 a.</li> <li><em>CVM_pareto_set+front.npz</em> is required for Figure 6.6 b. </li> <li><em>CVM_surrogate_model.mat</em> contains the surrogate model for the civil violence model</li> <li><em>Expl_iterations_*</em> contains Pareto set coverings after 8 and 12 iterations for Example 6.1.4 and Figure 6.1.</li> <li><em>VM_covering_iterations_12.mat</em> contains the Pareto set covering depicted in Figure 6.4 a.</li> <li><em>VM_ODE_covering_iterations_12_subset_front.mat</em> contains the Pareto set covering depicted in Figure 6.5 and 6.5 c.</li> <li><em>VM_ODE_covering_iterations_12_subset.mat</em> contains the Pareto set covering depicted in Figure 6.5 and 6.5 d.</li> <li><em>VM_ODE_covering_iterations_12.mat</em> contains the Pareto set covering depicted in Figure 6.4 b.</li> <li><em>VM_surrogate_model.mat</em> contains the surrogate model for the extended voter model.</li> <li><em>VM_test_points_non_pareto.npz</em> contains Non-Pareto points in Figure 6.5 and 6.5 d.</li> <li><em>VM_test_points_pareto.npz</em> contains Pareto points in Figure 6.5 and 6.5 c.</li> </ul>
Design and development remote monitoring agent for energy service companies in the telecommunication industry
<p>Data set downloaded from the remote monitoring agent (RMA) over its local webpage using WiFi connection.</p>
20220516-DOLA: Agent adapt ontologies to agree on decision taking. Introducing rarity index for agents to consider before adapting.
This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20220516-DOLA<br><br>Experiment design: Agent adapt ontologies to agree on decision taking. Introducing rarity index for agents to consider before adapting.<br><br>Experiment setting: Agents learn decision trees (transformed into ontologies); get income from environment; adapt by splitting their leaf nodes<br><br>Hypotheses: Success rate converges to 1. Improve the average accuracy at the end of the experiment.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20220516-DOLA">https://sake.re/20220516-DOLA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br>
20220302-DOLA: Agent adapt ontologies to agree on decision taking. Introducing conformity index for agents to consider before adapting.
This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br><br>Experiment Label: 20220302-DOLA<br><br>Experiment design: Agent adapt ontologies to agree on decision taking. Introducing conformity index for agents to consider before adapting.<br><br>Experiment setting: Agents learn decision trees (transformed into ontologies); get income from environment; adapt by splitting their leaf nodes<br><br>Hypotheses: Success rate converges to 1. Improve the average accuracy at the end of the experiment.<br><br>Detailed information can be found in index.html or notebook.ipynb.<br><br>[1] <a href="https://sake.re/20220302-DOLA">https://sake.re/20220302-DOLA</a><br>[2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a><br><br>
20210224-DOLA: Agent adapt ontologies to agree on decision taking (ontology initialisation comparison)
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br> <br> Experiment Label: 20210224-DOLA<br> <br> Experiment design: Agent adapt ontologies to agree on decision taking (ontology initialisation comparison)<br> <br> Experiment setting: Agents learn decision trees (transformed into ontologies); get income from environment; adapt by splitting their leaf nodes; environment generated from dataset<br> <br> Hypotheses: hypotheses of 20200623-DOLA are still verified for different training algorithms and starting with random ontologies.<br> <br> Detailed information can be found in index.html or notebook.ipynb.<br> <br> [1] <a href="https://sake.re/20210224-DOLA">https://sake.re/20210224-DOLA</a><br> [2] <a href="https://gitlab.inria.fr/moex/lazylav/">https://gitlab.inria.fr/moex/lazylav/</a></p>
Cystargolide-based amide and ester Pz analogs as proteasome inhibitors and anticancer agents
<p>A series of cystargolide-based beta-lactone analogs containing nitrogen atoms at the Pz portion of the scaffold were prepared and evaluated as proteasome inhibitors and for their cytotoxicity profile towards several cancer cell lines. Inclusion of one, two or even three nitrogen atoms at the Pz portion of the cystargolide scaffold is well-tolerated, producing analogs with low nanomolar proteasome inhibition activity, in many cases superior to carfilzomib. Additionally, analog 8g, containing an ester and pyrazine group at Pz, was shown to possess significant activity towards RPMI 8226 cells (IC50 = 21 nM) and to be less cytotoxic towards the normal tissue model MCF10A cells than carfilzomib.</p>
Pathfinding for agents in games using a tetrahedron mesh dataset
<p>Dataset produced during testing for the Thesis Pathfinding for agents in games using a tetrahedron mesh.<br> <a href="https://www.researchgate.net/publication/362801751_Pathfinding_for_agents_in_games_using_a_tetrahedron_mesh">Thesis link</a></p>
Data and code availibility for the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al.
<p>The data and codes used in the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al., published in <em>Science Advances</em> are included in this collection. Detailed descriptions of the files are given in the readme file.</p>
Figure 2 in Mites occurring in farm buildings as allergic agents and indicators in forensic analyses
Figure 2 Dominance [%] of the Astigmatina mite species in all examined farm buildings.
Novel radiocontrast agent dataset
<p>A newly synthesised and characterized diiodine-substituted natural eudesmic acid - 2,6-DiIodo-3,4,5-TriMethoxyBenzoic Acid (DITMBA) and its water soluble sodium salt (NaDITMB) is investigated in respect to commonly applied radiocontrast media in medical practice (Omnipaque®).</p> <p>A phantom with the iodine solutions was scanned with Philips Juno DRF system at different imaging conditions: 46 kV, 80 kV, 95 kV and 100 kV.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.