IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification, regression, and related tasks. Apart from the graph construction (based on AI planning problems) that is interesting in its own right, the data set possesses distinctly different characteristics from popularly used benchmarks. The data set, named IPC, consists of two self-contained versions, grounded and lifted, both including graphs of large and skewedly distributed sizes, posing substantial challenges for the computation of graph models such as graph kernels and graph neural networks. The graphs in this data set are directed and the lifted version is acyclic, offering the opportunity of benchmarking specialized models for directed (acyclic) structures. Moreover, the graph generator and the labeling are computer programmed; thus, the data set may be extended easily if a larger scale is desired. The data set is accessible from \url{https://github.com/IBM/IPC-graph-data}.Comment: ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. The data set is accessible from https://github.com/IBM/IPC-graph-dat

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and do- mains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph repre- sentations of planning tasks, we propose a graph neural net- work (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the con- volutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two- stage adaptive scheduling method to further improve the like- lihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at https://github.com/matenure/GNN planner

IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification, regression, and related tasks. Apart fromthe graph construction (based on AI planning problems) that is interesting in its own right, the data set possesses distinctly different characteristics from popularly used benchmarks. The dataset, named IPC, consists of two self-contained versions, grounded and lifted, both including graphs of large and skewedly distributed sizes,posing substantial challenges for the computation of graph models such as graph kernels and graph neural networks. The graphs in this data set are directed and the lifted version is acyclic, offering the opportunity of benchmarking specialized models for directed (acyclic) structures. Moreover, the graph generator and the labelingare computer programmed; thus, the data set may be extended easily if a larger scale is desired

Endemic microorganisms of a Drosophila simulans strain and their relationships with the non-mendelian transmission of a character

International audienc

Modelling the electronic structure and magnetic properties of LiFeAs and FeSe using hybrid-exchange density functional theory

The electronic structure and magnetic properties of LiFeAs and FeSe have been studied using hybrid exchange density functional theory. The total energies for a unit cell in LiFeAs and FeSe with different spin states including non-magnetic and spin-2 are calculated. The spin-2 configuration has the lower energy for both LiFeAs and FeSe. The computed anti-ferromagnetic exchange interactions between spins on the nearest (next nearest) neighbouring Fe atoms in LiFeAs and FeSe are approximately 14 (17) meV and 6 (13) meV respectively. The total energies of the checkerboard and stripe-type anti-ferromagnetic ordering for LiFeAs and FeSe are compared, yielding that for LiFeAs the checkerboard is lower whereas for FeSe the stripe-type is lower. However, owing to the fact that the exchange interaction of the next nearest neighbour is larger than that of the nearest one, which means that the collinear ordering might be the ground state. These results are in agreement with previous theoretical calculations and experiments. Especially the calculations for LiFeAs indicate a co-existence of conducting d-bands at the Fermi surface and d-orbital magnetism far below the Fermi surface. The theoretical results presented here might be useful for the experimentalists working on the electronic structure and magnetism of iron-based superconductors.Comment: 7 pages, 4 figures, 1 table, accepted by Solid State Communication

A quantitative meta-analysis and qualitative meta-synthesis of aged care residents’ experiences of autonomy, being controlled, and optimal functioning

Background and Objectives The poor mental health of adults living in aged care needs addressing. Improvements to nutrition and exercise are important, but mental health requires a psychological approach. Self-determination theory finds that autonomy is essential to wellbeing while experiences of being controlled undermine it. A review of existing quantitative data could underscore the importance of autonomy in aged care, and a review of the qualitative literature could inform ways to promote autonomy and avoid control. Testing these possibilities was the objective of this research. Research Design and Methods We conducted a mixed-methods systematic review of studies investigating autonomy, control, and indices of optimal functioning in aged care settings. The search identified 30 eligible reports (19 quantitative, 11 qualitative), including 141 quantitative effect sizes, 84 qualitative data items, and N = 2,668. Quantitative effects were pooled using three-level meta-analytic structural equation models, and the qualitative data were meta-synthesized using a grounded theory approach. Results As predicted, the meta-analysis showed a positive effect of aged care residents’ autonomy and their wellness, r = 0.33 [95% CI: 0.27, 0.39], and a negative effect of control, r = −0.16 [95% CI: −0.27, −0.06]. The meta-synthesis revealed seven primary and three sub-themes describing the nuanced ways autonomy, control, and help seeking are manifest in residential aged care settings. Discussion and Implications The results suggest that autonomy should be supported, and unnecessary external control should be minimized in residential aged care, and we discuss ways the sector could strive for both aims

Magnetism and Charge Dynamics in Iron Pnictides

In a wide variety of materials, such as copper oxides, heavy fermions, organic salts, and the recently discovered iron pnictides, superconductivity is found in close proximity to a magnetically ordered state. The character of the proximate magnetic phase is thus believed to be crucial for understanding the differences between the various families of unconventional superconductors and the mechanism of superconductivity. Unlike the AFM order in cuprates, the nature of the magnetism and of the underlying electronic state in the iron pnictide superconductors is not well understood. Neither density functional theory nor models based on atomic physics and superexchange, account for the small size of the magnetic moment. Many low energy probes such as transport, STM and ARPES measured strong anisotropy of the electronic states akin to the nematic order in a liquid crystal, but there is no consensus on its physical origin, and a three dimensional picture of electronic states and its relations to the optical conductivity in the magnetic state is lacking. Using a first principles approach, we obtained the experimentally observed magnetic moment, optical conductivity, and the anisotropy of the electronic states. The theory connects ARPES, which measures one particle electronic states, optical spectroscopy, probing the particle hole excitations of the solid and neutron scattering which measures the magnetic moment. We predict a manifestation of the anisotropy in the optical conductivity, and we show that the magnetic phase arises from the paramagnetic phase by a large gain of the Hund's rule coupling energy and a smaller loss of kinetic energy, indicating that iron pnictides represent a new class of compounds where the nature of magnetism is intermediate between the spin density wave of almost independent particles, and the antiferromagnetic state of local moments.Comment: 4+ pages with additional one-page supplementary materia

An invitation to grieve: reconsidering critical incident responses by support teams in the school setting

This paper proposes that consideration could be given to an invitational intervention rather than an expectational intervention when support personnel respond to a critical incident in schools. Intuitively many practitioners know that it is necessary for guidance/counselling personnel to intervene in schools in and following times of trauma. Most educational authorities in Australia have mandated the formulation of a critical incident intervention plan. This paper defines the term critical incident and then outlines current intervention processes, discussing the efficacy of debriefing interventions. Recent literature suggests that even though it is accepted that a planned intervention is necessary, there is scant evidence as to the effectiveness of debriefing interventions in stemming later symptoms of post traumatic stress disorder. The authors of this paper advocate for an expressive therapy intervention that is invitational rather than expectational, arguing that not all people respond to trauma in the same way and to expect that they will need to recall and retell what has happened is most likely a dangerous assumption. A model of invitation using Howard Gardner’s (1983) multiple intelligences is proposed so that students are invited to grieve and understand emotionally what is happening to them following a critical incident

Reactivity of (1-methoxycarbonylpentadienyl)iron(1+) cations with hydride, methyl, and nitrogen nucleophiles

The reaction of tricarbonyl and (dicarbonyl)triphenylphosphine (1-methoxycarbonyl-pentadientyl)iron(1+) cations 7 and 8 with methyl lithium, NaBH3CN, or potassium phthalimide affords (pentenediyl)iron complexes 9a-c and 11a-b, while reaction with dimethylcuprate, gave (E,Z-diene)iron complexes 10 and 12. Oxidatively induced-reductive elimination of 9a-c gave vinylcyclopropanecarboxylates 17a-c. The optically active vinylcyclopropane (+)-17a, prepared from (1S)-7, undergoes olefin cross-metathesis with excess (+)-18 to yield (+)-19, a C9C16 synthon for the antifungal agent ambruticin. Alternatively reaction of 7 with methanesulfonamide or trimethylsilylazide gave (E,E-diene)iron complexes 14d and e. Huisgen [3 + 2] cyclization of the (azidodienyl)iron complex 14e with alkynes afforded triazoles 25a-e