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

Synthesizing Imperative Programs from Examples Guided by Static Analysis

We present a novel algorithm that synthesizes imperative programs for introductory programming courses. Given a set of input-output examples and a partial program, our algorithm generates a complete program that is consistent with every example. Our key idea is to combine enumerative program synthesis and static analysis, which aggressively prunes out a large search space while guaranteeing to find, if any, a correct solution. We have implemented our algorithm in a tool, called SIMPL, and evaluated it on 30 problems used in introductory programming courses. The results show that SIMPL is able to solve the benchmark problems in 6.6 seconds on average.Comment: The paper is accepted in Static Analysis Symposium (SAS) '17. The submission version is somewhat different from the version in arxiv. The final version will be uploaded after the camera-ready version is read

Global Growth and Trends of In-Body Communication Research—Insight From Bibliometric Analysis

A bibliometric analysis was conducted to examine research on in-body communication. This study aimed to assess the research growth in different countries, identify influential authors for potential international collaboration, investigate research challenges, and explore future prospects for in-body communication. A total of 148 articles written in English from journals and conference proceedings were gathered from the Scopus database. These articles cover the period from 2006 until August 2023. VOS Viewer 1.6.19 and Tableau Cloud were used to analyze the data. The analysis reveals that research on in-body communication has shown fluctuations but overall tends to increase. The United States, Finland, and Japan were identified as the leading countries (top three) in terms of publication quantity, while researchers from Norway, Finland, and Morocco received the highest number of citations. The University of Oulu in Finland has emerged as a productive institution in this field. Collaborative research opportunities exist with the countries mentioned above or with authors who have expertise in this topic. The dominant research topic within this field pertains to ultra-wideband (UWB) technology. One of the future challenges in this field is the exploration of optical wireless communication (OWC) as a potential communication medium for in-body devices, such as electronic devices implanted in the human body. This includes improving performance to meet the requirements for in-body communication devices. Additionally, this paper provides further insights into the progress of research on OWC for in-body communication conducted in our laboratory

Complex refractive index of non-spherical particles in the vis-NIR region - application to Bacillus Subtilis spores

A method is presented for the estimation of optical constants in the ultraviolet-visible-near-infrared (UVVis-NIR) region of nonspherical particles in a suspension at concentrations where multiple scattering issignificant. The optical constants are obtained by an inversion technique using the adding-doubling method to solve the radiative transfer equation in combination with the single scattering theories for modelling scattering by nonspherical particles. Two methods for describing scattering by single scatteringare considered: the T-matrix method and the approximate but computationally simpler Rayleigh-Gans-Debye (RGD) approximation. The method is then applied to obtain the optical constants of Bacillussubtilis spores in the wavelength region 400-1200 nm. It is found that the optical constants obtained using the RGD approximation matches those obtained using the T-matrix method to within experimental error

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

The Asymptotic Form of Cosmic Structure: Small Scale Power and Accretion History

We explore the effects of small scale structure on the formation and equilibrium of dark matter halos in a universe dominated by vacuum energy. We present the results of a suite of four N-body simulations, two with a LCDM initial power spectrum and two with WDM-like spectra that suppress the early formation of small structures. All simulations are run into to far future when the universe is 64Gyr/h old, long enough for halos to essentially reach dynamical equilibrium. We quantify the importance of hierarchical merging on the halo mass accretion history, the substructure population, and the equilibrium density profile. We modify the mass accretion history function of Wechsler et al. (2002) by introducing a parameter, \gamma, that controls the rate of mass accretion, dln(M) / dln(a) ~ a^(-\gamma), and find that this form characterizes both hierarchical and monolithic formation. Subhalo decay rates are exponential in time with a much shorter time scale for WDM halos. At the end of the simulations, we find truncated Hernquist density profiles for halos in both the CDM and WDM cosmologies. There is a systematic shift to lower concentration for WDM halos, but both cosmologies lie on the same locus relating concentration and formation epoch. Because the form of the density profile remains unchanged, our results indicate that the equilibrium halo density profile is set independently of the halo formation process.Comment: 17 pages, submitted to ApJ. Full resolution version avaliable at http://www-personal.umich.edu/~mbusha/Papers/AccretionHistory.pd

Effect of fruit and vegetable concentrates on endothelial function in metabolic syndrome: A randomized controlled trial

<p>Abstract</p> <p>Background and Objective</p> <p>Dehydrated fruit and vegetable concentrates provide an accessible form of phytonutrient supplementation that may offer cardioprotective effects. This study assessed the effects of two blends of encapsulated juice powder concentrates (with and without added berry powders) on endothelial function in persons with metabolic syndrome, a risk factor for type 2 diabetes and cardiovascular disease.</p> <p>Methods</p> <p>Randomized, double blind, placebo controlled crossover clinical trial with three treatment arms. 64 adults with metabolic syndrome were enrolled and received 8-week sequences of each blend of the concentrates and placebo. The primary outcome measure was change in endothelial function (assessed as flow-mediated dilatation of the brachial artery) 2 hr after consuming a 75 g glucose load, after 8-weeks of daily consumption (sustained) or 2 hr after consumption of a single dose (acute). Secondary outcome measures included plasma glucose, serum insulin, serum lipids, and body weight.</p> <p>Results</p> <p>No significant between-group differences in endothelial function with daily treatment for 8 weeks were seen. No other significant treatment effects were discerned in glucose, insulin, lipids, and weight.</p> <p>Conclusion</p> <p>Encapsulated fruit and vegetable juice powder concentrates did not alter insulin or glucose measures in this sample of adults with metabolic syndrome.</p> <p>Trial Registration</p> <p>clinicaltrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT01224743">NCT01224743</a></p

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018