Statistical Mechanical Approach to Lossy Data Compression:Theory and Practice

The encoder and decoder for lossy data compression of binary memoryless sources are developed on the basis of a specific-type nonmonotonic perceptron. Statistical mechanical analysis indicates that the potential ability of the perceptron-based code saturates the theoretically achievable limit in most cases although exactly performing the compression is computationally difficult. To resolve this difficulty, we provide a computationally tractable approximation algorithm using belief propagation (BP), which is a current standard algorithm of probabilistic inference. Introducing several approximations and heuristics, the BP-based algorithm exhibits performance that is close to the achievable limit in a practical time scale in optimal cases.Comment: 10 pages, 2 figures, REVTEX preprin

A non-destructive study of crack development during thermal cycling of Al wire bonds using x-ray computed tomography

This paper concerns the non-destructive visualisation of the evolution of damage within ultrasonically bonded alumini-um wires using three dimensional x-ray computed tomography. We demonstrate the potential to observe the progressive accumulation of damage within the same wires during passive thermal cycling between -55°C and 190°C. Tomography datasets were obtained prior to and after cycling. Cracks could be seen emerging from the extreme ends of the bonds when imaged after 105 cycles. Subsequent cycling lead to the advancement of these cracks toward the centres of the bonds. In addition, damage developed within the interior of the bonds; these also grew with increase in number of cy¬cles, and merged with existing cracks. Virtual cross-sections have been analysed to quantify the rate of damage build up

Are All Perspective Taking Tasks Created Equal? The Relationship Between Performance on Perspective Taking Tasks in Children

Spatial abilities assist in manipulating, constructing, and navigating the physical world (Newcombe & Shipley, 1992; Montello, 2001). In this study, a variety of tasks were utilized to measure various constructs of spatial abilities. One of the constructs measured was perspective taking which consists of the ability to understand and recognize situations at different points of view. This allows individuals to relate to others, understand spatial relations, and view objects in different spaces (Newcombe & Huttenlocker, 1992). Two tasks were employed to measure perspective taking: Piaget’s Three Mountains task and a task modeled after a study by Newcombe and Huttenlocher (1992). The aim of the current analysis was to examine how these two commonly used perspective taking tasks are related using a sample of typically developing children (Mage = 6; Range = 4-9). We hypothesized that these tasks would be highly correlated, even when controlling for age, as they are both meant to measure the same construct of perspective taking. Results indicated that performance on the two tasks was moderately and significantly correlated (r = .46), however when conducting a partial correlation analysis controlling for age, the correlation was no longer statistically significant (r = .06). This implies that although these two tasks are common perspective taking tasks used in research, they may not be uniformly measuring the same type of perspective taking. These findings lay the foundation for future research to examine if there may be differences between the two tasks such as difficulty level or different facets of perspective taking

Differences in Prenatal Tobacco Exposure Patterns among 13 Race/Ethnic Groups in California.

Prenatal tobacco exposure is a significant, preventable cause of childhood morbidity, yet little is known about exposure risks for many race/ethnic subpopulations. We studied active smoking and environmental tobacco smoke (ETS) exposure in a population-based cohort of 13 racially/ethnically diverse pregnant women: white, African American, Hispanic, Native American, including nine Asian/Pacific Islander subgroups: Chinese, Japanese, Korean, Filipino, Cambodian, Vietnamese, Laotian, Samoan, and Asian Indians (N = 3329). Using the major nicotine metabolite, cotinine, as an objective biomarker, we analyzed mid-pregnancy serum from prenatal screening banked in 1999⁻2002 from Southern California in an effort to understand differences in tobacco exposure patterns by race/ethnicity, as well as provide a baseline for future work to assess secular changes and longer-term health outcomes. Prevalence of active smoking (based on age- and race-specific cotinine cutpoints) was highest among African American, Samoan, Native Americans and whites (6.8⁻14.1%); and lowest among Filipinos, Chinese, Vietnamese and Asian Indians (0.3⁻1.0%). ETS exposure among non-smokers was highest among African Americans and Samoans, followed by Cambodians, Native Americans, Vietnamese and Koreans, and lowest among Filipinos, Japanese, whites, and Chinese. At least 75% of women had detectable cotinine. While for most groups, levels of active smoking corresponded with levels of ETS, divergent patterns were also found. For example, smoking prevalence among white women was among the highest, but the group's ETS exposure was low among non-smokers; while Vietnamese women were unlikely to be active smokers, they experienced relatively high ETS exposure. Knowledge of race/ethnic differences may be useful in assessing disparities in health outcomes and creating successful tobacco interventions

Damage evolution in Al wire bonds subjected to a junction temperature fluctuation of 30 K

Ultrasonically bonded heavy Al wires subjected to a small junction temperature fluctuation under power cycling from 40°C to 70°C were investigated using a non-destructive three-dimensional (3-D) x-ray tomography evaluation approach. The occurrence of irreversible deformation of the microstructure and wear-out under such conditions were demonstrated. The observed microstructures consist of interfacial and inter-granular cracks concentrated in zones of stress intensity, i.e., near heels and emanating from interface precracks. Interfacial voids were also observed within the bond interior. Degradation rates of ‘first’ and ‘stitch’ bonds are compared and contrasted. A correlative microscopy study combining perspectives from optical microscopy with the x-ray tomography results clarifies the damage observed. An estimation of lifetime is made from the results and discussed in the light of existing predictions

A common framework for learning causality

[EN] Causality is a fundamental part of reasoning to model the physics of an application domain, to understand the behaviour of an agent or to identify the relationship between two entities. Causality occurs when an action is taken and may also occur when two happenings come undeniably together. The study of causal inference aims at uncovering causal dependencies among observed data and to come up with automated methods to find such dependencies. While there exist a broad range of principles and approaches involved in causal inference, in this position paper we argue that it is possible to unify different causality views under a common framework of symbolic learning.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jimenez by the RYC15/18009, both programs funded by the Spanish government.Onaindia De La Rivaherrera, E.; Aineto, D.; Jiménez-Celorrio, S. (2018). A common framework for learning causality. Progress in Artificial Intelligence. 7(4):351-357. https://doi.org/10.1007/s13748-018-0151-yS35135774Aineto, D., Jiménez, S., Onaindia, E.: Learning STRIPS action models with classical planning. In: International Conference on Automated Planning and Scheduling, ICAPS-18 (2018)Amir, E., Chang, A.: Learning partially observable deterministic action models. J. Artif. Intell. Res. 33, 349–402 (2008)Asai, M., Fukunaga, A.: Classical planning in deep latent space: bridging the subsymbolic–symbolic boundary. In: National Conference on Artificial Intelligence, AAAI-18 (2018)Cresswell, S.N., McCluskey, T.L., West, M.M.: Acquiring planning domain models using LOCM. Knowl. Eng. Rev. 28(02), 195–213 (2013)Ebert-Uphoff, I.: Two applications of causal discovery in climate science. In: Workshop Case Studies of Causal Discovery with Model Search (2013)Ebert-Uphoff, I., Deng, Y.: Causal discovery from spatio-temporal data with applications to climate science. In: 13th International Conference on Machine Learning and Applications, ICMLA 2014, Detroit, MI, USA, 3–6 December 2014, pp. 606–613 (2014)Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N., Turner, H.: Nonmonotonic causal theories. Artif. Intell. 153(1–2), 49–104 (2004)Halpern, J.Y., Pearl, J.: Causes and explanations: a structural-model approach. Part I: Causes. Br. J. Philos. Sci. 56(4), 843–887 (2005)Heckerman, D., Meek, C., Cooper, G.: A Bayesian approach to causal discovery. In: Jain, L.C., Holmes, D.E. (eds.) Innovations in Machine Learning. Theory and Applications, Studies in Fuzziness and Soft Computing, chapter 1, pp. 1–28. Springer, Berlin (2006)Li, J., Le, T.D., Liu, L., Liu, J., Jin, Z., Sun, B.-Y., Ma, S.: From observational studies to causal rule mining. ACM TIST 7(2), 14:1–14:27 (2016)Malinsky, D., Danks, D.: Causal discovery algorithms: a practical guide. Philos. Compass 13, e12470 (2018)McCain, N., Turner, H.: Causal theories of action and change. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference, AAAI 97, IAAI 97, 27–31 July 1997, Providence, Rhode Island, pp. 460–465 (1997)McCarthy, J.: Epistemological problems of artificial intelligence. In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, USA, 22–25 August 1977, pp. 1038–1044 (1977)McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of artificial intelligence. Mach. Intell. 4, 463–502 (1969)Pearl, J.: Reasoning with cause and effect. AI Mag. 23(1), 95–112 (2002)Pearl, J.: Causality: Models, Reasoning and Inference, 2nd edn. Cambridge University Press, Cambridge (2009)Spirtes, C.G.P., Scheines, R.: Causation, Prediction and Search, 2nd edn. The MIT Press, Cambridge (2001)Spirtes, P., Zhang, K.: Causal discovery and inference: concepts and recent methodological advances. Appl. Inform. 3, 3 (2016)Thielscher, M.: Ramification and causality. Artif. Intell. 89(1–2), 317–364 (1997)Triantafillou, S., Tsamardinos, I.: Constraint-based causal discovery from multiple interventions over overlapping variable sets. J. Mach. Learn. Res. 16, 2147–2205 (2015)Yang, Q., Kangheng, W., Jiang, Y.: Learning action models from plan examples using weighted MAX-SAT. Artif. Intell. 171(2–3), 107–143 (2007)Zhuo, H.H., Kambhampati, S: Action-model acquisition from noisy plan traces. In: International Joint Conference on Artificial Intelligence, IJCAI-13, pp. 2444–2450. AAAI Press (2013

A risk assessment approach to improve the resilience of a seaport system using Bayesian networks

Over the years, many efforts have been focused on developing methods to design seaport systems, yet disruption still occur because of various human, technical and random natural events. Much of the available data to design these systems are highly uncertain and difficult to obtain due to the number of events with vague and imprecise parameters that need to be modelled. A systematic approach that handles both quantitative and qualitative data, as well as means of updating existing information when new knowledge becomes available is required. Resilience, which is the ability of complex systems to recover quickly after severe disruptions, has been recognised as an important characteristic of maritime operations. This paper presents a modelling approach that employs Bayesian belief networks to model various influencing variables in a seaport system. The use of Bayesian belief networks allows the influencing variables to be represented in a hierarchical structure for collaborative design and modelling of the system. Fuzzy Analytical Hierarchy Process (FAHP) is utilised to evaluate the relative influence of each influencing variable. It is envisaged that the proposed methodology could provide safety analysts with a flexible tool to implement strategies that would contribute to the resilience of maritime systems

GINNs:Graph-Informed Neural Networks for Multiscale Physics

We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate for physics-based representations of multiscale and multiphysics systems. GINNs address the twin challenges of removing intrinsic computational bottlenecks in physics-based models and generating large data sets for estimating probability distributions of quantities of interest (QoIs) with a high degree of confidence. Both the selection of the complex physics learned by the NN and its supervised learning/prediction are informed by the PGM, which includes the formulation of structured priors for tunable control variables (CVs) to account for their mutual correlations and ensure physically sound CV and QoI distributions. GINNs accelerate the prediction of QoIs essential for simulation-based decision-making where generating sufficient sample data using physics-based models alone is often prohibitively expensive. Using a real-world application grounded in supercapacitor-based energy storage, we describe the construction of GINNs from a Bayesian network-embedded homogenized model for supercapacitor dynamics, and demonstrate their ability to produce kernel density estimates of relevant non-Gaussian, skewed QoIs with tight confidence intervals.Comment: 20 pages, 8 figure

Comparison of thermal and reliability performance between a SiC MOSFET module with embedded decoupling capacitors and commercial Si IGBT power modules

This paper characterizes thermal and reliability performance of a SiC MOSFET power module with embedded decoupling capacitors and without anti-parallel diodes. Active and passive temperature cycling, supported by transient thermal impedance characterisation and scanning acoustic microscopy, are used to evaluate key degradation mechanisms. The forward voltage drop of the body diode is used as a thermo-sensitive electrical parameter to estimate the junction temperature and the thermal structure function is analysed to elucidate the degradation in the heat flow path inside the module. Comparisons are made with commercial Si IGBT power modules