Causal Geometry

Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here, we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore, we introduce a geometric version of “effective information”—a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that is well matched to those interventions. This is a consequence of “causal emergence,” wherein macroscopic causal relationships may carry more information than “fundamental” microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions—as we illustrate on toy examples.     

Optimal causal inference: estimating stored information and approximating causal architecture

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.     

Bayesian wavelet-based analysis of functional magnetic resonance time series

Wavelet methods for image regularization offer a data-driven alternative to Gaussian smoothing in functional magnetic resonance (fMRI) analysis. Their impact has been limited by the difficulties in integrating regularization in the wavelet domain and inference in the image domain, precluding the probabilistic decision on which areas are activated by a task. Here we present an integrated framework for Bayesian estimation and regularization in wavelet space that allows the usual voxelwise hypothesis testing. This framework is flexible, being an adaptation to fMRI time series of a more general wavelet-based functional mixed-effect model. Through testing on a combination of simulated and real fMRI data, we show evidence of improved signal recovery, without compromising test accuracy in image space.     

Lattice mismatch induced curved configurations of hybrid boron nitride–carbon nanotubes

A unique curved configuration is observed in freestanding hybrid boron nitride–carbon nanotubes (BN–CNTs) based on molecular dynamics simulations, which, in previous studies, was tacitly assumed as a straight configuration. The physical fundamentals of this phenomenon are explored by using the continuum mechanics theory, where the curved configuration of BN–CNTs is found to be induced by the bending effect due to the lattice mismatch between the C domain and the BN domain. In addition, our results show that the curvature of the curved BN–CNTs is determined by their radius and composition. The curvature of BN–CNTs decreases with growing radius of BN–CNTs and becomes ignorable when their radius is relatively large. A non-monotonic relationship is detected between the curvature and the composition of BN–CNTs. Specifically, the curvature of BN–CNTs increases with growing BN concentration when the molar fraction of BN atoms is smaller than a critical value 0.52, but decreases with growing BN concentration when the molar fraction of BN atoms is larger than this critical value.     

Reconstructing Nonparametric Productivity Networks

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.     

嵌入式系统可信启动机制设计与实现

嵌入式系统在生活中不可或缺,如何增强其安全性是急需解决的问题。现有解决办法局限于理论层面且不符合嵌入式系统实际需求。为实现嵌入式系统安全启动并满足其实际应用需求,设计并实现了嵌入式系统可信启动机制。该机制以可信计算理论为基础,提出一种适用于嵌入式环境的高效、透明的可信框架模型,设计完成嵌入式可信计算硬件模块（ETHM）以及其逻辑结构,构造可靠稳定的接口机制,实现了完整的可信链传递、操作系统高可信启动机制等技术的集成设计。通过实验验证,该可信机制对操作系统安全性可以进行准确判定,并做出正常启动或发出警告的正确指令。实验结果表明,该可信机制具备安全性、可靠性、高效性的特点,并满足嵌入式系统实际应用需求。     

Graphical Models in Reconstructability Analysis and Bayesian Networks

Reconstructability Analysis (RA) and Bayesian Networks (BN) are both probabilistic graphical modeling methodologies used in machine learning and artificial intelligence. There are RA models that are statistically equivalent to BN models and there are also models unique to RA and models unique to BN. The primary goal of this paper is to unify these two methodologies via a lattice of structures that offers an expanded set of models to represent complex systems more accurately or more simply. The conceptualization of this lattice also offers a framework for additional innovations beyond what is presented here. Specifically, this paper integrates RA and BN by developing and visualizing: (1) a BN neutral system lattice of general and specific graphs, (2) a joint RA-BN neutral system lattice of general and specific graphs, (3) an augmented RA directed system lattice of prediction graphs, and (4) a BN directed system lattice of prediction graphs. Additionally, it (5) extends RA notation to encompass BN graphs and (6) offers an algorithm to search the joint RA-BN neutral system lattice to find the best representation of system structure from underlying system variables. All lattices shown in this paper are for four variables, but the theory and methodology presented in this paper are general and apply to any number of variables. These methodological innovations are contributions to machine learning and artificial intelligence and more generally to complex systems analysis. The paper also reviews some relevant prior work of others so that the innovations offered here can be understood in a self-contained way within the context of this paper.     

Mechanisms of postsynthesis doping of boron nitride nanostructures with carbon from first-principles simulations

Electron-beam-mediated postsynthesis doping of boron-nitride nanostructures with carbon atoms [Nature (London) 464, 571 (2010); J. Am. Chem. Soc. 132, 13?692 (2010)] was recently demonstrated, thus opening a new way to control the electronic properties of these systems. Using density-functional theory static and dynamic calculations, we show that the substitution process is governed not only by the response of such systems to irradiation, but also by the energetics of the atomic configurations, especially when the system is electrically charged. We suggest using spatially localized electron irradiation for making carbon islands and ribbons embedded into BN sheets. We further study the magnetic and electronic properties of such hybrid nanostructures and show that triangular carbon islands embedded into BN sheets possess magnetic moments, which can be switched on and off by electrically charging the structure.     

激光装置片状放大器组件的氙灯可靠性分析

 整理惯性约束聚变激光装置的运行记录和氙灯的故障数据，得到10种氙灯故障现象，并且归纳为3类,即触发故障、绝缘故障和氙灯爆炸，按故障发生时间可以分为“引发失效”的故障和“舍生取义”的故障。分析3类氙灯故障与片状放大器组件以及装置打靶成功率的逻辑关系，生成基于氙灯故障的装置打靶故障树，描述氙灯可靠性对于装置打靶成功的重要性。分析氙灯的生产工艺流程和工艺缺陷，生成氙灯的故障与工艺的鱼刺图，揭示氙灯故障的根源。     

Thermal properties of compressed expanded graphite: photothermal measurements

The thermal diffusivity and the thermal conductivity of compressed expanded graphite (CEG) samples were investigated by photothermal measurements in two geometries differing by a place of temperature disturbance detection. This disturbance can be detected on a surface opposite to the one at which the disturbance was generated (rear detection) or on the same surface (front detection). A measurement based on the rear detection allowed us to determine the effective thermal diffusivity of the sample, while the method with front detection gives the possibility of analysis of homogeneity of the sample. It is shown that the thermal diffusivity of CEG strongly depends on its apparent density. Moreover, CEG samples reveal anisotropy of the thermal properties. The thermal diffusivity in the direction parallel to the compacting axis is lower than the one in the direction perpendicular to it. The parallel thermal diffusivity decreases with growing apparent density, while the perpendicular thermal diffusivity significantly grows when the apparent density grows. The perpendicular thermal conductivity exhibits the same behavior as the perpendicular thermal diffusivity. The parallel thermal conductivity slightly grows with growing density and then reaches a plateau. The anisotropy of CEG samples grows with growing apparent density and vanishes for low-density samples. The photothermal measurement with front signal detection revealed that the CEG samples are non-homogeneous in the direction of the compacting axis and can be modeled by a two-layer system.     

以影像逼真度为约束条件的变换域指纹嵌入强度

原始图像与嵌入指纹（水印）后图像的影像逼真度取决于数字指纹的嵌入强度及嵌入版本数.在现有影像逼真度透明性指标约束下,对DCT变换域数字指纹各AC系数嵌入强度的上限进行了讨论,给出了满足影像逼真度约束的嵌入强度上限及嵌入版本数的解析表达式.该结论及其推论可作为影像逼真度透明性指标的充分条件,用于确定数字指纹或数字水印的嵌入强度及嵌入版本数等各项参量.实验结果证明了该结论的有效性.     

Heterogeneous Graphical Granger Causality by Minimum Message Length

The heterogeneous graphical Granger model (HGGM) for causal inference among processes with distributions from an exponential family is efficient in scenarios when the number of time observations is much greater than the number of time series, normally by several orders of magnitude. However, in the case of “short” time series, the inference in HGGM often suffers from overestimation. To remedy this, we use the minimum message length principle (MML) to determinate the causal connections in the HGGM. The minimum message length as a Bayesian information-theoretic method for statistical model selection applies Occam’s razor in the following way: even when models are equal in their measure of fit-accuracy to the observed data, the one generating the most concise explanation of data is more likely to be correct. Based on the dispersion coefficient of the target time series and on the initial maximum likelihood estimates of the regression coefficients, we propose a minimum message length criterion to select the subset of causally connected time series with each target time series and derive its form for various exponential distributions. We propose two algorithms—the genetic-type algorithm (HMMLGA) and exHMML to find the subset. We demonstrated the superiority of both algorithms in synthetic experiments with respect to the comparison methods Lingam, HGGM and statistical framework Granger causality (SFGC). In the real data experiments, we used the methods to discriminate between pregnancy and labor phase using electrohysterogram data of Islandic mothers from Physionet databasis. We further analysed the Austrian climatological time measurements and their temporal interactions in rain and sunny days scenarios. In both experiments, the results of HMMLGA had the most realistic interpretation with respect to the comparison methods. We provide our code in Matlab. To our best knowledge, this is the first work using the MML principle for causal inference in HGGM.     

Thermally affected characterization region by Barkhausen noise

The controlling of some industrial components require the development of new and particular nondestructive testing techniques. The testing method using Barkhausen noise (BN) is a particular one which can be applied to ferromagnetic materials. It is a magnetic nondestructive evaluation method and can provide very important information about the material structure. The aim of our work is to study the material structure using this technique to characterize the region submitted to thermal processing. Samples of steel have been heated at temperatures between 650 degrees C and 1,200 degrees C with variable parameters (time processing, maintenance time, etc.). Acoustic BN processing allows an easy interpretation of results. Micrographs of samples have been obtained to confirm the results obtained by BN.     

The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism

Causal inference methods based on conditional independence construct Markov equivalent graphs and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be inferred for two observations. In our contribution, we pose a challenge to reconcile these two research directions. We study the role of latent variables such as latent instrumental variables and hidden common causes in the causal graphical structures. We show that methods based on the independence of cause and mechanism indirectly contain traces of the existence of the hidden instrumental variables. We derive a novel algorithm to infer causal relationships between two variables, and we validate the proposed method on simulated data and on a benchmark of cause-effect pairs. We illustrate by our experiments that the proposed approach is simple and extremely competitive in terms of empirical accuracy compared to the state-of-the-art methods.     

基于虚拟测试仪器的诊断技术及在VMTS中应用

故障诊断是一门交叉学科,广泛应用于各个领域,在飞机维修虚拟训练系统(VMTS, Virtual Maintenance Training System)平台基础上,提出基于虚拟仪器进行测试、故障定位的仿真训练策略,构建了基于需求的虚拟测试仪器模型框架,并在VMTS系统中得到应用。通过虚拟测试仪器与推理模块协同配合的方法解决了维修虚拟训练过程中普遍存在的故障诊断类型套路化问题,并且能够完成复杂状态下的故障诊断任务,满足各种故障诊断训练要求,最后以尾桨故障诊断为例,证明该方法的可行性。     

Bayesian and E-Bayesian Estimations of Bathtub-Shaped Distribution under Generalized Type-I Hybrid Censoring

For the purpose of improving the statistical efficiency of estimators in life-testing experiments, generalized Type-I hybrid censoring has lately been implemented by guaranteeing that experiments only terminate after a certain number of failures appear. With the wide applications of bathtub-shaped distribution in engineering areas and the recently introduced generalized Type-I hybrid censoring scheme, considering that there is no work coalescing this certain type of censoring model with a bathtub-shaped distribution, we consider the parameter inference under generalized Type-I hybrid censoring. First, estimations of the unknown scale parameter and the reliability function are obtained under the Bayesian method based on LINEX and squared error loss functions with a conjugate gamma prior. The comparison of estimations under the E-Bayesian method for different prior distributions and loss functions is analyzed. Additionally, Bayesian and E-Bayesian estimations with two unknown parameters are introduced. Furthermore, to verify the robustness of the estimations above, the Monte Carlo method is introduced for the simulation study. Finally, the application of the discussed inference in practice is illustrated by analyzing a real data set.     

Constraint equations for general hypersurfaces and applications to shells

Hypersurfaces of arbitrary causal character embedded in a spacetime are studied with the aim of extracting necessary and sufficient free data on the submanifold suitable for reconstructing the spacetime metric and its first derivative along the hypersurface. The constraint equations for hypersurfaces of arbitrary causal character are then computed explicitly in terms of this hypersurface data, thus providing a framework capable of unifying, and extending, the standard constraint equations in the spacelike and in the characteristic cases to the general situation. This may have interesting applications in well-posedness problems more general than those already treated in the literature. As a simple application of the constraint equations for general hypersurfaces, we derive the field equations for shells of matter when no restriction whatsoever on the causal character of the shell is imposed.     

Causality in Reversed Time Series: Reversed or Conserved?

The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens—either perfectly, approximately, or not at all—using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed.     

基于变分推断的磁共振图像群组配准

为解决基于深度学习的成对配准方法精度低和传统配准算法耗时长的问题,本文提出一种基于变分推断的无监督端到端的群组配准以及基于局部归一化互相关（NCC）和先验的配准框架,该框架能够将多个图像配准到公共空间并有效地控制变形场的正则化,且不需要真实的变形场和参考图像．该方法得到的预估变形场可建模为概率生成模型,使用变分推断的方法求解;然后借助空间转换网络和损失函数来实现无监督方式训练．对于公开数据集LPBA40的3D脑磁共振图像配准任务,测试结果表明：本文所提出的方法与基线方法相比,具有较好的Dice得分、运行时间少且产生更好的微分同胚域,同时对噪声具有鲁棒性．