Identification and optimal estimation of random fields from scattered point-wise data

A self-contained presentation of the interpolation problem in two-dimensional spatial random fields is given. We investigate the case where the random field is not necessarily stationary, where the data are so scarce and so scattered in space that sample covariance function estimates are not meaningful and where, therefore, an analytical parametric ‘variogram’ model is used in lieu of the covariance. We discuss several variogram parameter estimation techniques (LS, GLS, ML, Interpolation Error Method) and we show how to derive estimates of various functionals of the random field from the variogram. A numerical simulation and two typical engineering applications illustrate the variogram estimation techniques and provide a good measure of their respective performances.     

Consensus networks over finite fields

This work studies consensus strategies for networks of agents with limited memory, computation, and communication capabilities. We assume that agents can process only values from a finite alphabet, and we adopt the framework of finite fields, where the alphabet consists of the integers {0,…,p−1}$\{0,\dots ,p-1\}$, for some prime number p$p$, and operations are performed modulo p$p$. Thus, we define a new class of consensus dynamics, which can be exploited in certain applications such as pose estimation in capacity and memory constrained sensor networks. For consensus networks over finite fields, we provide necessary and sufficient conditions on the network topology and weights to ensure convergence. We show that consensus networks over finite fields converge in finite time, a feature that can be hardly achieved over the field of real numbers. For the design of finite-field consensus networks, we propose a general design method, with high computational complexity, and a network composition rule to generate large consensus networks from smaller components. Finally, we discuss the application of finite-field consensus networks to distributed averaging and pose estimation in sensor networks.     

Modeling self-developing biological neural networks

Recent progress in chips–neuron interface suggests real biological neurons as long-term alternatives to silicon transistors. The first step to designing such computing systems is to build an abstract model of self-assembled biological neural networks, much like computer architects manipulate abstract models of transistors. In this article, we propose a model of the structure of biological neural networks. Our model reproduces most of the graph properties exhibited by Caenorhabditis elegans, including its small-world structure and allows generating surrogate networks with realistic biological structure, as would be needed for complex information processing/computing tasks.     

Polarization of light scattered by clover

This study was undertaken in order to better understand the factors that govern the polarization of light scattered from vegetation and soils. This phenomenon is not well understood but is potentially of interest for remote sensing of the earth. The intensity and polarization of light scattered by clover in vivo and soil were measured at a number of different angles of incidence and reflectance. Both individual leaves and natural patches of vegetation were measured. The Umov effect, or inverse relation between polarization and reflectance noted by many earlier workers, was observed here and is shown to be a very general property of diffusely scattering surfaces. The light transmitted through the leaves was found to be negatively polarized. The polarization of light scattered from aggregations of leaves is affected by this negatively polarized, transmitted light. The light scattered from the upper leaf surfaces was found to be positively polarized in a manner which could be accounted for quantitatively by specular Fresuel reflection from small, randomly oriented facets on the surfaces of the leaves.     

Material and device properties of highly birefringent nematic glasses and polymer networks for organic electroluminescence

Abstract— Light‐emitting nematic liquid crystals are promising materials for organic light‐emitting devices because their orientational anisotropy allows polarized electroluminescence and improved carrier transport. Two classes of nematics, i.e., room‐temperature glasses and crosslinked polymer networks are discussed. The latter class has an additional advantage in that photolithography can be used to pixelate a full‐color display. We show that the order parameter and birefringence of a new light‐emitting nematic liquid crystal with an extended aromatic core both have values greater than 0.9. The performance of green light‐emitting devices incorporating liquid crystals of different conjugation lengths is discussed. Efficacies up to 11.1 cd/A at 1160 cd/m² at an operating voltage of 7 V were obtained. A spatially graded, color organic light‐emitting device obtained by overlapping pixels of blue‐, green‐, and red‐emitting liquid crystals were demonstrated. Some regions of the red pixel were only partially photopolymerized in order to obtain different hues in the overlapping region with green. We also show that the photolithographic process has micron‐scale resolution.     

Consensus networks with time-delays over finite fields

In this paper, we investigate the consensus problem in networks with time-delays over finite fields. The delays are categorised into three cases: single constant delay, multiple constant delays, and time-varying bounded delays. For all cases, some sufficient and necessary conditions for consensus are derived. Furthermore, assuming that the communication graph is strongly connected, some of the obtained necessary conditions reveal that the conditions for consensus with time-delays over finite fields depend not only on the diagonal entries but also on the off-diagonal entries, something that is intrinsically distinct from the case over real numbers (where having at least one nonzero diagonal entry is a sufficient and necessary condition to guarantee consensus). In addition, it is shown that delayed networks cannot achieve consensus when the interaction graph is a tree if the corresponding delay-free networks cannot reach consensus, which is consistent with the result over real numbers. As for average consensus, we show that it can never be achieved for delayed networks over finite fields, although it indeed can be reached under several conditions for delay-free networks over finite fields. Finally, networks with time-varying delays are discussed and one sufficient condition for consensus is presented by graph-theoretic method.     

生物化学动力学网络体系的计算机模拟

生物网络体系中的化学动力学模拟已成为生物体系研究过程中的一个重要环节。目前,已有数种针对于不同生物网络体系的模拟算法。在这些算法的基础上,开发出相应的一些模拟软件。本文对国际上常用的28个模拟软件进行了分类和总结。根据软件所支持模拟算法的原理及适用范围,将该28个软件大致分成4类:均匀体系确定性模拟软件、均匀体系随机性模拟软件、均匀体系混合性随机软件和扩散反应体系模拟软件。结合各类模拟算法的局限性,针对如何根据实际体系来正确选择生物网络体系的化学动力学模拟算法和模拟软件给出了必要的讨论。     

A low-order model of biological neural networks

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.     

Spatial-frequency polarization phasometry of biological polycrystalline networks

A model of generalized optic anisotropy of the fibrillar protein matrices has been suggested and the method of Fourier polarimetry for the parameters of linear and circular birefringence of the tissue biopsy of the uterine wall with spatial-frequency selection of such coordinate distributions for a differentiation of benign (fibromyoma) and malignant (adenocarcinoma) conditions has been substantiated. A set of criteria for a polarization-phase differentiation of benign (fibromyoma) and malignant (adenocarcinoma) conditions of the uterine has been revealed and substantiated.     

Pyramidal neural networks with evolved variable receptive fields

Pyramidal neural networks (PNN) are computational systems inspired by the concept of receptive fields from the human visual system. These neural networks are designed for implicit feature extraction and have been applied in pattern recognition applications. In the original approach, the size of the receptive field within the same 2D layer is a constant parameter, while in the human visual system, the receptive field size is variable. This paper proposes a PNN with variable receptive fields determined by an evolutionary algorithm, called variable pyramidal neural network with evolutionary algorithms. We observed from experiments aiming at detecting faces in images that our approach can achieve better classification rates than the original PNN. We also observed that regions with more information (such as nose and eyes) are more emphasized by variable receptive fields. These results confirm the application of intelligent algorithms to determine adjustable receptive fields in neural networks is useful to find out relevant information for recognition task. Besides, the model is comparable to biological systems regarding the flexibility assigned to receptive fields.

     

Parameter estimation for Boolean models of biological networks

Boolean networks have long been used as models of molecular networks, and they play an increasingly important role in systems biology. This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean network models based on experimental data and biological input. The key feature is a discrete analog of parameter estimation for continuous models. With only experimental data as input, the software can be used as a tool for reverse-engineering of Boolean network models from experimental time course data.     

Reasoning about non-immediate triggers in biological networks

Modeling molecular interactions in signalling networks is important from various perspectives such as predicting side effects of drugs, explaining unusual cellular behavior and drug and therapy design. Various formal languages have been proposed for representing and reasoning about molecular interactions. The interactions are modeled as triggered events in most of the approaches. The triggering of events is assumed to be immediate: once an interaction is triggered, it should occur immediately. Although working well for engineering systems, this assumption poses a serious problem in modeling biological systems. Our knowledge about biological systems is inherently incomplete, thus molecular interactions are constantly elaborated and refined at different granularity of abstraction. The model of immediate triggers can not consistently deal with this refinement. In this paper we propose an action language to address this problem. We show that the language allows for refinements of biological knowledge, although at a higher cost in terms of complexity.      

Texture segmentation using Gaussian-Markov random fields and neuraloscillator networks

We propose an image segmentation method based on texture analysis. Our method is composed of two parts. The first part determines a novel set of texture features derived from a Gaussian-Markov random fields (GMRF) model. Unlike a GMRF-based approach, our method does not employ model parameters as features or require the extraction of features for a fixed set of texture types a priori. The second part is a 2D array of locally excitatory globally inhibitory oscillator networks (LEGION). After being filtered for noise suppression, features are used to determine the local couplings in the network. When LEGION runs, the oscillators corresponding to the same texture tend to synchronize, whereas different texture regions tend to correspond to distinct phases. In simulations, a large system of differential equations is solved for the first time using a recently proposed method for integrating relaxation oscillator networks. We provide results on real texture images to demonstrate the performance of our method.     

MUCIDS: an operative C environment for acquisition and processing of polarized-light scattered from biological specimens

In this work, we describe a software package, MUCIDS, completely developed in our laboratory, for acquisition and processing of differential polarization light-scattering data from specimens of biophysical interest. MUCIDS is a C environment that manages the whole activity of an instrument used for measurements of Mueller matrix scattering elements. It allows one to capture, analyse, process and display data from this or from other similar light-scattering experiments. The entire system is suitable for routine measurements in a general biophysical (or microbiological) laboratory because of its easy handling and maintenance. The software was written in C lattice and will run on IBM personal computers and similar. It uses IBM/DAC and GPIB/IBM interface cards.     

几种人类生物网络的自相似性实证研究

介绍了复杂网络自相似性的基本概念,阐述了自相似指数及其计算方法盒覆盖算法的基本原理和方法,并对四种不同类型的人类生物网络进行了实证分析。结果表明,无论是否考虑流通代谢物和小分子代谢物,新陈代谢网络都是自相似的,但是其他的生物网络大多不是自相似的。     

模拟退火算法在生物网络研究中的应用

生物网络的功能模块识别是当前生物信息学和系统生物研究领域的一个重要研究主题。首先介绍了模拟退火算法的基本原理,分析了聚集系数和模块性等与生物网络功能模块识别相关的一些基本概念,随后阐述了模拟退火算法在生物网络功能模块识别方面的应用。最后,通过新陈代谢网络和蛋白交互网络这2种具体生物网络的模块划分实例,证实了模拟退火算法在生物网络功能模块研究方面的高效性。     

大规模生物网络马尔可夫聚类的并行化算法

马尔可夫聚类算法（MCL）是在大规模生物网络中寻找模块的一个有效方法，能够挖掘网络结构和功能影响力较大的模块。算法涉及到大规模矩阵计算，因此复杂度可达立方阶次。针对复杂度高的问题，提出了基于消息传递接口（MPI）的并行化马尔可夫聚类算法以提高算法的计算性能。首先，生物网络转化成邻接矩阵；然后，根据算法的特性，按照矩阵的规模判断并重新生成新矩阵以处理非平方倍数矩阵的计算；其次，并行计算通过按块分配的方式能够有效地实现任意规模矩阵的运算；最后，循环并行计算直至收敛，得到网络聚类结果。通过模拟网络和真实生物网络数据集的实验结果表明，与全块集体式通信（FCC）并行方法相比，平均并行效率提升了10个百分点以上，因此可以将该优化算法应用在不同类型的大规模生物网络中。     

基于深度网络的可学习感受野算法在图像分类中的应用

作为图像检索,图像组织和机器人视觉的基本任务,图像分类在计算机视觉和机器学习中受到了广泛的关注.用于目标识别及图像分类的多种基于深度学习的模型同样引发了该领域内的极大兴趣.本文提出了一种取代尺度不变特征变换(SIFT)和方向梯度直方图(HOG)描述子的算法,即利用深度分层结构,按层级学习有效的图像表示,直接从原始像素点学习特征.该方法分别利用K--奇异值分解(K--SVD)和正交匹配追踪(OMP)进行字典训练和编码.此外,本文采用了同时学习分类器和用于池化的感受野方案.实验结果证明,上述算法在目标(Oxford flowers)和事件(UIUC--sports)图像分类测试集中取得了更好的分类性能.     

Neural networks with local receptive fields and superlinear VC dimension

Local receptive field neurons comprise such well-known and widely used unit types as radial basis function (RBF) neurons and neurons with center-surround receptive field. We study the Vapnik-Chervonenkis (VC) dimension of feedforward neural networks with one hidden layer of these units. For several variants of local receptive field neurons, we show that the VC dimension of these networks is superlinear. In particular, we establish the bound Omega(W log k) for any reasonably sized network with W parameters and k hidden nodes. This bound is shown to hold for discrete center-surround receptive field neurons, which are physiologically relevant models of cells in the mammalian visual system, for neurons computing a difference of gaussians, which are popular in computational vision, and for standard RBF neurons, a major alternative to sigmoidal neurons in artificial neural networks. The result for RBF neural networks is of particular interest since it answers a question that has been open for several years. The results also give rise to lower bounds for networks with fixed input dimension. Regarding constants, all bounds are larger than those known thus far for similar architectures with sigmoidal neurons. The superlinear lower bounds contrast with linear upper bounds for single local receptive field neurons also derived here.