Decomposing the global financial crisis: A Self-Organizing Time Map

A key starting point for financial stability surveillance is understanding past, current and possible future risks and vulnerabilities. Through temporal data and dimensionality reduction, or visual dynamic clustering, this paper aims to present a holistic view of cross-sectional macro-financial patterns over time. The Self-Organizing Time Map (SOTM) is a recent adaptation of the Self-Organizing Map for exploratory temporal structure analysis, which disentangles cross-sectional data structures over time. We apply the SOTM, as well as its combination with classical cluster analysis, in financial stability surveillance. Thus, this paper uses the SOTM for decomposing and identifying temporal structural changes in macro-financial data before, during and after the global financial crisis of 2007–2009.     

A biologically inspired object-based visual attention model

A biologically inspired object-based visual attention model is proposed in this paper. This model includes a training phase and an attention phase. In the training phase, all training targets are fused into a target class and all training backgrounds are fused into a background class. Weight vector is computed as the ratio of the mean target class saliency and the mean background class saliency for each feature. In the attention phase, for an attended scene, all feature maps are combined into a top-down salience map with the weight vector by a hierarchy method. Then, top-down and bottom-up salience map are fused into a global salience map which guides the visual attention. At last, the size of each salient region is obtained by maximizing entropy. The merit of our model is that it can attend a class target object which can appear in the corresponding background class. Experimental results indicate that: when the attended target object doesn’t always appear in the background corresponding to that in the training images, our proposed model is excellent to Navalpakkam’s model and the top-down approach of VOCUS.     

A biologically inspired framework for contour detection

In this paper, we present a simple and efficient biologically inspired edge detection algorithm which attempts to model some structural features of the Human Visual System. More precisely, our edge detection approach attempts to model the dynamic retina concept, i.e., the natural saccadic eye movement process which redirects the fovea’s attention from one point of interest to another within the image along with the inherent topological log-polar transformation (with its space-variant resolution) of the retinal image into its cortical projection. The experiments, reported in this paper and conducted on the Berkeley Segmentation Dataset, demonstrate that the proposed biologically inspired contour detection method performs well compared to the best existing state-of-the-art edge and contour detection methods recently proposed in the literature.     

Structured context-analysis techniques in biologically inspired ambient-intelligence systems

In this paper, techniques and related issues for the definition of a contextual knowledge in ambient-intelligence systems are explored. A logical structure for this kind of system, inspired by a neurobiological brain model, is proposed. Through these considerations, the role and the importance of context awareness in the definition of an artificial organism showing adaptability, pervasiveness, and scalability features are described. Techniques for the definition of a multilayer context representation are explained and practically demonstrated with a test-bed. In the proposed system, a complex event classification is obtained through the fusion of heterogeneous data coming from a set of sensors thanks to the design of a self-organizing map (SOM). The SOM represents the core of the system and testing proofs show good results in the classification of the events taking place in the monitored environment.     

A taxonomy of biologically inspired research in computer networking

The natural world is enormous, dynamic, incredibly diverse, and highly complex. Despite the inherent challenges of surviving in such a world, biological organisms evolve, self-organize, self-repair, navigate, and flourish. Generally, they do so with only local knowledge and without any centralized control. Our computer networks are increasingly facing similar challenges as they grow larger in size, but are yet to be able to achieve the same level of robustness and adaptability. Many research efforts have recognized these parallels, and wondered if there are some lessons to be learned from biological systems. As a result, biologically inspired research in computer networking is a quickly growing field. This article begins by exploring why biology and computer network research are such a natural match. We then present a broad overview of biologically inspired research, grouped by topic, and classified in two ways: by the biological field that inspired each topic, and by the area of networking in which the topic lies. In each case, we elucidate how biological concepts have been most successfully applied. In aggregate, we conclude that research efforts are most successful when they separate biological design from biological implementation – that is to say, when they extract the pertinent principles from the former without imposing the limitations of the latter.     

Optimal design of microfluidic networks using biologically inspired principles

From the earliest of times, Man has sought to replicate ideas that have evolved naturally in plants and animals. Understanding and extracting these “natural” design strategies has opened up a whole new field of research known as biomimetics. Designs formulated using biologically inspired principles range from novel surface treatments that mimic physiological processes to geometrical optimization for improving the performance of a system. In this paper, we will show how biomimetic principles based on the laws that govern biological vascular trees can be used to design artificial microfluidic distribution systems. The study focuses specifically on microfluidic manifolds composed of constant-depth rectangular- or trapezoidal-sectioned channels, as these geometries can readily be fabricated using standard micro-fabrication techniques. We will show that it is possible to introduce a prescribed element of flow control into the system by carefully selecting the branching parameter that governs the change in channel dimension at each bifurcation.     

A biologically inspired solution for fuzzy shortest path problems

By considering the uncertainty that exists in the edge weights of the network, fuzzy shortest path problems, as one of the derivative problems of shortest path problems, emerge from various practical applications in different areas. A path finding model, inspired by an amoeboid organism, Physarum polycephalum, has been shown as an effective approach for deterministic shortest path problems. In this paper, a biologically inspired algorithm called Fuzzy Physarum Algorithm (FPA) is proposed for fuzzy shortest path problems. FPA is developed based on the path finding model, while utilizing fuzzy arithmetic and fuzzy distance to deal with fuzzy issues. As a result, FPA can represent and handle the fuzzy shortest path problem flexibly and effectively. Distinct from many existing methods, no order relation has been assumed in the proposed FPA. Several examples, including a tourist problem, are given to illustrate the effectiveness and flexibility of the proposed method and the results are compared with existing methods.     

A biologically inspired spatio-chromatic feature for color object recognition

Multimedia Tools and Applications - Color information has been acknowledged for its important role in object recognition and scene classification. How to describe the color characteristics and...     

A fast biologically inspired algorithm for recurrent motion estimation

We have previously developed a neurodynamical model of motion segregation in cortical visual area V1 and MT of the dorsal stream. The model explains how motion ambiguities caused by the motion aperture problem can be solved for coherently moving objects of arbitrary size by means of cortical mechanisms. The major bottleneck in the development of a reliable biologically inspired technical system with real-time motion analysis capabilities based on this neural model is the amount of memory necessary for the representation of neural activation in velocity space. We propose a sparse coding framework for neural motion activity patterns and suggest a means by which initial activities are detected efficiently. We realize neural mechanisms such as shunting inhibition and feedback modulation in the sparse framework to implement an efficient algorithmic version of our neural model of cortical motion segregation. We demonstrate that the algorithm behaves similarly to the original neural model and is able to extract image motion from real world image sequences. Our investigation transfers a neuroscience model of cortical motion computation to achieve technologically demanding constraints such as real-time performance and hardware implementation. In addition, the proposed biologically inspired algorithm provides a tool for modeling investigations to achieve acceptable simulation time     

Economic theory and the financial crisis

I want to probe in the role of the market in allocating resources in this very preliminary essay. One does not have to see study deeply to that the failure of markets for various kinds of derivative securities to perform properly is an essential element of the current financial crisis. Actually, financial crises are not a new phenomenon. The history of capitalism has been marked by repeated collapses of the financial system, situations in which the ??markets?? for loans disappear for extensive periods of time. The 18th century saw some bubbles, but these might not be quite modern. But from 1819 on, there have a succession of failures of banks and other financial institutions. These have typically been unpredicted and did not correspond in time to any particular exogenous event (e.g., wars). Economists from Joho Stuart Mill (1848) on did recognize the phenomenon. But the discussion was and is not at all integrated with the general exposition of classical economics. No one could be a more vigorous advocate of unrestrained markets than Milton Friedman; yet, to my reading, the account that he and Anna Schwartz gave of monetary developments in the United States and particularly with regard to the Great Depression emphasizes, not prices, not even interest rates, but the supply of money, and, by inference, of liquidity. (Friedman and Schwartz (1963)). I start with the neoclassical general equilibrium framework, to which I have given a good deal of attention and effort. I seek to identify a possible point at which it fails to supply a coherent theory of securities markets and so might possibly lead to some understanding of the repeated crises of the financial system underlying the development of capitalism.     

Solving the minimum bisection problem using a biologically inspired computational model

The traditional trend of DNA computing aims at solving computationally intractable problems. The minimum bisection problem (MBP) is a well-known NP-hard problem, which is intended to partition the vertices of a given graph into two equal halves so as to minimize the number of those edges with exactly one end in each half. Based on a biologically inspired computational model, this paper describes a novel algorithm for the minimum bisection problem, which requires a time cost and a DNA strand length that are linearly proportional to the instance size.     

新型仿生六足机器人导航系统的建模与仿真

针对新型仿生六足机器人工作任务和作业环境的要求,设计了一种基于INS-GPS器件的专用组合式导航系统。该导航系统采用集中开环式组合方式,以INS和GPS器件输出的导航数据差作为滤波器的输入值,运用经典卡尔曼滤波理论对该导航系统进行了实时修正,并根据仿生六足机器人运动特性和测量任务的要求,建立了该导航系统的位置、速度组合测量方程,并运用MATLAB软件进行了仿真,仿真结果表明:采用该组合式导航系统可大大提高仿生六足机器人的导航精度,为仿生六足机器人实现智能化、实时化控制奠定了基础。     

Control of real-world complex robots using a biologically inspired algorithm

Elementary living beings, like bacteria, are able to reach food sources using only limited and very noisy sensory information. In this paper, we describe a very simple algorithm inspired from bacteria chemotaxis. We present a Markov chain model for studying the effect of noise on the behavior of an agent that moves according to this algorithm, and we show that, counterintuitively, the application of noise can increase the expected average performance over a fixed available time. After this theoretical analysis, experiments on real-world application of this algorithm are introduced. In particular, we show that the algorithm is able to control a complex robot arm, actuated by 17 McKibben pneumatic artificial muscles, without the need of any model of the robot or of its environment.     

FPGA implementation of a real-time biologically inspired image enhancement algorithm

This paper presents an FPGA implementation of a novel image enhancement algorithm, which compensates for the under-/over-exposed image regions, caused by the limited dynamic range of contemporary standard dynamic range image sensors. The algorithm, which is motivated by the attributes of the shunting center-surround cells of the human visual system, is implemented in Altera Stratix II GX: EP2SGX130GF1508C5 FPGA device. The proposed implementation, which is synthesized in an FPGA technology, employs reconfigurable pipeline, structured memory management, and data reuse in spatial operations, to render in real-time the huge amount of input data that the video signal comprises. It also avoids the use of computationally intensive operations, achieving the required specifications in terms of flexibility, timing, performance and visual quality. The proposed implementation allows real-time processing of color images with sizes up to 2.5 Mpixels, at frame rate of 25 fps. As a result, the architectural solution described in this work offers a low-cost implementation for automatic exposure correction in real-time video systems.

A biologically inspired multimedia content repurposing system in heterogeneous environments

The application of biologically inspired algorithms to multimedia repurposing systems in heterogeneous network environments is gaining importance due to its ability to adapt and customize multimedia content to frequently changing network environments. This paper presents a biologically inspired proxy-based multimedia content adaptation system, which is used to repurpose multimedia content dynamically for the transmission over heterogeneous networks and end devices. We use a series of repurposing proxies’ services in a chain fashion between the sender and multiple end devices. In order to find the appropriate chain of repurposing services that satisfy the Quality of Experience (QoE) requirements, the proposed service selection algorithm uses the ant colony metaphor. During the communication session, the algorithm uses biological foraging behavior inspired from ant agents to find optimal service paths between the media sender and the destination. Experimental results demonstrate that the proposed algorithm provides significant performance gain over traditional, state of the art selection algorithms and saves the average delay.     

Image recommendation based on a novel biologically inspired hierarchical model

Image recommendation has become an increasingly relevant problem recently, since strong demand to quickly find interested images from vast amounts of image library. We describe a biologically inspired hierarchical model for image recommendation. The biologically inspired model (BIM) for invariant feature representation has attracted widespread attention, which approximately follows the organization of cortex visuel. BIM is a computation architecture with four layers. With the image data size increases, the four-layer framework is prone to be overfitting, which limits its application. To address this issue, we propose a biologically inspired hierarchical model (BIHM) for feature representation, which adds two more discriminative layers upon the conventional four-layer framework. In contrast to the conventional BIM that mimics the inferior temporal cortex, which corresponds to the low level feature, the proposed BIHM adds two more layers upon the conventional framework to simulate inferotemporal cortex, exploring higher level feature invariance and selectivity. Furthermore, we firstly utilize the BIHM in the image recommendation. To demonstrate the effectiveness of proposed model, we use it to image classification and retrieval tasks and perform experiments on CalTech5, Imagenet and CalTech256 datasets. The experiment results show that BIHM exhibits better performance than the conventional model in the tasks and is very comparable to existing architectures.     

A biologically inspired method for vision-based docking of wheeled mobile robots

We present a new control law for the problem of docking a wheeled robot to a target at a certain location with a desired heading. Recent research into insect navigation has inspired a solution which uses only one video camera. The control law is of the “behavioral” type in that all control actions are based on immediate visual information. Docking success under certain conditions is proved mathematically and simulation studies show the control law to be robust to camera intrinsic parameter errors. Experiments were performed for verification of the control law.     

Genetic design of biologically inspired receptive fields for neural pattern recognition

This paper proposes a new method for the design, through simulated evolution, of biologically inspired receptive fields in feedforward neural networks (NNs). The method is intended to enhance pattern recognition performance by creating new neural architectures specifically tuned for a particular pattern recognition problem. It proposes a combined neural architecture composed of two networks in cascade: a feature extraction network (FEN) followed by a neural classifier. The FEN is composed of several layers with receptive fields constructed by additive superposition of excitatory and inhibitory fields. A genetic algorithm (GA) is used to select receptive field parameters to improve classification performance. The parameters are receptive field size, orientation, and bias as well as the number of different receptive fields in each layer. Based on a random initial population where each individual represents a different neural architecture, the GA creates new enhanced individuals. The method is applied to handwritten digit classification and face recognition. In both problems, results show strong dependency between NN classification performance and receptive field architecture. GA selected parameters of the receptive fields produced improvements in the classification performance on the test set up to 90.8% for the problem of handwritten digit classification and up to 84.2% for the face recognition problem. On the same test sets, results were compared advantageously to standard feedforward multilayer perceptron (MLP) NNs where receptive fields are not explicitly defined. The MLP reached a maximum classification performance of 84.9% and 77.5% in both problems, respectively.