Learning of Spatio–Temporal Codes in a Coupled Oscillator System

In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a “winnerless competition” process into spatio–temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using “weighted order parameters (WOPs)” that are analogous to “local field potentials” in neural systems. Since spatio–temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.      

An Expectation–Maximization Method for Spatio–Temporal Blind Source Separation Using an AR-MOG Source Model

In this paper, we develop a maximum-likelihood (ML) spatio-temporal blind source separation (BSS) algorithm, where the temporal dependencies are explained by assuming that each source is an autoregressive (AR) process and the distribution of the associated independent identically distributed (i.i.d.) innovations process is described using a mixture of Gaussians. Unlike most ML methods, the proposed algorithm takes into account both spatial and temporal information, optimization is performed using the expectation-maximization (EM) method, the source model is adapted to maximize the likelihood, and the update equations have a simple, analytical form. The proposed method, which we refer to as autoregressive mixture of Gaussians (AR-MOG), outperforms nine other methods for artificial mixtures of real audio. We also show results for using AR-MOG to extract the fetal cardiac signal from real magnetocardiographic (MCG) data.     

Spatio–Temporal Adaptation in the Unsupervised Development of Networked Visual Neurons

There have been many computational models mimicking the visual cortex that are based on spatial adaptations of unsupervised neural networks. In this paper, we present a new model called neuronal cluster which includes spatial as well as temporal weights in its unified adaptation scheme. The “in-place” nature of the model is based on two biologically plausible learning rules, Hebbian rule and lateral inhibition. We present the mathematical demonstration that the temporal weights are derived from the delay in lateral inhibition. By training with the natural videos, this model can develop spatio–temporal features such as orientation selective cells, motion sensitive cells, and spatio–temporal complex cells. The unified nature of the adaption scheme allows us to construct a multilayered and task-independent attention selection network which uses the same learning rule for edge, motion, and color detection, and we can use this network to engage in attention selection in both static and dynamic scenes.      

Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio–Temporal System Identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.     

Spatio‐Temporal Contours from Deep Volume Raycasting

We visualize contours for spatio‐temporal processes to indicate where and when non‐continuous changes occur or spatial bounds are encountered. All time steps are comprised densely in one visualization, with contours allowing to efficiently analyze processes in the data even in case of spatial or temporal overlap. Contours are determined on the basis of deep raycasting that collects samples across time and depth along each ray. For each sample along a ray, its closest neighbors from adjacent rays are identified, considering time, depth, and value in the process. Large distances are represented as contours in image space, using color to indicate temporal occurrence. This contour representation can easily be combined with volume rendering‐based techniques, providing both full spatial detail for individual time steps and an outline of the whole time series in one view. Our view‐dependent technique supports efficient progressive computation, and requires no prior assumptions regarding the shape or nature of processes in the data. We discuss and demonstrate the performance and utility of our approach via a variety of data sets, comparison and combination with an alternative technique, and feedback by a domain scientist.     

Strategies for Creating A Learning Organization

Managers having a hard time turning their organizations into learning ones may be having difficulty because there is more than one way to create a learning organization. Equally confounding is that there is more than one way to define what a learning organization is. This article gives IT managers a working definition of a learning organization along with six strategies for creating one. These strategies may also be used to revitalize an organization's learning initiative.     

溢出代码和访存压力敏感的快速机器学习*

基于遗传算法提出了溢出代码和访存压力敏感的机器学习来调试寄存器分配的权值函数。不同于以往采用目标程序的运行时间作为适应值,通过静态分析寄存器分配产生的溢出代码和基本块中的访存压力来构建适应值,以减少学习时间。这些分析被限定在热点函数中,在保证适应值精度的同时进一步加快了学习速度。实验表明,快速学习仅需要考虑热点函数的编译时间,整个CPU2000CINT测试集在5 h内即可学习完毕。大部分CPU2000CINT测试例子的性能得到了提高。其中perlbmk的性能提升最高可达到7.2%。     

Special Tutorial: The Organization of Electronic Cyclic Memories

Cyclic memories are memories in which data rotate cyclically past a read/write port. The most common cyclic memories in use today are mechanical drums and fixed-head disks. In the past, small electronic cyclic memories have been made from MOS technology; more recently they have been made from charge-coupled devices and magnetic bubbles. Because of advances in integrated technology, large electronic cyclic memories that might be replacements for drum and disk memories are likely to become available. Whereas it is difficult to predict what technology will eventually emerge for electronic auxiliary memories, present projections suggest that cyclically organized memories based upon magnetic bubbles or charge-coupled devices are potential candidates. The disadvantage of cyclic organization is that access time due to memory latency may be excessive. The problem we investigate in this paper is how to modify electronic cyclic memories in relatively minor and inexpensive ways so as to decrease rotational latency in a system environment.     

一种基于机器学习的虚拟机放置方法

为改善云数据中心的能耗、负载均衡性和服务等级协议(SLA)违背率,对虚拟机放置策略进行优化。基于IaaS环境,提出一种基于机器学习的虚拟机迁移调整方法。根据资源消耗的互补性和不均衡性对虚拟机进行预放置,使用深度神经网络预测物理机负载等级,并利用深度Q网络调整物理机数量。实验结果表明,该方法能够有效均衡负载分布,降低能源开销和SLA违背率。     

Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory

Recent interest in human-level intelligence suggests a rethink of the role of machine learning in computational intelligence. We argue that without cognitive learning the goal of achieving human-level synthetic intelligence is far from completion. Here we review the principles underlying human learning and memory, and identify three of them, i.e., continuity, glocality, and compositionality, as the most fundamental to human-level machine learning. We then propose the recently-developed hypernetwork model as a candidate architecture for cognitive learning and memory. Hypernetworks are a random hypergraph structure higher-order probabilistic relations of data by an evolutionary self-organizing process based on molecular self- assembly. The chemically-based massive interaction for information organization and processing in the molecular hypernetworks, referred to as hyperinteractionism, is contrasted with the symbolist, connectionist, and dynamicist approaches to mind and intelligence. We demonstrate the generative learning capability of the hypernetworks to simulate linguistic recall memory, visual imagery, and language-vision crossmodal translation based on a video corpus of movies and dramas in a multimodal memory game environment. We also offer prospects for the hyperinteractionistic molecular mind approach to a unified theory of cognitive learning.     

Surgical Tool Datasets for Machine Learning Research: A Survey

International Journal of Computer Vision - This paper is a comprehensive survey of datasets for surgical tool detection and related surgical data science and machine learning techniques and...     

基于机器学习的软件模块访存压力优化仿真

采用当前方法对软件模块访存压力进行优化时,优化后的软件模块带宽较高、数据传输延时高,存在有效性差的问题。将机器学习应用在软件模块的访存压力优化过程中,提出基于机器学习的软件模块访存压力优化方法。计算链路的使用率,并将计算结果传送到每条流对应的发送端中,发送端根据接收到的信息对发送速率进行调整,实现拥塞控制。采用多目标规划方法,根据预算值和实际值之间存在的偏差,构建软件模块访存压力优化模型,通过二进制粒子群算法对软件模块访存压力优化模型进行求解,实现软件模块访存压力的优化。仿真结果表明,所提方法的带宽高、数据传输延时小,验证了基于机器学习的软件模块访存压力优化方法的有效性。     

机器学习案例化教学方法研究

如何让学生正确理解机器学习理论、准确利用所学技术解决新问题,是机器学习课程教学的重点.提出一种由浅入深的案例化教学方法,设计综合案例实现机器学习全过程的融合,讲解每一个算法的理论知识和实现过程,并通过调整算法步骤、参数等,展示各个步骤、自变量和系数等对学习结果的影响.实践结果表明,该方法能够有效帮助学生理解理论知识,强...     

DeepValue: A Comparable Framework for Value-Based Strategy by Machine Learning

Computational Economics - Value relevant analysis is one of the key stock trading strategies of stock investment which is based on financial statement that represents the intrinsic investing value...     

Machine Learning in Drug Discovery: A Review

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

     

A New Sparse Learning Machine

This paper investigates the problem of global exponential synchronization for memristor-based neural networks with delay. Based on the nonsmooth analysis and differential inclusion theory, a new analytic technique is employed to design a discontinuous state feedback controller, which ensures the memristor-based drive system exponential synchronize with the response system. The succinct synchronization conditions is closely relate to the switching jumps. The estimated rate of the exponential synchronization can be obtained by solving a sample algebra equation. Simulation results are given to show the effectiveness and benefits of the proposed methods.     

A Two-Month Field Trial in an Elementary School for Long-Term Human–Robot Interaction

Interactive robots participating in our daily lives should have the fundamental ability to socially communicate with humans. In this paper, we propose a mechanism for two social communication abilities: forming long-term relationships and estimating friendly relationships among people. The mechanism for long-term relationships is based on three principles of behavior design. The robot we developed, Robovie, is able to interact with children in the same way as children do. Moreover, the mechanism is designed for long-term interaction along the following three design principles: (1) it calls children by name using radio frequency identification tags; (2) it adapts its interactive behaviors for each child based on a pseudo development mechanism; and (3) it confides its personal matters to the children who have interacted with the robot for an extended period of time. Regarding the estimation of friendly relationships, the robot assumes that people who spontaneously behave as a group together are friends. Then, by identifying each person in the interacting group around the robot, it estimates the relationships between them. We conducted a two-month field trial at an elementary school. An interactive humanoid robot, Robovie, was placed in a classroom at the school. The results of the field trial revealed that the robot successfully continued interacting with many children for two months, and seemed to have established friendly relationships with them. In addition, it demonstrated reasonable performance in identifying friendships among children. We believe that these results demonstrate the potential of current interactive robots to establish social relationships with humans in our daily lives.     

FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

In this paper, we introduce a new algorithm for incremental learning of a specific form of Takagi–Sugeno fuzzy systems proposed by Wang and Mendel in 1992. The new data-driven online learning approach includes not only the adaptation of linear parameters appearing in the rule consequents, but also the incremental learning of premise parameters appearing in the membership functions (fuzzy sets), together with a rule learning strategy in sample mode. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The modifications include an automatic generation of new clusters based on the nature, distribution, and quality of new data and an alternative strategy for selecting the winning cluster (rule) in each incremental learning step. Antecedent and consequent learning are connected in a stable manner, meaning that a convergence toward the optimal parameter set in the least-squares sense can be achieved. An evaluation and a comparison to conventional batch methods based on static and dynamic process models are presented for high-dimensional data recorded at engine test benches and at rolling mills. For the latter, the obtained data-driven fuzzy models are even compared with an analytical physical model. Furthermore, a comparison with other evolving fuzzy systems approaches is carried out based on nonlinear dynamic system identification tasks and a three-input nonlinear function approximation example.