Extended Self Organizing Maps for Structured Domain: Models and Learning

Many learning problems in computer vision can be posed as structured prediction problems, where the input and output instances are structured objects such as trees, graphs or strings rather than, single labels {+1, -1} or scalars. Kernel methods such as Structured Support Vector Machines, Twin Gaussian Processes (TGP), Structured Gaussian Processes, and vector-valued Reproducing Kernel Hilbert Spaces (RKHS), offer powerful ways to perform learning and inference over these domains. Positive definite kernel functions allow us to quantitatively capture similarity between a pair of instances over these arbitrary domains. A poor choice of the kernel function, which decides the RKHS feature space, often results in poor performance. Automatic kernel selection methods have been developed, but have focused only on kernels on the input domain (i.e.'one-way'). In this work, we propose a novel and efficient algorithm for learning kernel functions simultaneously, on both input and output domains. We introduce the idea of learning polynomial kernel transformations, and call this method Simultaneous Twin Kernel Learning (STKL). STKL can learn arbitrary, but continuous kernel functions, including 'one-way' kernel learning as a special case. We formulate this problem for learning covariances kernels of Twin Gaussian Processes. Our experimental evaluation using learned kernels on synthetic and several real-world datasets demonstrate consistent improvement in performance of TGP's.

Introduction: Protein conformational switches are often constructed by fusing an input domain, which recognizes a target ligand, to an output domain that establishes a biological response. Prior designs have employed binding-induced folding of the input domain to drive a conformational change in the output domain. Adding a second input domain can in principle harvest additional binding energy for performing useful work. It is not obvious, however, how to fuse two binding domains to a single output domain such that folding of both binding domains combine to effect conformational change in the output domain.Methods: Here, we converted the ribonuclease barnase (Bn) to a switchable enzyme by duplicating a C-terminal portion of its sequence and appending it to its N-terminus, thereby establishing a native fold (OFF state) and a circularly permuted fold (ON state) that competed for the shared core in a mutually exclusive fashion. Two copies of FK506 binding protein (FKBP), both made unstable by the V24A mutation and one that had been circularly permuted, were inserted into the engineered barnase at the junctions between the shared and duplicated sequences.Results: Rapamycin-induced folding of FK506 binding protein stretched and unfolded the native fold of barnase via the mutually exclusive folding effect, and rapamycin-induced folding of permuted FK506 binding protein stabilized the permuted fold of barnase by the loop-closure entropy principle. These folding events complemented each other to turn on RNase function. The cytotoxic switching mechanism was validated in yeast and human cells, and in vitro with purified protein.Discussion: Thermodynamic modeling and experimental results revealed that the dual action of loop-closure entropy and mutually exclusive folding is analogous to an engine transmission in which loop-closure entropy acts as the low gear, providing efficient switching at low ligand concentrations, and mutually exclusive folding acts as the high gear to allow the switch to reach its maximum response at high ligand concentrations.

A primary issue in black-box testing is how to generate adequate test cases from input domain of the system under test on the basis of user's requirement specification. However, for some types of systems including embedded systems, developing test cases from output domain is more suitable than developing from input domain, especially, when the output domain is smaller. This approach ensures better reliability of the system under test. In this paper, the authors present a new approach to automate the generation of test cases from output domain of a pilot project "Temperature Monitoring and Controlling of Nuclear Reactor System" (TMCNRS) which is an embedded system developed using modified Cleanroom Software Engineering methodology. An Automated Test Case Generator (ATCG) that uses Genetic algorithms (GAs) extensively and generates test cases from output domain is proposed. The ATCG generates test cases which are useful to conduct pseudo - exhaustive testing to detect single, double and several multimode faults in the system. The generator considers most of the combinations of outputs, and finds the corresponding inputs while optimizing the number of test cases generated. In order to investigate the effectiveness of this approach, test cases were generated by ATCG and the tests were conducted on the target embedded system at a minimum cost and time. Experimental results show that this approach is very promising.

Deep learning (DL) has shown unprecedented performance for many image analysis and image enhancement tasks. Yet, solving large-scale inverse problems like tomographic reconstruction remains challenging for DL. These problems involve non-local and space-variant integral transforms between the input and output domains, for which no efficient neural network models are readily available. A prior attempt to solve tomographic reconstruction problems with supervised learning relied on a brute-force fully connected network and only allowed reconstruction with a 1284 system matrix size. This cannot practically scale to realistic data sizes such as 5124 and 5126 for three-dimensional datasets. Here we present a novel framework to solve such problems with DL by casting the original problem as a continuum of intermediate representations between the input and output domains. The original problem is broken down into a sequence of simpler transformations that can be well mapped onto an efficient hierarchical network architecture, with exponentially fewer parameters than a fully connected network would need. We applied the approach to computed tomography (CT) image reconstruction for a 5124 system matrix size. This work introduces a new kind of data-driven DL solver for full-size CT reconstruction without relying on the structure of direct (analytical) or iterative (numerical) inversion techniques. This work presents a feasibility demonstration of full-scale learnt reconstruction, whereas more developments will be needed to demonstrate superiority relative to traditional reconstruction approaches. The proposed approach is also extendable to other imaging problems such as emission and magnetic resonance reconstruction. More broadly, hierarchical DL opens the door to a new class of solvers for general inverse problems, which could potentially lead to improved signal-to-noise ratio, spatial resolution and computational efficiency in various areas.

In view of present advancements in computing, with the development of distributed environment, many problems have to deal with distributed input data where individual data privacy is the most important issue to be addressed, for the concern of data owner by extending the privacy preserving notion to the original learning algorithms. Privacy Preserving Data Mining has become an active research area in addressing various privacy issues while bringing out solutions for them. There has been lot of progress in developing secure algorithms and models, able to preserve privacy using various data mining techniques like association, classification and clustering, where as importance of privacy preserving techniques applied for learning algorithms related to neural networks for mining problems are still in infancy. Our work in this paper focused on preserving privacy of an individual, using self organizing map (SOM) adopted for collaborative clustering of distributed data between multiple parties. We present Privacy Preserving Collaborative Clustering method using SOM (PPCSOM) for Horizontal Data Distribution, which allows multiple parties perform clustering in a collaborative approach using SOM neural network, without revealing their data directly to each other, in order to preserve privacy of all parties. Our simulation results shows that implementation of PPCSOM method achieves comparable accuracy and assured Privacy than the original non privacy preserving SOM algorithm.

Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification

Autonomous underwater vehicles (AUVs) have great advantages for activities in deep sea, and expected as the attractive tool. However, AUVs have various problems which should be solved. In this paper, the Self-Organizing Map (SOM) is applied as the clustering method for the navigation system. The SOM is known as one of the effective methods to extract the principle feature from many parameters and decrease the dimension of parameters. Through the competitive learning algorithms, the obtained is tuned to express specific features of the input signals. We have been investigating the possibility of navigation system based on SOM through simulations are experiments with an AUV called Twin-Burger. The learning algorithm of usual SOM is unsupervised learning. However, supervised learning algorithms should be introduced because the relationship between distances information and desirable behavior of the robot, that is, the relationship from inputs to outputs should be acquired and learned. In this paper, a supervised learning algorithm is introduced into SOM and a method to adapt the local to its environment by learning and evaluating the trajectory of robot is proposed. In the proposed method, the map is made static and digital value as teaching data. In order to include more information of environment in the initial map, the trajectories of robot are evaluated, and the evaluation is utilized in the learning process. This method enables the to have both the effect of dynamics of robot and environmental information. The efficiency of the method is investigated through the simulations and experiments.

An incremental adaptive neural network model for online noisy data regression and its application to compartment fire studies

Accurately translating medical images between different modalities, such as Computed Tomography (CT) to Magnetic Resonance Imaging (MRI), has numerous downstream clinical and machine learning applications. While several methods have been proposed to achieve this, they often prioritize perceptual quality with respect to output domain features over preserving anatomical fidelity. However, maintaining anatomy during translation is essential for many tasks, e.g., when leveraging masks from the input domain to develop a segmentation model with images translated to the output domain. To address these challenges, we propose ContourDiff with Spatially Coherent Guided Diffusion (SCGD), a novel framework that leverages domain-invariant anatomical contour representations of images. These representations are simple to extract from images, yet form precise spatial constraints on their anatomical content. We introduce a diffusion model that converts contour representations of images from arbitrary input domains into images in the output domain of interest. By applying the contour as a constraint at every diffusion sampling step, we ensure the preservation of anatomical content. We evaluate our method on challenging lumbar spine and hip-and-thigh CT-to-MRI translation tasks, via (1) the performance of segmentation models trained on translated images applied to real MRIs, and (2) the foreground FID and KID of translated images with respect to real MRIs. Our method outperforms other unpaired image translation methods by a significant margin across almost all metrics and scenarios. Moreover, it achieves this without the need to access any input domain information during training and we further verify its zero-shot capability, showing that a model trained on one anatomical region can be directly applied to unseen regions without retraining. Our code is available at <a href='https://github.com/mazurowski-lab/ContourDiff'>https://github.com/mazurowski-lab/ContourDiff</a>

The Self-Organizing Map (SOM) is a popular algorithm to analyze the structure of a dataset. However, some topological constraints of the SOM are fixed before the learning and may not be relevant regarding to the data structure. In this paper we propose to improve the SOM performance with a new algorithm which learn the topological constraints of the map using data structure information. Experiments on artificial and real databases show that algorithm achieve better results than SOM. This is not the case with trivial topological constraint relaxation because of the high increase of the Topological error.

<!--[if gte mso 10]> <mce:style><! /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Norm&aacute;l t&aacute;bl&aacute;zat"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} > <! [endif] ><p class="MsoNormal" style="text-align: justify; line-height: 200%;" _mce_style="text-align: justify; line-height: 200%;"><span style="font-size: 10pt; line-height: 200%; font-family: ";Arial";,";sans-serif";;" lang="EN-US">Traditional techniques of identification of a depositional body from core data are costly and sometimes difficult to extrapolate to uncored wells. However, application of Kohonen&rsquo;s Self Organized Map (SOM) approach may be regarded to be a potential method for pattern recognition problems. A combination of Indicator Kriging and SOM for log-porosity and sand content data coming from quantitative well-log interpretations is used for identifying the spatial pattern of some delta-plain sub-environments. The basic high-dimensional property fields are defined by 3D shapes of well known depositional facieses. Many parameters as log-porosity and sand content data can be used to determine geo-property as a lithological pattern using SOM. This step of method can discover spatial patterns as clusters in unstructured data set because SOM is based on clustering algorithm. However, this approach not necessarily makes sure, that the resulting disjunctive clusters can show any meaningful depositional geometry. So at last the final geometry is given using Indicator Kriging method, which uses threshold values derived from property values of clusters.</span><-->Traditional techniques to identify a depositional body from core data are costly and sometimes difficult to extrapolate to uncored wells. The application of Kohonen&rsquo;s Self Organized Map (SOM) approach may be useful for the interpretation of a depositional rock body through well-log data. SOM is based on a clustering algorithm and this method can be used to discover spatial patterns occurring as clusters in unstructured data sets. An example of the application of SOM is presented whereby clusters through SOM can indicate the contours of well-known depositional patterns such as sub-environments.

In this paper, two new families of constructive self-organizing maps (SOMs), SAM-SOM* and MAM-SOM*, are proposed. These families are specially useful for information retrieval from large databases, high-dimensional spaces and complex distance functions which usually consume a long time. They are generated by incorporating spatial access method (SAM) and metric access method (MAM) into SOM with the maximum insertion rate, i.e. the case when a new unit is created for each pattern presented to the network. In this specific case, the network presents interesting advantages and acquires new properties which are quite different of traditional SOM. In a constructive SOM, if new units are rarely inserted into network, the training algorithm would probably need a long time to converge. On the other hand, if new units are inserted frequently, the training algorithm would not have enough time to adapt these units to the data distribution. Besides, training time is increased because the search for the winning neuron is traditionally performed sequentially. The use of SAM and MAM combined with SOM open the possibility of training constructive SOM with as much units as existing patterns with less time and interesting advantages compared with both models: Kohonen network SOM and SAMSOM model (SOM using SAM). Advantages and drawbacks of these new families are also discussed. These new families are useful to improve both SOM and SAM techniques.

With an onset of social network amount of data is increasing day by day. In order to analyze the data and extract useful information, the data mining technology can be used. Clustering is one of the popular method of data mining. Clustering can be used for visualizing and analyzing of data. We are discussing Kohonen SOM. We are using neural networks, as a data mining tool which provides statistical observation and layout from big data-sets. We determine how Self-Organizing Kohonen Maps, can be efficiently used for data mining purposes. The Self Organizing Map (SOM) unsupervised learning is an effective computational tool in data mining processes. Self-Organizing Maps (SOMs) used to visualize social network dataset. We used Self-Organizing Map for clustering and analyzing high-dimensional and complex social network datasets. This paper also visualizes SOM neighbor connection, SOM neighbor weight distance, SOM weight position. We perform self organizing map algorithm for social network dataset in matlab.

This chapter focuses on the self-organizing map (SOM) model, which was applied to predict outflow nutrient concentrations for ICW treating farmyard runoff. The SOM showed that the outflow ammonia–nitrogen concentrations were strongly correlated with water temperature and salt concentrations, indicating that ammonia–nitrogen removal is effective as low salt concentrations and comparatively high temperatures in ICW. The SRP removal was predominantly affected by salt and dissolved oxygen concentrations. In addition, pH and temperature were weakly correlated with SRP removal, suggesting that SRP was easily removed within ICW if salt concentrations were low and dissolved oxygen, temperature, and pH values were high. The SOM model performed very well in predicting the nutrient concentrations with water quality variables such as temperature, conductivity, and dissolved oxygen, which can be measured cost-effectively. The results indicate that the SOM model was an appropriate approach to monitor wastewater treatment processes in ICW.

In this paper, the Self Organizing Maps (SOM) learning and classification algorithms are firstly modified. Then via the introduction of match-degree, reduction-rate and quantification error of reducing sample, a novel approach to intrusion detection based on Multi-layered modified SOM neural network and Principal Component Analysis (PCA) is proposed. In this model, PCA is applied to feature selection, and Multi-layered SOM is designed to subdivide the imprecise clustering in single-layered SOM layer by layer. Experimental results demonstrate that this model can provide a precise and efficient way for implementing the classifier in intrusion detection.KeywordsFalse Positive RateIntrusion DetectionTest DatasetAnomaly DetectionQuantification ErrorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.