NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

3D Magneto-Hydrodynamic Simulations of Parker Instability with Cosmic Rays

This study investigates Parker instability in an interstellar medium (ISM) near the Galactic plane using three-dimensional magneto-hydrodynamic simulations. Parker instability arises from the presence of a magnetic field in a plasma, wherein the magnetic buoyant pressure expels the gas and cause the gas to move along the field lines. The process is thought to induce the formation of giant molecular clouds in the Galaxy. In this study, the effects of cosmic-ray (CR) diffusion are examined. The ISM at equilibrium is assumed to comprise a plasma fluid and a CR fluid at various temperatures, with a uniform magnetic field passing through it in the azimuthal direction of the Galactic disk. After a small perturbation, the unstable gas aggregates at the footpoint of the magnetic fields and forms dense blobs. The growth rate of the instability increases with the strength of the CR diffusion. The formation of dense clouds is enhanced by the effect of cosmic rays (CRs), whereas the shape of the clouds depends sensitively on the initial conditions of perturbation.Comment: 4 pages, Computer Physics Communications 2011, 182, p177-17

Is the Clinical Version of the Iowa Gambling Task Relevant for Assessing Choice Behavior in Cases of Internet Addiction?

Objective: A critical issue in research related to the Iowa gambling task (IGT) is the use of the alternative factors expected value and gain–loss frequency to distinguish between clinical cases and control groups. When the IGT has been used to examine cases of Internet addiction (IA), the literature reveals inconsistencies in the results. However, few studies have utilized the clinical version of IGT (cIGT) to examine IA cases. The present study aims to resolve previous inconsistencies and to examine the validity of the cIGT by comparing performances of controls with cases of Internet gaming disorder (IGD), a subtype of IA defined by the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders.Methods: The study recruited 23 participants with clinically diagnosed IGD and 38 age-matched control participants. Based on the basic assumptions of IGT and the gain–loss frequency viewpoint, a dependent variables analysis was carried out.Results: The results showed no statistical difference between the two groups in most performance indices and therefore support the findings of most IGT-IA studies; in particular, expected value and gain–loss frequency did not distinguish between the IGD cases and controls. However, the participants in both groups were influenced by the gain–loss frequency, revealing the existence of the prominent deck B phenomenon.Conclusion: The findings provide two possible interpretations. The first is that choice behavior deficits do not constitute a characteristic feature of individuals who have been diagnosed with IGD/IA. The second is that, as the cIGT was unable to distinguish the choice behavior of the IGD/IA participants from that of controls, the cIGT may not be relevant for assessing IGD based on the indices provided by the expected value and gain–loss frequency perspectives in the standard administration of IGT

Ileocecal Burkitt's Lymphoma Presenting as Ileocolic Intussusception With Appendiceal Invagination and Acute Appendicitis

Intussusception is a common cause of abdominal pain in children. Although most cases are idiopathic, about 10% of cases have a pathologic lead point. Burkitt's lymphoma is not a common etiology. Burkitt's lymphoma might present primarily as intussusception in children but has rarely been associated with appendicitis. We report a case in which a 10-year-old obese boy who initially presented with acute appendicitis due to ileocolic intussusception with appendiceal invagination. He underwent one-trocar laparoscopy and antibiotic treatment. The symptoms recurred 10 days after discharge. Colonoscopy disclosed ileocecal Burkitt's lymphoma as the pathological lead point. This case emphasizes the importance of the age of the patient and the anatomic location of the intussusception related to possible etiology, and hence the most appropriate surgical procedure

Image operator learning coupled with CNN classification and its application to staff line removal

Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.Comment: To appear in ICDAR 201

Production of xylooligosaccharides from forest waste by membrane separation and Paenibacillus xylanase hydrolysis

Xylooligosaccharides (XO), derived from the alkaline (NaOH) extractant of Mikania micrantha, were produced using multiple staged membrane separation and enzymatic xylanolysis. Staged nanofiltration (NMX), ultrafiltration (EUMX), and centrifugation (EMX) processes for the ethanol precipitates were conducted. NMX recovered 97.26% of total xylose and removed 73.18% of sodium ions. Concentrations of total xylose were raised from 10.98 to 51.85 mg/mL by the NMX process. Recovered xylan-containing solids were hydrolyzed by the recombinant Paenibacillus xylanase. 68% XO conversions from total xylose of NMX was achieved in 24 hours. Xylopentaose (DP 5) was the major product from NMX and EMX hydrolysis. Xylohexaose (DP 6) was the major product from EUMX hydrolysis. Results of the present study suggest the applicability for XO production by nanofiltration, as NMX gave higher XO yields compared to those from a conventional ethanol-related lignocellulosic waste conversion process