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

Supported 3-D Pt nanostructures: the straightforward synthesis and enhanced electrochemical performance for methanol oxidation in an acidic medium

Noble metal nanostructures with branched morphologies [i.e., 3-D Pt nanoflowers (NFs)] by tridimensionally integrating onto conductive carbon materials are proved to be an efficient and durable electrocatalysts for methanol oxidation. The well-supported 3-D Pt NFs are readily achieved by an efficient cobalt-induced/carbon-mediated galvanic reaction approach. Due to the favorable nanostructures (3-D Pt configuration allowing a facile mass transfer) and supporting effects (including framework stabilization, spatially separate feature, and improved charge transport effects), these 3-D Pt NFs manifest much higher electrocatalytic activity and stability toward methanol oxidation than that of the commercial Pt/C and Pt-based electrocatalysts.Web of Scienc

A receiver-initiated soft-state probabilistic multicasting protocol in wireless ad hoc networks

A novel Receiver-Initiated Soft-State Probabilistic multicasting protocol (RISP) for mobile ad hoc network is proposed in this paper. RISP introduces probabilistic forwarding and soft-state for making relay decisions. Multicast members periodically initiate control packets, through which intermediate nodes adjust the forwarding probability. With a probability decay function (soft-state), routes traversed by more control packets are reinforced, while the less utilized paths are gradually relinquished. In this way, RISP can adapt to node mobility: at low mobility, RISP performs similar to a tree-based protocol; at high mobility, it produces a multicast mesh in the network. Simulation results show RISP has lower delivery redundancy than meshbased protocols, while achieving higher delivery ratio. Further, the control overhead is lower than other compared protocols. © 2005 IEEE.published_or_final_versio

A receiver-initiated soft-state probabilistic multicasting protocol in wireless ad hoc networks

A novel Receiver-Initiated Soft-State Probabilistic multicasting protocol (RISP) for mobile ad hoc network is proposed in this paper. RISP introduces probabilistic forwarding and soft-state for making relay decisions. Multicast members periodically initiate control packets, through which intermediate nodes adjust the forwarding probability. With a probability decay function (soft-state), routes traversed by more control packets are reinforced, while the less utilized paths are gradually relinquished. In this way, RISP can adapt to node mobility: at low mobility, RISP performs similar to a tree-based protocol; at high mobility, it produces a multicast mesh in the network. Simulation results show RISP has lower delivery redundancy than meshbased protocols, while achieving higher delivery ratio. Further, the control overhead is lower than other compared protocols. © 2005 IEEE.published_or_final_versio

Label Propagation for Graph Label Noise

Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node features and graph topology as input, and become more susceptible to label noise through message-passing mechanisms. Recently, only a few works have been proposed to tackle the label noise on graphs. One major limitation is that they assume the graph is homophilous and the labels are smoothly distributed. Nevertheless, real-world graphs may contain varying degrees of heterophily or even be heterophily-dominated, leading to the inadequacy of current methods. In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes. We begin by conducting two empirical analyses to explore the impact of graph homophily on graph label noise. Following observations, we propose a simple yet efficient algorithm, denoted as LP4GLN. Specifically, LP4GLN is an iterative algorithm with three steps: (1) reconstruct the graph to recover the homophily property, (2) utilize label propagation to rectify the noisy labels, (3) select high-confidence labels to retain for the next iteration. By iterating these steps, we obtain a set of correct labels, ultimately achieving high accuracy in the node classification task. The theoretical analysis is also provided to demonstrate its remarkable denoising "effect". Finally, we conduct experiments on 10 benchmark datasets under varying graph heterophily levels and noise types, comparing the performance of LP4GLN with 7 typical baselines. Our results illustrate the superior performance of the proposed LP4GLN

Development and Investigation of Low Collagen Degradability Unhairing Enzyme by Gene Modification

Content: Unhairing process brought serious pollution, and enzyme application for replacing polluting chemicals in unhairing process attracted much attention in recent years. However, the unhairing enzymes haven’t been accepted widely in actual production due to low purity, complex composition and poor stability. To solve these problems, unhairing enzyme is suggested to be improved by genetic modification in this research. The High-keratinase-producing gene (KerT), which was extracted from B. amyloliquefaciens TCCC11319, was introduced into the B.subtilis WB600 by heterologous expression. Because Bacillus subtilis WB600 is deficient in six extracellular proteases, this process successfully reduced the collagenolytic protease content in crude broth as well as improved the keratinase content. Meantime, the recombinant KerT produced by B.subtilis WB600 had the obviously unhairing effect to remove hairs. The results showed that the collagen degradability of recombinant KerT was slightly and it did not cause any adverse effects on the hide quality. This research will contribute to the development of unhairing enzyme, and the novel unhairing enzyme might be applied as the key factor for the advanced cleaning biotechnology in leather production process. Take-Away: 1. The keratinase gene KerT was firstly reported and analyzed which was extracted from B. amyloliquefaciens TCCC11319. 2. The collagenolytic protease activity of unhairing enzyme was successfully inhibited by heterologous expression of kerT. 3. The unhairing effect of this novel unhairing enzyme was similar to current Sulphur-lime method without damaging hide structure

Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an $H_{\infty}$ filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.Comment: to appear in IEEE Trans. Multimedi