Network Pruning via Feature Shift Minimization

Channel pruning is widely used to reduce the complexity of deep network models. Recent pruning methods usually identify which parts of the network to discard by proposing a channel importance criterion. However, recent studies have shown that these criteria do not work well in all conditions. In this paper, we propose a novel Feature Shift Minimization (FSM) method to compress CNN models, which evaluates the feature shift by converging the information of both features and filters. Specifically, we first investigate the compression efficiency with some prevalent methods in different layer-depths and then propose the feature shift concept. Then, we introduce an approximation method to estimate the magnitude of the feature shift, since it is difficult to compute it directly. Besides, we present a distribution-optimization algorithm to compensate for the accuracy loss and improve the network compression efficiency. The proposed method yields state-of-the-art performance on various benchmark networks and datasets, verified by extensive experiments. Our codes are available at: https://github.com/lscgx/FSM

Impact of wind farm wake steering control on blade root load

Yaw misalignment is known to affect blade root loads on wind turbines. Most of previous studies concentrate on yaw misalignment in the context of wake steering control, aiming at increasing the total output power of the wind farm. There, wake steering is compared with greedy control, in which yaw misalignment is considered to be 0. In reality, yaw misalignment also occurs in greedy control due to changes in wind direction arising from varying inflow conditions (e.g. turbulence). This paper aims at comparing these two sources of yaw misalignment-naturally changing wind direction versus active yaw in wake steering-in terms of blade root loads. To this end, SCADA data from a real wind farm is used to get yaw misalignment statistics in actual greedy control conditions. FAST.Farm is used to simulate three wind turbines arranged in series, to study maximum and damage-equivalent loads corresponding to in-plane and out-of-plane bending moments on the blades. The results show that compared with actual greedy control, wake steering control reduces the maximum load from the upstream wind turbine, but increases it from other wind turbines. Concerning the damage-equivalent loads from all wind turbines, the blade's in-plane moment is reduced, but the blade's out-of-plane moment is increased.Impact of wind farm wake steering control on blade root loadacceptedVersio

MicroRNA-22 suppresses NLRP3/CASP1 inflammasome pathway-mediated proinflammatory cytokine production by targeting the HIF-1α and NLRP3 in human dental pulp fibroblasts

Aim To investigate the synergetic regulatory effect of miR-22 on HIF-1α and NLRP3, subsequently regulating the production of the NLRP3/CASP1 inflammasome pathway-mediated proinflammatory cytokines IL-1β and IL-18 in human dental pulp fibroblasts (HDPFs) during the progression of pulpitis. Methodology Fluorescence in situ hybridization (FISH) and immunofluorescence (IF) were performed to determine the localization of miR-22-3p, NLRP3 and HIF-1α in human dental pulp tissues (HDPTs). The miR-22 mimics and inhibitor or plasmid of NLRP3 or HIF-1α were used to upregulate or downregulate miR-22 or NLRP3 or HIF-1α in HDPFs, respectively. Computational prediction via TargetScan 5.1 and a luciferase reporter assay were conducted to confirm target association. The mRNA and protein expression of HIF-1α, NLRP3, caspase-1, IL-1β and IL-18 were determined by qRT-PCR and western blotting, respectively. The release of IL-1β and IL-18 was analysed by ELISA. The significance of the differences between the experimental and control groups was determined by one-way analysis of variance, p < .05 indicated statistical significance. Results A decrease in miR-22 and an increase in HIF-1α and NLRP3 in HDPTs occurred during the transformation of reversible pulpitis into irreversible pulpitis compared with that in the healthy pulp tissues (p < .05). In the normal HDPTs, miR-22-3p was extensively expressed in dental pulp cells. HIF-1α and NLRP3 were mainly expressed in the odontoblasts and vascular endothelial cells. Whereas in the inflamed HDPTs, the odontoblast layers were disrupted. HDPFs were positive for miR-22-3p, HIF-1α and NLRP3. Computational prediction via TargetScan 5.1 and luciferase reporter assays confirmed that both NLRP3 and HIF-1α were direct targets of miR-22 in HDPFs. The miR-22 inhibitor further promoted the activation of NLRP3/CASP1 inflammasome pathway induced by ATP plus LPS and hypoxia (p < .05). In contrast, the miR-22 mimic significantly inhibited the NLRP3/CASP1 inflammasome pathway activation induced by ATP plus LPS and hypoxia (p < .05). Conclusion MiR-22, as a synergetic negative regulator, is involved in controlling the secretion of proinflammatory cytokines mediated by the NLRP3/CASP1 inflammasome pathway by targeting NLRP3 and HIF-1α. These results provide a novel function and mechanism of miR-22-HIF-1α-NLRP3 signalling in the control of proinflammatory cytokine secretion, thus indicating a potential therapeutic strategy for future endodontic treatment

Anomalous stopping of laser-accelerated intense proton beam in dense ionized matter

Ultrahigh-intensity lasers (10$^{18}$-10$^{22}$W/cm$^{2}$) have opened up new perspectives in many fields of research and application [1-5]. By irradiating a thin foil, an ultrahigh accelerating field (10$^{12}$ V/m) can be formed and multi-MeV ions with unprecedentedly high intensity (10$^{10}$A/cm$^2$) in short time scale ($\sim$ps) are produced [6-14]. Such beams provide new options in radiography [15], high-yield neutron sources [16], high-energy-density-matter generation [17], and ion fast ignition [18,19]. An accurate understanding of the nonlinear behavior of beam transport in matter is crucial for all these applications. We report here the first experimental evidence of anomalous stopping of a laser-generated high-current proton beam in well-characterized dense ionized matter. The observed stopping power is one order of magnitude higher than single-particle slowing-down theory predictions. We attribute this phenomenon to collective effects where the intense beam drives an decelerating electric field approaching 1GV/m in the dense ionized matter. This finding will have considerable impact on the future path to inertial fusion energy.Comment: 8 pages, 4 figure

Energy loss enhancement of very intense proton beams in dense matter due to the beam-density effect

Thoroughly understanding the transport and energy loss of intense ion beams in dense matter is essential for high-energy-density physics and inertial confinement fusion. Here, we report a stopping power experiment with a high-intensity laser-driven proton beam in cold, dense matter. The measured energy loss is one order of magnitude higher than the expectation of individual particle stopping models. We attribute this finding to the proximity of beam ions to each other, which is usually insignificant for relatively-low-current beams from classical accelerators. The ionization of the cold target by the intense ion beam is important for the stopping power calculation and has been considered using proper ionization cross section data. Final theoretical values agree well with the experimental results. Additionally, we extend the stopping power calculation for intense ion beams to plasma scenario based on Ohm's law. Both the proximity- and the Ohmic effect can enhance the energy loss of intense beams in dense matter, which are also summarized as the beam-density effect. This finding is useful for the stopping power estimation of intense beams and significant to fast ignition fusion driven by intense ion beams

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol); however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy

Plane Machining by Inner-Jet Electrochemical Milling of TiB2/7050 Aluminum Matrix Composite

Electrochemical milling (ECM) is an ideal technique for machining thin-walled structural parts of aluminum matrix composites. Adopting a reasonable tool cathode structure, feed rate, and processing method can improve the machining efficiency. In this study, a tool cathode with a reasonable structure was selected through flow field simulation. Then, the material removal rate (MRR) and surface roughness were studied using various ECM parameters. Finally, the transverse movement and processing method in which the starting position was rotated 90° were studied, and a plane of 59 × 59 mm was machined. The experimental results show that using an appropriate tool cathode can create a more uniform flow field. The MRR was 168.6 mm3/min and the surface roughness (Ra) was 3.329 µm at a feed rate of 30 mm/min. For machining larger plane structures, a transverse movement of 7 mm is verified to be the most suitable because of the best smoothness in the middle of the two processes. By using the same machining method and rotating the starting position 90°, the flatness of the processing plane decreased from 0.296 mm to 0.251 mm, a reduction of 15.2% compared to that obtained in the first processing

Holistic prediction for public transport crowd flows: A spatio dynamic graph network approach

Ministry of Education, Singapore under its Academic Research Funding Tier

A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs

Electronic nose (E-nose), as a device intended to detect odors or flavors, has been widely used in many fields. Many labeled samples are needed to gain an ideal E-nose classification model. However, the labeled samples are not easy to obtain and there are some cases where the gas samples in the real world are complex and unlabeled. As a result, it is necessary to make an E-nose that cannot only classify unlabeled samples, but also use these samples to modify its classification model. In this paper, we first introduce a semi-supervised learning algorithm called S4VMs and improve its use within a multi-classification algorithm to classify the samples for an E-nose. Then, we enhance its performance by adding the unlabeled samples that it has classified to modify its model and by using an optimization algorithm called quantum-behaved particle swarm optimization (QPSO) to find the optimal parameters for classification. The results of comparing this with other semi-supervised learning algorithms show that our multi-classification algorithm performs well in the classification system of an E-nose after learning from unlabeled samples