Context-Aware Semantic Inpainting

IEEE In recent times, image inpainting has witnessed rapid progress due to the generative adversarial networks (GANs) that are able to synthesize realistic contents. However, most existing GAN-based methods for semantic inpainting apply an auto-encoder architecture with a fully connected layer, which cannot accurately maintain spatial information. In addition, the discriminator in existing GANs struggles to comprehend high-level semantics within the image context and yields semantically consistent content. Existing evaluation criteria are biased toward blurry results and cannot well characterize edge preservation and visual authenticity in the inpainting results. In this paper, we propose an improved GAN to overcome the aforementioned limitations. Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context. Furthermore, we also introduce two novel measures to better assess the quality of image inpainting results. The experimental results demonstrate that our method outperforms the state-of-the-art under a wide range of criteria

Towards Adversarial Robustness via Feature Matching

Image classification systems are known to be vulnerable to adversarial attacks, which are imperceptibly perturbed but lead to spectacularly disgraceful classification. Adversarial training is one of the most effective defenses for improving the robustness of classifiers. We introduce an enhanced adversarial training approach in this work. Motivated by human's consistently accurate perception of surroundings, we explore the artificial attention of deep neural networks in the context of adversarial classification. We begin with an empirical analysis of how the attention of artificial systems will change as the model undergoes adversarial attacks. Observation is that the class-specific attention gets diverted and subsequently induces wrong prediction. To that end, we propose a regularizer encouraging the consistency in the artificial attention on the clean image and its adversarial counterpart. Our method shows improved empirical robustness over the state-of-the-art, secures 55.74% adversarial accuracy on CIFAR-10 with perturbation budget of 8/255 under the challenging untargeted attack in white-box settings. Further evaluations on CIFAR-100 also show our potential for a desirable boost in adversarial robustness for deep neural networks. Code and trained models of our work are available at: https://github.com/lizhuorong/Towards-Adversarial-Robustness-via-Feature-matching

PCA Based Robust Motion Data Recovery.

Human motion tracking is a prevalent technique in many fields. A common difficulty encountered in motion tracking is the corrupted data is caused by detachment of markers in 3D motion data or occlusion in 2D tracking data. Most methods for missing markers problem may quickly become ineffective when gaps exist in the trajectories of multiple markers for an extended duration. In this paper, we propose the principal component eigenspace based gap filling methods that leverage a training sample set for estimation. The proposed method is especially beneficial in the scenario of motion data with less predictable or repeated movement patterns, and that of even missing entire frames within an interval of a sequence. To highlight algorithm robustness, we perform algorithms on twenty test samples for comparison. The experimental results show that our methods are numerical stable and fast to work

Fabrication and Characterization of Al/NiO Energetic Nanomultilayers

The redox reaction between Al and metallic oxide has its advantage compared with intermetallic reaction and Al/NiO nanomutlilayers are a promising candidate for enhancing the performance of energetic igniter. Al/NiO nanomutlilayers with different modulation periods are prepared on alumina substrate by direct current (DC) magnetron sputtering. The thicknesses of each period are 250 nm, 500 nm, 750 nm, 1000 nm, and 1500 nm, respectively, and the total thickness is 3 μm. The X-ray diffraction (XRD) and scanning electron microscope (SEM) results of the as-deposited Al/NiO nanomutlilayers show that the NiO films are amorphous and the layered structures are clearly distinguished. The X-ray photoelectron spectroscopy (XPS) demonstrates that the thickness of Al2O3 increases on the side of Al monolayer after annealing at 450°C. The thermal diffusion time becomes greater significantly as the amount of thermal boundary conductance across the interfaces increases with relatively smaller modulation period. Differential scanning calorimeter (DSC) curve suggests that the energy release per unit mass is below the theoretical heat of the reaction due to the nonstoichiometric ratio between Al and NiO and the presence of impurities

PEI-coated Fe3O4 nanoparticles enable efficient delivery of therapeutic siRNA targeting REST into glioblastoma cells

Glioblastomas (GBM) are the most frequent brain tumors lacking efficient treatment. The increasingly elucidated gene targets make siRNA-based gene therapy a promising anticancer approach, while an efficient delivery system is urgently needed. Here, polyethyleneimine (PEI)-coated Fe3O4 nanoparticles (NPs) have been developed and applied for siRNA delivery into GBM cells to silence repressor element 1-silencing transcription factor (REST). The prepared PEI-coated Fe3O4 NPs were characterized as magnetic nanoparticles with a positive charge, by transmission electronic microscopy, dynamic light-scattering analysis and a magnetometer. By gel retardation assay, the nanoparticles were found to form complexes with siRNA and the interaction proportion of NP to siRNA was 2.8:1. The cellular uptake of NP/siRNA complexes was verified by prussian blue staining, fluorescence labeling and flow cytometry in U-87 and U-251 GBM cells. Furthermore, the REST silencing examined by realtime polymerase chain reaction (PCR) and Western blotting presented significant reduction of REST in transcription and translation levels. Upon the treatment of NP/siRNA targeting REST, the GBM cell viabilities were inhibited and the migration capacities were repressed remarkably, analyzed by cell counting kit-8 and transwell assay separately. In this study, we demonstrated the PEI-coated Fe3O4 nanoparticle as a vehicle for therapeutic siRNA delivery, at an appropriate NP/siRNA weight ratio for REST silencing in GBM cells, inhibiting cell proliferation and migration efficiently. These might represent a novel potential treatment strategy for GBM

Spin Speed and Supportedness Correlation and Evolution of Galaxy-Halo Systems

Galaxy angular momenta (spins) contain valuable cosmological information, complementing with their positions and velocities. The baryonic spin direction of galaxies have been probed as a reliable tracer of their host halos and the primordial spin modes. Here we use the TNG100 simulation of the IllustrisTNG project to study the spin magnitude correlations between dark matter, gas and stellar components of galaxy-halo systems, and their evolutions across the cosmic history. We find that these components generate similar initial spin magnitudes from the same tidal torque in Lagrangian space. At low redshifts, the gas component still traces the spin magnitude of dark matter halo and the primordial spin magnitude. However, the traceability of stellar component depends on the $ex$ $situ$ stellar mass fraction, $f_{\rm acc}$. Our results suggest that the galaxy baryonic spin magnitude can also serve as a tracer of their host halo and the initial perturbations, and the similarity of their evolution histories affects the galaxy-halo correlations.Comment: 9 pages, 7 figures, comments welcom

An RBF-based reparameterization method for constrained texture mapping

Texture mapping has long been used in computer graphics to enhance the realism of virtual scenes. However, to match the 3D model feature points with the corresponding pixels in a texture image, surface parameterization must satisfy specific positional constraints. However, despite numerous research efforts, the construction of a mathematically robust, foldover‐free parameterization that is subject to positional constraints continues to be a challenge. In the present paper, this foldover problem is addressed by developing radial basis function (RBF) based reparameterization. Given initial 2D embedding of a 3D surface, the proposed method can reparameterize 2D embedding into a foldover ‐free 2D mesh, satisfying a set of user‐specified constraint points. In addition, this approach is mesh‐free. Therefore, generating smooth texture mapping results is possible without extra smoothing optimization

Coherent postionization dynamics of molecules based on adiabatic strong-field approximation

Open-system density matrix methods typically employ incoherent population injection to investigate the postionization dynamics in strong laser fields. The presence of coherence injection has long been a subject of debate. In this context, we introduce a coherence injection model based on the adiabatic strong-field approximation (ASFA). This model effectively predicts ionic coherence resulting from directional tunnel ionization. With increasing field strength, the degree of coherence predicted by the ASFA model gradually deviates from that of the SFA model but remains much milder compared to the results of the simple and partial-wave expansion models. The impact of coherence injection on the postionization molecular dynamics is explored in O$_2$ and N$_2$. We find that the ionization-induced vibrational coherence strongly enhances the population inversion of $X^2 \Sigma _g^+ -B^2 \Sigma _u^+$ in N$_2^+$ and the dissociation probability of O$_2^+$. Conversely, the ionization-induced vibronic coherences have inhibitory effects on the related transitions. These findings reveal the significance of including the vibronic-state-resolved coherence injection in simulating molecular dynamics following strong-field ionization.Comment: 12 pages, 7 figure

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model. Results: The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density ($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and orientation ($p$ = 0.012). Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC. The codes and dataset are available at https://github.com/bupt-ai-cz/BALNMPComment: Update Table 1 and corresponding description