We propose a simple but efficient multichannel correlation network (MCCNet) so that the output frames can be directly aligned with inputs in the hidden feature space, while maintaining the desired stylistic patterns. A similarity loss function focused on inner channels is utilized to counteract the negative consequences of omitting non-linear operations such as softmax, thereby enforcing strict alignment. Furthermore, to boost MCCNet's proficiency in diverse lighting environments, we introduce a training component that accounts for illumination loss. Evaluations, both qualitative and quantitative, show that MCCNet effectively handles style transfer across a wide variety of video and image types. At https://github.com/kongxiuxiu/MCCNetV2, the MCCNetV2 code is readily available.
Despite the success of deep generative models in facial image editing, their direct use in video editing is complicated by several inherent issues. These challenges include enforcing 3D constraints, sustaining subject identity, and guaranteeing temporal coherence throughout the video sequence. To effectively address these difficulties, we introduce a novel framework operating within the StyleGAN2 latent space, for identity- and shape-aware editing propagation on face videos. insect biodiversity To address the difficulties of maintaining the identity, preserving the original 3D motion, and preventing shape distortions in human face video frames, we disentangle the StyleGAN2 latent vectors to separate appearance, shape, expression, and motion from the identity. To map a sequence of image frames to continuous latent codes with 3D parametric control, an edit encoding module is trained in a self-supervised manner, using both identity loss and triple shape losses. Our model has the ability to propagate edits using various approaches; these include: I. direct modification of a particular keyframe's visual characteristics, and II. An implicit procedure alters a face's form, mirroring a reference image, with III being another point. Semantic edits are facilitated by latent variables. Experiments across a multitude of video types in diverse settings show our method's superiority over animation-based techniques and the latest deep generative models.
The efficacy of decision-making reliant on high-quality data is fully contingent upon well-structured processes designed to ensure data appropriateness. There are variations in processes across organizations, and also in how these processes are conceived and enacted by those with the tasks of doing so. E multilocularis-infected mice We present a survey of 53 data analysts, across numerous industry sectors, encompassing in-depth interviews with 24 of them, about the application of computational and visual methods in the context of data characterization and quality investigation. The paper presents contributions across two significant areas. Our data profiling tasks and visualization techniques, far exceeding those found in other published material, highlight the necessity of grasping data science fundamentals. The application's second query, concerning the nature of effective profiling, analyzes the diverse profiling activities, highlighting the unconventional practices, showcasing examples of effective visualizations, and recommending the formalization of procedures and the creation of comprehensive rule sets.
The precise determination of SVBRDFs from 2D images of lustrous, diverse 3D objects is a highly desired outcome in fields such as cultural heritage preservation, where precisely capturing color fidelity is essential. Prior work, exemplified by the promising framework of Nam et al. [1], simplified the problem by assuming specular highlights exhibit symmetry and isotropy around an estimated surface normal. This work significantly refines the prior foundation with substantial alterations. We analyze the surface normal's role as a symmetry axis and compare nonlinear optimization for normals with the linear approximation of Nam et al., finding nonlinear optimization to be more effective, although emphasizing that accurate surface normal estimates are critical for the reconstructed color appearance of the object. selleck chemical Examining the use of a monotonicity constraint for reflectance, we develop a broader approach that extends to encompassing continuity and smoothness when optimizing continuous monotonic functions found in microfacet distributions. Last, we delve into the consequences of substituting an arbitrary 1-dimensional basis function with the standard GGX parametric microfacet distribution, discovering this substitution to be a reasonable approximation, exchanging precision for expediency in certain implementations. For high-fidelity applications, like those in cultural heritage or e-commerce, both representations can be used within pre-existing rendering systems, including game engines and online 3D viewers, while upholding accurate color rendering.
Biomolecules, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), are essential components in a wide array of crucial biological processes. Disease biomarkers, they can be, due to their dysregulations that cause complex human diseases. Biomarker identification assists in the process of diagnosing, treating, predicting the course of, and preventing diseases. DFMbpe, a novel deep neural network combining factorization machines and binary pairwise encoding, is presented in this study to identify disease-related biomarkers. For a comprehensive analysis of the interplay between characteristics, a binary pairwise encoding method is developed to obtain the basic feature representations for every biomarker-disease combination. Subsequently, the raw features are mapped to equivalent embedding vector representations. Thereafter, the factorization machine is applied for the purpose of obtaining extensive low-order feature interdependence, whilst the deep neural network is leveraged to derive deep high-order feature interdependence. Ultimately, the merging of two feature varieties leads to the definitive prediction. Unlike other methods for identifying biomarkers, the binary pairwise encoding strategy considers the relationship between features regardless of their non-cooccurrence in any single data point, and the DFMbpe architecture equally prioritizes both the impacts of first-order and subsequent-order feature interactions. Empirical evidence gathered from the experiment highlights the substantial superiority of DFMbpe over the existing state-of-the-art identification models across cross-validation and independent data evaluation. Finally, the impressive performance of this model is further substantiated by three case study analyses.
Emerging x-ray imaging technologies, able to capture phase and dark-field information, grant medicine a complementary sensitivity to the established technique of conventional radiography. The application of these methods spans a multitude of scales, from virtual histology analysis to clinical chest imaging, commonly involving the integration of optical components such as gratings. We examine the process of extracting x-ray phase and dark-field signals from bright-field images collected using a coherent x-ray source and a detector alone. The diffusive generalization of the transport-of-intensity equation—the Fokker-Planck equation—is the foundation for our paraxial imaging approach. The Fokker-Planck equation, when used in propagation-based phase-contrast imaging, proves that two intensity images are sufficient to acquire both the sample's projected thickness and its dark-field signal. Employing simulated and experimental data sets, we showcase the efficacy of the algorithm's results. The x-ray dark-field signal, as demonstrated, can be extracted from propagation-based image data, and the accurate determination of sample thickness benefits from considering the effects of dark-field imaging. The proposed algorithm's anticipated benefits encompass biomedical imaging, industrial settings, and additional applications focused on non-invasive imaging.
The desired controller's design, implemented within the confines of a lossy digital network, is achieved via this work through the application of a dynamic coding method and optimized packet lengths. Sensor node transmissions are initially scheduled using the weighted try-once-discard (WTOD) protocol. To substantially improve coding accuracy, a time-varying coding length encoding function, coupled with a state-dependent dynamic quantizer, has been developed. A designed state-feedback controller will ensure the controlled system's mean-square exponential ultimate boundedness in the face of potential packet dropouts. Importantly, the coding error is shown to directly affect the convergent upper limit, which is further refined through the optimization of the coding lengths. Finally, the simulation's results are shown using the double-sided linear switched reluctance machine systems.
The shared inherent knowledge of a population of individuals is instrumental to the capabilities of evolutionary multitasking optimization (EMTO). Even so, the current EMTO methods mostly emphasize improving convergence by employing parallel processing insights linked to different tasks. This fact, a consequence of the unexploited knowledge concerning the diversity, may result in local optimization problems affecting EMTO. Employing a diversified knowledge transfer strategy, termed DKT-MTPSO, this article presents a solution to this multifaceted problem in the context of multitasking particle swarm optimization algorithms. Given the current trajectory of population evolution, an adaptive mechanism for task selection is established to control the source tasks contributing to the target tasks. Secondarily, a reasoning process for knowledge, incorporating elements of convergence and the multiplicity of diverse knowledges, is implemented. A method for knowledge transfer, diversified to encompass various transfer patterns, is developed in order to broaden the range of generated solutions guided by accumulated knowledge, enabling a comprehensive exploration of the task search space, improving EMTO's effectiveness by minimizing reliance on local optima.