120 research outputs found
Figure 10 from: Alharbi SA, Al-Qthanin RN (2021) Taxonomic revision of Ceropegia sect. Huernia (Asclepiadoideae, Apocynaceae) in Saudi Arabia with three new combinations. PhytoKeys 174: 47-80. https://doi.org/10.3897/phytokeys.174.58867
No full textFigure 10 Ceropegia lodarensis var. lodarensisA ex J Lavranos 1789, sub DP3604, Yemen, (H. lodarensis, Type) BAlharbi S6B (H. collenetteae) CAlharbi S9B (H. collenetteae) D ex Collenette 549 sub DP6865, Jabal Al Sawdah, (H. saudi-arabica, Type) EAlharbi S2B (H. collenetteae) FAlharbi S18a (H. collenetteae) GCollenette 2227, Al-Hadda, (H. collenetteae) H ex Collenette 8232 sub DP8126, (H. saudi-arabica) IAlharbi S4B (H. collenetteae) J ex Collenette 1176 sub DP6868, Jabal Al Sawdah, (H. collenetteae, Type) K maroon uniform colour of corolla tube in ex Collenette sub DP6594, Abha, (H. saudi-arabica) L concentric broken maroon lines of corolla tube in Alharbi S6B (H. collenetteae). (A) reproduced from Plowes (2014); (D, G, H, J, K) reproduced from Plowes (2012); (B, C, E, F, I, L) photo by the first author from Wadi Thee Gazal, Ash Shafa
Stylistic and Spatial Disentanglement in GANs
No full textThis dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations. We examine disentanglement in several kinds of deep networks. We examine image-to-image translation GANs, unconditional GANs, and sketch extraction networks.
The task in image-to-image translation GANs is to translate images from one domain to another. It is immediately clear that disentanglement is necessary in this case. The network must maintain the core contents of the image while changing the stylistic appearance to match the target domain. We propose latent filter scaling to achieve multimodality and disentanglement. Previous methods require complicated network architectures to enforce that disentanglement. Our approach, on the other hand, maintains the traditional GAN loss with a minor change in architecture. Unlike image-to-image GANs, unconditional GANs are generally entangled. Unconditional GANs offer one method of changing the generated output which is changing the input noise code. Therefore, it is very difficult to resample only some parts of the generated images. We propose structured noise injection to achieve disentanglement in unconditional GANs. We propose using two input codes: one to specify spatially-variable details, and one to specify spatially-invariable details. In addition to the ability to change content and style independently, it also allows users to change the content only at certain locations.
Combining our previous findings, we improve the performance of sketch-to-image translation networks. A crucial problem is how to correct input sketches before feeding them to the generator. By extracting sketches in an unsupervised way only from the spatially-variable branch of the image, we are able to produce sketches that show the content in many different styles. Those sketches can serve as a dataset to train a sketch-to-image translation GAN
Marker Detection in Aerial Images
No full textThe problem that the thesis is trying to solve is the detection of small markers in high-resolution aerial images. Given a high-resolution image, the goal is to return the pixel coordinates corresponding to the center of the marker in the image. The marker has the shape of two triangles sharing a vertex in the middle, and it occupies no more than 0.01% of the image size.
An improvement on the Histogram of Oriented Gradients (HOG) is proposed, eliminating the majority of baseline HOG false positives for marker detection. The improvement is guided by the observation that standard HOG description struggles to separate markers from negatives patches containing an X shape. The proposed method alters intensities with the aim of altering gradients. The intensity-dependent gradient alteration leads to more separation between filled and unfilled shapes.
The improvement is used in a two-stage algorithm to achieve high recall and high precision in detection of markers in aerial images. In the first stage, two classifiers are used: one to quickly eliminate most of the uninteresting parts of the image, and one to carefully select the marker among the remaining interesting regions. Interesting regions are selected by scanning the image with a fast classifier trained on the HOG features of markers in all rotations and scales. The next classifier is more precise and uses our method to eliminate the majority of the false positives of standard HOG. In the second stage, detected markers are tracked forward and backward in time. Tracking is needed to detect extremely blurred or distorted markers that are missed by the previous stage. The algorithm achieves 94% recall with minimal user guidance. An average of 30
guesses are given per image; the user verifies for each whether it is a marker or not. The brute force approach would return 100,000 guesses per image
LASPA: Latent Spatial Alignment for Fast Training-free Single Image Editing
No full textWe present a novel, training-free approach for textual editing of real images using diffusion models. Unlike prior methods that rely on computationally expensive finetuning, our approach leverages LAtent SPatial Alignment (LASPA) to efficiently preserve image details. We demonstrate how the diffusion process is amenable to spatial guidance using a reference image, leading to semantically coherent edits. This eliminates the need for complex optimization and costly model finetuning, resulting in significantly faster editing compared to previous methods. Additionally, our method avoids the storage requirements associated with large finetuned models. These advantages make our approach particularly well-suited for editing on mobile devices and applications demanding rapid response times. While simple and fast, our method achieves 62-71\% preference in a user-study and significantly better model-based editing strength and image preservation scores
LASPA: Latent Spatial Alignment for Fast Training-free Single Image Editing
Get PDFWe present a novel, training-free approach for textual editing of real images
using diffusion models. Unlike prior methods that rely on computationally
expensive finetuning, our approach leverages LAtent SPatial Alignment (LASPA)
to efficiently preserve image details. We demonstrate how the diffusion process
is amenable to spatial guidance using a reference image, leading to
semantically coherent edits. This eliminates the need for complex optimization
and costly model finetuning, resulting in significantly faster editing compared
to previous methods. Additionally, our method avoids the storage requirements
associated with large finetuned models. These advantages make our approach
particularly well-suited for editing on mobile devices and applications
demanding rapid response times. While simple and fast, our method achieves
62-71\% preference in a user-study and significantly better model-based editing
strength and image preservation scores
Disentangled Image Generation Through Structured Noise Injection
No full textWe explore different design choices for injecting noise into generative adversarial networks (GANs) with the goal of disentangling the latent space. Instead of traditional approaches, we propose feeding multiple noise codes through separate fully-connected layers respectively. The aim is restricting the influence of each noise code to specific parts of the generated image. We show that disentanglement in the first layer of the generator network leads to disentanglement in the generated image. Through a grid-based structure, we achieve several aspects of disentanglement without complicating the network architecture and without requiring labels. We achieve spatial disentanglement, scale-space disentanglement, and disentanglement of the foreground object from the background style allowing fine-grained control over the generated images. Examples include changing facial expressions in face images, changing beak length in bird images, and changing car dimensions in car images. This empirically leads to better disentanglement scores than state-of-the-art methods on the FFHQ dataset
An Exploration of the Current Leadership Style in Umluj College at Tabuk University, Saudi Arabia: The Relationship between Leadership, Gender and Work Engagement
No full textAbstract: It is commonly stated that culture is one of the factors that may affect leadership styles, but the question arises as to the extent of this impact. This article seeks to explore the current leadership style in Umluj College at Tabuk University, Saudi Arabia and to determine the relationship between leadership, gender and work engagement. To discover whether or not the findings could be applied to Umluj College, this study devised hypotheses to test the theories of leadership, which could either be supported or rejected after analysing the gathered data. Both quantitative and qualitative approaches were used to gather data and reach findings. Triangulation was implemented to compare outcomes and reach conclusive results. After testing the hypotheses and using theories to compare and contrast data, findings revealed that transformational leadership is the dominant style of leadership at Umluj College. Furthermore, both males and females in the college were found to be transformational leaders. This shows that leadership style at Umluj College is independent from gender. Moreover, transformational leadership does not have a significant influence on work engagement at this college.
Keywords: Leadership; Gender; Work Engagement; Saudi Arabia.
Title: An Exploration of the Current Leadership Style in Umluj College at Tabuk University, Saudi Arabia: The Relationship between Leadership, Gender and Work Engagement
Author: Yasraa Alharbi, Suze Mathews
International Journal of Social Science and Humanities Research
ISSN 2348-3156 (Print), ISSN 2348-3164 (online)
Vol. 11, Issue 1, January 2023 - March 2023
Page No: 49-69
Research Publish Journals
Website: www.researchpublish.com
Published Date: 11-January-2023
DOI: https://doi.org/10.5281/zenodo.7524488
Paper Download Link (Source)
https://www.researchpublish.com/papers/an-exploration-of-the-current-leadership-style-in-umluj-college-at-tabuk-university-saudi-arabia-the-relationship-between-leadership-gender-and-work-engagementInternational Journal of Social Science and Humanities Research, ISSN 2348-3156 (Print), ISSN 2348-3164 (online), Research Publish Journals, Website: www.researchpublish.co
Latent Filter Scaling for Multimodal Unsupervised Image-To-Image Translation
No full textIn multimodal unsupervised image-to-image translation tasks, the goal is to translate an image from the source domain to many images in the target domain. We present a simple method that produces higher quality images than current state-of-the-art while maintaining the same amount of multimodal diversity. Previous methods follow the unconditional approach of trying to map the latent code directly to a full-size image. This leads to complicated network architectures with several introduced hyperparameters to tune. By treating the latent code as a modifier of the convolutional filters, we produce multimodal output while maintaining the traditional Generative Adversarial Network (GAN) loss and without additional hyperparameters. The only tuning required by our method controls the tradeoff between variability and quality of generated images. Furthermore, we achieve disentanglement between source domain content and target domain style for free as a by-product of our formulation. We perform qualitative and quantitative experiments showing the advantages of our method compared with the state-of-the art on multiple benchmark image-to-image translation datasets.The project was funded in part by the KAUST Office of Sponsored Research (OSR) under Award No. URF/1/3426-01-01
Role and implications of nanodiagnostics in the changing trends of clinical diagnosis
No full textAbstractNanodiagnostics is the term used for the application of nanobiotechnology in molecular diagnosis, which is important for developing personalized cancer therapy. It is usually based on pharmacogenetics, pharmacogenomics, and pharmacoproteomic information but also takes into consideration environmental factors that influence response to therapy. Nanotechnology in medicine involves applications of nanoparticles currently under development, as well as longer range research that involves the use of manufactured nano-robots to make repairs at the cellular level. Nanodiagnostic technologies are also being used to refine the discovery of biomarkers, as nanoparticles offer advantages of high volume/surface ratio and multifunctionality. Biomarkers are important basic components of personalized medicine and are applicable to the management of cancer as well. The field of nano diagnostics raises certain ethical concerns related with the testing of blood. With advances in diagnostic technologies, doctors will be able to give patients complete health checks quickly and routinely. If any medication is required this will be tailored specifically to the individual based on their genetic makeup, thus preventing unwanted side-effects
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