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In this paper we will search in analysis the matrices (SVD) and (GSVD) in terms of the relationship between their matrices after touching and briefly about the idea of each. Where we will two analyze matrices A(m, p) and B(n, p) using the techniques above and we compare between each of the two analyzes 'for the purpose of reaching a final relationship connecting between the matrices of the two analyze. This relationship can be utilized it in image processors, encryption and image enhancement as well as text and video. Perhaps in a later research we will focus on determining the relationships between the matrices of other analyzes such as (QR) and (LU) analysis.

All-optical image processing based on metasurfaces is a swiftly advancing field of technology, due to its high speed, large integrability and inherently low energy requirements. So far, the proposed devices have been focusing on canonical operations, such as differentiations to perform edge detection across all objects in a complex scene. Yet, undesired background noise and clutter can hinder such operations, requiring target selection with digital post-processing which inherently limits the overall accuracy, efficiency and speed. Here, we introduce an optical solution for real-time size-selective image processing and experimentally demonstrate the concept with a metal-dielectric-metal film performing a spatial band-pass filter in momentum space. We show high-resolution (~0.9 μm) edge detection and real-time dynamic denoising, ideally suited for bio-imaging applications and target recognitions. Our demonstrated k -space filtering metasurface expands the scope of nonlocal flat optics for analog image processing, ushering in opportunities for ultra-compact, cost-effective, and multifunctional image processors. All-optical image processing using metasurfaces is advancing due to its high speed, integrability, and low energy consumption, yet background noise and clutter hinder the progress. In this work, a real-time optical image processor is introduced, using a metal-dielectric-metal film to perform spatial band-pass filtering in momentum space enabling high-resolution edge detection and real-time dynamic denoising.

Strongly scattering conditions are detrimental to imaging. Conventional methods rely solely on post-processing techniques to enhance the ballistic components of an obscured target to recover the image; however, they struggle in complex conditions where the ballistic components are heavily overwhelmed by scattering. In this work, we present an optical meta-image-processor (MIP) that tailors the scattered point spread function of the imaging system to enable high-quality, deep imaging through strongly scattering media. The MIP performs both Laplacian and Gaussian operations in a single device, effectively suppressing background interference and Gaussian noise in the obscured image. Experimental results demonstrate that clear information can be recognized with the MIP, even when the optical thickness of the scattering medium reaches a challenging value of 17.05. Without the MIP, such imaging depth cannot be achieved through direct imaging, even when combined with any other post-processing techniques. Additionally, the MIP shows potential for enhancing the diagnostic performance of fundus cameras in the presence of cataracts. Both simulated and experimental evidence confirms the MIP’s capability to reveal hidden information under strongly scattering conditions, underscoring its promising potential to enhance imaging depth in biomedical imaging, machine vision, and artificial intelligence, especially in complex environments. This work presents a meta-image-processor enabling optical image processing system to achieve improved penetration depth through strongly scattering media, advancing imaging technology for visualization through deeper and more complex media.

Researchers gave successive versions of a large language model information produced by previous generations of the AI — and observed rapid collapse. Researchers gave successive versions of a large language model information produced by previous generations of the AI — and observed rapid collapse. Credit: M. Boháček & H. Farid /ArXiv (CC BY 4.0) Example images generated after iterative retraining increasing from 25% (top row) to 100% (bottom row) of SD-generated faces.

Artificial intelligence (AI) has transformed art and design education, giving students new ways to create, explore, and learn. Unfortunately, there is fear among academicians that students will use AI, especially text-to-image generators like Midjourney or Dall-E, as an illegal shortcut in creating their work. This article examines how generative AI solutions, such as text-to-image generators, can help students create innovative and sustainable designs while promoting academic integrity. The article shows how AI in art and design education can equip students with the skills and knowledge to succeed in a rapidly changing digital landscape. This research uses a qualitative method by analyzing the apps and literature reviews in journals and documents related to the problems studied. Case studies show how AI-based solutions can help students create innovative and sustainable designs while promoting academic integrity. Integrating controlled AI- based approaches in art and design education can promote academic integrity, creativity, and sustainability. AI-based art and design education solutions may help society become more innovative and sustainable. This article concludes that art and design educators must embrace AI-based solutions to prepare students for a rapidly changing digital world.

Tabular data is the predominant format for statistical analysis and machine learning across domains such as finance, biomedicine, and environmental sciences. However, conventional methods often face challenges when dealing with high dimensionality and complex nonlinear relationships. In contrast, deep learning models, particularly Convolutional Neural Networks (CNNs), are well-suited for automatic feature extraction and achieve high predictive accuracy, but are primarily designed for image-based inputs. This study presents a comparative evaluation of non-Euclidean distance metrics within the Image Generator for Tabular Data (IGTD) framework, which transforms tabular data into image representations for CNN-based classification. While the original IGTD relies on Euclidean distance, we extend the framework to adopt alternative metrics, including one minus correlation, Geodesic distance, Jensen-Shannon distance, Wasserstein distance, and Tropical distance. These metrics are designed to better capture complex, nonlinear relationships among features. Through systematic experiments on both simulated and real-world genomics datasets, we compare the performance of each distance metric in terms of classification accuracy and structural fidelity of the generated images. The results demonstrate that non-Euclidean metrics can significantly improve the effectiveness of CNN-based classification on tabular data. By enabling a more accurate encoding of feature relationships, this approach broadens the applicability of CNNs and offers a flexible, interpretable solution for high-dimensional, structured data across disciplines.

Researchers are tracing sources of racial and gender bias in images generated by artificial intelligence, and making efforts to fix them. Researchers are tracing sources of racial and gender bias in images generated by artificial intelligence, and making efforts to fix them.

We report on the experimental realization of a quasi-one-dimensional photonic graphene ribbon supporting four flat-bands (FBs). We study the dynamics of fundamental and dipolar modes, which are analogous to the s and p orbitals, respectively. In the experiment, both modes (orbitals) are effectively decoupled from each other, implying two sets of six bands, where two of them are completely flat (dispersionless). Using an image generator setup, we excite the s and p FB modes and demonstrate their non-diffracting propagation for the first time. Our results open an exciting route towards photonic emulation of higher orbital dynamics.

Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which can be time-consuming and costly. This manuscript presents a generative artificial intelligence method that creates digital versions of drug products from images of exemplar products. This approach employs an image generator guided by critical quality attributes, such as particle size and drug loading, to create realistic digital product variations that can be analyzed and optimized digitally. This paper shows how this method was validated through two case studies: one for the determination of the amount of material that will create a percolating network in an oral tablet product and another for the optimization of drug distribution in a long-acting HIV inhibitor implant. The results demonstrate that the generative AI method accurately predicts a percolation threshold of 4.2% weight of microcrystalline cellulose and generates implant formulations with controlled drug loading and particle size distributions. Comparisons with real samples reveal that the synthesized structures exhibit comparable particle size distributions and transport properties in release media. Pharmaceutical drug dosage forms are traditionally determined through extensive physical experimentation. Here, the authors present a generative AI method that creates digital drug products from images, matching and improving critical quality attributes such as particle size and drug loading.

Studies of emotion often rely on standardized stimulus sets to elicit affective responses. Although established databases provide images with normative valence and arousal ratings, selecting suitable stimuli can be difficult when experiments require specific thematic or content constraints. This challenge is especially pronounced for negative stimuli, which are central to research on maladaptive emotions and behaviors in clinical contexts but are often scarce in necessary quantity or specificity. The present study evaluated the feasibility of using generative AI, specifically text-to-image generators, to create tailored negative and neutral affective stimuli. To assess whether these images can serve as alternatives to traditional stimuli, we compared their affective properties to those reported in standardized image databases. Across two studies, participants rated the valence and arousal of 160 and 200 AI-generated images. Our findings revealed that AI-generated negative and neutral images reproduced the characteristic inverse association between valence and arousal observed in standardized databases, with moderate to strong correlations between these dimensions. These results highlight the potential of generative AI as a practical methodological tool for creating customized affective stimuli aligned with specific research objectives and experimental designs.