To assist the readers following the design framework, we use a section to summarize the design of the fictitious estimators. H.1 finite horizon, non-stationary case Now let us introduce our estimators zt and gt in the finite-horizon non-stationary case (the choices for the stationary case and the infinite-horizon case will be introduced later).

Clinical diagnosis of breast malignancy (BM) is a challenging problem in the recent era. In particular, Deep learning (DL) models have continued to offer important solutions for early BM diagnosis but their performance experiences overfitting due to the limited volume of breast ultrasound (BUS) image data. Further, large BUS datasets are difficult to manage due to privacy and legal concerns. Hence, image augmentation is a necessary and challenging step to improve the performance of the DL models. However, the current DL-based augmentation models are inadequate and operate as a black box resulting lack of information and justifications about their suitability and efficacy. Additionally, pre and post-augmentation need high-performance computational resources and time to produce the augmented image and evaluate the model performance. Thus, this study aims to develop a novel efficient augmentation approach for BUS images with advanced neural style transfer (NST) and Explainable AI (XAI) harnessing GPU-based parallel infrastructure. We scale and distribute the training of the augmentation model across 8 GPUs using the Horovod framework on a DGX cluster, achieving a 5.09 speedup while maintaining the model's accuracy. The proposed model is evaluated on 800 (348 benign and 452 malignant) BUS images and its performance is analyzed with other progressive techniques, using different quantitative analyses. The result indicates that the proposed approach can successfully augment the BUS images with 92.47% accuracy.

Neural style transfer (NST) is widely adopted in computer vision to generate new images with arbitrary styles. This process leverages neural networks to merge aesthetic elements of a style image with the structural aspects of a content image into a harmoniously integrated visual result. However, unauthorized NST can exploit artwork. Such misuse raises socio-technical concerns regarding artists' rights and motivates the development of technical approaches for the proactive protection of original creations. Adversarial attack is a concept primarily explored in machine learning security. Our work introduces this technique to protect artists' intellectual property. In this paper Locally Adaptive Adversarial Color Attack (LAACA), a method for altering images in a manner imperceptible to the human eyes but disruptive to NST. Specifically, we design perturbations targeting image areas rich in high-frequency content, generated by disrupting intermediate features. Our experiments and user study confirm that by attacking NST using the proposed method results in visually worse neural style transfer, thus making it an effective solution for visual artwork protection.

We release a new Bayesian neural network library for PyTorch for large-scale deep networks. Our library implements mainstream approximate Bayesian inference algorithms: variational inference, MC-dropout, stochastic-gradient MCMC, and Laplace approximation. The main differences from other existing Bayesian neural network libraries are as follows: 1) Our library can deal with very large-scale deep networks including Vision Transformers (ViTs).

In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokm{\aa}l and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech datasets. Additionally, we measure the performance of these models against previous state-of-the-art ASR models, as well as on out-of-domain datasets. We improve the state of the art on the Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of 17.10\% to 7.60\%, with models achieving 5.81\% for Bokm{\aa}l and 11.54\% for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.

It had a great new idea for a movie--Elemental, based on characters from The Good Dinosaur's director Peter Sohn--but actually animating the film's titular elements was proving to be a problem. After all, it's one thing to draw a crumbling mound of sentient dirt, but how do you capture the ethereal nature of fire onscreen, and how would a corporeal body made of water even work? Can you see through it? Do the eyes just float around? While some of those questions could be answered with good old-fashioned suspension of disbelief, Pixar's animators thought the fire issue was a real conundrum, especially considering that one of their movie's leads, Ember, was actually supposed to be made of the stuff. They had tools to make a flame effect from years of previous animations, but when you actually tried to shape it into a character, the results were pretty terrifying, a cross between Studio Ghibli's Calcifer and Nicolas Cage's Ghost Rider, but somehow harsher.

Neural Style Transfer (NST) is an image processing optimization technique which adopts style from an image and imposes it over the content of another given image. In simple terms, this NST takes the content of an image and changes the style of it using other image. It uses the content image to extract the actual picture content and presents it in the style extracted from style reference image. It combines the content and style from two images and generates a single output image. For Example, If you wish to experiment your favorite art with different style format you can use this NST.

In today's world, Neural Style Transfer (NST) has become a trendsetting term. NST combines two pictures, a content picture and a reference image in style (such as the work of a renowned painter) in a way that makes the output image look like an image of the material, but rendered with the form of a reference picture. However, there is no study using the artwork or painting of Bangladeshi painters. Bangladeshi painting has a long history of more than two thousand years and is still being practiced by Bangladeshi painters. This study generates NST stylized image on Bangladeshi paintings and analyzes the human point of view regarding the aesthetic preference of NST on Bangladeshi paintings. To assure our study's acceptance, we performed qualitative human evaluations on generated stylized images by 60 individual humans of different age and gender groups. We have explained how NST works for Bangladeshi paintings and assess NST algorithms, both qualitatively \& quantitatively. Our study acts as a pre-requisite for the impact of NST stylized image using Bangladeshi paintings on mobile UI/GUI and material translation from the human perspective. We hope that this study will encourage new collaborations to create more NST related studies and expand the use of Bangladeshi artworks.

Likewise, we admire the story of musicians, artists, writers and every creative human because of their personal struggles, how they overcome life's challenges and find inspiration from everything they've been through. That's something that can't be automated, even if we achieve the always-elusive general artificial intelligence. This article will be a tutorial on using neural style transfer (NST) learning to generate professional-looking artwork like the one above. NST has been around for a while and there are websites that perform all of the functions before you, however, it is very fun to play around and create your own images. NST is quite computationally intensive, so in this case, you are limited not by your imagination, but primarily by your computational resources.

Multilabel classification is a classification problem where multiple target labels can be assigned to each observation instead of only one, like in multiclass classification. It can be regarded as a special case of multivariate classification or multi-target prediction problems, for which the scale of each response variable can be of any kind, for example nominal, ordinal or interval. Originally, multilabel classification was used for text classification (McCallum, 1999; Schapire and Singer, 2000) and is now used in several applications in different research fields. For example, in image classification, a photo can belong to the classes mountain and sunset simultaneously. Zhang and Zhou (2008) and others (Boutell et al., 2004) used multilabel algorithms to classify scenes on images of natural environments.