The next big VR avatar performance will be the late Notorious B.I.G., East Coast rap legend. Broadcast in Meta's Horizon Worlds, the show will use a virtual recreation of '90s Brooklyn as a backdrop and will have performances by guest artists like Bad Boy Records founder Sean "Diddy" Combs. It will also feature a narrative journey of Biggie's life by music journalist Touré. Bringing an artist back from the dead in avatar form often meets a wave of criticism – and that was true this time as well. Meta responded, saying it received the blessing of the Notorious B.I.G. We've also seen holograms of Michael Jackson and Whitney Houston.

Ubisoft knew Assassin's Creed was going to be huge. Over its three years of development, the game went from a Prince of Persia spin-off curio to the gem in the publisher's forthcoming games list. It became the talk of the industry in 2006 after a well-constructed trailer teased at the imaginations of gamers and history fanatics alike. Intrigued by the promise of a game set during the Third Crusade in the Holy Land in 1191 – with some mysterious sci-fi elements projected over the top – we all waited with bated breath for this historical action experiment to land in late 2007. The publisher needed the best people it could get on the project.

The Monitor is a weekly column devoted to everything happening in the WIRED world of culture, from movies to memes, TV to Twitter. On Tuesday morning, Ruben Martinez Jr. was staring at his computer screen, calculating his chances. He was on a group chat trying to strategize the best way to score Taylor Swift tickets, and it was looking bleak. Everyone seemed to have 2,000-plus people ahead of them in line. Martinez, a software engineer at OkCupid, checked the browser developer tools to see if he could figure out his actual place in the queue. He thought he could find a percentage for how far back he was.

To understand how radically gaming is about to be transformed by Generative AI, look no further than this recent Twitter post by @emmanuel_2m. In this post he explores using Stable Diffusion Dreambooth, popular 2D Generative AI models, to generate images of potions for a hypothetical game. What's transformative about this work is not just that it saves time and money while also delivering quality – thus smashing the classic "you can only have two of cost, quality, or speed" triangle. Artists are now creating high-quality images in a matter of hours that would otherwise take weeks to generate by hand. What's truly transformative is that: There hasn't been a technology this revolutionary for gaming since real-time 3D. Spend any time at all talking to game creators, and the sense of excitement and wonder is palpable. So where is this technology going? And how will it transform gaming?

The project's ultimate goal is to meet the country's need for improved telecommunications. On the second day of the mammoth event at the Expo Centre in Karachi, attendees of the International Defence Exhibition and Seminar 2022 (IDEAS-22) showed great enthusiasm for the'One Network' initiative of the Frontier Works Organization (FWO). In a cutting-edge communication initiative called "One Network," workers in Pakistan's highway tunnels are laying 3,000 kilometers of fiber optic cable underneath. Once the project is finished, it will fulfill all of Pakistan's communication needs. The COO of One Network claims that 2,000 kilometers of fiber optic cable have been deployed under the communication backbone of major highways.

In particular, the above approximation leads to important transferability results of graph neural In this work we introduce a convolution operation over the tangent networks (GNNs) [17,18], as well as to the introduction of Graphon bundle of Riemannian manifolds exploiting the Connection Laplacian and Manifold Neural Networks, continuous architectures shown to operator. We use the convolution to define tangent bundle filters be limit objects of GNNs [19, 20]. However, most of the previous and tangent bundle neural networks (TNNs), novel continuous works focus on scalar signals, e.g. one or more scalar values architectures operating on tangent bundle signals, i.e. vector fields attached to each node of graphs or point of manifolds; recent developments over manifolds. We discretize TNNs both in space and time domains, [21] show that processing vector data defined on tangent showing that their discrete counterpart is a principled variant bundles of manifolds or discrete vector bundles [22, 23] comes with of the recently introduced Sheaf Neural Networks.

We propose high dynamic range (HDR) radiance fields, HDR-Plenoxels, that learn a plenoptic function of 3D HDR radiance fields, geometry information, and varying camera settings inherent in 2D low dynamic range (LDR) images. Our voxel-based volume rendering pipeline reconstructs HDR radiance fields with only multi-view LDR images taken from varying camera settings in an end-to-end manner and has a fast convergence speed. To deal with various cameras in real-world scenarios, we introduce a tone mapping module that models the digital in-camera imaging pipeline (ISP) and disentangles radiometric settings. Our tone mapping module allows us to render by controlling the radiometric settings of each novel view. Finally, we build a multi-view dataset with varying camera conditions, which fits our problem setting. Our experiments show that HDR-Plenoxels can express detail and high-quality HDR novel views from only LDR images with various cameras.

Propaganda is the expression of an opinion or an action by an individual or a group deliberately designed to influence the opinions or the actions of other individuals or groups with reference to predetermined ends, which is achieved by means of well-defined rhetorical and psychological devices. Propaganda techniques are commonly used in social media to manipulate or to mislead users. Thus, there has been a lot of recent research on automatic detection of propaganda techniques in text as well as in memes. However, so far the focus has been primarily on English. With the aim to bridge this language gap, we ran a shared task on detecting propaganda techniques in Arabic tweets as part of the WANLP 2022 workshop, which included two subtasks. Subtask~1 asks to identify the set of propaganda techniques used in a tweet, which is a multilabel classification problem, while Subtask~2 asks to detect the propaganda techniques used in a tweet together with the exact span(s) of text in which each propaganda technique appears. The task attracted 63 team registrations, and eventually 14 and 3 teams made submissions for subtask 1 and 2, respectively. Finally, 11 teams submitted system description papers.

Speech Emotion Recognition (SER) is one of the essential perceptual methods of humans in understanding the situation and how to interact with others, therefore, in recent years, it has been tried to add the ability to recognize emotions to human-machine communication systems. Since the SER process relies on labeled data, databases are essential for it. Incomplete, low-quality or defective data may lead to inaccurate predictions. In this paper, we fixed the inconsistencies in Sharif Emotional Speech Database (ShEMO), as a Persian database, by using an Automatic Speech Recognition (ASR) system and investigating the effect of Farsi language models obtained from accessible Persian text corpora. We also introduced a Persian/Farsi ASR-based SER system that uses linguistic features of the ASR outputs and Deep Learning-based models.

Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained much attention in community. The main idea is to bridge the gap between NLP downstream tasks and language modeling (LM), by mapping these tasks into natural language prompts, which are then filled by pre-trained language models (PLMs). However, for prompt learning, there are still two salient gaps between NLP tasks and pretraining. First, prompt information is not necessarily sufficiently present during LM pretraining. Second, task-specific data are not necessarily well represented during pretraining. We address these two issues by proposing AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs by making use of both task and prompt characteristics. In addition, we make use of knowledge in Natural Language Inference models for deriving adaptive verbalizers. Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings. In addition, in zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35\% relative error reduction.