In-betweening is a technique for generating transitions given initial and target character states. The majority of existing works require multiple (often $>$10) frames as input, which are not always accessible. Our work deals with a focused yet challenging problem: to generate the transition when given exactly two frames (only the first and last). To cope with this challenging scenario, we implement our bi-directional scheme which generates forward and backward transitions from the start and end frames with two adversarial autoregressive networks, and stitches them in the middle of the transition where there is no strict ground truth. The autoregressive networks based on conditional variational autoencoders (CVAE) are optimized by searching for a pair of optimal latent codes that minimize a novel stitching loss between their outputs. Results show that our method achieves higher motion quality and more diverse results than existing methods on both the LaFAN1 and Human3.6m datasets.

Chest X-rays remains to be the most common imaging modality used to diagnose lung diseases. However, they necessitate the interpretation of experts (radiologists and pulmonologists), who are few. This review paper investigates the use of deep transfer learning techniques to detect COVID-19, pneumonia, and tuberculosis in chest X-ray (CXR) images. It provides an overview of current state-of-the-art CXR image classification techniques and discusses the challenges and opportunities in applying transfer learning to this domain. The paper provides a thorough examination of recent research studies that used deep transfer learning algorithms for COVID-19, pneumonia, and tuberculosis detection, highlighting the advantages and disadvantages of these approaches. Finally, the review paper discusses future research directions in the field of deep transfer learning for CXR image classification, as well as the potential for these techniques to aid in the diagnosis and treatment of lung diseases.

This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta. Through this paper, we identify key challenges and opportunities for NLP research in Ethiopia. Furthermore, we provide a centralized repository on GitHub that contains publicly available resources for various NLP tasks in these languages. This repository can be updated periodically with contributions from other researchers. Our objective is to identify research gaps and disseminate the information to NLP researchers interested in Ethiopian languages and encourage future research in this domain.

Model-based deep learning has achieved astounding successes due in part to the availability of large-scale real-world data. However, processing such massive amounts of data comes at a considerable cost in terms of computations, storage, training and the search for good neural architectures. Dataset distillation has thus recently come to the fore. This paradigm involves distilling information from large real-world datasets into tiny and compact synthetic datasets such that processing the latter ideally yields similar performances as the former. State-of-the-art methods primarily rely on learning the synthetic dataset by matching the gradients obtained during training between the real and synthetic data. However, these gradient-matching methods suffer from the so-called accumulated trajectory error caused by the discrepancy between the distillation and subsequent evaluation. To mitigate the adverse impact of this accumulated trajectory error, we propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7% on a subset of images of the ImageNet dataset with higher resolution images. We also validate the effectiveness and generalizability of our method with datasets of different resolutions and demonstrate its applicability to neural architecture search. Code is available at https://github.com/AngusDujw/FTD-distillation.

Every human being in this world produces waste. South Africa is a developing country with many townships that have limited waste resources. Over-increasing population growth overpowers the volume of most municipal authorities to provide even the most essential services. Waste in townships is produced via littering, dumping of bins, cutting of trees, dumping of waste near rivers, and overrunning of waste bins. Waste increases diseases, air pollution, and environmental pollution, and lastly increases gas emissions that contribute to the release of greenhouse gases. The ungathered waste is dumped widely in the streets and drains contributing to flooding, breeding of insects, rodent vectors, and spreading of diseases. Therefore, the aim of this paper is to design a smart waste management system for the city of Johannesburg. The city of Johannesburg contains waste municipality workers and has provided some areas with waste resources such as waste bins and trucks for collecting waste. But the problem is that the resources only are not enough to solve the problem of waste in the city. The waste municipality uses traditional ways of collecting waste such as going to each street and picking up waste bins. The traditional way has worked for years but as the population is increasing more waste is produced which causes various problems for the waste municipalities and the public at large. The proposed system consists of sensors, user applications, and a real-time monitoring system. This paper adopts the experimental methodology.

A new study led by faculty at the University of Georgia demonstrates the potential of using artificial intelligence to transform tuberculosis treatment in low-resource communities. And while the study focused on TB patients, it has applications across the health care sector, freeing up health care workers to perform other necessary tasks. Growing evidence has demonstrated the potential for AI to increase productivity, reduce health care worker burnout, and improve quality of care in clinical settings. The study, which was published last month in the Journal of Medical Internet Research AI, pilots the use of AI to watch thousands of submitted videos of TB patients taking their medication. This application could automate the job of a health care worker watching a patient take their pill at a clinic, known as directly observed therapy (DOT).

Nikki Haley, presidential candidate and former U.S. ambassador to the U.N., weighs in after President Biden authorized an air strike in response to an Iranian drone that killed an American. The U.S. can no longer take a reactive stance toward Iran after a new Pentagon report revised the total number of troops killed by Iran-backed groups continues to rise, experts told Fox News Digital. "Iran's regional strategy of working through proxies and carve outs is continuing unabated," Behnam Ben Taleblu, a senior fellow and Iran expert at the Foundation for Defense of Democracies, said. "The open question is, when will the Biden administration ditch tit-for-tat strikes and work to rollback Iran's Shiite militia network in the heartland of the Middle East?" President Biden ordered a series of retaliatory precision airstrikes in Syria on Thursday, reportedly killing eight Iranians, after Iran's Islamic Revolutionary Guards Corps crashed a UAV into a building, killing a U.S. contractor and wounding six other Americans. U.S. intelligence assessed the UAV that crashed into a coalition base, which killed the contractor, was of Iranian origin -- so President Biden authorized the military to retaliate, the Pentagon said.

Major Regions Covered (North America, Europe, Asia Pacific, Mid East and Africa) 1.4 Years Considered for the Study (2015-2029) 1.5 Currency Considered (U.S. Dollar) 1.6 Stakeholders 2 Key Findings of the Study 3 Market Dynamics 3.1 Driving Factors for this Market 3.2 Factors Challenging the Market 3.3 Opportunities of the Global Artificial Intelligence in Market (Regions, Growing/Emerging Downstream Market Analysis) 3.4 Technological and Market Developments in the Artificial Intelligence in Market 3.5 Industry News by Region 3.6 Regulatory Scenario by Region/Country 3.7 Market Investment Scenario Strategic Recommendations Analysis

Dedan Kimathi University of Technology (DeKUT) is a Chartered Public University that has two campuses: the Main Campus in Nyeri and Nairobi Campus along Loita street.

America's credibility among its adversaries has dwindled under President Biden, with some experts arguing a line can be drawn from the disastrous U.S. withdrawal from Afghanistan to more recent events such as the Chinese spy balloon and the downing of a U.S. drone by Russian forces. "I think the Biden administration's disastrous withdrawal from Afghanistan was a key catalyst for multiple trends that have undermined U.S. influence and deterrence," James Phillips, the senior research fellow for foreign policy at the Heritage Foundation, told Fox News Digital. "U.S. allies were shocked by the naive assumptions behind the withdrawal, the speed with which Washington abandoned longtime allies, and the incompetence of the policymakers that supervised the withdrawal." Phillips argues that it was not just American allies who took note of the administration's hastily executed exit from Afghanistan, but also adversaries such as China and Russia, who no longer fear U.S. deterrence. "U.S. adversaries perceived the withdrawal from Afghanistan as a manifestation of U.S. weakness and a desire to rapidly exit the Middle East," Phillips said.