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.

The United States military has said it carried out multiple air strikes in eastern Syria against Iran-aligned groups who it blamed for a deadly drone attack earlier that killed a contractor, injured another, and wounded five US troops, the Pentagon said. The US attacks late on Thursday night were in retaliation for an attack against a US-led coalition base near Hassakeh in northeast Syria at approximately 01:38pm (10:38 GMT) the same day, the Pentagon said in a statement. US intelligence has assessed that the drone was Iranian in origin and US Defence Secretary Lloyd Austin said the strikes targeted groups affiliated with Iran's Islamic Revolutionary Guards Corps in eastern Syria. "The airstrikes were conducted in response to today's attack as well as a series of recent attacks against Coalition forces in Syria by groups affiliated with the IRGC," Austin said in a statement. Austin said he authorised the retaliatory strikes at the direction of US President Joe Biden.

Our engineering team is split into organisations which we call Fleets. Each Fleet focuses on a core customer journey (onboarding, security, payments, support, new business, growth, and marketing, etc.). Each of these fleets contains multiple smaller teams called Pods, each of which focuses on a specific aspect of the product. Pods will include a product owner, product designer, back-end engineers, Android, iOS, and Web developers, who each bring a unique perspective to the problem you are all contributing towards. Luno offers a "Remote but Reachable" working approach.