Heatwaves, intensified by climate change and rapid urbanisation, pose significant threats to urban systems, particularly in the Global South, where adaptive capacity is constrained. This study investigates the relationship between heatwaves and nighttime light (NTL) radiance, a proxy of nighttime economic activity, in four hyperdense cities: Delhi, Guangzhou, Cairo, and Sao Paulo. We hypothesised that heatwaves increase nighttime activity. Using a double machine learning (DML) framework, we analysed data from 2013 to 2019 to quantify the impact of heatwaves on NTL while controlling for local climatic confounders. Results revealed a statistically significant increase in NTL intensity during heatwaves, with Cairo, Delhi, and Guangzhou showing elevated NTL on the third day, while S\~ao Paulo exhibits a delayed response on the fourth day. Sensitivity analyses confirmed the robustness of these findings, indicating that prolonged heat stress prompts urban populations to shift activities to night. Heterogeneous responses across cities highlight the possible influence of urban morphology and adaptive capacity to heatwave impacts. Our findings provide a foundation for policymakers to develop data-driven heat adaptation strategies, ensuring that cities remain liveable and economically resilient in an increasingly warming world.

The widespread use of large language models has brought up essential questions about the potential biases these models might learn. This led to the development of several metrics aimed at evaluating and mitigating these biases. In this paper, we first demonstrate that prompt-based fairness metrics exhibit poor agreement, as measured by correlation, raising important questions about the reliability of fairness assessment using prompts. Then, we outline six relevant reasons why such a low correlation is observed across existing metrics. Based on these insights, we propose a method called Correlated Fairness Output (CAIRO) to enhance the correlation between fairness metrics. CAIRO augments the original prompts of a given fairness metric by using several pre-trained language models and then selects the combination of the augmented prompts that achieves the highest correlation across metrics. We show a significant improvement in Pearson correlation from 0.3 and 0.18 to 0.90 and 0.98 across metrics for gender and religion biases, respectively. Our code is available at https://github.com/chandar-lab/CAIRO.

The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become one of the important thrusts. In addition to the advancement in wireless technology, it is important to efficiently use the resources available and meet the users' requirements. Graph Neural Networks (GNNs) have emerged as a promising paradigm for effectively modeling and extracting insights which inherently exhibit complex network structures due to its high performance and accuracy, scalability, adaptability, and resource efficiency. There is a lack of a comprehensive survey that focuses on the applications and advances GNN has made in the context of IoT and Next Generation (NextG) networks. To bridge that gap, this survey starts by providing a detailed description of GNN's terminologies, architecture, and the different types of GNNs. Then we provide a comprehensive survey of the advancements in applying GNNs for IoT from the perspective of data fusion and intrusion detection. Thereafter, we survey the impact GNN has made in improving spectrum awareness. Next, we provide a detailed account of how GNN has been leveraged for networking and tactical systems. Through this survey, we aim to provide a comprehensive resource for researchers to learn more about GNN in the context of wireless networks, and understand its state-of-the-art use cases while contrasting to other machine learning approaches. Finally, we also discussed the challenges and wide range of future research directions to further motivate the use of GNN for IoT and NextG Networks.

This research introduces a sophisticated transfer learning model based on Google's MobileNetV2 for breast cancer tumor classification into normal, benign, and malignant categories, utilizing a dataset of 1576 ultrasound images (265 normal, 891 benign, 420 malignant). The model achieves an accuracy of 0.82, precision of 0.83, recall of 0.81, ROC-AUC of 0.94, PR-AUC of 0.88, and MCC of 0.74. It examines image intensity distributions and misclassification errors, offering improvements for future applications. Addressing dataset imbalances, the study ensures a generalizable model. This work, using a dataset from Baheya Hospital, Cairo, Egypt, compiled by Walid Al-Dhabyani et al., emphasizes MobileNetV2's potential in medical imaging, aiming to improve diagnostic precision in oncology. Additionally, the paper explores Streamlit-based deployment for real-time tumor classification, demonstrating MobileNetV2's applicability in medical imaging and setting a benchmark for future research in oncology diagnostics.

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits(VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What's more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely ST-VQC, to integrate non-linearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm_cairo with 27 qubits and ibmq_lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the ST-VQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

Spark Foundry is one of four global media agency brands within Publicis Media. The force of acceleration, or the speed of change is so strong today that it has the potential to leave industries including our own behind. So, at Spark Foundry we are working towards driving positive change. We are an Acceleration Agency. At Spark Foundry acceleration applies to every layer of our business.

A U.S. drone strike in Afghanistan this weekend killed Ayman al-Zawahri, who helped Osama bin Laden plot the Sept. 11, 2001, attacks on the United States and ensured al-Qaida survived and spread in the years after. President Joe Biden on Monday announced the killing of al-Zawahri, delivering a significant counterterrorism win just 11 months after American troops left the country. A look at the al-Qaida leader, who evaded U.S. capture for 21 years after the suicide airliner attacks that in many ways changed America and its relations with the rest of the world. Americans who lived through the 9/11 attacks may not remember al-Zawahri's name, but many know his face more than two decades on: a man in glasses, slightly smiling, invariably shown in photos by the side of bin Laden as the two arranged the strike on the United States. An Egyptian, al-Zawahri was born June 19, 1951, to a comfortable family in a leafy, drowsy Cairo suburb.

Salah Khalil is the founder and chief executive officer of Macat International, a company that measures and develops critical thinking skills in higher education and in the corporate sector. Khalil is former strategy consultant at the Westminster Foundation for Democracy in London. He also serves on the advisory board of the Business School at the American University in Cairo. Khalil says many skills that we're using in the current economy might be surpassed by machines in the future. These skills will decay with time, and critical thinking is one of those skills that will not decay with time.

A British-built robot that uses artificial intelligence and a mechanical arm to create art has been released by customs officials in Egypt ahead of an exhibition this week. Ai-Da, named after the mathematician Ada Lovelace, was seized by officials earlier this month over concerns "her" machinery could contain espionage tools. The device was held for 10 days as the British embassy worked with Cairo on the matter. "The Embassy is glad to see that Ai-Da the artist robot has now been cleared through customs," the UK's embassy in Cairo said in a statement. "Customs clearance procedures can be lengthy, and are required before importation of any artworks or IT equipment."

The medical industry is sitting on a huge trove of data, but in many cases it can be a challenge to realize the value of it because that data is unstructured and in disparate places. Today, a startup called Mendel, which has built an AI platform both to ingest and bring order to that body of information, is announcing $18 million in funding to continue its growth and to build out what it describes as a "clinical data marketplace" for people not just to organize, but also to share and exchange that data for research purposes. It's also going to be using the funding to hire more talent -- technical and support -- for its two offices, in San Jose, CA and Cairo, Egypt. The Series A round is being led by DCM, with OliveTree and MTVLP, and previous backers Launch Capital, SOSV, Bootstrap Labs and Chairman of UCSF Health Hub Mark Goldstein also participating. The funding comes on the heels of what Mendel says is a surge of interest among research and pharmaceutical companies in sourcing better data to gain a better understanding of longer-term patient care and progress, in particular across wider groups of users, not just at a time when it has been more challenging to observe people and run trials, but in light of the understanding that using AI to leverage much bigger data sets can produce better insights.