Three aid workers killed, 4 wounded in RSF drone attack in Sudan's Kordofan At least three aid workers have been killed and four others wounded in a drone attack by the paramilitary Rapid Support Forces (RSF) on an aid convoy in Sudan's South Kordofan state, according to the Sudan Doctors Network, in the latest carnage against civilians caught up in the nation's brutal civil war. The convoy of trucks carrying food and humanitarian supplies was targeted by the RSF, and its ally, the Sudan People's Liberation Movement-North, while travelling through the Kartala area on its way to the cities of Kadugli and Dilling on Thursday. The network said that this attack marked the "second such incident in less than a month, following the shelling of a United Nations aid convoy in the town of Al-Rahad," adding: "this dangerous escalation threatens the safety of humanitarian operations and further exacerbates civilian suffering". The Sudan Doctors Network reiterated its call to the "international community, the United Nations, and human rights organisations to exert urgent and effective pressure on the leadership of the Rapid Support Forces to ensure the protection of aid convoys and their workers, to open safe and sustainable humanitarian corridors, and to hold those responsible for targeting aid accountable". Al Jazeera could not independently verify the latest RSF attack, which came a month after the government-aligned Sudanese Armed Forces (SAF) announced that it had broken a nearly two-year-long RSF siege on Dilling.

Electroencephalogram (EEG) signals play a crucial role in understanding brain activity and diagnosing neurological diseases. Because supervised EEG encoders are unable to learn robust EEG patterns and rely too heavily on expensive signal annotation, research has turned to general-purpose self-supervised EEG encoders, known as EEG-based models (EEG-FMs), to achieve robust and scalable EEG feature extraction. However, the readiness of early EEG-FMs for practical applications and the standards for long-term research progress remain unclear . Therefore, a systematic and comprehensive review of first-generation EEG-FMs is necessary to understand their current state-of-the-art and identify key directions for future EEG-FMs. T o this end, this study reviews 14 early EEG-FMs and provides a critical comprehensive analysis of their methodologies, empirical findings, and unaddressed research gaps. This review focuses on the latest developments in EEG-based models (EEG-FMs), which have shown great potential for processing and analyzing EEG data. We discuss various EEG-FMs, including their architectures, pretraining strategies, pretraining and downstream datasets, and other details. This review also highlights challenges and future directions in the field, aiming to provide a comprehensive overview for researchers and practitioners interested in EEG analysis and related EEG-FM. EG is a non-invasive technique that records the electrical activity of the brain.

Brain-computer interfaces (BCIs) turn brain signals into functionally useful output, but they are not always accurate. A good Machine Learning classifier should be able to indicate how confident it is about a given classification, by giving a probability for its classification. Standard classifiers for Motor Imagery BCIs do give such probabilities, but research on uncertainty quantification has been limited to Deep Learning. We compare the uncertainty quantification ability of established BCI classifiers using Common Spatial Patterns (CSP-LDA) and Riemannian Geometry (MDRM) to specialized methods in Deep Learning (Deep Ensembles and Direct Uncertainty Quantification) as well as standard Convolutional Neural Networks (CNNs). We found that the overconfidence typically seen in Deep Learning is not a problem in CSP-LDA and MDRM. We found that MDRM is underconfident, which we solved by adding Temperature Scaling (MDRM-T). CSP-LDA and MDRM-T give the best uncertainty estimates, but Deep Ensembles and standard CNNs give the best classifications. We show that all models are able to separate between easy and difficult estimates, so that we can increase the accuracy of a Motor Imagery BCI by rejecting samples that are ambiguous.

Recently, the potential of large language models (LLMs) has been widely used in assisting programming. However, current research does not explore the artist potential of LLMs in creative coding within artist and AI collaboration. Our work probes the reflection type of artists in the creation process with such collaboration. We compare two common collaboration approaches: invoking the entire program and multiple subtasks. Our findings exhibit artists' different stimulated reflections in two different methods. Our finding also shows the correlation of reflection type with user performance, user satisfaction, and subjective experience in two collaborations through conducting two methods, including experimental data and qualitative interviews. In this sense, our work reveals the artistic potential of LLM in creative coding. Meanwhile, we provide a critical lens of human-AI collaboration from the artists' perspective and expound design suggestions for future work of AI-assisted creative tasks.

Batch Normalization (BN) is widely used to stabilize the optimization process and improve the test performance of deep neural networks. The regularization effect of BN depends on the batch size and explicitly using smaller batch sizes with Batch Normalization, a method known as Ghost Batch Normalization (GBN), has been found to improve generalization in many settings. We investigate the effectiveness of GBN by disentangling the induced ``Ghost Noise'' from normalization and quantitatively analyzing the distribution of noise as well as its impact on model performance. Inspired by our analysis, we propose a new regularization technique called Ghost Noise Injection (GNI) that imitates the noise in GBN without incurring the detrimental train-test discrepancy effects of small batch training. We experimentally show that GNI can provide a greater generalization benefit than GBN. Ghost Noise Injection can also be beneficial in otherwise non-noisy settings such as layer-normalized networks, providing additional evidence of the usefulness of Ghost Noise in Batch Normalization as a regularizer.

Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image. We present a novel method of reconstructing lost artwork, by applying neural style transfer to x-radiographs of artwork with secondary interior artwork beneath a primary exterior, so as to reconstruct lost artwork. Finally we reflect on AI art exhibitions and discuss the social, cultural, ethical, and philosophical impact of these technical innovations.

Creativity is sometimes taken to be an inexplicable aspect of human activity. By summarizing a considerable body of literature on creativity, I hope to show how to turn some of the best ideas about creativity into programs that are demonstrably more creative than any we have seen to date. I believe the key to building more creative programs is to give them the ability to reflect on and modify their own frameworks and criteria. That is, I believe that the key to creativity is at the metalevel.