Abstract--Introduction: This study presents FetalSleepNet, the first published deep learning approach to classifying sleep states from the ovine electroencephalogram (EEG). Fetal EEG is complex to acquire and difficult and laborious to interpret consistently. However, accurate sleep stage classification may aid in the early detection of abnormal brain maturation associated with pregnancy complications (e.g. Methods: EEG electrodes were secured onto the ovine dura over the parietal cortices of 24 late-gestation fetal sheep. A lightweight deep neural network originally developed for adult EEG sleep staging was trained on the ovine EEG using transfer learning from adult EEG. A spectral equalisation-based domain adaptation strategy was used to reduce cross-domain mismatch. Results: We demonstrated that while direct transfer performed poorly, full fine-tuning combined with spectral equalisation achieved the best overall performance (accuracy: 86.6%, macro F1-score: 62.5), outperforming baseline models. Conclusions: T o the best of our knowledge, FetalSleepNet is the first deep learning framework specifically developed for automated sleep staging from the fetal EEG. Beyond the laboratory, the EEG-based sleep stage classifier functions as a label engine, enabling large-scale weak/semi-supervised labeling and distillation to facilitate training on less invasive signals that can be acquired in the clinic, such as Doppler Ultrasound or electrocardiogram data. FetalSleepNet's lightweight design makes it well suited for deployment in low-power, real-time, and wearable fetal monitoring systems. LEEP state patterns reflect fetal neurophysiological function and development [1], and are clinically relevant for detecting abnormal neurodevelopment, which may result from conditions such as chronic hypoxia, infection or hypertensive disorders of pregnancy (HDP) [2]-[4]. J. V argas-Calixto, N. Katebi, and G. D. Clifford are with the Department of Biomedical Informatics, Emory University, Atlanta, USA. Nhi Tran, R. Galinsky and S. B. Kelly are with the Ritchie Centre, Hudson Institute of Medical Research, Melbourne, Australia. G. D. Clifford is also with the Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.

Graph neural networks (GNNs) have been widely used in graph-related contexts. It is known that the separation power of GNNs is equivalent to that of the Weisfeiler-Lehman (WL) test; hence, GNNs are imperfect at identifying all non-isomorphic graphs, which severely limits their expressive power. This work investigates $k$-hop subgraph GNNs that aggregate information from neighbors with distances up to $k$ and incorporate the subgraph structure. We prove that under appropriate assumptions, the $k$-hop subgraph GNNs can approximate any permutation-invariant/equivariant continuous function over graphs without cycles of length greater than $2k+1$ within any error tolerance. We also provide an extension to $k$-hop GNNs without incorporating the subgraph structure. Our numerical experiments on established benchmarks and novel architectures validate our theory on the relationship between the information aggregation distance and the cycle size.

Bouciegues said he has had some performers with whom he has worked express concerns about being scanned for projects but noted that it's been a relatively common practice for some time in the entertainment industry. "These particular scans are by no means suspicious," Bouciegues said. "They've been around since the early 2000s. And almost every movie with heavy VFX or whatever, every performer is scanned." WATCH: ARNOLD SCHWARZENEGGER'S STUNT COORDINATOR EXPLAINS WHY HE WANTS A FAIR CONTRACT IN STRIKE NEGOTIATIONS "What I think that the fear is is that AI is going to improve on [and] going to make more efficient the pipelines that are in place already," he continued, citing the example of creating a large digital army through AI versus hiring dozens or hundreds of performers.

In video reviews of the latest drone models to his 80,000 YouTube subscribers, Indiana college student Carson Miller doesn't seem like an unwitting tool of Chinese spies. Yet that's how the U.S. is increasingly viewing him and thousands of other Americans who purchase drones built by Shenzhen-based SZ DJI Technology Co., the world's top producer of unmanned aerial vehicles. Miller, who bought his first DJI model in 2016 for $500 and now owns six of them, shows why the company controls more than half of the U.S. drone market. "If tomorrow DJI were completely banned," the 21-year-old said, "I would be pretty frightened." Critics of DJI warn the dronemaker may be channeling reams of sensitive data to Chinese intelligence agencies on everything from critical infrastructure like bridges and dams to personal information such as heart rates and facial recognition.

The ultimate goal of clinicians is to provide accurate and rapid clinical interpretation, which guides appropriate treatment of CMF deformities. Cone-beam computed tomography (CBCT) is the newest conventional imaging modality for the diagnosis and treatment planning of patients with skeletal CMF deformities. Not only do CBCT scanners expose patients to lower doses of radiation compared to spiral CT scanners, but also CBCT scanners are compact, fast and less expensive, which makes them widely available. On the other hand, CBCT scans have much greater noise and artifact presence, leading to challenges in image analysis tasks. CBCT-based image analysis plays a significant role in diagnosing a disease or deformity, characterizing its severity, planning the treatment options, and estimating the risk of potential interventions. The core image analysis framework involves the detection and measurement of deformities, which requires precise segmentation of CMF bones. Landmarks, which identify anatomically distinct locations on the surface of the segmented bones, are placed and measurements are performed to determine the severity of the deformity compared to traditional 2D norms as well as to assist in treatment and surgical planning. Figure 1 shows nine anatomical landmarks defined on the mandible. Surgical planning, patient-specific prediction of deformities, and quantification as well as clinical assessment of the deformities require precise segmentation and anatomical landmarking. However, automatically segmenting bones from the CMF regions, and accurately identifying clinically relevant anatomical landmarks on the surface of these bones continue to be a significant challenge and a persistent problem. Currently, the landmarks have not evolved from traditional 2D anatomical landmarks for cephalometric analysis though 3D imaging has become more commonplace for clinical application. Additionally, landmarking on CT images is tedious and manual or semi-automated and prone to operator variability. Despite some recent elaborative efforts towards making a fully automated and accurate software for segmentation of bones and landmarking for deformation analysis in dental applications [3], [4], the problem remains largely unsolved for global CMF deformity analysis, especially for those who have congenital or developmental deformities for whom the diagnosis and treatment planning are most critically needed. The main reason for this research gap is high anatomical variability in the shape of these bones due to their deformities in such patient populations. Abstract--In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking.

If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.

If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.

We propose a new and efficient technique for incorporating contextual information into object classification. Most of the current techniques face the problem of exponential computation cost. In this paper, we propose a new general framework that incorporates partial context at a linear cost. This technique is applied to microscopic urinalysis image recognition, resulting in a significant improvement of recognition rate over the context free approach. This gain would have been impossible using conventional context incorporation techniques.

We propose a new and efficient technique for incorporating contextual information into object classification. Most of the current techniques face the problem of exponential computation cost. In this paper, we propose a new general framework that incorporates partial context at a linear cost. This technique is applied to microscopic urinalysis image recognition, resulting in a significant improvement of recognition rate over the context free approach. This gain would have been impossible using conventional context incorporation techniques.

In this paper we propose a technique to incorporate contextual information into object classification. In the real world there are cases where the identity of an object is ambiguous due to the noise in the measurements based on which the classification should be made. It is helpful to reduce the ambiguity by utilizing extra information referred to as context, which in our case is the identities of the accompanying objects. This technique is applied to white blood cell classification. Comparisons are made against "no context" approach, which demonstrates the superior classification performance achieved by using context. In our particular application, it significantly reduces false alarm rate and thus greatly reduces the cost due to expensive clinical tests.