If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation?

In this section, we provide an open-ended discussion on how the prior distribution ( i.e., imbalanced

Large Language Models (LLMs) have achieved great success in various reasoning tasks. In this work, we focus on the graph reasoning ability of LLMs. Although theoretical studies proved that LLMs are capable of handling graph reasoning tasks, empirical evaluations reveal numerous failures. To deepen our understanding on this discrepancy, we revisit the ability of LLMs on three fundamental graph tasks: graph description translation, graph connectivity, and the shortest-path problem. Our findings suggest that LLMs can fail to understand graph structures through text descriptions and exhibit varying performance for all these three fundamental Figure 1: The overview of datasets in accuracy and distribution tasks. Meanwhile, we perform a realworld across different connectivity types. We evaluate investigation on knowledge graphs and GPT-3 on determining whether a path exists between make consistent observations with our findings.

The principal benefit of unsupervised graph representation learning is that a pre-trained model can be fine-tuned where data or labels are scarce. Existing approaches are domain specific, maintaining consistent node and edge attributes across the pre-training and target datasets. This precludes transfer to other domains. A model capable of positive transfer on arbitrary tasks and domains would represent the first foundation graph model. In this work we use adversarial contrastive learning to present FoToM, a graph pre-training method based on node and edge feature exclusion. We use FoToM to pre-train models over multiple graph domains, producing the first foundation graph models. We demonstrate positive transfer on evaluation datasets from multiple domains, including domains not present in pre-training data. On all datasets performance is at worst on-par and on 76% significantly better than a supervised baseline ($P \leq 0.01$), with an 8 to 40% reduction in error at 95% confidence. Contrary to other research, pre-training on a dataset with the target domain excluded leads us to better performance than pre-training on a dataset from only the target domain. The multi-domain model at worst, matches, and on 56% of tasks, significantly outperforms single-domain ($P \leq 0.01$). These results include when node labels are used in evaluation, where performance is consistently superior to single-domain or non-pre-trained models. Notably, FoToM benefits scenarios in both large or scarce data regimes for the target domains.

Manual delineation of tumor regions from magnetic resonance (MR) images is time-consuming, requires an expert, and is prone to human error. In recent years, deep learning models have been the go-to approach for the segmentation of brain tumors. U-Net and its' variants for semantic segmentation of medical images have achieved good results in the literature. However, U-Net and its' variants tend to over-segment tumor regions and may not accurately segment the tumor edges. The edges of the tumor are as important as the tumor regions for accurate diagnosis, surgical precision, and treatment planning. In the proposed work, the authors aim to extract edges from the ground truth using a derivative-like filter followed by edge reconstruction to obtain an edge ground truth in addition to the brain tumor ground truth. Utilizing both ground truths, the author studies several U-Net and its' variant architectures with and without tumor edges ground truth as a target along with the tumor ground truth for brain tumor segmentation. The author used the BraTS2020 benchmark dataset to perform the study and the results are tabulated for the dice and Hausdorff95 metrics. The mean and median metrics are calculated for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) regions. Compared to the baseline U-Net and its variants, the models that learned edges along with the tumor regions performed well in core tumor regions in both training and validation datasets. The improved performance of edge-trained models trained on baseline models like U-Net and V-Net achieved performance similar to baseline state-of-the-art models like Swin U-Net and hybrid MR-U-Net. The edge-target trained models are capable of generating edge maps that can be useful for treatment planning. Additionally, for further explainability of the results, the activation map generated by the hybrid MR-U-Net has been studied.

Humans rely heavily on shape information to recognize objects. Conversely, convolutional neural networks (CNNs) are biased more towards texture. This is perhaps the main reason why CNNs are vulnerable to adversarial examples. Here, we explore how shape bias can be incorporated into CNNs to improve their robustness. Two algorithms are proposed, based on the observation that edges are invariant to moderate imperceptible perturbations. In the first one, a classifier is adversarially trained on images with the edge map as an additional channel. At inference time, the edge map is recomputed and concatenated to the image. In the second algorithm, a conditional GAN is trained to translate the edge maps, from clean and/or perturbed images, into clean images. Inference is done over the generated image corresponding to the input's edge map. Extensive experiments over 10 datasets demonstrate the effectiveness of the proposed algorithms against FGSM and $\ell_\infty$ PGD-40 attacks. Further, we show that a) edge information can also benefit other adversarial training methods, and b) CNNs trained on edge-augmented inputs are more robust against natural image corruptions such as motion blur, impulse noise and JPEG compression, than CNNs trained solely on RGB images. From a broader perspective, our study suggests that CNNs do not adequately account for image structures that are crucial for robustness. Code is available at:~\url{https://github.com/aliborji/Shapedefence.git}.

Cancer has complex patterns of progression that include converging as well as diverging progressional pathways. Vogelstein's path model of colon cancer was a pioneering contribution to cancer research. Since then, several attempts have been made at obtaining mathematical models of cancer progression, devising learning algorithms, and applying these to cross-sectional data. Beerenwinkel {\em et al.} provided, what they coined, EM-like algorithms for Oncogenetic Trees (OTs) and mixtures of such. Given the small size of current and future data sets, it is important to minimize the number of parameters of a model. For this reason, we too focus on tree-based models and introduce Hidden-variable Oncogenetic Trees (HOTs). In contrast to OTs, HOTs allow for errors in the data and thereby provide more realistic modeling. We also design global structural EM algorithms for learning HOTs and mixtures of HOTs (HOT-mixtures). The algorithms are global in the sense that, during the M-step, they find a structure that yields a global maximum of the expected complete log-likelihood rather than merely one that improves it. The algorithm for single HOTs performs very well on reasonable-sized data sets, while that for HOT-mixtures requires data sets of sizes obtainable only with tomorrow's more cost-efficient technologies.