Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP. Traditional traffic safety studies often examine risk factors in isolation, overlooking the spatial complexity and contextual interactions inherent in the built environment. Furthermore, conventional Neural Network-based risk estimators typically generate point estimates without conveying model uncertainty, limiting their utility in critical decision-making. To address these shortcomings, we introduce a novel geospatial deep learning framework that leverages satellite imagery as a comprehensive spatial input. This approach enables the model to capture the nuanced spatial patterns and embedded environmental risk factors that contribute to fatal crash risks. Rather than producing a single deterministic output, our model estimates a full Beta probability distribution over fatal crash risk, yielding accurate and uncertainty-aware predictions--a critical feature for trustworthy AI in safety-critical applications. Our model outperforms baselines by achieving a 17-23% improvement in recall, a key metric for flagging potential dangers, while delivering superior calibration. By providing reliable and interpretable risk assessments from satellite imagery alone, our method enables safer autonomous navigation and offers a highly scalable tool for urban planners and policymakers to enhance roadway safety equitably and cost-effectively.

Neural network model calibration is crucial in medical imaging, where accurate probabilistic predictions are essential for informed decision-making. Existing calibration techniques often introduce additional complexity and may not fully capture the inherent uncertainty associated with the tasks. To address these challenges, we propose a novel approach based on probabilistic embedding that models uncertainty through a Gaussian distribution. By embedding the model’s predictions into a probabilistic space, the proposed method enables effective uncertainty quantification. We demonstrate the effectiveness of our approach on multiple medical imaging tasks. The experimental result shows our method outperforms existing techniques in terms of both calibration and accuracy.

In this poster, we introduce a set of modules designed to enhance security education in introductory programming courses. The modules cover a range of prevalent security concerns, including topics such as integer overflow, buffer overflow, and user input validation. These critical security problems are addressed to equip students with essential knowledge and skills in secure coding. The modules feature code examples within sandbox environments, as well as presented using the Python Tutor code visualizer. The eight modules align seamlessly with chapters found in popular introductory programming courses. Each example has been implemented in C++, Java, and Python, ensuring broad applicability across different programming languages.

In this poster, we introduce LEARNDB (Learning Environment and Resource Network for Databases), a platform designed to meet the unique demands of database education. The platform offers a comprehensive set of tools designed to facilitate the creation and management of tutorials, exercises, quizzes, and laboratory assignments, encompassing topics spanning from database design to SQL (Structured Query Language) proficiency. We have also developed content that encompasses topics spanning from database design to SQL (Structured Query Language) proficiency, providing students with practical skills and knowledge.

Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are implicitly associated with neuropsychiatric disorders (NDs), and integrating multi-modal to the binary classification of NDs has been thoroughly explored. However, accurately classifying multiple classes of NDs remains a challenge due to the complexity of disease subclass. In our study, we develop a heterogeneous neural network (H-Net) that integrates sMRI and fMRI modes for classifying multi-class NDs. To account for the differences between the two modes, H-Net adopts a heterogeneous neural network strategy to extract information from each mode. Specifically, H-Net includes an multi-layer perceptron based (MLP-based) encoder, a graph attention network based (GAT-based) encoder, and a cross-modality transformer block. The MLP-based and GAT-based encoders extract semantic features from sMRI and features from fMRI, respectively, while the cross-modality transformer block models the attention of two types of features. In H-Net, the proposed MLP-mixer block and cross-modality alignment are powerful tools for improving the multi-classification performance of NDs. H-Net is validate on the public dataset (CNP), where H-Net achieves 90% classification accuracy in diagnosing multi-class NDs. Furthermore, we demonstrate the complementarity of the two MRI modalities in improving the identification of multi-class NDs. Both visual and statistical analyses show the differences between ND subclasses.

Segmenting stroke lesions and assessing the thrombolysis in cerebral infarction (TICI) grade are two important but challenging prerequisites for an auxiliary diagnosis of the stroke. However, most previous studies have focused only on a single one of two tasks, without considering the relation between them. In our study, we propose a simulated quantum mechanics-based joint learning network (SQMLP-net) that simultaneously segments a stroke lesion and assesses the TICI grade. The correlation and heterogeneity between the two tasks are tackled with a single-input double-output hybrid network. SQMLP-net has a segmentation branch and a classification branch. These two branches share an encoder, which extracts and shares the spatial and global semantic information for the segmentation and classification tasks. Both tasks are optimized by a novel joint loss function that learns the intra- and inter-task weights between these two tasks. Finally, we evaluate SQMLP-net with a public stroke dataset (ATLAS R2.0). SQMLP-net obtains state-of-the-art metrics (Dice:70.98% and accuracy:86.78%) and outperforms single-task and existing advanced methods. An analysis found a negative correlation between the severity of TICI grading and the accuracy of stroke lesion segmentation.

Neural networks (NNs) have demonstrated exciting results on various tasks within the last decade. For example, the performance on image classification tasks has been improved dramatically. However, the performance evaluations are often based on a black-box performance, such as accuracy, while insightful analysis of the black-box, such as the prediction formation mechanism, is often missing. Empirically, a NN usually produces a stable overall performance on the same task across multiple training trials when treating it as a black-box. However, when unveiling the black-box, the performance is usually volatile. The decision-making criteria learned by the training trials are often significantly different, which is problematic in many ways. We believe achieving consistent criteria between different training trials is equally important to achieving high performance, if not more. This work, firstly, evaluates the decision-making criteria of NNs via inputs sensitivity using feature-attribution explanation methods in combination with computational analysis and clustering analysis. Through intensive experimentation, we find that decision-making criteria are easily distinguishable between training trials of the same architecture and task, suggesting the criteria learned between training trials are significantly inconsistent. To mitigate this inconsistency, we propose three general training schemes. Our demonstration result shows that the proposed methods effectively reduce the inconsistency of the decision-making criteria learned by different training trials while maintaining the overall performance.

A key challenge in training neural networks for a given medical imaging task is the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports are often readily available in medical records and contain rich but unstructured interpretations written by experts as part of standard clinical practice. We propose using these textual reports as a form of weak supervision to improve the image interpretation performance of a neural network without requiring additional manually labeled examples. We use an image-text matching task to train a feature extractor and then fine-tune it in a transfer learning setting for a supervised task using a small labeled dataset. The end result is a neural network that automatically interprets imagery without requiring textual reports during inference. We evaluate our method on three classification tasks and find consistent performance improvements, reducing the need for labeled data by 67%-98%.

This paper addresses the task of classifying galaxy clusters, which are the largest known objects in the Universe. Galaxy clusters can be categorized as cool-core (CC), weak-cool-core (WCC), and non-cool-core (NCC), depending on their central cooling times. Traditional classification approaches used in astrophysics are inaccurate and rely on measuring surface brightness concentrations or central gas densities. In this work, we propose a multi-branch attention network that uses spatial attention to classify a given cluster. To evaluate our network, we use a database of simulated X-ray emissivity images, which contains 954 projections of 318 clusters. Experimental results show that our network outperforms several strong baseline methods and achieves a macro-averaged F1 score of 0.83. We highlight the value of our proposed spatial attention module through an ablation study.

Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.

Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaining slice-to-slice changes. We propose a novel deep 2D convolutional neural network (CNN) architecture for DBT classification that simultaneously overcomes both challenges. Our approach operates on the full volume, regardless of the number of slices, and allows the use of pre-trained 2D CNNs for feature extraction, which is important given the limited amount of annotated training data. In an extensive evaluation on a real-world clinical dataset, our approach achieves 0.854 auROC, which is 28.80% higher than approaches based on 3D CNNs. We also find that these improvements are stable across a range of model configurations.

Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks. Many works focus on adversarial attacks and defenses on 2D images, but few focus on 3D point clouds. In this paper, our goal is to enhance the adversarial robustness of PointNet, which is one of the most widely used models for 3D point clouds. We apply the fast gradient sign attack method (FGSM) on 3D point clouds and find that FGSM can be used to generate not only adversarial images but also adversarial point clouds. To minimize the vulnerability of PointNet to adversarial attacks, we propose Defense-PointNet. We compare our model with two baseline approaches and show that Defense-PointNet significantly improves the robustness of the network against adversarial samples.

In this paper, we propose a leg-driven physiology framework for pedestrian detection. The framework is introduced to reduce the search space of candidate regions of pedestrians. Given a set of vertical line segments, we can generate a space of rectangular candidate regions, based on a model of body proportions. The proposed framework can be either integrated with or without learning-based pedestrian detection methods to validate the candidate regions. A symmetry constraint is then applied to validate each candidate region to decrease the false positive rate. The experiment demonstrates the promising results of the proposed method by comparing it with Dalal & Triggs method. For example, rectangular regions detected by the proposed method has much similar area to the ground truth than regions detected by Dalal & Triggs method.