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SynWTS is a high-fidelity synthetic dataset built as a Digital Twin of the Woven Traffic Safety (WTS) dataset. It is developed for the 2026 AI City Challenge (Track 2) to advance Sim2Real research in transportation safety understanding. It is used to develop multi-modal AI that can describe anomalies from video, such as pedestrian accidents. 

Cortical thickness is a widely used biomarker of brain morphology and health, yet it is dependent on local cortical folding. Because gyral crowns are consistently thicker than sulcal fundi and cortical folds vary widely across individuals, these fluctuations introduce unmodeled nuisance variance that can obscure meaningful biological effects of interest. Previous global methods of folding compensation incompletely compensate for folding effects on cortical thickness. Spatial smoothing is commonly used to reduce these effects in the literature, but this markedly degrades spatial localization precision. To address these limitations, we developed a novel method for folding-compensated cortical thickness estimation that uses nonlinear local multiple regression with five folding measures to model and more completely remove folding-related variance from cortical thickness. This approach estimates what cortical thickness would have been in the absence of folding, yielding a more biologically interpretable measure of cortical architecture. We applied this new approach to data from the Young Adult Human Connectome Project (HCP-YA) and Aging Human Connectome Project (HCA), demonstrating substantial reductions in intra-areal and inter-individual variability, substantially increasing standardized effect sizes of age on cortical thickness (41% increase) while preserving neurobiologically expected patterns, and avoiding the loss of spatial precision that occurs with the spatial smoothing that has traditionally been used in the literature. The method has been integrated into the HCP pipelines, facilitating its widespread use. By attenuating folding-induced variability, this technique enhances cortical thickness as a structural phenotype and may support more accurate cortical parcellation, longitudinal tracking, and biomarker discovery in brain health and disease. 

Antifungal drug resistance is a major challenge in fungal infection management. Numerous genomic changes are known to contribute to acquired drug resistance in clinical isolates of specific pathogens, but whether they broadly explain natural resistance across entire lineages is unknown. We leveraged genomic, ecological, and phenotypic trait data from naturally sampled strains from nearly all known species in subphylumSaccharomycotinato examine the evolution of resistance to eight antifungal drugs. The phylogenetic distribution of drug resistance varied by drug; fluconazole resistance was widespread, while 5-fluorocytosine resistance was rare, except inLipomycetales. A random forest algorithm trained on genomic data predicted drug-resistant yeasts with 54–75% accuracy. Fluconazole resistance was consistently predicted with the highest accuracy (75.2%). Furthermore, fluconazole resistance prediction accuracy was similar between models trained on genome-wide variation in the presence and number of InterPro protein annotations acrossSaccharomycotina(75.2%) and those trained on amino acid sequence alignment data of Erg11, a protein known to be involved in fluconazole resistance (74.3-74.9%). Interestingly, the top Erg11 residues for predicting fluconazole resistance acrossSaccharomycotinado not overlap with, are not spatially close to, and are less conserved than those previously linked to resistance in clinical isolates ofCandida albicans.In silicodeep mutational scanning of theC. albicansErg11 protein reveals that amino acid variants implicated in clinical cases of resistance are almost universally destabilizing while variants in our most informative residues are energetically more neutral, explaining why the latter are much more common than the former in natural populations. Importantly, previous experimental analyses ofC. albicansErg11 have shown that amino acid variation in our most informative residues, despite having never been directly implicated in clinical cases, can directly contribute to resistance. Our results suggest that studies of natural resistance in yeast species never encountered in the clinic will yield a fuller understanding of antifungal drug resistance. 

Field-theoretic simulations that rely on a partial saddle-point approximation have become powerful tools for studying complex polymer materials. The computational cost of such simulations depends critically upon the efficiency of the iterative algorithm used to identify a partial saddle-point field configuration during each step of a stochastic simulation. We introduce a new algorithm for this purpose that relies on a physically motivated approximation in which the linear response of the density to a small change in a pressure-like field is approximated by the response of a hypothetical homogeneous system. The computational cost of the resulting algorithm is significantly less than that of the commonly used Anderson mixing algorithm. 

In recent years, there has been a rise in recognition of the need for computing education to bridge the gap between academia and industry. In addition, educational researchers are also interested in increasing student engagement by grounding learning experiences in real-life concerns, community issues, or personal interests. Unfortunately, traditional lecture-based teaching techniques often fail to prepare students for the challenges they will face in real-world software development scenarios. Project-Based Learning (PjBL) takes a different approach by immersing students in real-world software engineering projects, allowing them to apply theoretical knowledge in practical contexts, building practical skills, fostering critical thinking, and improving problem-solving abilities. Prior literature reviews have explored aspects of PjBL in computing education, such as communication support, educational effectiveness, sprint organization, and capstone course design. However, no literature review extensively and comprehensively examines the following questions as a whole: where PjBL is used, how it is taught, why it should be used, and what challenges to expect in software-related computing courses. The review takes a systematic approach, incorporating a thorough search strategy across four academic databases and targeting keywords associated with PjBL and software computing in higher education. A total of 34 PjBL course attributes were extracted from 184 selected primary studies, which contributed to answering six research questions: (1) What computing courses use PjBL? (2) What is the nature of software projects used? (3) How are these projects organized? (4) How are students assessed and evaluated? (5) What are the reported impacts of PjBL? and (6) How are students supported throughout the projects? The literature review makes four key contributions: a description of the nature of software projects used and how these projects are organized, a highlight of the impacts of PjBL and the methods used to measure those impacts, a summary of the various forms of support provided to students throughout their projects, and the list of challenges encountered in implementing PjBL and recommendations to alleviate those challenges. This comprehensive review offers new insights and serves as a catalog of best practices for computing educators.