Tootfinder

A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT
Tanjin Taher Toma, Tejas Sudharshan Mathai, Bikash Santra, Pritam Mukherjee, Jianfei Liu, Wesley Jong, Darwish Alabyad, Vivek Batheja, Abhishek Jha, Mayank Patel, Darko Pucar, Jayadira del Rivero, Karel Pacak, Ronald M. Summers
https://

A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT
Accurate segmentation of pheochromocytoma (PCC) in abdominal CT scans is essential for tumor burden estimation, prognosis, and treatment planning. It may also help infer genetic clusters, reducing reliance on expensive testing. This study systematically evaluates anatomical priors to identify configurations that improve deep learning-based PCC segmentation. We employed the nnU-Net framework to evaluate eleven annotation strategies for accurate 3D segmentation of pheochromocytoma, introducing a …

I, Robot? Socio-Technical Implications of Ultra-Personalized AI-Powered AAC; an Autoethnographic Account
Tobias Weinberg, Ricardo E. Gonzalez Penuela, Stephanie Valencia, Thijs Roumen
https://arxiv.org/abs/2509.13671

I, Robot? Socio-Technical Implications of Ultra-Personalized AI-Powered AAC; an Autoethnographic Account
Generic AI auto-complete for message composition often fails to capture the nuance of personal identity, requiring significant editing. While harmless in low-stakes settings, for users of Augmentative and Alternative Communication (AAC) devices, who rely on such systems for everyday communication, this editing burden is particularly acute. Intuitively, the need for edits would be lower if language models were personalized to the communication of the specific user. While technically feasible, su…

Parameter Identifiability of Linear-Compartmental Mammillary Models
Katherine Clemens, Jonathan Martinez, Anne Shiu, Michaela Thompson, Benjamin Warren
https://arxiv.org/abs/2506.21889

Parameter Identifiability of Linear-Compartmental Mammillary Models
Linear compartmental models are a widely used tool for analyzing systems arising in biology, medicine, and more. In such settings, it is essential to know whether model parameters can be recovered from experimental data. This is the identifiability problem. For a class of linear compartmental models with one input and one output, namely, those for which the underlying graph is a bidirected tree, Bortner et al. completely characterized which such models are structurally identifiability, which me…

“If the abundance movement is to succeed, it must identify the defenders of scarcity and broaden the playbook to either overcome those interests or change their incentives to bring them on the team.” 🎯
https://fed.brid.gy/r/https://bsky.app

Higher-order transmissibility and its linear approximation for in-service crack identification in train wheelset axles
Ehsan Naghizadeh, Paolo Tiso, Eleni Chatzi
https://arxiv.org/abs/2507.18636

Higher-order transmissibility and its linear approximation for in-service crack identification in train wheelset axles
In-service structural health monitoring is a so far rarely exploited, yet potent option for early-stage crack detection and identification in train wheelset axles. This procedure is non-trivial to enforce on the basis of a purely data-driven approach and typically requires the adoption of numerical, e.g. finite element-based, simulation schemes of the dynamic behavior of these axles. Damage in this particular case can be formulated as a breathing crack problem, which further complicates simulat…

Time-Fair Benchmarking for Metaheuristics: A Restart-Fair Protocol for Fixed-Time Comparisons
Junbo Jacob Lian
https://arxiv.org/abs/2509.08986 https://arx…

Time-Fair Benchmarking for Metaheuristics: A Restart-Fair Protocol for Fixed-Time Comparisons
Numerous purportedly improved metaheuristics claim superior performance based on equivalent function evaluations (FEs), yet often conceal additional computational burdens in more intensive iterations, preprocessing stages, or hyperparameter tuning. This paper posits that wall-clock time, rather than solely FEs, should serve as the principal budgetary constraint for equitable comparisons. We formalize a fixed-time, restart-fair benchmarking protocol wherein each algorithm is allotted an identica…

Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems
Qian Xiao, Conn Breathnach, Ioana Ghergulescu, Conor O'Sullivan, Keith Johnston, Vincent Wade
https://arxiv.org/abs/2508.18925

Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems
The surge in the adoption of Intelligent Tutoring Systems (ITSs) in education, while being integral to curriculum- based learning, can inadvertently exacerbate performance gaps. To address this problem, student profiling becomes crucial for tracking progress, identifying struggling students, and alleviating disparities among students. Such profiling requires measuring student behaviors and performance across different aspects, such as content coverage, learning intensity, and proficiency in dif…

As Canada Day approaches, this story of a Mom finding her Canadian identity through helping Syrian refugees settle here after 2015 is poignant.
"In 2025, being a Canadian woman to me means looking out for our neighbours, leaning into differences in culture, religious practices and learning how to help others in need. Because we are not different at all. We are all just looking for safety and peace. It's our Canadian values of equality, respect and freedom in action.
It is the singular privilege of my life to walk alongside these families from Cape Breton and those who are newcomers to Canada. It's changed how I live, and I intend to do this work as long as I am able. I learn from them grace, service, faith and hope. They taught me how to be a Canadian.”
#Canada #CanPoli #CdnPoli #Refugees #Immigration #Syria #AlanKurdi
https://www.cbc.ca/news/canada/nova-scotia/first-person-cape-breton-welcomes-syrian-refugees-1.7559341

Animated Visual Encoding and Layer Blending for Identification of Educational Game Strategies
Braden Roper, William Thompson, Chris Weaver
https://arxiv.org/abs/2507.01134

Animated Visual Encoding and Layer Blending for Identification of Educational Game Strategies
Game-Based Learning has proven to be an effective method for enhancing engagement with educational material. However, gaining a deeper understanding of player strategies remains challenging. Sequential game-state and action-based tracking tools often gather extensive data that can be difficult to interpret as long-term strategy. This data presents unique problems to visualization, as it can be fairly natural, noisy data but is constrained within synthetic, controlled environments, leading to is…