A logistics provider’s profit maximization facility location problem with random utility maximizing followers
David Pinzon Ulloa
Bernard Gendron
Implementation of a Global Pediatric Trauma Course in an Upper Middle–Income Country: A Pilot Study
Abbie Naus
Madeleine Carroll
Ayla Gerk
David P. Mooney
Natalie L. Yanchar
Julia Ferreira
Karen E. Gripp
Caroline Ouellet
Fabio Botelho
ChatGPT: What Every Pediatric Surgeon Should Know About Its Potential Uses and Pitfalls
Raquel González
Russell Woo
A Francois Trappey
Stewart Carter
David Darcy
Ellen Encisco
Brian Gulack
Doug Miniati
Edzhem Tombash
Eunice Y. Huang
An improved column-generation-based matheuristic for learning classification trees
Krunal Kishor Patel
Guy Desaulniers
Patient-Centered Surgical Care for Children in Low and Lower-Middle Income Countries (LMICs) - A Systematic Scoping Review of the Literature
Riya Sawhney
Kacylia Roy Proulx
Ayla Gerk
Elena Guadagno
A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
Zeinab Sherkatghanad
Amin Ghazanfari
Human local field potentials in motor and non-motor brain areas encode upcoming movement direction.
Etienne Combrisson
Franck Di Rienzo
Anne-Lise Saive
Marcela Perrone-Bertolotti
Juan LP Soto
Philippe Kahane
Jean-Philippe Lachaux
Aymeric Guillot
Penalty weight tuning in high dose rate brachytherapy using multi-objective Bayesian optimization.
Hossein Jafarzadeh
Majd Antaki
Ximeng Mao
Marie Duclos
Farhad Maleki
OBJECTIVE Treatment plan optimization in high dose rate (HDR) brachytherapy often requires manual fine-tuning of penalty weights for each ob… (see more)jective, which can be time-consuming and dependent on the planner's experience. To automate this process, this study used a multi-criteria approach called multi-objective Bayesian optimization with q-noisy expected hypervolume improvement as its acquisition function (MOBO-qNEHVI). Approach: The treatment plans of 13 prostate cancer patients were retrospectively imported to a research treatment planning system, RapidBrachyMTPS, where fast mixed integer optimization (FMIO) performs dwell time optimization given a set of penalty weights to deliver 15 Gy to the target volume. MOBO-qNEHVI was used to find patient-specific Pareto optimal penalty weight vectors that yield clinically acceptable dose volume histogram metrics. The relationship between the number of MOBO-qNEHVI iterations and the number of clinically acceptable plans per patient (acceptance rate) was investigated. The performance time was obtained for various parameter configurations. Main results: MOBO-qNEHVI found clinically acceptable treatment plans for all patients. With increasing the number of MOBO-qNEHVI iterations, the acceptance rate grew logarithmically while the performance time grew exponentially. Fixing the penalty weight of the tumour volume to maximum value, adding the target dose as a parameter, initiating MOBO-qNEHVI with 25 parallel sampling of FMIO, and running 6 MOBO-qNEHVI iterations found solutions that delivered 15 Gy to the hottest 95% of the clinical target volume while respecting the dose constraints to the organs at risk. The average acceptance rate for each patient was 89.74% ± 8.11%, and performance time was 66.6 ± 12.6 seconds. The initiation took 22.47 ± 7.57 s, and each iteration took 7.35 ± 2.45 s to find one Pareto solution. Significance: MOBO-qNEHVI can automatically explore the trade-offs between treatment plan objectives in a patient-specific manner within a minute. This approach can reduce the dependency of plan quality on planner's experience.
Autoregressive Networks with Dependent Edges
Jinyuan Chang
Qin Fang
Peter W. MacDonald
Qiwei Yao
Radiation hardness of open Fabry-Pérot microcavities
Fernanda C. Rodrigues-Machado
Erika Janitz
Simon Bernard
H. Bekerat
Malcolm McEwen
James Renaud
Lilian Childress
Jack C Sankey
SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision
Ankit Vani
Bac Nguyen
Samuel Lavoie
Ranjay Krishna
Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception al… (see more)lows us to robustly generalize under distractions and to new compositions of perceivable concepts. Transformers employ a similar notion of attention in their architecture, but representation learning models with transformer backbones like CLIP and DINO often fail to demonstrate robustness and compositionality. We highlight a missing architectural prior: unlike human perception, transformer encodings do not separately attend over individual concepts. In response, we propose SPARO, a read-out mechanism that partitions encodings into separately-attended slots, each produced by a single attention head. Using SPARO with CLIP imparts an inductive bias that the vision and text modalities are different views of a shared compositional world with the same corresponding concepts. Using SPARO, we demonstrate improvements on downstream recognition, robustness, retrieval, and compositionality benchmarks with CLIP (up to +14% for ImageNet, +4% for SugarCrepe), and on nearest neighbors and linear probe for ImageNet with DINO (+3% each). We also showcase a powerful ability to intervene and select individual SPARO concepts to further improve downstream task performance (up from +4% to +9% for SugarCrepe) and use this ability to study the robustness of SPARO's representation structure. Finally, we provide insights through ablation experiments and visualization of learned concepts.
Fairness Incentives in Response to Unfair Dynamic Pricing
Jesse Thibodeau
Hadi Nekoei
Afaf Taïk
Janarthanan Rajendran
The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' … (see more)demand responses to a given pricing strategy. Notably, dynamic pricing may result in buyer distributions unreflective of those of the underlying population, which can be problematic in markets where fair representation is socially desirable. To address this, policy makers might leverage tools such as taxation and subsidy to adapt policy mechanisms dependent upon their social objective. In this paper, we explore the potential for AI methods to assist such intervention strategies. To this end, we design a basic simulated economy, wherein we introduce a dynamic social planner (SP) to generate corporate taxation schedules geared to incentivizing firms towards adopting fair pricing behaviours, and to use the collected tax budget to subsidize consumption among underrepresented groups. To cover a range of possible policy scenarios, we formulate our social planner's learning problem as a multi-armed bandit, a contextual bandit and finally as a full reinforcement learning (RL) problem, evaluating welfare outcomes from each case. To alleviate the difficulty in retaining meaningful tax rates that apply to less frequently occurring brackets, we introduce FairReplayBuffer, which ensures that our RL agent samples experiences uniformly across a discretized fairness space. We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13.19% in the full RL setting.