We use cookies to analyze the browsing and usage of our website and to personalize your experience. You can disable these technologies at any time, but this may limit certain functionalities of the site. Read our Privacy Policy for more information.
Setting cookies
You can enable and disable the types of cookies you wish to accept. However certain choices you make could affect the services offered on our sites (e.g. suggestions, personalised ads, etc.).
Essential cookies
These cookies are necessary for the operation of the site and cannot be deactivated. (Still active)
Analytics cookies
Do you accept the use of cookies to measure the audience of our sites?
Multimedia Player
Do you accept the use of cookies to display and allow you to watch the video content hosted by our partners (YouTube, etc.)?
Publications
Molar Pregnancy in a Quadruplet Conception Following IVF: A Case Report
Real-world decision-making problems involve Type 1 decision-dependent uncertainty, where the probability distribution of the stochastic proc… (see more)ess depends on the model decisions. However, few studies focus on two-stage stochastic programs with this type of endogenous uncertainty, and those that do lack general methodologies. We thus propose herein a general method for solving a class of these programs based on the transformation of random variables, a technique widely employed in probability and statistics. The proposed method is tailored to large-scale problems with discrete or continuous endogenous random variables. The random variable transformation allows the use of the sample average approximation (SAA) method, which provides optimality convergence guarantees under certain conditions. We show that, for some classical distributions, the proposed method reduces to solving mixed-integer linear or convex programs. Finally, we validate this method by applying it to a network design and facility-protection problem, considering distinct decision-dependent distributions for the random variables. Whereas most distributions result in a nonlinear nonconvex deterministic equivalent program, the proposed method solves mixed-integer linear programs in all cases. In addition, it produces attractive performance estimators for the SAA method in a reasonable computational time and outperforms the case in which the endogenous distribution defines a mixed-integer deterministic equivalent.
Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are r… (see more)epresented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.
While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symboli… (see more)c planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard
While Transformers have enabled tremendous progress in various application settings, such architectures still trail behind traditional symbo… (see more)lic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks. This is accomplished by training an encoder-decoder Transformer model to predict the search dynamics of the
While Transformers have enabled tremendous progress in various application settings, such architectures still trail behind traditional symbo… (see more)lic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks. This is accomplished by training an encoder-decoder Transformer model to predict the search dynamics of the
