Domain Generalization (DG) requires models to generalize across unseen data distributions. Kernel-based theory reveals a No-Free-Lunch problem: any model with a fixed representation is fundamentally sub-optimal for all possible shifts. While large ensembles mitigate this, they are computationally expensive and remain static once trained, inheriting the same theoretical limitation. We introduce MoE² (Mixture-of-Mixtures of Experts), a framework that uses a single frozen backbone to dynamically synthesize a bespoke adapter for each input, allowing it to continuously adapt its effective kernel. We provide a theoretical grounding for this process, proving our routing mechanism is a principled non-parametric estimator for the optimal Bayes mixture of experts. We derive a generalization bound that cleanly separates the router's estimation error from the reduction in a kernel-mismatch penalty achieved via synthesis. MoE² matches or exceeds state-of-the-art ensemble baselines on major DG benchmarks while using only a single, compact model. MoE² thus provides a theoretically-grounded and lightweight alternative to large-scale ensembles for robust domain generalization.

Integer errors continue to create vulnerabilities. In fact, Integer Overflow or Wraparound is listed at position 11 in the 2020 CWE Top 25 Most Dangerous Software Weaknesses. This poster describes the Expression Evaluation (EE) visualization tool that helps students understand the type conversions that take place implicitly within a C program. This tool depicts step-wise the coercions that take place within the evaluation of a user specified expression with mixed integer type operands. The system enables students to create unlimited examples to test their understanding. The tool was evaluated in the classroom and shown to be easy to use and effective.

Schema integration is the problem of creating a unified target schema based on a set of existing source schemas and based on a set of correspondences that are the result of matching the source schemas. Previous methods for schema integration rely on the exploration, implicit or explicit, of the multiple design choices that are possible for the integrated schema. Such exploration relies heavily on user interaction; thus, it is time consuming and labor intensive. Furthermore, previous methods have ignored the additional information that typically results from the schema matching process, that is, the weights and in some cases the directions that are associated with the correspondences.

This paper describes Orchid, a system that converts declarative mapping specifications into data flow specifications (ETL jobs) and vice versa. Orchid provides an abstract operator model that serves as a common model for both transformation paradigms; both mappings and ETL jobs are transformed into instances of this common model. As an additional benefit, instances of this common model can be optimized and deployed into multiple target environments. Orchid is being deployed inFastTrack, a data transformation toolkit in IBM Information Server.