Shape-from-intrinsic operator

Shape-from-X is an important class of problems in the fields of geometry processing, computer graphics, and vision, attempting to recover the structure of a shape from some observations. In this paper, we formulate the problem of shape-from-operator (SfO), recovering an embedding of a mesh from intrinsic differential operators defined on the mesh. Particularly interesting instances of our SfO problem include synthesis of shape analogies, shape-from-Laplacian reconstruction, and shape exaggeration. Numerically, we approach the SfO problem by splitting it into two optimization sub-problems that are applied in an alternating scheme: metric-from-operator (reconstruction of the discrete metric from the intrinsic operator) and embedding-from-metric (finding a shape embedding that would realize a given metric, a setting of the multidimensional scaling problem)

EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments

EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities and miss important aspects, such as fairness, bias, usefulness, informativeness. This workshop builds on the success of last year's workshop at CIKM, but with a broader scope and an interactive format.Comment: EvalRS 2023 will be a workshop hosted at KDD2

E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems

Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user retention and fairness, often taking a back seat. Moreover, reconciling multiple performance perspectives is by definition indeterminate, presenting a stumbling block to those in the pursuit of rounded evaluation of Recommender Systems. EvalRS 2022 -- a data challenge designed around Multi-Objective Evaluation -- was a first practical endeavour, providing many insights into the requirements and challenges of balancing multiple objectives in evaluation. In this work, we reflect on EvalRS 2022 and expound upon crucial learnings to formulate a first-principles approach toward Multi-Objective model selection, and outline a set of guidelines for carrying out a Multi-Objective Evaluation challenge, with potential applicability to the problem of rounded evaluation of competing models in real-world deployments.Comment: 15 pages, under submissio

Minimal Superstrings and Loop Gas Models

We reformulate the matrix models of minimal superstrings as loop gas models on random surfaces. In the continuum limit, this leads to the identification of minimal superstrings with certain bosonic string theories, to all orders in the genus expansion. RR vertex operators arise as operators in a Z_2 twisted sector of the matter CFT. We show how the loop gas model implements the sum over spin structures expected from the continuum RNS formulation. Open string boundary conditions are also more transparent in this language.Comment: 36 pages, 3 figure

Research on collaborative information sharing systems

Collaborative systems are systems designed to help people involved in a common task achieve their goals. They are widely used today, and they’re gaining a great consensus both inside corporations and on the World Wide Web. There are many kinds of collaborative systems, such as Wikis (like Wikipedia), blogs, tag-based systems (like Flickr, del.icio.us and Bibsonomy) and even collaborative maps (as in Google Maps). One of the main reasons of this success is that, as applications are becoming more and more data-driven, spontaneous user participation adds value to a system because it helps in creating a new, unique and hard to recreate source of data [1]. The main objective of this research project is to study collaborative systems and the possibility to enhance them through semantics. The aim of a contamination between these systems and Semantic Web technologies is twofold: on one side, we think that the huge quantity of information created by the participation of many users can be better managed and searched thanks to added semantics; on the other side, Semantic Web community can exploit spontaneou