Split Learning has been recently introduced to facilitate applications where user data privacy is a requirement. However, it has not been thoroughly studied in the context of Privacy-Preserving Record Linkage, a problem in which the same real-world entity should be identified among databases from different dataholders, but without disclosing any additional information. In this paper, we investigate the potentials of Split Learning for Privacy-Preserving Record Matching, by introducing a novel training method through the utilization of Reference Sets, which are publicly available data corpora, showcasing minimal matching impact against a traditional centralized SVM-based technique.

Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecule generation, integrating multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models remains unexplored. We introduce multi-conditional diffusion guidance. The proposed Transformer-based denoising model has a condition encoder that learns the representations of numerical and categorical conditions. The denoising model, consisting of a structure encoder-decoder, is trained for denoising under the representation of conditions. The diffusion process becomes graph-dependent to accurately estimate graph-related noise in molecules, unlike the previous models that focus solely on the marginal distributions of atoms or bonds. We extensively validate our model for multi-conditional polymer and small molecule generation. Results demonstrate our superiority across metrics from distribution learning to condition control for molecular properties. An inverse polymer design task for gas separation with feedback from domain experts further demonstrates its practical utility.

Researchers from MIT have developed new self-driving car system capable of navigating in low visibility settings, including in fog and snow. The system relies on Localizing Ground Penetrating Radar (LGPR), which takes readings the shape and composition of the road directly below and around the car with electromagnetic pulses. Other self-driving car systems use a combination of Lidar, radar, and cameras to develop a real-time topographical model of where the car is in space. These systems are generally reliable but have been vulnerable to visual tricks like fake road signs and lane makers, and can become significantly less reliable during bad weather conditions. The LGPR system aims to improve on these vulnerabilities by focusing on the road itself and not the open space in front of the car.

In Statistical Learning, the Vapnik-Chervonenkis (VC) dimension is an important combinatorial property of classifiers. To our knowledge, no theoretical results yet exist for the VC dimension of edited nearest-neighbour (1NN) classifiers with reference set of fixed size. Related theoretical results are scattered in the literature and their implications have not been made explicit. We collect some relevant results and use them to provide explicit lower and upper bounds for the VC dimension of 1NN classifiers with a prototype set of fixed size. We discuss the implications of these bounds for the size of training set needed to learn such a classifier to a given accuracy. Further, we provide a new lower bound for the two-dimensional case, based on a new geometrical argument.