Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach

Over 44 million Americans currently suffer from food insecurity, of whom 13 million are children. Across the United States, thousands of food banks and pantries serve as vital sources of food and other forms of aid for food insecure families. By optimizing food bank and pantry locations, food would become more accessible to families who desperately require it. In this work, we introduce a novel two-level optimization framework, which utilizes the K-Medoids clustering algorithm in conjunction with the Open-Source Routing Machine engine, to optimize food bank and pantry locations based on real road distances to houses and house blocks. Our proposed framework also has the adaptability to factor in considerations such as median household income using a pseudo-weighted K-Medoids algorithm. Testing conducted with California and Indiana household data, as well as comparisons with real food bank and pantry locations showed that interestingly, our proposed framework yields food pantry locations superior to those of real existing ones and saves significant distance for households, while there is a marginal penalty on the first level food bank to food pantry distance. Overall, we believe that the second-level benefits of this framework far outweigh any drawbacks and yield a net benefit result.

A Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery

As a crucial and intricate task in robotic minimally invasive surgery, reconstructing surgical scenes using stereo or monocular endoscopic video holds immense potential for clinical applications. NeRF-based techniques have recently garnered attention for the ability to reconstruct scenes implicitly. On the other hand, Gaussian splatting-based 3D-GS represents scenes explicitly using 3D Gaussians and projects them onto a 2D plane as a replacement for the complex volume rendering in NeRF. However, these methods face challenges regarding surgical scene reconstruction, such as slow inference, dynamic scenes, and surgical tool occlusion. This work explores and reviews state-of-the-art (SOTA) approaches, discussing their innovations and implementation principles. Furthermore, we replicate the models and conduct testing and evaluation on two datasets. The test results demonstrate that with advancements in these techniques, achieving real-time, high-quality reconstructions becomes feasible.

myAURA: Personalized health library for epilepsy management via knowledge graph sparsification and visualization

Objective: We report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and researchers in making decisions about care and self-management. Materials and Methods: myAURA rests on the federation of an unprecedented collection of heterogeneous data resources relevant to epilepsy, such as biomedical databases, social media, and electronic health records. A generalizable, open-source methodology was developed to compute a multi-layer knowledge graph linking all this heterogeneous data via the terms of a human-centered biomedical dictionary. Results: The power of the approach is first exemplified in the study of the drug-drug interaction phenomenon. Furthermore, we employ a novel network sparsification methodology using the metric backbone of weighted graphs, which reveals the most important edges for inference, recommendation, and visualization, such as pharmacology factors patients discuss on social media. The network sparsification approach also allows us to extract focused digital cohorts from social media whose discourse is more relevant to epilepsy or other biomedical problems. Finally, we present our patient-centered design and pilot-testing of myAURA, including its user interface, based on focus groups and other stakeholder input. Discussion: The ability to search and explore myAURA's heterogeneous data sources via a sparsified multi-layer knowledge graph, as well as the combination of those layers in a single map, are useful features for integrating relevant information for epilepsy. Conclusion: Our stakeholder-driven, scalable approach to integrate traditional and non-traditional data sources, enables biomedical discovery and data-powered patient self-management in epilepsy, and is generalizable to other chronic conditions.

Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting

In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to prominence for their exceptional ability to reconstruct scenes. Nonetheless, these methods are hampered by slow inference, prolonged training, and substantial computational demands. Additionally, some rely on stereo depth estimation, which is often infeasible due to the high costs and logistical challenges associated with stereo cameras. Moreover, the monocular reconstruction quality for deformable scenes is currently inadequate. To overcome these obstacles, we present Endo-4DGS, an innovative, real-time endoscopic dynamic reconstruction approach that utilizes 4D Gaussian Splatting (GS) and requires no ground truth depth data. This method extends 3D GS by incorporating a temporal component and leverages a lightweight MLP to capture temporal Gaussian deformations. This effectively facilitates the reconstruction of dynamic surgical scenes with variable conditions. We also integrate Depth-Anything to generate pseudo-depth maps from monocular views, enhancing the depth-guided reconstruction process. Our approach has been validated on two surgical datasets, where it can effectively render in real-time, compute efficiently, and reconstruct with remarkable accuracy. These results underline the vast potential of Endo-4DGS to improve surgical assistance.

Sum Rate Maximization under AoI Constraints for RIS-Assisted mmWave Communications

The concept of age of information (AoI) has been proposed to quantify information freshness, which is crucial for time-sensitive applications. However, in millimeter wave (mmWave) communication systems, the link blockage caused by obstacles and the severe path loss greatly impair the freshness of information received by the user equipments (UEs). In this paper, we focus on reconfigurable intelligent surface (RIS)-assisted mmWave communications, where beamforming is performed at transceivers to provide directional beam gain and a RIS is deployed to combat link blockage. We aim to maximize the system sum rate while satisfying the information freshness requirements of UEs by jointly optimizing the beamforming at transceivers, the discrete RIS reflection coefficients, and the UE scheduling strategy. To facilitate a practical solution, we decompose the problem into two subproblems. For the first per-UE data rate maximization problem, we further decompose it into a beamforming optimization subproblem and a RIS reflection coefficient optimization subproblem. Considering the difficulty of channel estimation, we utilize the hierarchical search method for the former and the local search method for the latter, and then adopt the block coordinate descent (BCD) method to alternately solve them. For the second scheduling strategy design problem, a low-complexity heuristic scheduling algorithm is designed. Simulation results show that the proposed algorithm can effectively improve the system sum rate while satisfying the information freshness requirements of all UEs.