Ground and aerial robots, with distinct sensing perspectives, acquire heterogeneous point clouds that exhibit limited overlap, presenting significant challenges for collaborative mapping. To address these challenges, this article proposes a robust LiDAR-based aerial-ground collaborative mapping framework for large-scale outdoor environments. Firstly, to perform reliable cross-source place recognition and detect loop closure between aerial-ground robots, a deep network that fuses multi-level bird’s-eye view (BEV) and geometric features is developed to ensure consistent feature extraction and emphasize overlaps between heterogeneous point clouds. Next, an overlap-aware registration method is proposed to align point clouds within a detected loop closure. This method can strategically perform point cloud sparsification based on overlap ratio estimation, and mitigate the adverse effects of interfering points in non-overlapping regions. Furthermore, a graph optimization is implemented to consider all loop closure constraints simultaneously and ensure global map consistency. Comparative experiments on public and self-collected datasets demonstrate the superiority of the proposed approach. We open source code on GitHub1to benefit the community.

Online reviews on products and services can be very useful for customers, but they need to be protected from manipulation. So far, most studies have focused on analyzing online reviews from a single hosting site. How could one leverage information from multiple review hosting sites? This is the key question in our work. In response, we develop a systematic methodology to merge, compare, and evaluate reviews from multiple hosting sites. We focus on hotel reviews and use more than 15 million reviews from more than 3.5 million users spanning three prominent travel sites. Our work consists of three thrusts: (a) we develop novel features capable of identifying cross-site discrepancies effectively, (b) we conduct arguably the first extensive study of cross-site variations using real data, and develop a hotel identity-matching method with 93% accuracy, (c) we introduce the TrueView score, as a proof of concept that cross-site analysis can better inform the end user. Our results show that: (1) we detect 7 times more suspicious hotels by using multiple sites compared to using the three sites in isolation, and (2) we find that 20% of all hotels appearing in all three sites seem to have low trustworthiness score. Our work is an early effort that explores the advantages and the challenges in using multiple reviewing sites towards more informed decision making.

We present a low-cost medical embedded system for detecting diabetic peripheral neuropathy (DPN). More specifically, we use low-cost infrared sensors to capture in a non-contact fashion the thermal response of the feet following cold stimulation. The rate of thermal regulation is then calculated based on temperature recovery and used to quantify the degree of the foot sensation. This device and methodology enables early detection of risk for DPN for in-home use or at family care clinics where fancy, expensive tests cannot be conducted.

Nonnegative Least Squares (NNLS) is a general form for many important problems. We consider a special case of NNLS where the input is nonnegative. It is called Totally Nonnegative Least Squares (TNNLS) in the literature. We show a reduction of TNNLS to a single class Support Vector Machine (SVM), thus relating the sparsity of a TNNLS solution to the sparsity of supports in a SVM. This allows us to apply any SVM solver to the TNNLS problem. We get an order of magnitude improvement in running time by first obtaining a smaller version of our original problem with the same solution using a fast approximate SVM solver. Second, we use an exact NNLS solver to obtain the solution. We present experimental evidence that this approach improves the performance of state-of-the-art NNLS solvers by applying it to both randomly generated problems as well as to real datasets, calculating radiation therapy dosages for cancer patients.

This paper presents a leaf sequencing software called SLS (static leaf sequencing) for intensity-modulated radiation therapy (IMRT). SLS seeks to produce improved clinical IMRT treatment plans by (1) shortening their treatment times and (2) minimizing their machine delivery errors. Our SLS software is implemented using the C programming language on Linux workstations and is designed as a separate module to complement the current commercial treatment planning systems. The input to SLS is discrete radiation intensity maps computed by current planning systems, and its output is (modified) optimized control sequences for the radiotherapy machines. Our SLS approach is very different from the commonly used planning methods in medical literature in that it is based on graph algorithms and computational geometry techniques. Comparisons of SLS with the CORVUS commercial planning system indicated that for the same set of discrete radiation intensity maps, treatment times can be shortened by over 30% by our SLS plans while maintaining the same treatment quality. We have used SLS in clinical applications at two cancer treatment centers. This paper discusses the various aspects of the implementation, installation, commissioning, and testing of our SLS software system

The static leaf sequencing (SLS) problem arises in radiation therapy for cancer treatments, aiming to accomplish the delivery of a radiation prescription to a target tumor in the minimum amount of delivery time. Geometrically, the SLS problem can be formulated as a 3-D partition problem for which the 2-D problem of partitioning a polygonal domain (possibly with holes) into a minimum set of monotone polygons is a special case. In this paper, we present new geometric algorithms for a basic case of the 3-D SLS problem (which is also of clinical value) and for the general 3-D SLS problem. Our basic 3-D SLS algorithm, based on new geometric observations, produces guaranteed optimal quality solutions using Steiner points in polynomial time; the previously best known basic 3-D SLS algorithm gives optimal outputs only for the case without any Steiner points, and its time bound involves a multiplicative factor of a factorial function of the input. Our general 3-D SLS algorithm is based on our basic 3-D SLS algorithm and a polynomial time algorithm for partitioning a polygonal domain (possibly with holes) into a minimum set of x-monotone polygons, and has a fast running time. Experiments and comparisons using real medical data and on a real radiotherapy machine have shown that our 3-D SLS algorithms and software produce treatment plans that use significantly shorter delivery time and give better treatment quality than the current most popular commercial treatment planning system and the most well-known SLS algorithm. Some of our techniques and geometric procedures (e.g., for the problem of partitioning a polygonal domain into a minimum set of x-monotone polygons) are interesting in their own right.