MOTIVATION: Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual's genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. RESULTS: Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual's genetic similarity to a continental group. AVAILABILITY AND IMPLEMENTATION: AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

Most of existing path finding problems focused on searching a path with the minimum cost, such as shortest-path length and shortest travel time. In this paper, we consider a new path finding problem, i.e., length-constrained maximum density path (LDP) problem. Given a graph with length and weight on each edge, the LDP problem aims to find the maximum density path between two nodes under a specified length constraint, where the density of the path is defined as the ratio of the path weight to the path length. To the best of our knowledge, there are no existing works that focus on this problem. We prove the problem is NP-hard. Then we propose an A*-based exact algorithm to acquire the optimal solution. Due to the expensive computational overhead of the A*-based exact algorithm, we further propose two effective approximation algorithms, i.e., the label setting algorithm and the top-k based network expansion (k-NE) algorithm. Extensive experiments on four real and synthetic datasets verify the efficiency and effectiveness of the proposed algorithms.

In this paper, we focus on a new route recommendation problem, i.e., when a user gives a keyword and range constraint, the route that contains the maximum number of POIs tagged with the keyword or similar POIs in the range will be returned for him. This is a practical problem when people want to explore a place, e.g., find a route within 2 km containing as many clothing stores as possible. To solve the problem, we first calculate the score of each edge in road networks based on the number and similarity of POIs. Then, we reformulate the problem into finding the path in a graph with the maximum score within the distance constraint problem, which is proved NP-hard. Given this, we not only propose an exact branch and bound (BnB) algorithm, but also devise a more efficient top-k based network expansion (k-NE) algorithm to find the near-optimal solution. Extensive experiments on real datasets not only verify the effectiveness of the proposed route recommendation algorithm, but also show that the efficiency and accuracy of k-NE algorithm are completely acceptable.

Due to the advent and pervasive deployment of wireless local area networks, WiFi-based indoor localization systems have received increasing attention in the last few years. However, their localization accuracy has always been a challenging issue. In addition, because of diverse interference such as multipath effects, the block of signals, an unstable or weak signal in itself, etc., not all the access points (APs) are informative for the localization. Faced with these problems, we propose a WiFi-based localization model by modifying the large localization errors and enhancing the Gaussian process regression (MEGPR). 1) To select the AP subsets that contribute more to the localization and further reduce the computational load, the AP discrimination criterion (APDC) is introduced to quantify the discernibility of the APs detected in the workspace and filter out the APs with low discrimination. 2) Second, to enhance the localization model, the localization residual is fed and learnt by the model. 3) Furthermore, the large localization errors are mitigated by the location modification method (LMM). Experiments were conducted in a real environment with an area of more than 1200 m2and the results show that compared with other existing localization models, the average localization error of the proposed MEGPR model is minimum, which further verifies the effectiveness of the proposed MEGPR localization model.

Heterogeneous Computing is becoming an important technology trend in HPC, where more and more heterogeneous processors are used. However, in traditional node architecture, heterogeneous processors are always used as coprocessors. Such usage increases the communication latency between heterogeneous processors and prevents the node from achieving high density. With the purpose of improving communication efficiency between heterogeneous processors, this paper proposed a new node architecture named HeteNode. In HeteNode, general purpose processors and heterogeneous processors are interconnected by a system controller directly and play the same role in both process of communication and process of computation. The prototype of HeteNode which contains nine processors in 1U chassis is built. Evaluation carried out on the prototype shows that 580ns minimum intra-node latency and 1.78us minimum inter-node latency between heterogeneous processors are achieved. Besides, NPB benchmarks show good scalability in HeteNode.