Currently, 3D rendering for large-scale free camera trajectories, namely, arbitrary input camera trajectories, poses significant challenges: 1) The distribution and observation angles of the cameras are irregular, and various types of scenes are included in the free trajectories; 2) Processing the entire point cloud and all images at once for large-scale scenes requires a substantial amount of GPU memory. This paper presents a Toy-GS method for accurately rendering large-scale free camera trajectories. Specifically, we propose an adaptive spatial division approach for free trajectories to divide cameras and the sparse point cloud of the entire scene into various regions according to camera poses. Training each local Gaussian in parallel for each area enables us to concentrate on texture details and minimize GPU memory usage. Next, we use the multi-view constraint and position-aware point adaptive control (PPAC) to improve the rendering quality of texture details. In addition, our regional fusion approach combines local and global Gaussians to enhance rendering quality with an increasing number of divided areas. Extensive experiments have been carried out to confirm the effectiveness and efficiency of Toy-GS, leading to state-of-the-art results on two public large-scale datasets as well as our SCUTic dataset. Our proposal demonstrates an enhancement of 1.19 dB in PSNR and conserves 7 G of GPU memory when compared to various benchmarks.

We consider the problem of minimizing emission of a heavy-duty truck transporting freight between two locations subject to a hard deadline constraint. The truck is equipped with a multi-speed transmission and a modern combustion engine that intelligently switches among multiple fuel injection strategies at certain engine speeds (called switching speeds) to achieve lower emission profiles. Our objective is to minimize the emission by optimizing both path and speed planning for heavy-duty trucks with multi-speed transmission and multiple injection strategies in the engine. This emission minimization problem, while pervasive in practice, has two challenges: i) the emission rate function is discontinuous and non-convex due to switching of the fuel injections and gear ratios, which makes the common practice of driving at a constant speed on a road segment not eco-friendly; ii) the problem is NP-hard due to the combinatorial nature of the simultaneous path and speed planning. We tackle the first challenge by considering the case where the truck can travel at a heterogeneous speed profile over a road segment and then formulate the speed planning problem as a convex problem. We further identify special structures in this problem and provide an efficient method for computing the optimal speed profile. We then tackle the second challenge by developing an efficient heuristic for both path planning and speed planning to solve the emission minimization problem on the scale of national highway systems. Our extensive simulations on the US highway system show that our solution reduces up to 46% NOx emission as compared to the commonly-adopted fastest path approach. We also find that optimizing heterogeneous speed profiles reduce up to 32% emission as compared to their homogeneous counterpart, thus are necessary to be considered in eco-friendly truck operations.

We consider the scenario where an energy harvesting source sends its updates to a receiver. The source optimizes its energy allocation over a decision period to maximize a sum of time-varying functions of the age of information (AoI), representing the value of providing timely information. In a practical online setting, we need to make irrevocable energy allocation decisions at each time while the time-varying functions and the energy arrivals are only revealed sequentially. The problem is then challenging as 1) we are facing uncertain energy harvesting arrivals and time-varying functions, and 2) the energy allocation decisions and the energy harvesting process are coupled due to the capacity-limited battery. In this paper, we develop an optimal online algorithm$\textsf {CR-RePursuit}$and show it achieves$(\ln \theta +1)$-competitiveness, where$\theta $is a parameter representing the level of uncertainty of the time-varying functions. It is the optimal competitive ratio among all deterministic and randomized online algorithms. We also introduce an adaptive variant of the algorithm, +, that further exploits the revealed information to obtain significantly improved empirical performance. We conduct simulations based on real-world traces and compare our algorithms with conceivable alternatives. The results show that our algorithms achieve 15% performance improvement as compared to the state-of-the-art baseline.

We consider the scenario where an energy harvesting source sends its updates to a receiver. The source optimizes its energy allocation over a decision period to maximize a sum of time-varying functions of the age of information (AoI), representing the value of providing timely information. In a practical online setting, we need to make irrevocable energy allocation decisions at each time while the time-varying functions and the energy arrivals are only revealed sequentially. The problem is then challenging as 1) we are facing uncertain energy harvesting arrivals and time-varying functions, and 2) the energy allocation decisions and the energy harvesting process are coupled due to the capacity-limited battery. In this paper, we develop an optimal online algorithm CR-Reserve and show it achieves (lnθ + 1)-competitive, where θ is a parameter representing the level of uncertainty of the time-varying functions. It is the optimal competitive ratio among all deterministic and randomized online algorithms. We conduct simulations based on real-world traces and compare our algorithms with conceivable alternatives. The results show that our algorithms achieve 12% performance improvement as compared to the state-of-the-art baseline.

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

The motion of planar ground vehicles is often non-holonomic, and as a result may be modelled by the 2 DoF Ackermann steering model. We analyse the feasibility of estimating such motion with a downward facing camera that exerts fronto-parallel motion with respect to the ground plane. This turns the motion estimation into a simple image registration problem in which we only have to identify a 2-parameter planar homography. However, one difficulty that arises from this setup is that ground-plane features are indistinctive and thus hard to match between successive views. We encountered this difficulty by introducing the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation. The solution relies on the branch-and-bound optimisation technique. Through the low-dimensional parametrisation, a derivation of tight bounds, and an efficient implementation, we demonstrate how this technique is eventually amenable to accurate real-time motion estimation. We prove its property of global optimality and analyse the impact of assuming a locally constant centre of rotation. Our results on real data finally demonstrate a significant advantage over the more traditional, correspondence-based hypothesise-and-test schemes.