Towards Real-time Scalable Dense Mapping using Robot-centric Implicit Representation

Real-time and high-quailty dense mapping is essential for robots to perform fine tasks. However, most existing methods can not achieve both speed and quality. Recent works have shown that implicit neural representations of 3D scenes can produce remarkable results, but they are limited to small scenes and lack real-time performance. To address these limitations, we propose a real-time scalable mapping method using robot-centric implicit representation. We train implicit features with a multi-resolution local map and decode them as signed distance values through a shallow neural network. We maintain the learned features in a scalable manner using a global map that consists of a hash table and a submap set. We exploit the characteristics of the local map to achieve highly efficient training and mitigate the catastrophic forgetting problem in incremental implicit mapping. Extensive experiments validate that our method outperforms existing methods in reconstruction quality, real-time performance, and applicability. The code of our system will be available at \url{https://github.com/HITSZ-NRSL/RIM.git}.Comment: Submitted to IEEE Robotics and Automation Letter

RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments

Current simultaneous localization and mapping (SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM systems to reduce the influence of dynamic objects. However, it is still challenging to apply a robust localization in dynamic environments for resource-restricted robots. This paper proposes a real-time RGB-D inertial odometry system for resource-restricted robots in dynamic environments named Dynamic-VINS. Three main threads run in parallel: object detection, feature tracking, and state optimization. The proposed Dynamic-VINS combines object detection and depth information for dynamic feature recognition and achieves performance comparable to semantic segmentation. Dynamic-VINS adopts grid-based feature detection and proposes a fast and efficient method to extract high-quality FAST feature points. IMU is applied to predict motion for feature tracking and moving consistency check. The proposed method is evaluated on both public datasets and real-world applications and shows competitive localization accuracy and robustness in dynamic environments. Yet, to the best of our knowledge, it is the best-performance real-time RGB-D inertial odometry for resource-restricted platforms in dynamic environments for now. The proposed system is open source at: https://github.com/HITSZ-NRSL/Dynamic-VINS.gi

Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimziation

Actively planning sensor views during object reconstruction is essential to autonomous mobile robots. This task is usually performed by evaluating information gain from an explicit uncertainty map. Existing algorithms compare options among a set of preset candidate views and select the next-best-view from them. In contrast to these, we take the emerging implicit representation as the object model and seamlessly combine it with the active reconstruction task. To fully integrate observation information into the model, we propose a supervision method specifically for object-level reconstruction that considers both valid and free space. Additionally, to directly evaluate view information from the implicit object model, we introduce a sample-based uncertainty evaluation method. It samples points on rays directly from the object model and uses variations of implicit function inferences as the uncertainty metrics, with no need for voxel traversal or an additional information map. Leveraging the differentiability of our metrics, it is possible to optimize the next-best-view by maximizing the uncertainty continuously. This does away with the traditionally-used candidate views setting, which may provide sub-optimal results. Experiments in simulations and real-world scenes show that our method effectively improves the reconstruction accuracy and the view-planning efficiency of active reconstruction tasks. The proposed system is going to open source at https://github.com/HITSZ-NRSL/ActiveImplicitRecon.git.Comment: 8 pages, 10 figures, Submitted to IEEE Robotics and Automation Letters (RA-L

A brief review and clinical evidences of teriparatide therapy for atypical femoral fractures associated with long-term bisphosphonate treatment

The risk of bisphosphonate (BP)-associated atypical femur fracture (AFF) has markedly increased over recent decades due to suppression of bone turnover, accumulation of structural micro-damage and reduction of bone remodeling consequent to long-term BP treatment. These medications further delay bone union and result in challenging clinical management. Teriparatide (TPTD), a synthetic human parathyroid hormone, exhibits unique anabolic effects and can increase bone remodeling and improve bone microarchitecture, further promoting fracture healing and reducing the rate of bone non-union. In this study, we briefly define AFF as well as the effects of BPs on AFFs, detailed the role of TPTD in AFF management and the latest clinical therapeutic findings. We have confirmed that TPTD positively promotes the healing of AFFs by reducing the time to bone union and likelihood of non-union. Thus, teriparatide therapy could be considered as an alternative treatment for AFFs, however, further research is required for the establishment of effective clinical guidelines of TPTD use in the management of AFF

MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation

Developing conversational agents to interact with patients and provide primary clinical advice has attracted increasing attention due to its huge application potential, especially in the time of COVID-19 Pandemic. However, the training of end-to-end neural-based medical dialogue system is restricted by an insufficient quantity of medical dialogue corpus. In this work, we make the first attempt to build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG, with more than 17K conversations collected from the online health consultation community. Five different categories of entities, including diseases, symptoms, attributes, tests, and medicines, are annotated in each conversation of MedDG as additional labels. To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. To acquire a clear comprehension on these two medical dialogue tasks, we implement several state-of-the-art benchmarks, as well as design two dialogue models with a further consideration on the predicted entities. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset, and the response quality can be enhanced with the help of auxiliary entity information. From human evaluation, the simple retrieval model outperforms several state-of-the-art generative models, indicating that there still remains a large room for improvement on generating medically meaningful responses.Comment: Data and code are available at https://github.com/lwgkzl/MedD

A near-infrared fluorescent probe based on a FRET rhodamine donor linked to a cyanine acceptor for sensitive detection of intracellular pH alternations

A fluorescence resonance energy transfer (FRET)-based near-infrared fluorescent probe (B+) for double-checked sensitive detection of intracellular pH changes has been synthesized by binding a near-infrared rhodamine donor to a near-infrared cyanine acceptor through robust C-N bonds via a nucleophilic substitution reaction. To demonstrate the double-checked advantages of probe B+, a near-infrared probe (A) was also prepared by modification of a near-infrared rhodamine dye with ethylenediamine to produce a closed spirolactam residue. Under basic conditions, probe B+ shows only weak fluorescence from the cyanine acceptor while probe A displays nonfluorescence due to retention of the closed spirolactam form of the rhodamine moiety. Upon decrease in solution pH level, probe B+ exhibits a gradual fluorescence increase from rhodamine and cyanine constituents at 623 nm and 743 nm respectively, whereas probe A displays fluorescence increase at 623 nm on the rhodamine moiety as acidic conditions leads to the rupture of the probe spirolactam rings. Probes A and B+ have successfully been used to monitor intracellular pH alternations and possess pKa values of 5.15 and 7.80, respectively

Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data

In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks. Armed with the Luo-Tseng error bound condition~\citep{luo1992error}, two proposed algorithms, called Bregman Alternating Projected Gradient (BAPG) and hybrid Bregman Proximal Gradient (hBPG) enjoy the convergence guarantees. Upon task-specific properties, our analysis further provides novel theoretical insights to guide how to select the best-fit method. As a result, we are able to provide comprehensive experiments to validate the effectiveness of our methods on a host of tasks, including graph alignment, graph partition, and shape matching. In terms of both wall-clock time and modeling performance, the proposed methods achieve state-of-the-art results

A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd

Mobile CrowdSensing (MCS), through employing considerable workers to sense and collect data in a participatory manner, has been recognized as a promising paradigm for building many large-scale applications in a cost-effective way, such as combating COVID-19. The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies assume that the qualities of workers are known in advance, or the platform knows the qualities of workers once it receives their collected data. In reality, to reduce their costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform. So, it is very hard for the platform to evaluate the authenticity of the received data. In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem, and design an UCB-based algorithm to separate the exploration and exploitation, considering the Sensing Rates (SRs) of recruited workers as the gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We prove that our SCMABA achieves truthfulness and individual rationality. Additionally, we exhibit outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.Comment: 18 pages, 14 figure

Detecting Zn(II) Ions in Live Cells with Near-Infrared Fluorescent Probes.

Two near-infrared fluorescent probes (A and B) containing hemicyanine structures appended to dipicolylamine (DPA), and a dipicolylamine derivative where one pyridine was substituted with pyrazine, respectively, were synthesized and tested for the identification of Zn(II) ions in live cells. In both probes, an acetyl group is attached to the phenolic oxygen atom of the hemicyanine platform to decrease the probe fluorescence background. Probe A displays sensitive fluorescence responses and binds preferentially to Zn(II) ions over other metal ions such as Cd2+ ions with a low detection limit of 0.45 nM. In contrast, the emission spectra of probe B is not significantly affected if Zn(II) ions are added. Probe A possesses excellent membrane permeability and low cytotoxicity, allowing for sensitive imaging of both exogenously supplemented Zn(II) ions in live cells, and endogenously releases Zn(II) ions in cells after treatment of 2,2-dithiodipyridin

Detecting Zn(II) Ions in Live Cells with Near-Infrared Fluorescent Probes.

Two near-infrared fluorescent probes (A and B) containing hemicyanine structures appended to dipicolylamine (DPA), and a dipicolylamine derivative where one pyridine was substituted with pyrazine, respectively, were synthesized and tested for the identification of Zn(II) ions in live cells. In both probes, an acetyl group is attached to the phenolic oxygen atom of the hemicyanine platform to decrease the probe fluorescence background. Probe A displays sensitive fluorescence responses and binds preferentially to Zn(II) ions over other metal ions such as Cd2+ ions with a low detection limit of 0.45 nM. In contrast, the emission spectra of probe B is not significantly affected if Zn(II) ions are added. Probe A possesses excellent membrane permeability and low cytotoxicity, allowing for sensitive imaging of both exogenously supplemented Zn(II) ions in live cells, and endogenously releases Zn(II) ions in cells after treatment of 2,2-dithiodipyridin