Asynchronous Collaborative Autoscanning with Mode Switching for Multi-Robot Scene Reconstruction

When conducting autonomous scanning for the online reconstruction of unknown indoor environments, robots have to be competent at exploring scene structure and reconstructing objects with high quality. Our key observation is that different tasks demand specialized scanning properties of robots: rapid moving speed and far vision for global exploration and slow moving speed and narrow vision for local object reconstruction, which are referred as two different scanning modes: explorer and reconstructor, respectively. When requiring multiple robots to collaborate for efficient exploration and fine-grained reconstruction, the questions on when to generate and how to assign those tasks should be carefully answered. Therefore, we propose a novel asynchronous collaborative autoscanning method with mode switching, which generates two kinds of scanning tasks with associated scanning modes, i.e., exploration task with explorer mode and reconstruction task with reconstructor mode, and assign them to the robots to execute in an asynchronous collaborative manner to highly boost the scanning efficiency and reconstruction quality. The task assignment is optimized by solving a modified Multi-Depot Multiple Traveling Salesman Problem (MDMTSP). Moreover, to further enhance the collaboration and increase the efficiency, we propose a task-flow model that actives the task generation and assignment process immediately when any of the robots finish all its tasks with no need to wait for all other robots to complete the tasks assigned in the previous iteration. Extensive experiments have been conducted to show the importance of each key component of our method and the superiority over previous methods in scanning efficiency and reconstruction quality.Comment: 13pages, 12 figures, Conference: SIGGRAPH Asia 202

Modeling impacts of carbon sequestration on net greenhouse gas emissions from agricultural soils in China

Soil organic carbon (SOC) contents in many farmlands have been depleted because of the long-term history of intensive cultivation in China. Chinese farmers are encouraged to adopt alternative management practices on their farms to sequester SOC. On the basis of the availability of carbon (C) resources in the rural areas in China, the most promising practices are (1) incorporating more crop residue in the soils and (2) resuming traditional manure fertilizer. By implementing the alternative practices, increase in SOC content has been observed in some fields. This paper investigates how the C sequestration strategies could affect nitrous oxide (N2O) and methane (CH4) emissions from the agricultural soils in six selected sites across China. A process-based model, denitrification-decomposition or DNDC, which has been widely validated against data sets of SOC dynamics and N2O and CH4 fluxes observed in China, was adopted in the study to quantify the greenhouse gas impacts of enhanced crop residue incorporation and manure amendment under the diverse climate, soil, and crop rotation conditions across the six agroecosystems. Model results indicated that (1) when the alternative management practices were employed C sequestration rates increased, however, N2O or CH4 emissions were also increased for these practices; and (2) reducing the application rates of synthetic fertilizer in conjunction with the alternative practices could decrease N2O emissions while at the same time maintaining existing crop yields and C sequestration rates. The modeling approach could help with development of spatially differentiated best management practices at large regional scales

Easily-prepared dinickel phosphide (Ni2P) nanoparticles as an efficient and robust electrocatalyst for hydrogen evolution

Polydispersed dinickel phosphide (Ni2P) nanoparticles were synthesized by a simple and scalable solid-state reaction. These nanoparticles are an excellent and robust catalyst for the electrochemical hydrogen evolution reaction, operating in both acidic and basic solutions

Developing resource consolidation frameworks for moldable virtual machines in clouds

This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed Virtual Clusters) are hosted in a Cloud system consisting of a cluster of physical nodes. Multiple Virtual Clusters (VCs) cohabit in the physical cluster, with each VC offering a particular type of service for the incoming requests. In this context, VM consolidation, which strives to use a minimal number of nodes to accommodate all VMs in the system, plays an important role in saving resource consumption. Most existing consolidation methods proposed in the literature regard VMs as “rigid” during consolidation, i.e., VMs’ resource capacities remain unchanged. In VC environments, QoS is usually delivered by a VC as a single entity. Therefore, there is no reason why VMs’ resource capacity cannot be adjusted as long as the whole VC is still able to maintain the desired QoS. Treating VMs as “moldable” during consolidation may be able to further consolidate VMs into an even fewer number of nodes. This paper investigates this issue and develops a Genetic Algorithm (GA) to consolidate moldable VMs. The GA is able to evolve an optimized system state, which represents the VM-to-node mapping and the resource capacity allocated to each VM. After the new system state is calculated by the GA, the Cloud will transit from the current system state to the new one. The transition time represents overhead and should be minimized. In this paper, a cost model is formalized to capture the transition overhead, and a reconfiguration algorithm is developed to transit the Cloud to the optimized system state with low transition overhead. Experiments have been conducted to evaluate the performance of the GA and the reconfiguration algorithm

Deca : a garbage collection optimizer for in-memory data processing

In-memory caching of intermediate data and active combining of data in shuffle buffers have been shown to be very effective in minimizing the recomputation and I/O cost in big data processing systems such as Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap. These generated objects may quickly saturate the garbage collector, especially when handling a large dataset, and hence, limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca,1 a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. When systems are processing very large data, Deca also provides field-oriented memory pages to ensure high compression efficiency. Extensive experimental studies using both synthetic and real datasets show that, in comparing to Spark, Deca is able to (1) reduce the garbage collection time by up to 99.9%, (2) reduce the memory consumption by up to 46.6% and the storage space by 23.4%, (3) achieve 1.2× to 22.7× speedup in terms of execution time in cases without data spilling and 16× to 41.6× speedup in cases with data spilling, and (4) provide similar performance compared to domain-specific systems

An Effective Pd–Ni2P/C Anode Catalyst for Direct Formic Acid Fuel Cells

The direct formic acid fuel cell is an emerging energy conversion device for which palladium is considered as the state-of-the-art anode catalyst. In this communication, we show that the activity and stability of palladium for formic acid oxidation can be significantly enhanced using nickel phosphide (Ni2P) nanoparticles as a cocatalyst. X-ray photoelectron spectroscopy (XPS) reveals a strong electronic interaction between Ni2P and Pd. A direct formic acid fuel cell incorporating the best Pd-Ni2P anode catalyst exhibits a power density of 550mWcm(-2), which is 3.5times of that of an analogous device using a commercial Pd anode catalyst

NHX antiporters regulate the pH of endoplasmic reticulum and auxin-mediated development

AtNHX5 and AtNHX6 are endosomal Na+,K+/H+ antiporters that are critical for growth and development in Arabidopsis, but the mechanism behind their action remains unknown. Here, we report that AtNHX5 and AtNHX6, functioning as H+ leak, control auxin homeostasis and auxin-mediated development. We found that nhx5 nhx6 exhibited growth variations of auxin-related defects. We further showed that nhx5 nhx6 was affected in auxin homeostasis. Genetic analysis showed that AtNHX5 and AtNHX6 were required for the function of the ER-localized auxin transporter PIN5. Although AtNHX5 and AtNHX6 were co-localized with PIN5 at ER, they did not interact directly. Instead, the conserved acidic residues in AtNHX5 and AtNHX6, which are essential for exchange activity, were required for PIN5 function. AtNHX5 and AtNHX6 regulated the pH in ER. Overall, AtNHX5 and AtNHX6 may regulate auxin transport across the ER via the pH gradient created by their transport activity. H+-leak pathway provides a fine-tuning mechanism that controls cellular auxin fluxes

Trans-lymphatic Contrast-Enhanced Ultrasound in Combination with Blue Dye Injection is Feasible for Detection and Biopsy of Sentinel Lymph Nodes in Breast Cancer

Objective: The best method for sentinel lymph node biopsy (SLNB) in early-staged breast cancer (EBC) remains controversial. This study aimed to evaluate a novel method by combining trans-lymphatic contrast-enhanced ultrasound (TLCEUS) with blue dye injection as a guidance of SLNB. Methods: TLCEUS was performed in 88 patients with newly diagnosed EBC. Methylene blue dye was percutaneously injected into enhanced sentinel lymph nodes (SLNs) under ultrasound guidance, followed by standard SLNB and axillary lymph node dissection. Enhancement patterns and the arriving time (AT) of contrast agent within SLNs were evaluated. Histopathological examination of dissected nodes was performed to confirm metastasis. Results: A total of 95 enhanced SLNs were identified and biopsied in 86 of 88 patients with identification rate of 97.7%. The specificity was 75.0%, sensitivity was 83.3%, and false-negative rate was 16.7%. Contrast-enhanced SLNs with type I, type II, and type III patterns had a metastatic positive rate of 11.4% (5/44), 57.1% (12/21) and 80.0% (24/30), respectively. Metastatic positive SLNs showed a mean AT of 61.6 ± 58.7 s while metastatic negative SLNs showed a mean AT of 41.3 ± 19.9 s, which was statistically significantly different. Conclusion: The TLCEUS/blue dye method can be used as an alternative to the radioisotope/blue dye method for its feasibility and accuracy