Online Robot Introspection via Wrench-based Action Grammars

Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan act paradigm, however more recently robots are undergoing a sense-plan-act-verify paradigm. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we are trying to enable the robot to answer the question: what did I do? Is my behavior as expected or not? To this end, we analyze noisy wrench data and postulate that the latter inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by segmenting and encoding the data. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming a sentence (set of words with grammar rules) for a given sub-task; allowing the latter to be uniquely represented. The grammar, which can also include unexpected events, was classified in offline and online scenarios as well as for simulated and real robot experiments. Multiclass Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were are used to give temporal confidence to the introspection result. The contribution of our work is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or abnormal. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. This verification mechanism can be used by high-level planners or reasoning systems to enable intelligent failure recovery or determine the next most optima manipulation skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494

Text detection and recognition based on a lensless imaging system

Lensless cameras are characterized by several advantages (e.g., miniaturization, ease of manufacture, and low cost) as compared with conventional cameras. However, they have not been extensively employed due to their poor image clarity and low image resolution, especially for tasks that have high requirements on image quality and details such as text detection and text recognition. To address the problem, a framework of deep-learning-based pipeline structure was built to recognize text with three steps from raw data captured by employing lensless cameras. This pipeline structure consisted of the lensless imaging model U-Net, the text detection model connectionist text proposal network (CTPN), and the text recognition model convolutional recurrent neural network (CRNN). Compared with the method focusing only on image reconstruction, UNet in the pipeline was able to supplement the imaging details by enhancing factors related to character categories in the reconstruction process, so the textual information can be more effectively detected and recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed lensless images. By performing experiments on datasets of different complexities, the applicability to text detection and recognition on lensless cameras was verified. This study reasonably demonstrates text detection and recognition tasks in the lensless camera system,and develops a basic method for novel applications

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major challenge is that existing GAL strategies may introduce semantic confusion to the selected training set, particularly when graphs are noisy. Specifically, most existing methods assume all aggregating features to be helpful, ignoring the semantically negative effect between inter-class edges under the message-passing mechanism. In this work, we present Semantic-aware Active learning framework for Graphs (SAG) to mitigate the semantic confusion problem. Pairwise similarities and dissimilarities of nodes with semantic features are introduced to jointly evaluate the node influence. A new prototype-based criterion and query policy are also designed to maintain diversity and class balance of the selected nodes, respectively. Extensive experiments on the public benchmark graphs and a real-world financial dataset demonstrate that SAG significantly improves node classification performances and consistently outperforms previous methods. Moreover, comprehensive analysis and ablation study also verify the effectiveness of the proposed framework.Comment: Accepted by CIKM 202

Immunization against inhibin DNA vaccine as an alternative therapeutic for improving follicle development and reproductive performance in beef cattle

The objective of the present study was to investigate the potential role of immunization against INH on follicular development, serum reproductive hormone (FSH, E2, and P4) concentrations, and reproductive performance in beef cattle. A total of 196 non-lactating female beef cattle (4-5 years old) with identical calving records (3 records) were immunized with 0.5, 1.0, 1.5, or 2.0 mg [(T1, n = 58), (T2, n = 46), (T3, n = 42) and (T4, n = 36), respectively] of the pcISI plasmid. The control (C) group (n = 14) was immunized with 1.0 mL 0.9% saline. At 21d after primary immunization, all beef cattle were boosted with half of the primary immunization dose. On day 10 after primary immunization, the beef cattle immunized with INH DNA vaccine evidently induced anti-INH antibody except for the T1 group. The T3 group had the greatest P/N value peak among all the groups. The anti-INH antibody positive rates in T2, T3 and T4 groups were significantly higher than that in C and T1 groups. RIA results indicated that serum FSH concentration in T2 group increased markedly on day 45 after booster immunization; the E2 amount in T3 group was significantly increased on day 10 after primary immunization, and the levels of E2 also improved in T2 and T3 groups after booster immunization; the P4 concentration in T2 group was significantly improved on day 21 after primary immunization. Ultrasonography results revealed that the follicles with different diameter sizes were increased, meanwhile, the diameter and growth speed of ovulatory follicle were significantly increased. Furthermore, the rates of estrous, ovulation, conception, and twinning rate were also significantly enhanced. These findings clearly illustrated that INH DNA vaccine was capable of promoting the follicle development, thereby improving the behavioral of estrous and ovulation, eventually leading to an augment in the conception rates and twinning rate of beef cattle

Effect of Double-Ovsynch and Presynch-Ovsynch on postpartum ovarian cysts and inactive ovary in high-yielding dairy cows

IntroductionOptimizing the management of dairy cattle reproduction can reduce postpartum ovarian disease in high-yielding dairy cows and thus enhance ranch economic benefit. The hypothesis of this study was that the Double-Ovsynch (DO) protocol in high-producing dairy cows would result in a lower incidence of follicular cysts but a higher incidence of luteal cysts compared to those undergoing the Presynch-Ovsynch (PS) protocol.MethodsIn this experiment, 384 cows (204 primiparous and 180 multiparous) were allocated to the DO group, which followed the protocol: GnRH-7d-PGF2α-3d-GnRH-7d-Ovsynch-56 h (GnRH-7d-PGF2α-56 h-GnRH-16hTAI), starting on 39 ± 3 days in milk (DIM). Additionally, 359 cows (176 primiparous and 183 multiparous) were assigned to the PS group, which followed the protocol: PGF2α-14d-PGF2α-12d-Ovsynch-56 h, starting on 31 ± 3 DIM. In DO, B-mode ultrasound examinations were conducted 1 day after the GnRH-7d-PGF2α-3d-GnRH protocol to diagnose the presence of ovarian diseases followed by reexamination after 7 days of suspected cases. In PS, B-mode ultrasound examinations were conducted 1 day after the PGF2α-14d-PGF2α protocol to diagnose the presence of ovarian diseases followed by reexamination after 7 days. For all cows confirmed to having ovarian diseases, a second B-mode ultrasound examination was conducted at the time of the second GnRH and timed artificial insemination (TAI). If the ovary showed a normal developing follicle in combination with normal ovulation, the ovarian disease was considered to be cured.ResultsThe current study revealed no significant difference in the overall incidence and cure rate of postpartum ovarian diseases between DO and PS (incidence rate: 3.9% vs. 6.7%, cure rate: 50% vs. 41.7%, DO vs. PS). Also, there was no significant difference in the incidence and cure rate of luteal cysts between DO and PS (incidence rate: 2.9% vs. 2.2%, cure rate: 50.0% vs. 50.0%). The incidence of follicular cysts was significantly lower in the DO group than in the PS group (0.8% vs. 2.8%, DO vs. PS, p = 0.037), but there was no significant difference in the cure rates (66.7% vs. 50%). The occurrence of inactive ovary was lower in DO compared to PS (0.2% vs. 1.7%, p = 0.047). There was no significant difference in the pregnancy rate between the DO and PS groups (48.2% vs. 41.8%), although the DO group had a higher rate. What is different from our assumption is that PS did not effectively reduce the incidence of postpartum luteal cysts

The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region

Data and code availability The authors declare that the majority of the data supporting the findings of this study are available through the links given in the paper. The unpublished data are available from the corresponding author upon request. The new estimate of Tibetan soil carbon stock and R code are available in a persistent repository (https://figshare.com/s/4374f28d880f366eff6d). Acknowledgements This study was supported by the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (XDA20050101), the National Natural Science Foundation of China (41871104), Key Research and Development Programs for Global Change and Adaptation (2017YFA0603604), International Partnership Program of the Chinese Academy of Sciences (131C11KYSB20160061) and the Thousand Youth Talents Plan project in China. Jinzhi Ding acknowledges the General (2017M620922) and the Special Grade (2018T110144) of the Financial Grant from the China Postdoctoral Science Foundation.Peer reviewedPublisher PD

Component fractionation of wood-tar by column chromatography with the packing material of silica gel

Bio-oil can be an important fuel resource for automobiles in the future,while its complex composition restricts the direct application of the bio-oil extremely.So it is necessary to separate the complex mixture to relatively simplified fractions for goal directed specific treatments to reach the fuel quality for automobiles,and meanwhile different functional chemical materials and fine chemicals can be obtained.So it is significant to investigate the bio-oil component separating methods.Herein the method of column chromatography by the packing material of silica gel with two series of eluants of cyclohexane-benzene-methanol and cyclohexane-dichloromethane-methanol were investigated for component fractionation of the raw wood tar(oil fraction of the liquid product by slow pyrolysis of wood).The analytical results show that the components in cyclohexane are rich in alkoxyl-monophenols;the components of alkyl-monophenols and five ring oxygen-containing compounds are abundant in benzene and in dichloromethane similarly;in the methanol fraction,the components are diverse and diphenols are relatively in higher content,comparatively small polar molecules and five ring oxygen-containing compounds are more abundant in the methanol fraction after being eluted by dichloromethane,and the content of 1-(4-hydroxy-3-methoxyphenyl)-2-propanone is higher after being eluted by benzene

Eulerian simulation of gas-solid flow in a countercurrent downer

Countercurrent gas-solid downer reactor has been used in various processes, however, fundamental studies on the hydrodynamics of such reactors are sparse, especially, there is no computational fluid dynamics study available. To this end, an empirical inter-phase drag correlation, based on experimental data available in literature, is proposed to address the key role of meso-scale particle clustering structure in determining effective inter-phase drag force. The proposed drag correlation is then integrated into Eulerian model to study the hydrodynamics of gas-solid flow in a countercurrent downer reactor. It was shown that the measured axial pressure distribution and radial solid concentration profiles can be reproduced reasonably well and the radial particle velocity profile can be qualitatively captured as well. Present study offers a preliminary validation of feasibility of modeling gas-solid in a countercurrent downer using Eulerian model. (C) 2013 Elsevier B.V. All rights reserved