Transfer of manipulation skills from human to machine through demonstration in a haptic rendered virtual environment

Robots are widely used as automation tools to improve productivity in industry. Force sensitive manipulation is a generic requirement for a large number of industrial tasks, especially those associated with assembly. One of the major factors preventing greater use of robots in assembly tasks to date has been the lack of availability of fast and reliable methods of programming robots to carry out such tasks. Hence robots have in practice been unable to economically replicate the complex force and torque sensitive capability of human operators. A new approach is explored to transfer human manipulation skills to a robotics system. The teaching of the human skills to the machine starts by demonstrating those skills in a haptic-rendered virtual environment. The experience is close to real operation as the forces and torques generated during the interaction of the parts are sensed by the operator. A skill acquisition algorithm utilizes the position and contact force/torque data generated in the virtual environment combined with a priori knowledge about the task to generate the skills required to perform such a task. Such skills are translated into actual robotic trajectories for implementation in real time. The peg-in-hole insertion problem is used as a case study. A haptic rendered 3D virtual model of the peg-in-hole insertion process is developed. The haptic or tactile rendering is provided through a haptic device. A multi-layer method is developed to derive and learn the basic manipulation skills from the virtual manipulation carried out by a human operator. The force and torque data generated through virtual manipulation are used for skill acquisition. The skill acquisition algorithm primarily learns the actions which result in a proper change of contact states. Both optimum sequences and normal operation rules are learned and stored in a skill database. If the contact state is not among or near any state in the optimum sequences stored in the skill database, a corrective strategy is applied until a state among or near a state in the optimal space is produced. On-line incremental learning is also used for new cases encountered during physical manipulation. The approach is fully validated through an experimental rig set up for this purpose and the results are reported

datasheet1_AVA: A Financial Service Chatbot Based on Deep Bidirectional Transformers.pdf

We develop a chatbot using deep bidirectional transformer (BERT) models to handle client questions in financial investment customer service. The bot can recognize 381 intents, decides when to say I don’t know, and escalate escalation/uncertain questions to human operators. Our main novel contribution is the discussion about the uncertainty measure for BERT, where three different approaches are systematically compared with real problems. We investigated two uncertainty metrics, information entropy and variance of dropout sampling, in BERT, followed by mixed-integer programming to optimize decision thresholds. Another novel contribution is the usage of BERT as a language model in automatic spelling correction. Inputs with accidental spelling errors can significantly decrease intent classification performance. The proposed approach combines probabilities from masked language model and word edit distances to find the best corrections for misspelled words. The chatbot and the entire conversational AI system are developed using open-source tools and deployed within our company’s intranet. The proposed approach can be useful for industries seeking similar in-house solutions in their specific business domains. We share all our code and a sample chatbot built on a public data set on GitHub.</p

DataSheet_1_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

DataSheet_3_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

DataSheet_2_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

Targeted Modification of the Cationic Anticancer Peptide HPRP-A1 with iRGD To Improve Specificity, Penetration, and Tumor-Tissue Accumulation

The chimeric peptide HPRP-A1-iRGD, composed of a chemically conjugated tumor-homing/penetration domain (iRGD) and a cationic anticancer peptide domain (HPRP-A1), was used to study the effect of targeted modification to enhance the peptide’s specificity, penetration, and tumor accumulation ability. The iRGD domain exhibits tumor-targeting and tumor-penetrating activities by specifically binding to the neuropilin-1 receptor. Acting as a homing/penetration domain, iRGD contributed to enhancing the tumor selectivity, permeability, and targeting of HPRP-A1 by targeted receptor dependence. As the anticancer active domain, HPRP-A1 kills cancer cells by disrupting the cell membrane and inducing apoptosis. The in vitro membrane selectivity toward cancer cells, such as A549 and MDA-MB-23, and human umbilical vein endothelial cells (HUVECs), normal cells, the penetrability assessment in the A549 3D multiple cell sphere model, and the in vivo tumor-tissue accumulation test in the A549 xenograft model indicated that HPRP-A1-iRGD exhibited significant increases in the selectivity toward membranes that highly express NRP-1, the penetration distance in 3D multiple cell spheres, and the accumulation in tumor tissues after intravenous injection, compared with HPRP-A1 alone. The mechanism of the enhanced targeting ability of HPRP-A1-iRGD was demonstrated by the pull-down assay and biolayer interferometry test, which indicated that the chimeric peptide could specifically bind to the neuropilin-1 protein with high affinity. We believe that chemical conjugation with iRGD to increase the specificity, penetration, and tumor-tissue accumulation of HPRP-A1 is an effective and promising approach for the targeted modification of peptides as anticancer therapeutics

Coadministration of kla peptide with HPRP-A1 to enhance anticancer activity

The apoptosis-inducing peptide kla (KLAKLAK)2 possesses the ability to disrupt mitochondrial membranes and induce cancer cell apoptosis, but this peptide has a poor eukaryotic cell-penetrating potential. Thus, it requires the assistance of other peptides for effective translocation at micromolar concentrations. In this study, breast and lung cancer cells were treated by kla peptide co-administrated with membrane-active anticancer peptide HPRP-A1. HPRP-A1 assisted kla to enter cancer cells and localized on mitochondrial membranes to result in cytochrome C releasing and mitochondrial depolarization which ultimately induced apoptosis.The apoptosis rate was up to 65%and 45% on MCF-7 and A549 cell lines, respectively, induced by HPRP-A1 coadministration with kla group. The breast cancer model was constructed in mice, and the anticancer peptides were injected to observe the changes in cancer volume, andimmunohistochemical analysis was performed on the tissues and organs after the drug was administered. Both the weight and volume of tumor tissue were remarkable lower in HPRP-A1 with kla group compared with thosepeptidealonggroups. The results showed that the combined drug group effectively inhibited the growth of cancer and did not cause toxic damage to normal tissues, as well as exhibited significantly improvement on peptide anticancer activity in vitro and in vivo.</div

H&E, TUNEL and Ki 67 staining in tumour tissues and histological examination of major organs.

(A) H&E, Ki67, and TUNEL staining of tumour tissues. (B) Histological examination by H&E staining of major organs (heart, liver, spleen, lungs, and kidneys) in the breast cancer mouse model.</p

Co-localization of peptides in MCF-7 cells.

Various concentrations of FITC-kla with or without 4 μM HPRP-A1 were applied to MCF-7 cells for 1 h. The blue colour was nuclei stained by Hoechst 33258, green colour was FITC-labelled kla and red colour was mitochondria stained by Mito-Tracker® Red, respectively. Images were obtained by confocal microscopy.</p

Violation behavior in vertical restraint: Empirical analyses in the case of retail price maintenance

Violation behavior in vertical restraint: Empirical analyses in the case of retail price maintenanc