Multi-contour initial pose estimation for 3D registration

Reliable manipulation of everyday household objects is essential to the success of service robots. In order to accurately manipulate these objects, robots need to know objects’ full 6-DOF pose, which is challenging due to sensor noise, clutters and occlusions. In this paper, we present a new approach for effectively guessing the object pose given an observation of just a small patch of the object, by leveraging the fact that many household objects can only keep stable on a planar surface under a small set of poses. In particular, for each stable pose of an object, we slice the object with horizontal planes and extract multiple cross-section contours. The pose estimation is then reduced to find a stable pose whose contour matches best with that of the sensor data, and this can be solved efficiently by convolution. Experiments on the manipulation tasks in the DARPA Robotics Challenge validate our approach. In addition, we also investigate our method’s performance on object recognition tasks raising in the challenge.postprin

Heterogeneity in ecosystem service values: Linking public perceptions and environmental policies

One way of linking research and environmental policies is to increase public participation and identify ecosystem services valued by society, but the reasons influencing ecosystem values can vary. Our study investigates the reasons influencing ecosystem service values at the third largest freshwater lake in China, Lake Tai (Taihu). We interviewed 257 rural and 257 urban respondents in four cities and their respective rural regions surrounding the lake. Respondents were more willing to pay to protect a provisioning ecosystem service than a cultural ecosystem service, and those emotionally attached to the lake may value it more highly. There is also spatial heterogeneity in respondents’ ecosystem values. Rural communities ranked directly used ecosystem services higher than urban communities. The city that respondents lived in also significantly affected the amount they were willing to pay for ecosystem services. Identifying potential reasons behind ecosystem service values can provide insights into linking public perception and policy making, helping to form environmental policies that reflect societal values.</jats:p

Performance-based Fire Safety Design for Existing Small-scale Hospitals

AbstractThe new era of National Health Insurance in 2000 has had a significant impacted on the management and operation of smallscale hospitals. In response to social needs, and in order to survive under the new insurance system, some small-scale hospitals have transformed or established new Respiratory Care Wards by using existing hospital space. According to the 2009 statistics released by Department of Health, Executive Yuan, there are a total of 307 small-scale medical institutes which provide servicesunder 99 beds. Compared with other large-scale medical centers and general hospitals, small-scale hospitals cannot properly deal with safety management and response to emergency evacuation due to lack of facilities, equipment and human resources. Therefore, small-scale hospitals face a major challenge in emergency response once a fire has occurred. As a result of such a situation, this study has focused mainly on Respiratory Care Wards (RCW) where patients are unable to evacuate. It hopes to analyse the safety management, and emergency response in small-scale hospitals by means of understanding the space characteristics and fire risk. Through on-site surveys, we can understand the fire risk, space features, patient characteristics, facilities and equipment. With reference to the related regulations of hospital emergency management and response, we will propose some fire safety engineering approaches, such as refuge areas in horizontal evacuation and so-called “besieged zones” for “defense-in-place”, etc., to provide some alternative measures to improve fire safety for those small-scale hospitals

A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding

Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention. In this work, we investigate the understanding ability of ChatGPT for zero-shot dialogue understanding tasks including spoken language understanding (SLU) and dialogue state tracking (DST). Experimental results on four popular benchmarks reveal the great potential of ChatGPT for zero-shot dialogue understanding. In addition, extensive analysis shows that ChatGPT benefits from the multi-turn interactive prompt in the DST task but struggles to perform slot filling for SLU. Finally, we summarize several unexpected behaviors of ChatGPT in dialogue understanding tasks, hoping to provide some insights for future research on building zero-shot dialogue understanding systems with Large Language Models (LLMs).Comment: Technical Repor

Genetic regulation of mouse liver metabolite levels.

We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohydrates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome-wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co-variation across various biological scales

Combined Treatment with MEK and mTOR Inhibitors is Effective in In Vitro and In Vivo Models of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer histotype, characterized by high biological aggressiveness and scarce treatment options. Recently, we have established a clinically relevant murine HCC model by co-expressing activated forms of v-akt murine thymoma viral oncogene homolog (AKT) and oncogene c-mesenchymal-epithelial transition (c-Met) proto-oncogenes in the mouse liver via hydrodynamic tail vein injection (AKT/c-MET mice). Tumor cells from these mice demonstrated high activity of the AKT/ mammalian target of rapamycin (mTOR) and Ras/ Mitogen-activated protein kinase (MAPK) signaling cascades, two pathways frequently co-induced in human HCC. Methods: Here, we investigated the therapeutic efficacy of sorafenib, regorafenib, the MEK inhibitor PD901 as well as the pan-mTOR inhibitor MLN0128 in the AKT/c-Met preclinical HCC model. Results: In these mice, neither sorafenib nor regorafenib demonstrated any efficacy. In contrast, administration of PD901 inhibited cell cycle progression of HCC cells in vitro. Combined PD901 and MLN0128 administration resulted in a pronounced growth constraint of HCC cell lines. In vivo, treatment with PD901 or MLN0128 alone moderately slowed HCC growth in AKT/c-MET mice. Importantly, the simultaneous administration of the two drugs led to a stable disease with limited tumor progression in mice. Mechanistically, combined mitogen-activated extracellular signal-regulated kinase (MEK) and mTOR inhibition resulted in a stronger cell cycle inhibition and growth arrest both in vitro and in vivo. Conclusions: Our study indicates that combination of MEK and mTOR inhibitors might represent an effective therapeutic approach against human HCC

MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing

Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider, we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.Comment: AAAI2023 Main Conference. Code: https://github.com/microsoft/ContextualS