39 research outputs found
Decomposed Human Motion Prior for Video Pose Estimation via Adversarial Training
Estimating human pose from video is a task that receives considerable
attention due to its applicability in numerous 3D fields. The complexity of
prior knowledge of human body movements poses a challenge to neural network
models in the task of regressing keypoints. In this paper, we address this
problem by incorporating motion prior in an adversarial way. Different from
previous methods, we propose to decompose holistic motion prior to joint motion
prior, making it easier for neural networks to learn from prior knowledge
thereby boosting the performance on the task. We also utilize a novel
regularization loss to balance accuracy and smoothness introduced by motion
prior. Our method achieves 9\% lower PA-MPJPE and 29\% lower acceleration error
than previous methods tested on 3DPW. The estimator proves its robustness by
achieving impressive performance on in-the-wild dataset
An Empirical Study on Large Language Models in Accuracy and Robustness under Chinese Industrial Scenarios
Recent years have witnessed the rapid development of large language models
(LLMs) in various domains. To better serve the large number of Chinese users,
many commercial vendors in China have adopted localization strategies, training
and providing local LLMs specifically customized for Chinese users.
Furthermore, looking ahead, one of the key future applications of LLMs will be
practical deployment in industrial production by enterprises and users in those
sectors. However, the accuracy and robustness of LLMs in industrial scenarios
have not been well studied. In this paper, we present a comprehensive empirical
study on the accuracy and robustness of LLMs in the context of the Chinese
industrial production area. We manually collected 1,200 domain-specific
problems from 8 different industrial sectors to evaluate LLM accuracy.
Furthermore, we designed a metamorphic testing framework containing four
industrial-specific stability categories with eight abilities, totaling 13,631
questions with variants to evaluate LLM robustness. In total, we evaluated 9
different LLMs developed by Chinese vendors, as well as four different LLMs
developed by global vendors. Our major findings include: (1) Current LLMs
exhibit low accuracy in Chinese industrial contexts, with all LLMs scoring less
than 0.6. (2) The robustness scores vary across industrial sectors, and local
LLMs overall perform worse than global ones. (3) LLM robustness differs
significantly across abilities. Global LLMs are more robust under
logical-related variants, while advanced local LLMs perform better on problems
related to understanding Chinese industrial terminology. Our study results
provide valuable guidance for understanding and promoting the industrial domain
capabilities of LLMs from both development and industrial enterprise
perspectives. The results further motivate possible research directions and
tooling support
SugarMate: Non-intrusive blood glucose monitoring with smartphones
Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference of blood glucose levels at a fine-grained time resolution. We propose Md3RNN, an efficient learning paradigm to make full use of the available blood glucose information. Specifically, the newly designed grouped input layers, together with the adoption of a deep RNN model, offer an opportunity to build blood glucose models for the general public based on limited personal measurements from single-user and grouped-users perspectives. Evaluations on 112 users demonstrate that Md3RNN yields an average accuracy of 82.14%, significantly outperforming previous learning methods those are either shallow, generically structured, or oblivious to grouped behaviors. Also, a user study with the 112 participants shows that SugarMate is acceptable for practical usage.</jats:p
Cyclic ADP ribose isomers: Production, chemical structures, and immune signaling
Cyclic adenosine diphosphate (ADP)–ribose (cADPR) isomers are signaling molecules produced by bacterial and plant Toll/interleukin-1 receptor (TIR) domains via nicotinamide adenine dinucleotide (oxidized form) (NAD+) hydrolysis. We show that v-cADPR (2′cADPR) and v2-cADPR (3′cADPR) isomers are cyclized by O-glycosidic bond formation between the ribose moieties in ADPR. Structures of 2′cADPR-producing TIR domains reveal conformational changes that lead to an active assembly that resembles those of Toll-like receptor adaptor TIR domains. Mutagenesis reveals a conserved tryptophan that is essential for cyclization. We show that 3′cADPR is an activator of ThsA effector proteins from the bacterial antiphage defense system termed Thoeris and a suppressor of plant immunity when produced by the effector HopAM1. Collectively, our results reveal the molecular basis of cADPR isomer production and establish 3′cADPR in bacteria as an antiviral and plant immunity–suppressing signaling molecule
Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition
Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3.
Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612.
Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ”
Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018.
Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026.
Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091.
Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190.
Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU).
Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762.
Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202.
Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001
Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition
Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3.
Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612.
Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ”
Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018.
Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026.
Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091.
Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190.
Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU).
Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762.
Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202.
Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe
Internet Traffic Prediction with Distributed Multi-Agent Learning
Internet traffic prediction has been considered a research topic and the basis for intelligent network management and planning, e.g., elastic network service provision and content delivery optimization. Various methods have been proposed in the literature for Internet traffic prediction, including statistical, machine learning and deep learning methods. However, most of the existing approaches are trained and deployed in a centralized approach, without considering the realistic scenario in which multiple parties are concerned about the prediction process and the prediction model can be trained in a distributed approach. In this study, a distributed multi-agent learning framework is proposed to fill the research gap and predict Internet traffic in a distributed approach, in which each agent trains a base prediction model and the individual models are further aggregated with the cooperative interaction process. In the numerical experiments, two sophisticated deep learning models are chosen as the base prediction model, namely, long short-term memory (LSTM) and gated recurrent unit (GRU). The numerical experiments demonstrate that the GRU model trained with five agents achieves state-of-the-art performance on a real-world Internet traffic dataset collected in a campus backbone network in terms of root mean square error (RMSE) and mean absolute error (MAE)