ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries

Existing autonomous driving pipelines separate the perception module from the prediction module. The two modules communicate via hand-picked features such as agent boxes and trajectories as interfaces. Due to this separation, the prediction module only receives partial information from the perception module. Even worse, errors from the perception modules can propagate and accumulate, adversely affecting the prediction results. In this work, we propose ViP3D, a visual trajectory prediction pipeline that leverages the rich information from raw videos to predict future trajectories of agents in a scene. ViP3D employs sparse agent queries throughout the pipeline, making it fully differentiable and interpretable. Furthermore, we propose an evaluation metric for this novel end-to-end visual trajectory prediction task. Extensive experimental results on the nuScenes dataset show the strong performance of ViP3D over traditional pipelines and previous end-to-end models.Comment: Project page is at https://tsinghua-mars-lab.github.io/ViP3

Evolutionary City: Towards a Flexible, Agile and Symbiotic System

Urban growth sometimes leads to rigid infrastructure that struggles to adapt to changing demand. This paper introduces a novel approach, aiming to enable cities to evolve and respond more effectively to such dynamic demand. It identifies the limitations arising from the complexity and inflexibility of existing urban systems. A framework is presented for enhancing the city's adaptability perception through advanced sensing technologies, conducting parallel simulation via graph-based techniques, and facilitating autonomous decision-making across domains through decentralized and autonomous organization and operation. Notably, a symbiotic mechanism is employed to implement these technologies practically, thereby making urban management more agile and responsive. In the case study, we explore how this approach can optimize traffic flow by adjusting lane allocations. This case not only enhances traffic efficiency but also reduces emissions. The proposed evolutionary city offers a new perspective on sustainable urban development, highliting the importance of integrated intelligence within urban systems.Comment: 11 pages, 11 figure

IR Design for Application-Specific Natural Language: A Case Study on Traffic Data

In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits. With the ceaseless progress in computer performance and the rapid development of large-scale models, the possibility of programming using natural language in specified applications - referred to as Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits greater flexibility and freedom, which, in turn, leads to an increase in computational complexity for parsing and a decrease in processing performance. To tackle this issue, our paper advances a design for an intermediate representation (IR) that caters to ASNL and can uniformly process transportation data into graph data format, improving data processing performance. Experimental comparisons reveal that in standard data query operations, our proposed IR design can achieve a speed improvement of over forty times compared to direct usage of standard XML format data

Multilevel Nitrogen Additions Alter Chemical Composition and Turnover of the Labile Fraction Soil Organic Matter via Effects on Vegetation and Microorganisms

Global nitrogen (N) deposition greatly impacts soil carbon sequestration. A 2- yr multiple N addition (0, 10, 20, 40, 80, and 160 kg N·ha- 1·yr- 1) experiment was conducted in alpine grassland to illustrate the mechanisms underlying the observed soil organic matter (SOM) dynamics on the Qinghai- Tibet Plateau (QTP). Labile fraction SOM (LF- SOM) fingerprints were characterized by pyrolysis- gas chromatography/tandem- mass spectrometry, and microbial functional genes (GeoChip 4.6) were analyzed in conjunction with LF- SOM fingerprints to decipher the responses of LF- SOM transformation to N additions. The significant correlations between LF- SOM and microbial biomass, between organic compounds in LF- SOM and compound degradation- related genes, and between LF- SOM and net ecosystem exchange implied LF- SOM were the main fraction utilized by microorganisms and the most sensitive fraction to N additions. The LF- SOM increased at the lowest N addition levels (10 and 20 kg N·ha- 1·yr- 1) and decreased at higher N addition levels (40 to 160 kg N·ha- 1·yr- 1), but the decrease of LF- SOM was weakened at 160 kg N·ha- 1·yr- 1 addition. The nonlinear response of LF- SOM to N additions was due to the mass balance between plant inputs and microbial degradation. Plant- derived compounds in LF- SOM were more sensitive to N addition than microbial- derived and aromatic compounds. It is predicted that when the N deposition rate increased by 10 kg N·ha- 1·yr- 1 on the QTP, carbon sequestration in the labile fraction may increase by nearly 170% compared with that under the current N deposition rate. These findings provide insight into future N deposition impacts on LF- SOM preservation on the QTP.Key PointsThe LF- SOM quantity increased at the lowest N additions (N10 and N20) and decreased from N40 to N160, but the decrease was weakened at the highest N addition (N160)Plant- derived compounds in LF- SOM were more sensitive to N addition than microbial- derived and aromatic compoundsThe organic compounds in LF- SOM were significantly correlated with compound degradation- related genesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154963/1/jgrg21637_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154963/2/jgrg21637.pd

TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems

Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs. However, real-world complex systems present three prevalent challenges concerning task planning and tool usage: (1) The real system usually has a vast array of APIs, so it is impossible to feed the descriptions of all APIs to the prompt of LLMs as the token length is limited; (2) the real system is designed for handling complex tasks, and the base LLMs can hardly plan a correct sub-task order and API-calling order for such tasks; (3) Similar semantics and functionalities among APIs in real systems create challenges for both LLMs and even humans in distinguishing between them. In response, this paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents operating within real-world systems. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs for the user task among the extensive array available; (2) LLM Finetuner tunes a base LLM so that the finetuned LLM can be more capable for task planning and API calling; (3) the Demo Selector adaptively retrieves different demonstrations related to hard-to-distinguish APIs, which is further used for in-context learning to boost the final performance. We validate our methods using a real-world commercial system as well as an open-sourced academic dataset, and the outcomes clearly showcase the efficacy of each individual component as well as the integrated framework

Wireless transmission of biosignals for hyperbaric chamber applications

[EN] This paper presents a wireless system to send biosignals outside a hyperbaric chamber avoiding wires going through the chamber walls. Hyperbaric chambers are becoming more and more common due to new indications of hyperbaric oxygen treatments. Metallic walls physically isolate patients inside the chamber, where getting a patient's vital signs turns into a painstaking task. The paper proposes using a ZigBee-based network to wirelessly transmit the patient's biosignals to the outside of the chamber. In particular, a wearable battery supported device has been designed, implemented and tested. Although the implementation has been conducted to transmit the electrocardiography signal, the device can be easily adapted to consider other biosignals.The authors would like to thanks the University of Balearic Islands (UIB), the Miguel Hernandez University (UMH), MEDIBAROX unit of the Perpetuo Socorro Hospital and the "Catedra de Medicina Hiperbarica" (UMH) for their support allowing the use of its facilities for this work. The authors would also like to thank Borja Mas Boned for his help designing the LabVIEW application. This research has been carried out with funding and promotion of "Catedra de Medicina Hiperbarica" of the Miguel Hernandez University. http://nbio.umh.es/es/2010/12/01/catedra-de-medicina-hiperbarica-medibarox/.Perez-Vidal, C.; Gracia Calandin, LI.; Carmona, C.; Alorda, B.; Salinas, A. (2017). Wireless transmission of biosignals for hyperbaric chamber applications. PLoS ONE. 12(3):1-19. https://doi.org/10.1371/journal.pone.0172768S119123Sureda, A., Batle, J. M., Martorell, M., Capó, X., Tejada, S., Tur, J. A., & Pons, A. (2016). Antioxidant Response of Chronic Wounds to Hyperbaric Oxygen Therapy. PLOS ONE, 11(9), e0163371. doi:10.1371/journal.pone.0163371Branco, B. H. M., Fukuda, D. H., Andreato, L. V., Santos, J. F. da S., Esteves, J. V. D. C., & Franchini, E. (2016). The Effects of Hyperbaric Oxygen Therapy on Post-Training Recovery in Jiu-Jitsu Athletes. PLOS ONE, 11(3), e0150517. doi:10.1371/journal.pone.0150517Xu, Y., Ji, R., Wei, R., Yin, B., He, F., & Luo, B. (2016). The Efficacy of Hyperbaric Oxygen Therapy on Middle Cerebral Artery Occlusion in Animal Studies: A Meta-Analysis. PLOS ONE, 11(2), e0148324. doi:10.1371/journal.pone.0148324Lin, B.-S., Lin, B.-S., Chou, N.-K., Chong, F.-C., & Chen, S.-J. (2006). RTWPMS: A Real-Time Wireless Physiological Monitoring System. IEEE Transactions on Information Technology in Biomedicine, 10(4), 647-656. doi:10.1109/titb.2006.874194Hu, S., Wei, H., Chen, Y., & Tan, J. (2012). A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks. Sensors, 12(9), 12844-12869. doi:10.3390/s120912844Burns, A., Greene, B. R., McGrath, M. J., O’Shea, T. J., Kuris, B., Ayer, S. M., … Cionca, V. (2010). SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research. IEEE Sensors Journal, 10(9), 1527-1534. doi:10.1109/jsen.2010.2045498Gil, Y., Wu, W., & Lee, J. (2012). A Synchronous Multi-Body Sensor Platform in a Wireless Body Sensor Network: Design and Implementation. Sensors, 12(8), 10381-10394. doi:10.3390/s120810381Chin-Teng Lin, Kuan-Cheng Chang, Chun-Ling Lin, Chia-Cheng Chiang, Shao-Wei Lu, Shih-Sheng Chang, … Li-Wei Ko. (2010). An Intelligent Telecardiology System Using a Wearable and Wireless ECG to Detect Atrial Fibrillation. IEEE Transactions on Information Technology in Biomedicine, 14(3), 726-733. doi:10.1109/titb.2010.2047401W. Y. Chung, Y. D. Lee, and S. J. Jung, 'A Wireless Sensor Network Compatible Wearable U-Healthcare Monitoring System Using Integrated Ecg, Accelerometer and Spo2', Conf Proc IEEE Eng Med Biol Soc, 2008 (2008), 1529–32.ZigBee Alliance; http://www.zigbee.org/Mahmood, A., Javaid, N., & Razzaq, S. (2015). A review of wireless communications for smart grid. Renewable and Sustainable Energy Reviews, 41, 248-260. doi:10.1016/j.rser.2014.08.036J.S. Lee, Y.W. Su, and C.C. Shen, "A comparative study of wireless protocols: Bluetooth, UWB, ZigBee, and Wi-Fi, 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON), 2007, pp. 46–51.P.P. Parikh, M.G. Kanabar, and T.S. Sidhu, "Opportunities and challenges of wireless communication technologies for smart grid applications, IEEE PES General Meeting, 2010, pp. 1–7.Fadlullah, Z. M., Fouda, M. M., Kato, N., Takeuchi, A., Iwasaki, N., & Nozaki, Y. (2011). Toward intelligent machine-to-machine communications in smart grid. IEEE Communications Magazine, 49(4), 60-65. doi:10.1109/mcom.2011.5741147A.C. Olteanu, G.D. Oprina, N. Tapus, and S. Zeisberg, "Enabling mobile devices for home automation using ZigBee, 19th IEEE International Conference on Control Systems and Computer Science, 2013, pp. 189–195.Shang, Y. (2014). Vulnerability of networks: Fractional percolation on random graphs. Physical Review E, 89(1). doi:10.1103/physreve.89.012813R. Barea-Navarro. Biomedical Instrumentation. Chapter 3. University of Alcala

Dual-confined SeS2 cathode based on polyaniline-assisted double-layered micro/mesoporous carbon spheres for advanced Li-SeS(2 )battery

Selenium-sulfur solid solutions (SexSy) attracts soaring attention owing to its improved electrical conductivity over sulfur and higher theoretical specific capacity than selenium. Herein, high-performance lithium-selenium/sulfur batteries with a dual-confined cathode configuration by encapsulating SeS2 in double-layered hollow micro/mesoporous carbon spheres (DSMCs) with a conductive polyaniline (PANI) protection sheath are proposed. Polysulfides/polyselenides are efficiently restricted in the cathode via physical and chemical entrapment from DSMCs and PANI as well as chemical binding between selenium and sulfur. Benefiting from the distinct advantages of SeS2 and the well-constructed host framework, the cathode achieves high capacity utilization of 1018 mAh g(-1) at 0.2 A g(-1) , together with outstanding rate capability of 619 mAh g(-1) at 2 A g(-1) and excellent cycle life over 500 cycles with almost 100% Coulombic efficiency. The novel SexSy based cathode demonstrates a promising route to surmount some bottlenecks of current lithium-sulfur systems for high-performance rechargeable batteries

Multiple core-shelled sulfur composite based on spherical double-layered hollow carbon and PEDOT:PSS as cathode for lithium-sulfur batteries

Nanostructured sulfur cathode with a multiple core-shelled structure, featured with the spherical double-layered hollow carbon/sulfur composite (DLHC/S) coated with a conductive layer of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), is designed and synthesized for lithium-sulfur batteries. Transmission electron microscope images of DLHC/S single nanoparticle show that the sulfur aggregates predominantly in the interior space between the two carbon shells by using a vacuum infiltration process. The electric conductivity of DLHC/S@PEDOT:PSS increases over 5 times as comparing to DLHC/S without PEDOT:PSS coating. The composite cathode exhibits a high reversible capacity of 1089 mAh g(-1) at 0.2C and superior rate capacity of 510 mAh g(-1 )even at 4 C, and also remarkable cycling stability with a capacity decay of 0.097% per cycle after 500 cycles at 1 C. The excellent electrochemical performances for DLHC/S@PEDOT:PSS cathode are primarily attributed to the engineering of the unique multiple core-shell structure of DLHC/S@PEDOT:PSS, which inhibits the sulfur dissolution into the electrolyte and the polysulfide shuttle effect, together with the conductivity enhancement due to PEDOT:PSS coating. (C) 2020 Elsevier B.V. All rights reserved

A Novel Hybrid Deep Learning Method for Predicting the Flow Fields of Biomimetic Flapping Wings

The physics governing the fluid dynamics of bio-inspired flapping wings is effectively characterized by partial differential equations (PDEs). Nevertheless, the process of discretizing these equations at spatiotemporal scales is notably time consuming and resource intensive. Traditional PDE-based computations are constrained in their applicability, which is mainly due to the presence of numerous shape parameters and intricate flow patterns associated with bionic flapping wings. Consequently, there is a significant demand for a rapid and accurate solution to nonlinear PDEs, to facilitate the analysis of bionic flapping structures. Deep learning, especially physics-informed deep learning (PINN), offers an alternative due to its great nonlinear curve-fitting capability. In the present work, a hybrid coarse-data-driven physics-informed neural network model (HCDD-PINN) is proposed to improve the accuracy and reliability of predicting the time evolution of nonlinear PDEs solutions, by using an order-of-magnitude-coarser grid than traditional computational fluid dynamics (CFDs) require as internal training data. The architecture is devised to enforce the initial and boundary conditions, and incorporate the governing equations and the low-resolution spatiotemporal internal data into the loss function of the neural network, to drive the training. Compared to the original PINN with no internal data, the training and predicting dynamics of HCDD-PINN with different resolutions of coarse internal data are analyzed on the problem relevant to the two-dimensional unsteady flapping wing, which involves unsteady flow features and moving boundaries. Additionally, a hyper-parametrical study is conducted to obtain an optimal model for the problem under consideration, which is then utilized for investigating the effects of the snapshot and fraction of the coarse internal data on the HCDD-PINN’s performances. The results show that the proposed framework has a sufficient stability and accuracy for solving the considered biomimetic flapping-wing problem, and its great potential means that it can be considered as an alternative to accelerate or replace traditional CFD solvers in the future. The interested variables of the flow field at any instant can be rapidly obtained by the trained HCDD-PINN model, which is superior to the traditional CFD method that usually needs to be re-run. For the three-dimensional and optimization problems of flapping wings, the advantages of the proposed method are supposedly even more apparent