Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition

Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems. One of the main challenges in VD is to promote diverse behaviors among agents, while existing methods directly encourage the diversity of learned agent networks with various strategies. However, we argue that these dedicated designs for agent networks are still limited by the indistinguishable VD network, leading to homogeneous agent behaviors and thus downgrading the cooperation capability. In this paper, we propose a novel Contrastive Identity-Aware learning (CIA) method, explicitly boosting the credit-level distinguishability of the VD network to break the bottleneck of multi-agent diversity. Specifically, our approach leverages contrastive learning to maximize the mutual information between the temporal credits and identity representations of different agents, encouraging the full expressiveness of credit assignment and further the emergence of individualities. The algorithm implementation of the proposed CIA module is simple yet effective that can be readily incorporated into various VD architectures. Experiments on the SMAC benchmarks and across different VD backbones demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://github.com/liushunyu/CIA

Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes

Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multi-view cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method.Comment: Accepted by CVPR 2023. Code and models are available at: https://github.com/ruili3/dynamic-multiframe-dept

INT: Towards Infinite-frames 3D Detection with An Efficient Framework

It is natural to construct a multi-frame instead of a single-frame 3D detector for a continuous-time stream. Although increasing the number of frames might improve performance, previous multi-frame studies only used very limited frames to build their systems due to the dramatically increased computational and memory cost. To address these issues, we propose a novel on-stream training and prediction framework that, in theory, can employ an infinite number of frames while keeping the same amount of computation as a single-frame detector. This infinite framework (INT), which can be used with most existing detectors, is utilized, for example, on the popular CenterPoint, with significant latency reductions and performance improvements. We've also conducted extensive experiments on two large-scale datasets, nuScenes and Waymo Open Dataset, to demonstrate the scheme's effectiveness and efficiency. By employing INT on CenterPoint, we can get around 7% (Waymo) and 15% (nuScenes) performance boost with only 2~4ms latency overhead, and currently SOTA on the Waymo 3D Detection leaderboard.Comment: accepted by ECCV202

Cost-effectiveness analysis of nivolumab combination therapy in the first-line treatment for advanced esophageal squamous-cell carcinoma

ObjectiveWe aimed to investigate the cost-effectiveness of nivolumab plus chemotherapy and nivolumab plus ipilimumab versus chemotherapy in the first-line treatment for advanced esophageal squamous-cell carcinoma (ESCC) patients from a healthcare system perspective in China.MethodsOn the basis of the CheckMate 648 trial, a partitioned survival model was constructed to estimate economic costs and health outcomes among overall and PD-L1-positive advanced ESCC patients over a 10-year lifetime horizon. The health-related costs and utilities were obtained from the local charges and published literature. The lifetime costs, life-years, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER) were measured. One-way and probabilistic sensitivity analyses (PSA) were performed to assess the robustness of the model.ResultsIn the base-case analysis, in overall and PD-L1-positive advanced ESCC patients, the ICERs were $415,163.81/QALY and$216,628.00/QALY for nivolumab plus chemotherapy, and$430,704.11/QALY and$185,483.94/QALY for nivolumab plus ipilimumab, respectively, compared with chemotherapy. One-way sensitivity analyses revealed that patients’ weight was the most influential parameter on ICER. The PSA demonstrated that the probability of nivolumab combination therapy being cost-effective was 0% over chemotherapy at the current price and willingness-to-pay threshold ($38,351.20/QALY). When the price of nivolumab and ipilimumab decreased 80%, the cost-effective probability of nivolumab plus ipilimumab increased to 40.44% and 86.38% in overall and PD-L1-positive advanced ESCC patients, respectively.ConclusionNivolumab combination therapy could improve survival time and health benefits over chemotherapy for advanced ESCC patients, but it is unlikely to be a cost-effective treatment option in China

Efficient DNA walker guided by ordered cruciform-shaped DNA track for ultrasensitive and rapid electrochemical detection of lead ion

The rational design of DNA tracks is an effective pathway to guide the autonomous movement and high-efficiency recognition in DNA walkers, showing outstanding advantages for the cascade signal amplification of electrochemical biosensors. However, the uncontrolled distance between two adjacent tracks on the electrode could increase the risk of derailment and interruption of the reaction. Hence, a novel four-way balanced cruciform-shaped DNA track (C-DNT) was designed as a structured pathway to improve the effectiveness and stability of the reaction in DNA walkers. In this work, two kinds of cruciform-shaped DNA were interconnected as a robust structure that could avoid the invalid movement of the designed DNA walker on the electrode. When hairpin H2 was introduced onto the electrode, the strand displacement reaction (SDR) effectively triggered movements of the DNA walker along the cruciform-shaped track while leaving ferrocene (Fc) on the electrode, leading to a significant enhancement of the electrochemical signal. This design enabled the walker to move in an excellent organized and controllable manner, thus enhancing the reaction speed and walking efficiency. Compared to other walkers moving on random tracks, the reaction time of the C-DNT-based DNA walker could be reduced to 20 min. Lead ion (Pb2+) was used as a model target to evaluate the analytical performance of this biosensor, which exhibited a low detection limit of 0.033 pM along with a wide detection ranging from 0.1 pM to 500 nM. This strategy presented a novel concept for designing a high-performance DNA walker-based sensing platform for the detection of contaminants

Integrated radiative and evaporative cooling beyond daytime passive cooling power limit

Radiative cooling technologies can passively gain lower temperature than that of ambient surroundings without consuming electricity, which has emerged as potential alternatives to traditional cooling methods. However, the limitations in daytime radiation intensity with a net cooling power of less than 150 W·m−2 have hindered progress toward commercial practicality. Here, we report an integrated radiative and evaporative chiller (IREC) based on polyacrylamide hydrogels combined with an upper layer of breathable poly(vinylidene fluoride-co-trifluoroethylene) fibers, which achieves a record high practical average daytime cooling power of 710 W·m−2. The breathable fiber layer has an average emissivity of over 76% in the atmospheric window, while reflecting 90% of visible light. This IREC possesses effective daytime radiative cooling while simultaneously ensuring evaporative cooling capability, enhancing daytime passive cooling effectively. As a result, IREC presents the practicability for both personal cooling managements and industrial auxiliary cooling applications. An IREC-based patch can assist in cooling human body by 13 °C low for a long term and biocompatible use, and IREC can maintain the temperature of industrial storage facilities such as oil tanks at room temperature even under strong sunlight irradiation. This work delivers the highest performance daytime passive cooling by simultaneous infrared radiation and water evaporation, and provides a new perspective for developing highly efficient, scalable, and affordable passive cooling strategy

RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

ABSTRACTUrban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects