Ridesplitting是合并的骑乘服务的一种形式，具有减轻骑行对环境的负面影响的巨大潜力。但是，大多数现有研究仅根据优化模型和仿真探索其理论环境益处。相比之下，这项研究旨在揭示基于观察到的中国骑车数据的数据及其决定因素的现实世界排放减少。本研究将Trip数据与Copert模型整合在一起，计算了共享乘车的CO2排放量（Ridesplitting）及其取代的单骑行（常规乘车），以估计每次骑行旅行的CO2排放降低。结果表明，并非所有的骑行旅行都减少了现实世界中的骑车的排放。二氧化碳的降低速度降低速率因行程到旅行而异，平均为43.15g/km。然后，应用可解释的机器学习模型，梯度提升机，用于探索二氧化碳排放率降低速度的关系及其决定因素之间的关系。基于Shapley添加剂解释（SHAP）方法，共享乘车的重叠率和弯路率被确定为确定二氧化碳排放率降低乘车率的最重要因素。增加重叠率，共享乘车的数量，平均速度和行驶距离比率，同时降低弯路率，实际行程距离和行驶距离差距可以增加二氧化碳排放率的降低骑行率。另外，通过部分依赖图研究了决定因素的非线性效应和相互作用。总而言之，这项研究为政府和骑车公司提供了一种科学方法，以更好地评估和优化乘车的环境利益。

风险的准确器官（OAR）分割对于减少治疗后并发症的放射治疗至关重要。达人指南推荐头部和颈部（H＆N）区域的一套超过40桨的桨，然而，由于这项任务的可预测的禁止劳动力成本，大多数机构通过划定较小的桨子和忽视的少数，选择了大量简化的协议与其他桨相关的剂量分布。在这项工作中，我们提出了一种使用深度学习的新颖，自动化和高效的分层OAR分段（SOARS）系统，精确地描绘了一套全面的42 H＆N OAR。 SOARS将42桨分层进入锚，中级和小型和硬质子类别，通过神经结构搜索（NAS）原则，专门为每个类别提供神经网络架构。我们在内在机构中使用176名培训患者建立了SOAR模型，并在六个不同的机构中独立评估了1327名外部患者。对于每个机构评估，它始终如一地表现出其他最先进的方法至少3-5％的骰子得分（在其他度量的相对误差减少36％）。更重要的是，广泛的多用户研究明显证明，98％的SOARE预测只需要非常轻微或没有直接临床验收的修订（节省90％的辐射脑神经工作负载），并且它们的分割和剂量准确度在于或小于帧 - 用户的变化。这些调查结果证实了H＆N癌症放射疗法工作流OAR描绘过程的强烈临床适用性，提高了效率，全面性和质量。

Visual Entity Linking (VEL) is a task to link regions of images with their corresponding entities in Knowledge Bases (KBs), which is beneficial for many computer vision tasks such as image retrieval, image caption, and visual question answering. While existing tasks in VEL either rely on textual data to complement a multi-modal linking or only link objects with general entities, which fails to perform named entity linking on large amounts of image data. In this paper, we consider a purely Visual-based Named Entity Linking (VNEL) task, where the input only consists of an image. The task is to identify objects of interest (i.e., visual entity mentions) in images and link them to corresponding named entities in KBs. Since each entity often contains rich visual and textual information in KBs, we thus propose three different sub-tasks, i.e., visual to visual entity linking (V2VEL), visual to textual entity linking (V2TEL), and visual to visual-textual entity linking (V2VTEL). In addition, we present a high-quality human-annotated visual person linking dataset, named WIKIPerson. Based on WIKIPerson, we establish a series of baseline algorithms for the solution of each sub-task, and conduct experiments to verify the quality of proposed datasets and the effectiveness of baseline methods. We envision this work to be helpful for soliciting more works regarding VNEL in the future. The codes and datasets are publicly available at https://github.com/ict-bigdatalab/VNEL.

相位函数是Monte Carlo（MC）仿真的光传播模型的关键元件，其通常配备有具有相关参数的分析功能。据报道，据报道，机器学习方法估计特定形式的相位函数的参数，例如Henyey-Greenstein相位功能，但是，对于我们的知识，没有进行研究以确定相位功能的形式。在这里，我们设计卷积神经网络，以估计来自漫反射光图像的相位函数而没有对相位函数的形式进行任何明确的假设。具体地，我们使用高斯混合模型作为示例来表示相位函数，并准确地学习模型参数。选择高斯混合模型，因为它提供了相位函数的分析表达，以便于MC模拟中促进偏转角采样，并且不会显着增加自由参数的数量。我们所提出的方法在典型的生物组织的MC模拟反射图像上使用不同的各向异性因子进行典型生物组织的MC模拟反射图像。分析了视野（FOV）和空间分辨率对误差的影响以优化估计方法。相位函数的平均平方误差为0.01，各向异性因子的相对误差为3.28％。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.