分配和验证国际公认的商品代码（HS编码）的任务是贸易货物的是海关办公室的关键职能之一。这一决定对于进口商和出口商至关重要，因为它决定了关税率。但是，类似于法官作出的法院决定，即使对于经验丰富的海关官员，任务也可能是非琐碎的。目前的论文提出了一个深入的学习模式，以协助这一看似挑战HS代码分类。与韩国海关服务一起，我们建立了基于科电的决策模型，该决策模型建议了HS代码的最有可能的标题和副标题（即，前四位和六位数）。在129,084件之前的情况下评估显示，我们模型的前3个建议在分类265个副标题方面的准确性为95.5％。这个有希望的结果意味着算法可以通过协助HS代码分类任务来减少海关官员所采取的时间和精力。

Single-image 3D human reconstruction aims to reconstruct the 3D textured surface of the human body given a single image. While implicit function-based methods recently achieved reasonable reconstruction performance, they still bear limitations showing degraded quality in both surface geometry and texture from an unobserved view. In response, to generate a realistic textured surface, we propose ReFu, a coarse-to-fine approach that refines the projected backside view image and fuses the refined image to predict the final human body. To suppress the diffused occupancy that causes noise in projection images and reconstructed meshes, we propose to train occupancy probability by simultaneously utilizing 2D and 3D supervisions with occupancy-based volume rendering. We also introduce a refinement architecture that generates detail-preserving backside-view images with front-to-back warping. Extensive experiments demonstrate that our method achieves state-of-the-art performance in 3D human reconstruction from a single image, showing enhanced geometry and texture quality from an unobserved view.

基于会话的建议旨在根据持续的会话预测用户的下一个行为。先前的作品是将会话建模为一系列项目的变量长度，并学习单个项目和汇总会话的表示。最近的研究应用了图形神经网络，具有注意机制，通过将会话建模为图形结构化数据来捕获复杂的项目过渡和依赖性。但是，他们仍然在数据和学习方法方面面临着根本的挑战，例如稀疏监督信号和会议中的嘈杂互动，从而导致次优性能。在本文中，我们提出了SR-GCL，这是一个基于会话建议的新型对比学习框架。作为对比学习的关键组成部分，我们提出了两种全球环境增强的数据增强方法，同时保持原始会话的语义。与其他最先进的方法相比，两个现实世界电子商务数据集的广泛实验结果证明了SR-GCL的优势。

机器学习（ML）的广泛部署正在引起严重的关注，以保护为收集培训数据做出贡献的用户的隐私。差异隐私（DP）作为保护保护的实用标准，在行业中迅速获得势头。尽管DP的重要性，但是在计算机系统社区中，几乎没有探索这种新兴ML算法对系统设计的影响。在这项工作中，我们对名为DP-SGD的最先进的私人ML培训算法进行了详细的工作量表征。我们发现了DP-SGD的几种独特属性（例如，其高内存能力和计算需求与非私人ML），从而引起其关键瓶颈。基于我们的分析，我们提出了一个名为Diva的差异私有ML的加速器，该加速器在计算利用率方面具有显着改善，从而导致2.6倍的能量效率与常规收缩期阵列。

Graph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational. A unique property of GCNs is that its two primary execution stages, aggregation and combination, exhibit drastically different dataflows. Consequently, prior GCN accelerators tackle this research space by casting the aggregation and combination stages as a series of sparse-dense matrix multiplication. However, prior work frequently suffers from inefficient data movements, leaving significant performance left on the table. We present GROW, a GCN accelerator based on Gustavson's algorithm to architect a row-wise product based sparse-dense GEMM accelerator. GROW co-designs the software/hardware that strikes a balance in locality and parallelism for GCNs, achieving significant energy-efficiency improvements vs. state-of-the-art GCN accelerators.

迅速探索随机树（RRT）已申请自动停车，因为迅速解决高维运动规划，易于反映约束。然而，规划时间通过延伸狭窄的停车点而无需碰撞而增加。为了减少规划时间，提出了目标树算法，用了向后停车路径的集合（目标树）代替RRT中的停车目标。然而，它由圆形和直线路径组成，并且由于曲率不连续性，自主车辆无法准确停放。此外，规划时间在复杂的环境中增加;障碍物可以阻挡向后路径。因此，本文介绍了复杂停车环境的连续曲率目标树算法。首先，目标树包括薄帘路径，以解决这种曲率不连续性。其次，为了进一步减少计划时间，定义了成本函数以构建考虑障碍物的目标树。与最佳变型RRT集成并搜索到达后向路径中的最短路径，所提出的算法在采样时间增加时获得近最佳路径。实验结果在真实环境中表明车辆更加准确地停放，并且连续曲率路径比与其他基于样品的算法所获得的那些获得更高的成功率。

诸如GELU，LIZESION和SOFTMAX之类的非线性操作是变压器模型的必备且昂贵的构建块。有几种先前的作品简化了这些操作，使用查找表或整数计算，但是这种近似值遭受了更低的精度或相当大的硬件成本，并且长期延迟。本文提出了一种精确且硬件友好的近似框架，用于高效变压器推断。我们的框架采用简单的神经网络作为通用近似器，其结构等效地转换成LUT。拟议的框架，称为NN-LUT可以准确地更换流行伯特模型中的所有非线性操作，在面积，功耗和延迟中显着降低。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.