A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators. Existing toolkits for visualizing computational graphs are not applicable when the structure is highly complicated and large-scale (e.g., BERT [1]). To address this problem, we propose leveraging a suite of visual simplification techniques, including a cycle-removing method, a module-based edge-pruning algorithm, and an isomorphic subgraph stacking strategy. We design and implement an interactive visualization system that is suitable for computational graphs with up to 10 thousand elements. Experimental results and usage scenarios demonstrate that our tool reduces 60% elements on average and hence enhances the performance for recognizing and diagnosing DNN models. Our contributions are integrated into an open-source DNN visualization toolkit, namely, MindInsight [2].

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Eliminating ghosting artifacts due to moving objects is a challenging problem in high dynamic range (HDR) imaging. In this letter, we present a hybrid model consisting of a convolutional encoder and a Transformer decoder to generate ghost-free HDR images. In the encoder, a context aggregation network and non-local attention block are adopted to optimize multi-scale features and capture both global and local dependencies of multiple low dynamic range (LDR) images. The decoder based on Swin Transformer is utilized to improve the reconstruction capability of the proposed model. Motivated by the phenomenal difference between the presence and absence of artifacts under the field of structure tensor (ST), we integrate the ST information of LDR images as auxiliary inputs of the network and use ST loss to further constrain artifacts. Different from previous approaches, our network is capable of processing an arbitrary number of input LDR images. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed method by comparing it with existing state-of-the-art HDR deghosting models. Codes are available at https://github.com/pandayuanyu/HSTHdr.

While large pre-trained models have transformed the field of natural language processing (NLP), the high training cost and low cross-lingual availability of such models prevent the new advances from being equally shared by users across all languages, especially the less spoken ones. To promote equal opportunities for all language speakers in NLP research and to reduce energy consumption for sustainability, this study proposes an effective and energy-efficient framework GreenPLM that uses bilingual lexicons to directly translate language models of one language into other languages at (almost) no additional cost. We validate this approach in 18 languages and show that this framework is comparable to, if not better than, other heuristics trained with high cost. In addition, when given a low computational cost (2.5\%), the framework outperforms the original monolingual language models in six out of seven tested languages. We release language models in 50 languages translated from English and the source code here.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

Motion planning is challenging for autonomous systems in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this paper, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in simulation and real world setting. Experimental results show that the proposed methods can generate smooth collision-free trajectories with less computation time compared with other benchmarks and perform robustly in cluttered environments.

如今，基础模型已成为人工智能中的基本基础设施之一，铺平了通往通用情报的方式。但是，现实提出了两个紧急挑战：现有的基础模型由英语社区主导；用户通常会获得有限的资源，因此不能总是使用基础模型。为了支持中文社区的发展，我们介绍了一个名为Fengshenbang的开源项目，该项目由认知计算与自然语言研究中心（CCNL）领导。我们的项目具有全面的功能，包括大型预培训模型，用户友好的API，基准，数据集等。我们将所有这些都包装在三个子项目中：风水次模型，风水框架和狂热基准。 Fengshenbang的开源路线图旨在重新评估中国预培训的大型大型模型的开源社区，促使整个中国大型模型社区的发展。我们还希望构建一个以用户为中心的开源生态系统，以允许个人访问所需的模型以匹配其计算资源。此外，我们邀请公司，大学和研究机构与我们合作建立大型开源模型的生态系统。我们希望这个项目将成为中国认知情报的基础。

变异量子算法（VQA）在NISQ时代表现出巨大的潜力。在VQA的工作流程中，Ansatz的参数迭代更新以近似所需的量子状态。我们已经看到了各种努力，以较少的大门起草更好的安萨兹。在量子计算机中，栅极Ansatz最终将转换为控制信号，例如TransMons上的微波脉冲。并且对照脉冲需要精心校准，以最大程度地减少误差（例如过度旋转和旋转）。在VQA的情况下，此过程将引入冗余，但是VQAS的变异性能自然可以通过更新幅度和频率参数来处理过度旋转和重组的问题。因此，我们提出了PAN，这是一种用于VQA的天然脉冲ANSATZ GENTARATOR框架。我们生成具有可训练参数用于振幅和频率的天然脉冲ansatz。在我们提出的锅中，我们正在调整参数脉冲，这些脉冲在NISQ计算机上得到了内在支持。考虑到本机 - 脉冲ANSATZ不符合参数迁移规则，我们需要部署非级别优化器。为了限制发送到优化器的参数数量，我们采用了一种生成本机 - 脉冲ANSATZ的渐进式方式。实验是在模拟器和量子设备上进行的，以验证我们的方法。当在NISQ机器上采用时，PAN获得的延迟平均提高了86％。 PAN在H2和HEH+上的VQE任务分别能够达到99.336％和96.482％的精度，即使NISQ机器中有很大的噪声。

动作检测的任务旨在在每个动作实例中同时推论动作类别和终点的本地化。尽管Vision Transformers推动了视频理解的最新进展，但由于在长时间的视频剪辑中，设计有效的架构以进行动作检测是不平凡的。为此，我们提出了一个有效的层次时空时空金字塔变压器（STPT）进行动作检测，这是基于以下事实：变压器中早期的自我注意力层仍然集中在局部模式上。具体而言，我们建议在早期阶段使用本地窗口注意来编码丰富的局部时空时空表示，同时应用全局注意模块以捕获后期的长期时空依赖性。通过这种方式，我们的STPT可以用冗余的大大减少来编码区域和依赖性，从而在准确性和效率之间进行有希望的权衡。例如，仅使用RGB输入，提议的STPT在Thumos14上获得了53.6％的地图，超过10％的I3D+AFSD RGB模型超过10％，并且对使用其他流量的额外流动功能的表现较少，该流量具有31％的GFLOPS ，它是一个有效，有效的端到端变压器框架，用于操作检测。

磁共振图像（MRI）中的脑肿瘤分割（BTS）对于脑肿瘤诊断，癌症管理和研究目的至关重要。随着十年小型挑战的巨大成功以及CNN和Transformer算法的进步，已经提出了许多出色的BTS模型来解决BTS在不同技术方面的困难。但是，现有研究几乎没有考虑如何以合理的方式融合多模式图像。在本文中，我们利用了放射科医生如何从多种MRI模态诊断脑肿瘤的临床知识，并提出了一种称为CKD-TRANSBTS的临床知识驱动的脑肿瘤分割模型。我们没有直接串联所有模式，而是通过根据MRI的成像原理将输入方式分为两组来重新组织输入方式。具有拟议模态相关的跨意义块（MCCA）的双支支混合式编码器旨在提取多模式图像特征。所提出的模型以局部特征表示能力的能力来继承来自变压器和CNN的强度，以提供精确的病变边界和3D体积图像的远程特征提取。为了弥合变压器和CNN功能之间的间隙，我们提出了解码器中的反式和CNN功能校准块（TCFC）。我们将提出的模型与五个基于CNN的模型和六个基于Transformer的模型在Brats 2021挑战数据集上进行了比较。广泛的实验表明，与所有竞争对手相比，所提出的模型可实现最先进的脑肿瘤分割性能。