Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.

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.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Modern retrieval system often requires recomputing the representation of every piece of data in the gallery when updating to a better representation model. This process is known as backfilling and can be especially costly in the real world where the gallery often contains billions of samples. Recently, researchers have proposed the idea of Backward Compatible Training (BCT) where the new representation model can be trained with an auxiliary loss to make it backward compatible with the old representation. In this way, the new representation can be directly compared with the old representation, in principle avoiding the need for any backfilling. However, followup work shows that there is an inherent tradeoff where a backward compatible representation model cannot simultaneously maintain the performance of the new model itself. This paper reports our ``not-so-surprising'' finding that adding extra dimensions to the representation can help here. However, we also found that naively increasing the dimension of the representation did not work. To deal with this, we propose Backward-compatible Training with a novel Basis Transformation ($BT^2$). A basis transformation (BT) is basically a learnable set of parameters that applies an orthonormal transformation. Such a transformation possesses an important property whereby the original information contained in its input is retained in its output. We show in this paper how a BT can be utilized to add only the necessary amount of additional dimensions. We empirically verify the advantage of $BT^2$ over other state-of-the-art methods in a wide range of settings. We then further extend $BT^2$ to other challenging yet more practical settings, including significant change in model architecture (CNN to Transformers), modality change, and even a series of updates in the model architecture mimicking the evolution of deep learning models.

CNN-based surrogates have become prevalent in scientific applications to replace conventional time-consuming physical approaches. Although these surrogates can yield satisfactory results with significantly lower computation costs over small training datasets, our benchmarking results show that data-loading overhead becomes the major performance bottleneck when training surrogates with large datasets. In practice, surrogates are usually trained with high-resolution scientific data, which can easily reach the terabyte scale. Several state-of-the-art data loaders are proposed to improve the loading throughput in general CNN training; however, they are sub-optimal when applied to the surrogate training. In this work, we propose SOLAR, a surrogate data loader, that can ultimately increase loading throughput during the training. It leverages our three key observations during the benchmarking and contains three novel designs. Specifically, SOLAR first generates a pre-determined shuffled index list and accordingly optimizes the global access order and the buffer eviction scheme to maximize the data reuse and the buffer hit rate. It then proposes a tradeoff between lightweight computational imbalance and heavyweight loading workload imbalance to speed up the overall training. It finally optimizes its data access pattern with HDF5 to achieve a better parallel I/O throughput. Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24.4X speedup over PyTorch Data Loader and 3.52X speedup over state-of-the-art data loaders.

RGB热点对象检测（SOD）结合了两个光谱，以分段图像中的视觉明显区域。大多数现有方法都使用边界图来学习锋利的边界。这些方法忽略了孤立的边界像素与其他自信像素之间的相互作用，从而导致了次优性能。为了解决这个问题，我们为基于SWIN Transformer的RGB-T SOD提出了一个职位感知关系学习网络（PRLNET）。 PRLNET探索像素之间的距离和方向关系，以增强阶层内的紧凑性和类间的分离，从而产生具有清晰边界和均匀区域的显着对象掩模。具体而言，我们开发了一个新颖的签名距离辅助模块（SDMAM）来改善编码器特征表示，该模块考虑了边界邻域中不同像素的距离关系。然后，我们使用定向字段（FRDF）设计一种功能改进方法，该方法通过利用明显对象内部的功能来纠正边界邻域的特征。 FRDF利用对象像素之间的方向信息有效地增强了显着区域的阶层紧凑性。此外，我们构成了一个纯变压器编码器 - 模块网络，以增强RGB-T SOD的多光谱特征表示。最后，我们对三个公共基准数据集进行了定量和定性实验。结果表明，我们所提出的方法的表现优于最新方法。

在狭窄的空间中，基于传统层次自治系统的运动计划可能会导致映射，定位和控制噪声引起碰撞。此外，当无映射时，它将被禁用。为了解决这些问题，我们利用深厚的加强学习，可以证明可以有效地进行自我决策，从而在狭窄的空间中自探索而无需地图，同时避免碰撞。具体而言，基于我们的Ackermann-Steering矩形Zebrat机器人及其凉亭模拟器，我们建议矩形安全区域来表示状态并检测矩形形状的机器人的碰撞，以及无需精心制作的奖励功能，不需要增强功能。目的地信息。然后，我们在模拟的狭窄轨道中基准了五种增强学习算法，包括DDPG，DQN，SAC，PPO和PPO-DISCRETE。经过训练，良好的DDPG和DQN型号可以转移到三个全新的模拟轨道上，然后转移到三个现实世界中。

在图像之间生成健壮和可靠的对应关系是多种应用程序的基本任务。为了在全球和局部粒度上捕获上下文，我们提出了Aspanformer，这是一种基于变压器的无探测器匹配器，建立在层次的注意力结构上，采用了一种新颖的注意操作，能够以自适应方式调整注意力跨度。为了实现这一目标，首先，在每个跨注意阶段都会回归流图，以定位搜索区域的中心。接下来，在中心周围生成一个采样网格，其大小不是根据固定的经验配置为固定的，而是根据与流图一起估计的像素不确定性的自适应计算。最后，在派生区域内的两个图像上计算注意力，称为注意跨度。通过这些方式，我们不仅能够维持长期依赖性，而且能够在高相关性的像素之间获得细粒度的注意，从而补偿基本位置和匹配任务中的零件平滑度。在广泛的评估基准上的最新准确性验证了我们方法的强匹配能力。

低光视频增强（LLVE）是许多应用程序，例如拍摄和自动驾驶，是一项重要但艰巨的任务。与单图像低光增强不同，大多数LLVE方法都利用相邻帧的时间信息来恢复颜色并删除目标框架的噪声。但是，这些算法基于多帧对齐和增强的框架，在遇到极端低光或快速运动时可能会产生多帧融合工件。在本文中，受到低潜伏期和高动态事件范围的启发，我们使用来自多个帧的合成事件来指导低光视频的增强和恢复。我们的方法包含三个阶段：1）事件合成和增强，2）事件和图像融合，以及3）低光增强。在此框架中，我们分别为第二阶段和第三阶段设计了两个新型模块（事件图像融合变换和事件引导的双分支）。广泛的实验表明，我们的方法在合成数据集和真实LLVE数据集上都优于现有的低光视频或单个图像增强方法。

否决单图是一项普遍但又具有挑战性的任务。复杂的降雪降解和各种降解量表需要强大的代表能力。为了使否定的网络看到各种降雪并建模本地细节和全球信息的上下文相互作用，我们提出了一种称为Snowformer的功能强大的建筑。首先，它在编码器中执行比例感知功能聚合，以捕获各种降解的丰富积雪信息。其次，为了解决大规模降级，它使用了解码器中的新颖上下文交互变压器块，该互动器块在全球上下文交互中从前范围内的局部细节和全局信息进行了上下文交互。并引入本地上下文互动可改善场景细节的恢复。第三，我们设计了一个异质的特征投影头，该功能投影头逐渐融合了编码器和解码器的特征，并将精制功能投影到干净的图像中。广泛的实验表明，所提出的雪诺形雪孔比其他SOTA方法取得了重大改进。与SOTA单图像HDCW-NET相比，它在CSD测试集上将PSNR度量提高了9.2dB。此外，与一般图像恢复体系结构NAFNET相比，PSNR的增加5.13db，这验证了我们的雪诺形雪地降雪任务的强大表示能力。该代码在\ url {https://github.com/ephemeral182/snowformer}中发布。