Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the minimum binding energy - the adsorption energy - for an adsorbate and a catalyst surface of interest. Traditionally, the identification of low energy adsorbate-surface configurations relies on heuristic methods and researcher intuition. As the desire to perform high-throughput screening increases, it becomes challenging to use heuristics and intuition alone. In this paper, we demonstrate machine learning potentials can be leveraged to identify low energy adsorbate-surface configurations more accurately and efficiently. Our algorithm provides a spectrum of trade-offs between accuracy and efficiency, with one balanced option finding the lowest energy configuration, within a 0.1 eV threshold, 86.63% of the time, while achieving a 1387x speedup in computation. To standardize benchmarking, we introduce the Open Catalyst Dense dataset containing nearly 1,000 diverse surfaces and 87,045 unique configurations.

建模原子系统的能量和力是计算化学中的一个基本问题，有可能帮助解决世界上许多最紧迫的问题，包括与能源稀缺和气候变化有关的问题。这些计算传统上是使用密度函数理论进行的，这在计算上非常昂贵。机器学习有可能从天数或小时到秒从天数大幅提高这些计算的效率。我们建议球形通道网络（SCN）对原子能量和力进行建模。 SCN是一个图神经网络，节点代表原子并边缘其相邻原子。原子嵌入是使用球形谐波表示的一组球形函数，称为球形通道。我们证明，通过基于3D边缘方向旋转嵌入式，可以在保持消息的旋转模糊性的同时使用更多信息。虽然均衡性是理想的属性，但我们发现，通过在消息传递和聚合中放松这种约束，可以提高准确性。我们在大规模开放催化剂2020数据集中展示了最新的结果，这些数据集在能源和力量预测中，用于许多任务和指标。

计算催化和机器学习社区在开发用于催化剂发现和设计的机器学习模型方面取得了长足的进步。然而，跨越催化的化学空间的一般机器学习潜力仍然无法触及。一个重大障碍是在广泛的材料中获得访问培训数据的访问。缺乏数据的一类重要材料是氧化物，它抑制模型无法更广泛地研究氧气进化反应和氧化物电催化。为了解决这个问题，我们开发了开放的催化剂2022（OC22）数据集，包括62,521个密度功能理论（DFT）放松（〜9,884,504个单点计算），遍及一系列氧化物材料，覆盖范围，覆盖率和吸附物（ *H， *o， *o， *o， *o， *o， * n， *c， *ooh， *oh， *oh2， *o2， *co）。我们定义广义任务，以预测催化过程中适用的总系统能量，发展几个图神经网络的基线性能（Schnet，Dimenet ++，Forcenet，Spinconv，Painn，Painn，Gemnet-DT，Gemnet-DT，Gemnet-OC），并提供预先定义的数据集分割以建立明确的基准，以实现未来的努力。对于所有任务，我们研究组合数据集是否会带来更好的结果，即使它们包含不同的材料或吸附物。具体而言，我们在Open Catalyst 2020（OC20）数据集和OC22上共同训练模型，或OC22上的微调OC20型号。在最一般的任务中，Gemnet-OC看到通过微调来提高了约32％的能量预测，通过联合训练的力预测提高了约9％。令人惊讶的是，OC20和较小的OC22数据集的联合培训也将OC20的总能量预测提高了约19％。数据集和基线模型是开源的，公众排行榜将遵循，以鼓励社区的持续发展，以了解总能源任务和数据。

近年来，分子模拟数据集的出现是大数量级，更多样化的阶。这些新数据集在复杂性的四个方面有很大差异：1。化学多样性（不同元素的数量），2。系统大小（每个样品原子数），3。数据集大小（数据样本数）和4.域移动（培训和测试集的相似性）。尽管存在这些较大的差异，但在狭窄和狭窄的数据集上的基准仍然是证明分子模拟的图形神经网络（GNN）进展的主要方法，这可能是由于较便宜的训练计算要求所致。这就提出了一个问题 - GNN在小和狭窄的数据集上的进展是否转化为这些更复杂的数据集？这项工作通过首先根据大型开放催化剂2020（OC20）数据集开发Gemnet-OC模型来研究这个问题。 Gemnet-OC的表现优于OC20上的先前最新ART，同时将训练时间减少10倍。然后，我们比较了18个模型组件和超参数选择对多个数据集的性能的影响。我们发现，根据用于做出模型选择的数据集，所得模型将大不相同。为了隔离这种差异的来源，我们研究了OC20数据集的六个子集，这些子集分别测试了上述四个数据集方面的每个数据集。我们发现，OC-2M子集的结果与完整的OC20数据集良好相关，同时训练得更便宜。我们的发现挑战了仅在小型数据集上开发GNN的常见做法，但突出了通过中等尺寸的代表性数据集（例如OC-2M）以及Gemnet-oc等高效模型来实现快速开发周期和可推广结果的方法。我们的代码和预估计的模型权重是开源的。

视频可访问性对于盲人和低愿景用户来说至关重要，以获得教育，就业和娱乐的公平参与。尽管有专业和业余服务和工具，但大多数人类生成的描述都很昂贵且耗时。此外，人生成的描述的速率不能匹配视频产生的速度。为了克服视频可访问性的越来越多的空白，我们开发了两个工具的混合系统到1）自动生成视频的描述，2）提供响应于视频上的用户查询的答案或附加描述。与26例盲和低视力下的混合方法研究结果表明，当两种工具在串联中使用时，我们的系统会显着提高用户理解和享受所选视频的理解和享受。此外，参与者报告说，在呈现自生物的描述与人类修订的自动化描述相关时，没有显着差异。我们的结果表明了对发达系统的热情及其承诺提供对视频的定制访问。我们讨论了当前工作的局限性，并为自动视频描述工具的未来发展提供了建议。

We present a new AI task -Embodied Question Answering (EmbodiedQA) -where an agent is spawned at a random location in a 3D environment and asked a question ('What color is the car?'). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ('orange'). This challenging task requires a range of AI skills -active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.

We propose a technique for producing 'visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable.Our approach -Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say 'dog' in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept.Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fullyconnected layers (e.g. VGG), (2) CNNs used for structured outputs (e.g. captioning), (3) CNNs used in tasks with multimodal inputs (e.g. visual question answering) or reinforcement learning, all without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative vi-

Machine Translation (MT) system generally aims at automatic representation of source language into target language retaining the originality of context using various Natural Language Processing (NLP) techniques. Among various NLP methods, Statistical Machine Translation(SMT). SMT uses probabilistic and statistical techniques to analyze information and conversion. This paper canvasses about the development of bilingual SMT models for translating English to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset, all 15 languages are briefed with a short description related to our experimental need. Further, a detailed analysis of Samanantar and OPUS dataset for model building, along with standard benchmark dataset (Flores-200) for fine-tuning and testing, is done as a part of our experiment. Different preprocessing approaches are proposed in this paper to handle the noise of the dataset. To create the system, MOSES open-source SMT toolkit is explored. Distance reordering is utilized with the aim to understand the rules of grammar and context-dependent adjustments through a phrase reordering categorization framework. In our experiment, the quality of the translation is evaluated using standard metrics such as BLEU, METEOR, and RIBES

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

The Information Bottleneck theory provides a theoretical and computational framework for finding approximate minimum sufficient statistics. Analysis of the Stochastic Gradient Descent (SGD) training of a neural network on a toy problem has shown the existence of two phases, fitting and compression. In this work, we analyze the SGD training process of a Deep Neural Network on MNIST classification and confirm the existence of two phases of SGD training. We also propose a setup for estimating the mutual information for a Deep Neural Network through Variational Inference.