我们提供了证据表明,学到的密度功能理论(``dft')的力场已准备好进行基态催化剂发现。我们的关键发现是,尽管预测的力与地面真相有很大差异,但使用从超过50 \%的评估系统中使用RPBE功能的能量与使用RPBE功能相似或较低能量的力量的力量与使用RPBE功能相似或较低的力量放松。这具有令人惊讶的含义,即学习的潜力可能已经准备好在挑战性的催化系统中替换DFT,例如在Open Catalyst 2020数据集中发现的电位。此外,我们表明,在局部谐波能量表面上具有与目标DFT能量相同的局部谐波能量表面训练的力场也能够在50 \%的情况下找到较低或相似的能量结构。与在真实能量和力量训练的标准模型相比,这种``简易电位''的收敛步骤更少,这进一步加速了计算。它的成功说明了一个关键:即使模型具有高力误差,学到的电位也可以定位能量最小值。结构优化的主要要求仅仅是学到的电位具有正确的最小值。由于学到的电位与系统大小的速度快速且尺寸为线性,因此我们的结果开辟了快速找到大型系统基础状态的可能性。
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具有经典数字求解器的湍流模拟需要非常高分辨率的网格来准确地解决动态。在这里,我们以低空间和时间分辨率培训学习模拟器,以捕获高分辨率产生的湍流动态。我们表明我们所提出的模型可以比各种科学相关指标的相同低分辨率的经典数字求解器更准确地模拟湍流动态。我们的模型从数据训练结束到底,能够以低分辨率学习一系列挑战性的混乱和动态动态,包括最先进的雅典娜++发动机产生的轨迹。我们表明,我们的更简单,通用体系结构优于来自所学到的湍流模拟文献的各种专业的湍流特异性架构。一般来说,我们看到学习的模拟器产生不稳定的轨迹;但是,我们表明调整训练噪音和时间下采样解决了这个问题。我们还发现,虽然超出培训分配的泛化是学习模型,训练噪声,卷积架构以及增加损失约束的挑战。广泛地,我们得出的结论是,我们所知的模拟器优于传统的求解器在较粗糙的网格上运行,并强调简单的设计选择可以提供稳定性和鲁棒的泛化。
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培训大型神经网络架构的快速增长带来了对划分策略的需要,例如通过使用数据,模型或管道并行性。通过程序基元越来越多地支持这些方法,但识别有效的分区策略需要昂贵的实验和专业知识。我们介绍了自动分区器的原型,它无缝集成到现有的编译器和现有用户工作流中。我们的分区使SPMD风格的并行性能够包含数据并行性和参数/激活分片。通过归纳策略和在平台独立的分区IR中搜索的组合,Automap可以恢复用于变压器层的专家分区策略,如Megatron分片。
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Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another. Our framework-which we term "Graph Network-based Simulators" (GNS)-represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time. Our model was robust to hyperparameter choices across various evaluation metrics: the main determinants of long-term performance were the number of message-passing steps, and mitigating the accumulation of error by corrupting the training data with noise. Our GNS framework advances the state-of-the-art in learned physical simulation, and holds promise for solving a wide range of complex forward and inverse problems.
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This paper proposes a graph-based approach to representing spatio-temporal trajectory data that allows an effective visualization and characterization of city-wide traffic dynamics. With the advance of sensor, mobile, and Internet of Things (IoT) technologies, vehicle and passenger trajectories are being increasingly collected on a massive scale and are becoming a critical source of insight into traffic pattern and traveller behaviour. To leverage such trajectory data to better understand traffic dynamics in a large-scale urban network, this study develops a trajectory-based network traffic analysis method that converts individual trajectory data into a sequence of graphs that evolve over time (known as dynamic graphs or time-evolving graphs) and analyses network-wide traffic patterns in terms of a compact and informative graph-representation of aggregated traffic flows. First, we partition the entire network into a set of cells based on the spatial distribution of data points in individual trajectories, where the cells represent spatial regions between which aggregated traffic flows can be measured. Next, dynamic flows of moving objects are represented as a time-evolving graph, where regions are graph vertices and flows between them are treated as weighted directed edges. Given a fixed set of vertices, edges can be inserted or removed at every time step depending on the presence of traffic flows between two regions at a given time window. Once a dynamic graph is built, we apply graph mining algorithms to detect change-points in time, which represent time points where the graph exhibits significant changes in its overall structure and, thus, correspond to change-points in city-wide mobility pattern throughout the day (e.g., global transition points between peak and off-peak periods).
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Incivility remains a major challenge for online discussion platforms, to such an extent that even conversations between well-intentioned users can often derail into uncivil behavior. Traditionally, platforms have relied on moderators to -- with or without algorithmic assistance -- take corrective actions such as removing comments or banning users. In this work we propose a complementary paradigm that directly empowers users by proactively enhancing their awareness about existing tension in the conversation they are engaging in and actively guides them as they are drafting their replies to avoid further escalation. As a proof of concept for this paradigm, we design an algorithmic tool that provides such proactive information directly to users, and conduct a user study in a popular discussion platform. Through a mixed methods approach combining surveys with a randomized controlled experiment, we uncover qualitative and quantitative insights regarding how the participants utilize and react to this information. Most participants report finding this proactive paradigm valuable, noting that it helps them to identify tension that they may have otherwise missed and prompts them to further reflect on their own replies and to revise them. These effects are corroborated by a comparison of how the participants draft their reply when our tool warns them that their conversation is at risk of derailing into uncivil behavior versus in a control condition where the tool is disabled. These preliminary findings highlight the potential of this user-centered paradigm and point to concrete directions for future implementations.
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Labeling large image datasets with attributes such as facial age or object type is tedious and sometimes infeasible. Supervised machine learning methods provide a highly accurate solution, but require manual labels which are often unavailable. Zero-shot models (e.g., CLIP) do not require manual labels but are not as accurate as supervised ones, particularly when the attribute is numeric. We propose a new approach, CLIPPR (CLIP with Priors), which adapts zero-shot models for regression and classification on unlabelled datasets. Our method does not use any annotated images. Instead, we assume a prior over the label distribution in the dataset. We then train an adapter network on top of CLIP under two competing objectives: i) minimal change of predictions from the original CLIP model ii) minimal distance between predicted and prior distribution of labels. Additionally, we present a novel approach for selecting prompts for Vision & Language models using a distributional prior. Our method is effective and presents a significant improvement over the original model. We demonstrate an improvement of 28% in mean absolute error on the UTK age regression task. We also present promising results for classification benchmarks, improving the classification accuracy on the ImageNet dataset by 2.83%, without using any labels.
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Sequence models based on linear state spaces (SSMs) have recently emerged as a promising choice of architecture for modeling long range dependencies across various modalities. However, they invariably rely on discretization of a continuous state space, which complicates their presentation and understanding. In this work, we dispose of the discretization step, and propose a model based on vanilla Diagonal Linear RNNs ($\mathrm{DLR}$). We empirically show that $\mathrm{DLR}$ is as performant as previously-proposed SSMs in the presence of strong supervision, despite being conceptually much simpler. Moreover, we characterize the expressivity of SSMs (including $\mathrm{DLR}$) and attention-based models via a suite of $13$ synthetic sequence-to-sequence tasks involving interactions over tens of thousands of tokens, ranging from simple operations, such as shifting an input sequence, to detecting co-dependent visual features over long spatial ranges in flattened images. We find that while SSMs report near-perfect performance on tasks that can be modeled via $\textit{few}$ convolutional kernels, they struggle on tasks requiring $\textit{many}$ such kernels and especially when the desired sequence manipulation is $\textit{context-dependent}$. For example, $\mathrm{DLR}$ learns to perfectly shift a $0.5M$-long input by an arbitrary number of positions but fails when the shift size depends on context. Despite these limitations, $\mathrm{DLR}$ reaches high performance on two higher-order reasoning tasks $\mathrm{ListOpsSubTrees}$ and $\mathrm{PathfinderSegmentation}\text{-}\mathrm{256}$ with input lengths $8K$ and $65K$ respectively, and gives encouraging performance on $\mathrm{PathfinderSegmentation}\text{-}\mathrm{512}$ with input length $262K$ for which attention is not a viable choice.
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Emotions play an important role in interpersonal interactions and social conflict, yet their function in the development of controversy and disagreement in online conversations has not been explored. To address this gap, we study controversy on Reddit, a popular network of online discussion forums. We collect discussions from a wide variety of topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc. Our study has three main findings. First, controversial comments express more anger and less admiration, joy and optimism than non-controversial comments. Second, controversial comments affect emotions of downstream comments in a discussion, usually resulting in long-term increase in anger and a decrease in positive emotions, although the magnitude and direction of emotional change depends on the forum. Finally, we show that emotions help better predict which comments will become controversial. Understanding emotional dynamics of online discussions can help communities to better manage conversations.
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Practitioners prune neural networks for efficiency gains and generalization improvements, but few scrutinize the factors determining the prunability of a neural network the maximum fraction of weights that pruning can remove without compromising the model's test accuracy. In this work, we study the properties of input data that may contribute to the prunability of a neural network. For high dimensional input data such as images, text, and audio, the manifold hypothesis suggests that these high dimensional inputs approximately lie on or near a significantly lower dimensional manifold. Prior work demonstrates that the underlying low dimensional structure of the input data may affect the sample efficiency of learning. In this paper, we investigate whether the low dimensional structure of the input data affects the prunability of a neural network.
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