The strength of materials, like many problems in the natural sciences, spans multiple length and time scales, and the solution has to balance accuracy and performance. Peierls stress is one of the central concepts in crystal plasticity that measures the strength through the resistance of a dislocation to plastic flow. The determination of Peierls stress involves a multiscale nature depending on both elastic lattice responses and the energy landscape of crystal slips. Material screening by strength via the Peierls stress from first-principles calculations is computationally intractable for the nonlocal characteristics of dislocations, and not included in the state-of-the-art computational material databases. In this work, we propose a physics-transfer framework to learn the physics of crystal plasticity from empirical atomistic simulations and then predict the Peierls stress from chemically accurate density functional theory-based calculations of material parameters. Notably, the strengths of single-crystalline metals can be predicted from a few single-point calculations for the deformed lattice and on the {\gamma} surface, allowing efficient, high-throughput screening for material discovery. Uncertainty quantification is carried out to assess the accuracy of models and sources of errors, showing reduced physical and system uncertainties in the predictions by elevating the fidelity of training models. This physics-transfer framework can be generalized to other problems facing the accuracy-performance dilemma, by harnessing the hierarchy of physics in the multiscale models of materials science.
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Qini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms, that quantifies the value of optimally selecting among both units and treatment arms at different budget levels. We develop an efficient algorithm for computing these curves and propose bootstrap-based confidence intervals that are exact in large samples for any point on the curve. These confidence intervals can be used to conduct hypothesis tests comparing the value of treatment targeting using an optimal combination of arms with using just a subset of arms, or with a non-targeting assignment rule ignoring covariates, at different budget levels. We demonstrate the statistical performance in a simulation experiment and an application to treatment targeting for election turnout.
The generalized Gauss-Newton (GGN) optimization method incorporates curvature estimates into its solution steps, and provides a good approximation to the Newton method for large-scale optimization problems. GGN has been found particularly interesting for practical training of deep neural networks, not only for its impressive convergence speed, but also for its close relation with neural tangent kernel regression, which is central to recent studies that aim to understand the optimization and generalization properties of neural networks. This work studies a GGN method for optimizing a two-layer neural network with explicit regularization. In particular, we consider a class of generalized self-concordant (GSC) functions that provide smooth approximations to commonly-used penalty terms in the objective function of the optimization problem. This approach provides an adaptive learning rate selection technique that requires little to no tuning for optimal performance. We study the convergence of the two-layer neural network, considered to be overparameterized, in the optimization loop of the resulting GGN method for a given scaling of the network parameters. Our numerical experiments highlight specific aspects of GSC regularization that help to improve generalization of the optimized neural network. The code to reproduce the experimental results is available at //github.com/adeyemiadeoye/ggn-score-nn.
Language models that can learn a task at inference time, called in-context learning (ICL), show increasing promise in natural language inference tasks. In ICL, a model user constructs a prompt to describe a task with a natural language instruction and zero or more examples, called demonstrations. The prompt is then input to the language model to generate a completion. In this paper, we apply ICL to the design and evaluation of satisfaction arguments, which describe how a requirement is satisfied by a system specification and associated domain knowledge. The approach builds on three prompt design patterns, including augmented generation, prompt tuning, and chain-of-thought prompting, and is evaluated on a privacy problem to check whether a mobile app scenario and associated design description satisfies eight consent requirements from the EU General Data Protection Regulation (GDPR). The overall results show that GPT-4 can be used to verify requirements satisfaction with 96.7% accuracy and dissatisfaction with 93.2% accuracy. Inverting the requirement improves verification of dissatisfaction to 97.2%. Chain-of-thought prompting improves overall GPT-3.5 performance by 9.0% accuracy. We discuss the trade-offs among templates, models and prompt strategies and provide a detailed analysis of the generated specifications to inform how the approach can be applied in practice.
As assembly tasks grow in complexity, collaboration among multiple robots becomes essential for task completion. However, centralized task planning has become inadequate for adapting to the increasing intelligence and versatility of robots, along with rising customized orders. There is a need for efficient and automated planning mechanisms capable of coordinating diverse robots for collaborative assembly. To this end, we propose a Stackelberg game-theoretic learning approach. By leveraging Stackelberg games, we characterize robot collaboration through leader-follower interaction to enhance strategy seeking and ensure task completion. To enhance applicability across tasks, we introduce a novel multi-agent learning algorithm: Stackelberg double deep Q-learning, which facilitates automated assembly strategy seeking and multi-robot coordination. Our approach is validated through simulated assembly tasks. Comparison with three alternative multi-agent learning methods shows that our approach achieves the shortest task completion time for tasks. Furthermore, our approach exhibits robustness against both accidental and deliberate environmental perturbations.
Specifications for modular program verifiers are expressed as constraints on program states (e.g. preconditions) and relations on program states (e.g. postconditions). For programs whose domain is managing resources of any kind (e.g. cryptocurrencies), such state-based specifications must make explicit properties that a human would implicitly understand for free. For example, it's clear that depositing into your bank account will not change other balances, but classically this must be stated as a frame condition. As a result, classical specifications for resource-manipulating programs quickly become verbose and difficult to interpret, write and debug. In this paper, we present a novel methodology that extends a modular program verifier to support user-defined first-class resources, allowing resource-related operations and properties to be expressed directly and eliminating the need to reify implicit knowledge in the specifications. We implement our methodology as an extension of the program verifier Prusti, and use it to verify real-world smart contracts and a key part of a blockchain application. Our evaluation demonstrates that specifications written with our methodology are more concise and substantially simpler than specifications written purely in terms of program states.
Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: training a separate neural network for each individual task. Many real-world problems, however, call for a multi-modal approach and, therefore, for multi-tasking models. Multi-task learning (MTL) aims to leverage useful information across tasks to improve the generalization capability of a model. This thesis is concerned with multi-task learning in the context of computer vision. First, we review existing approaches for MTL. Next, we propose several methods that tackle important aspects of multi-task learning. The proposed methods are evaluated on various benchmarks. The results show several advances in the state-of-the-art of multi-task learning. Finally, we discuss several possibilities for future work.
Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio. Machine perception models, in stark contrast, are typically modality-specific and optimised for unimodal benchmarks, and hence late-stage fusion of final representations or predictions from each modality (`late-fusion') is still a dominant paradigm for multimodal video classification. Instead, we introduce a novel transformer based architecture that uses `fusion bottlenecks' for modality fusion at multiple layers. Compared to traditional pairwise self-attention, our model forces information between different modalities to pass through a small number of bottleneck latents, requiring the model to collate and condense the most relevant information in each modality and only share what is necessary. We find that such a strategy improves fusion performance, at the same time reducing computational cost. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks including Audioset, Epic-Kitchens and VGGSound. All code and models will be released.
Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequential recommendation problem, intending to predict the next items that the user might be interacted with. Recent works usually give an overall embedding from a user's behavior sequence. However, a unified user embedding cannot reflect the user's multiple interests during a period. In this paper, we propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our multi-interest module captures multiple interests from user behavior sequences, which can be exploited for retrieving candidate items from the large-scale item pool. These items are then fed into an aggregation module to obtain the overall recommendation. The aggregation module leverages a controllable factor to balance the recommendation accuracy and diversity. We conduct experiments for the sequential recommendation on two real-world datasets, Amazon and Taobao. Experimental results demonstrate that our framework achieves significant improvements over state-of-the-art models. Our framework has also been successfully deployed on the offline Alibaba distributed cloud platform.
Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. However, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. To address this challenge, we propose a novel knowledge graph embedding model---namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE)---which maps entities into the polar coordinate system. HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy. Specifically, the radial coordinate aims to model entities at different levels of the hierarchy, and entities with smaller radii are expected to be at higher levels; the angular coordinate aims to distinguish entities at the same level of the hierarchy, and these entities are expected to have roughly the same radii but different angles. Experiments demonstrate that HAKE can effectively model the semantic hierarchies in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore to represent hierarchical knowledge, such as the similarities and dissimilarities of entities in one domain. We proposed to learn a Domain Representations over existing knowledge graph embedding models, such that entities that have similar attributes are organized into the same domain. Such hierarchical knowledge of domains can give further evidence in link prediction. Experimental results show that domain embeddings give a significant improvement over the most recent state-of-art baseline knowledge graph embedding models.
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