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With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated using two industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow Facility (MFF). Compared to existing methods, Root-KGD not only gives more accurate root cause variable diagnosis results but also provides interpretable fault-related information by locating faults to corresponding physical entities in knowledge graph (such as devices and streams). In addition, combined with its lightweight nature, Root-KGD is more effective in online industrial applications.

Pump-probe experiments with femtosecond time resolution allow to disentangle the electronic dynamics from the lattice response and thus provide valuable insights into the non-equilibrium behavior of correlated materials. In Cr-doped V$_2$O$_3$, a multi-orbital Mott-Hubbard material which has been intensively investigated for decades, time-resolved experiments reported a photo-induced insulator-metal transition leading to a transient metal state with nonthermal properties. Here, we combine non-equilibrium dynamical mean-field theory with realistic first principles modeling to simulate the ultrafast response of this material to a laser excitation. Our calculations reproduce the insulating initial state, with orbital occupations in agreement with experiment, and reveal an ultrafast pump-induced gap filling associated with a charge reshuffling between the $e_g^\pi$ and $a_{1g}$ orbitals. However, in contrast to the related compound VO$_2$, the electronic system thermalizes within a few tens of femtoseconds and we find no evidence for the existence of a metastable nonthermal metal. This suggests that the reported nonthermal behavior in the experiments may be associated with the mismatch between the electronic and lattice temperatures.

The metal-insulator transition of VO$_2$, which in equilibrium is associated with a structural phase transition, has been intensively studied for decades. In particular, it is challenging to disentangle the role of Mott physics from dimerization effects in the insulating phase. Femtosecond time-resolved experiments showed that optical excitations can induce a transient metallic state in the dimerized phase, which is distinct from the known equilibrium phases. In this study, we combine non-equilibrium cluster dynamical mean-field theory with realistic first principles modeling to clarify the nature of this laser-induced metallic state. We show that the doublon-holon production by laser pulses with polarization along the V-V dimers and the subsequent inter-orbital reshuffling of the photo-carriers leads to a population of orbital-mixed states and the filling of the gap. The photo-induced metal state is qualitatively similar to a hot electronic state in the dimerized structure, and does not involve a collapse of the Mott gap.

Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents., Comment: Code available at https://github.com/aiwaves-cn/agents

Sr$_2$IrO$_4$ has attracted a lot of attention due to its structural and electronic similarities to La$_2$CuO$_4$ which is the parent compound of high-T$_c$ superconducting cuprates. It was proposed to be a strong spin-orbit coupled J$_{eff}$ = 1/2 Mott insulator, but the Mott nature of its insulating ground state and the origin of the gap have not been conclusively established. Here, we use ultrafast laser pulses to realize an insulator-metal transition in Sr$_2$IrO$_4$ and probe the resulting dynamics using time- and angle-resolved photoemission spectroscopy. We observe a closing of the gap and the formation of weakly-renormalized electronic bands in the gap region. Comparing these observations to the expected temperature and doping evolution of Mott gaps and Hubbard bands provides clear evidence that the insulating state does not originate from Mott correlations. We instead propose a correlated band insulator picture, where antiferromagnetic correlations play a key role in the opening of the gap. More broadly, our results demonstrate that energy-momentum resolved nonequilibrium dynamics can be used to clarify the nature of equilibrium states in correlated materials.

Recent theoretical studies showed that the electronic structure of 1$T$-TaS$_2$ in the low-temperature commensurate charge density wave phase exhibits a nontrivial interplay between band-insulating and Mott insulating behavior. This has important implications for the interpretation of photo-doping experiments. Here we use nonequilibrium dynamical mean-field theory simulations of a realistic multi-layer structure to clarify the charge carrier dynamics induced by a laser pulse. The solution is propagated up to the picosecond timescale by employing a memory-truncation scheme. While long-lived doublons and holons only exist in the surface state of a specific structure, the disturbance of bonding states in the bilayers which make up the bulk of the system explain the almost instantaneous appearance of in-gap states. Our simulations consistently explain the coexistence of a doublon feature with a prominent ``background" signal in previous time-resolved photoemission experiments, and they suggest strategies for the selective population of the ingap and doublon states by exploiting the sensitivity to the pump polarization and pump frequency., Comment: 12 pages, 9 figures

Using the triangular-lattice extended Hubbard model as a test system, we compare $GW$+EDMFT results for the recently proposed self-consistency scheme with causal auxiliary fields to those obtained from the standard implementation which identifies the impurity Green's functions with the corresponding local lattice Green's functions. Both for short-ranged and long-ranged interactions we find similar results, but the causal scheme yields slightly stronger correlation effects at half-filling. We use the two implementations of $GW$+EDMFT to compute spectral functions and dynamically screened interactions in the parameter regime relevant for 1$T$-TaS$_2$. We address the question whether or not the sample used in a recent photoemission study [Phys. Rev. Lett. 120, 166401 (2018)] was half-filled or hole-doped.

This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain. We explore this task with a clinical corpus consisting of 200 breast cancer follow-up treatment letters in which 16 distinct types of relations are annotated. We experiment with an approach to extracting typed relations called window-bounded co-occurrence (WBC), which uses an adjustable context window around entity mentions of a relevant type, and compare its performance with a more typical intra-sentential co-occurrence baseline. We further introduce a new bag-of-concepts (BoC) approach to feature engineering based on the state-of-the-art word embeddings and word synonyms. We demonstrate the competitiveness of BoC by comparing with methods of higher complexity, and explore its effectiveness on this small dataset., Comment: To appear in Proceedings of the Student Research Workshop at the North American Association for Computational Linguistics (NAACL) meeting 2019

Deep neural networks are vulnerable to adversarial examples. Prior defenses attempted to make deep networks more robust by either changing the network architecture or augmenting the training set with adversarial examples, but both have inherent limitations. Motivated by recent research that shows outliers in the training set have a high negative influence on the trained model, we studied the relationship between model robustness and the quality of the training set. We first show that outliers give the model better generalization ability but weaker robustness. Next, we propose an adversarial example detection framework, in which we design two methods for removing outliers from training set to obtain the sanitized model and then detect adversarial example by calculating the difference of outputs between the original and the sanitized model. We evaluated the framework on both MNIST and SVHN. Based on the difference measured by Kullback-Leibler divergence, we could detect adversarial examples with accuracy between 94.67% to 99.89%.