Quantum Entanglement Phase Transitions and Computational Complexity: Insights from Ising Models

In this paper, we construct 2-dimensional bipartite cluster states and perform single-qubit measurements on the bulk qubits. We explore the entanglement scaling of the unmeasured 1-dimensional boundary state and show that under certain conditions, the boundary state can undergo a volume-law to an area-law entanglement transition driven by variations in the measurement angle. We bridge this boundary state entanglement transition and the measurement-induced phase transition in the non-unitary 1+1-dimensional circuit via the transfer matrix method. We also explore the application of this entanglement transition on the computational complexity problems. Specifically, we establish a relation between the boundary state entanglement transition and the sampling complexity of the bipartite $2$d cluster state, which is directly related to the computational complexity of the corresponding Ising partition function with complex parameters. By examining the boundary state entanglement scaling, we numerically identify the parameter regime for which the $2$d quantum state can be efficiently sampled, which indicates that the Ising partition function can be evaluated efficiently in such a region

Numerical Study on Hydrogen Flow Behavior in Two Compartments with Different Connecting Pipes

Hydrogen accumulation in the containment compartments under severe accidents would result in high concentration, which could lead to hydrogen deflagration or detonation. Therefore, getting detailed hydrogen flow and distribution is a key issue to arrange hydrogen removal equipment in the containment compartments. In this study, hydrogen flow behavior in local compartments has been investigated in two horizontal compartments. The analysis model is built by 3-dimensional CFD code in Cartesian coordinates based on the connection structure of the Advanced Pressurized Water Reactor (PWR) compartments. It consists of two cylindrical vessels, representing the Steam Generator compartment (SG) and Core Makeup Tank compartment (CMT). With standard k-ε turbulence model, the effects of the connecting pipe size and location on hydrogen concentration distribution are investigated. Results show that increasing the diameter of connection pipe (IP) which is located at 800 mm from 150 mm to 300 mm facilitates hydrogen flow between compartments. Decreasing the length of IP which is located at 800 mm from 1000 mm to 500 mm can also facilitate hydrogen flow between compartments. Lower IP is in favor of hydrogen mixing with air in non-source compartment. Higher IP is helpful for hydrogen flow to the non-source term compartment from source term compartment

Evaluating Self-Supervised Learning for Molecular Graph Embeddings

Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels. However, GSSL methods are designed not for optimisation within a specific domain but rather for transferability across a variety of downstream tasks. This broad applicability complicates their evaluation. Addressing this challenge, we present "Molecular Graph Representation Evaluation" (MOLGRAPHEVAL), generating detailed profiles of molecular graph embeddings with interpretable and diversified attributes. MOLGRAPHEVAL offers a suite of probing tasks grouped into three categories: (i) generic graph, (ii) molecular substructure, and (iii) embedding space properties. By leveraging MOLGRAPHEVAL to benchmark existing GSSL methods against both current downstream datasets and our suite of tasks, we uncover significant inconsistencies between inferences drawn solely from existing datasets and those derived from more nuanced probing. These findings suggest that current evaluation methodologies fail to capture the entirety of the landscape.Comment: update result

An Empirical Study of Retrieval-enhanced Graph Neural Networks

Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby their theoretical expressive power is limited to the first-order Weisfeiler-Lehman test (1-WL). An effective approach to this challenge is to explicitly retrieve some annotated examples used to enhance GNN models. While retrieval-enhanced models have been proved to be effective in many language and vision domains, it remains an open question how effective retrieval-enhanced GNNs are when applied to graph datasets. Motivated by this, we want to explore how the retrieval idea can help augment the useful information learned in the graph neural networks, and we design a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. In GRAPHRETRIEVAL, for each input graph, similar graphs together with their ground-true labels are retrieved from an existing database. Thus they can act as a potential enhancement to complete various graph property predictive tasks. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs. Moreover, our empirical study also illustrates that retrieval enhancement is a promising remedy for alleviating the long-tailed label distribution problem.Comment: Accepted by ECAI 202

Ultra-high Performance Liquid Chromatography-Tandem Mass Spectrometry Analysis of Dynamic Changes in Non-volatile Compounds in Green Tea during Storage at Ambient Temperature

Herein, we used an ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) system to investigate changes in the profile of non-volatile metabolites in green tea after different periods (15, 40, 80 and 120 days) of storage at 37 ℃. The color of green tea and tea infusion changed after 40 days of storage at 37 ℃ when compared with the control (stored at −80 ℃). During 40 days of storage, the content of catechins did not significantly change, but the content of free amino acids significantly decreased. A total of 684 non-volatile components were identified, 77 of which were found to be characteristic differential metabolites, the major ones being polyphenols, lipids and organic acids. The content of octadecadien-6-ynoic acid, an umami-related compound, significantly decreased after 40 days and was undetected after 80 days. The content of theaflavine-3-catechin, related to the color and taste of tea infusion, significantly increased with storage time. Similarly, the content of flavone glycoside increased with storage time. Moreover, the content of lipids such as triacylglycerols (TAGs) and diacylglycerol (DAGs) changed significantly after 15 days, and the content of free fatty acids increased with storage time. The findings from this study showed that the contents of lipids and flavonoids in green tea significantly changed during storage, which played an important role in the quality deterioration of green tea

Diffusion Boundary Condition at Surface Steps

This Communication reports a geometrical factor that is necessary in the diffusion boundary condition across surface steps. Specifically, this factor relates adatom concentration to its spatial gradient at a surface step, and it describes the fraction of jump attempts that cross the step. In this Communication, the authors show that the factor is 1/\Pi using theoretical formulation and further verify the formulation using numerical simulations for triangular, square, and hexagonal surface lattices

Does Full Waveform Inversion Benefit from Big Data?

This paper investigates the impact of big data on deep learning models for full waveform inversion (FWI). While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained on OpenFWI, a collection of large-scale, multi-structural datasets published recently. Particularly, we train and evaluate the FWI models on a combination of 10 2D subsets in OpenFWI that contain 470K data pairs in total. Our experiments demonstrate that larger datasets lead to better performance and generalization of deep learning models for FWI. We further demonstrate that model capacity needs to scale in accordance with data size for optimal improvement

Can proline dehydrogenase—a key enzyme involved in proline metabolism—be a novel target for cancer therapy?

Emerging evidence suggests that proline metabolism is important for regulating the survival and death of different types of cancer cells. Proline dehydrogenase (PRODH), an enzyme catalyzing proline catabolism, and the degradation products of proline by PRODH, such as ATP and ROS, are known to play critical roles in cancer progression. Notably, the role of PRODH in cancer is still complicated and unclear, and primarily depends on the cancer type and tumor microenvironment. For instance, PRODH induces apoptosis and senescence through ROS signaling in different types of cancers, while as a protumor factor, PRODH promotes malignant phenotypes of certain tumors under stresses such as hypoxia. In order to assess whether PRODH can serve as a novel target for cancer therapy, we will provide an overview of the biological functions of PRODH and its double-edged role in cancer in this article

EFWI\mathbf{\mathbb{E}^{FWI}}: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/Comment: 20 pages, 11 figure