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Jia-Meng Shao,&ast; Bing Shen,&ast; Zhi-Xiang Zhou, Lucia D’Angelo, Shea Michaela James, Jia-Feng Lin, Cheng Zheng Department of Cardiology, the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Cheng Zheng; Jia-Feng Lin, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, East Section of Wenzhou Avenue, No. 1111, Wenzhou, Zhejiang Province, 325000, People’s Republic of China, Tel +86-13587688177 ; +86-13806685498, Email zhengcheng_wzmufey@163.com; linjiafeng_wzmcfey@163.comBackground: The role of total bilirubin (TBIL) in cardiovascular disease has been increasingly recognized in recent decades. Studies have shown a correlation between total bilirubin levels and the prognosis of patients after heart surgery. This study aimed to investigate the clinical significance of bilirubin elevation in persistent atrial fibrillation (PAF) patients who received radiofrequency catheter ablation (RFCA).Methods and Results: A total of 184 patients with PAF who received RFCA were retrospectively studied. Laboratory examinations and demographic data were analyzed to identify independent predictors of TBIL elevation. The relationship between TBIL and prognosis was further investigated. Our results indicated that TBIL increased significantly after RFCA. Multiple linear regression analysis showed that TBIL elevation owned a negative correlation with the percentile of low voltage areas (LVAs) in left atria (β=− 0.490, P< 0.001). In contrast, a positive correlation was observed with the white blood cell (WBC) ratio (β=0.153, P=0.042) and left atrial diameter (LAD) (β=0.232, P=0.025). It was found that postoperative TBIL levels increased and then gradually decreased to baseline within 5 days without intervention. The bilirubin ratio < 1.211 indicated the possibility of

Limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the widefield imaging modalities used for optically cleared or thin specimens. We recently introduced “De-scattering with Excitation Patterning” or “DEEP” as a widefield alternative to point-scanning geometries. Using patterned multiphoton excitation, DEEP encodes spatial information inside tissue before scattering. However, to de-scatter at typical depths, hundreds of such patterned excitations were needed. In this work, we present DEEP2, a deep learning-based model that can de-scatter images from just tens of patterned excitations instead of hundreds. Consequently, we improve DEEP’s throughput by almost an order of magnitude. We demonstrate our method in multiple numerical and experimental imaging studies, including in vivo cortical vasculature imaging up to 4 scattering lengths deep in live mice.

We introduce neural lithography to address the ‘design-to-manufacturing’ gap in computational optics. Computational optics with large design degrees of freedom enable advanced functionalities and performance beyond traditional optics. However, the existing design approaches often overlook the numerical modeling of the manufacturing process, which can result in significant performance deviation between the design and the fabricated optics. To bridge this gap, we, for the first time, propose a fully differentiable design framework that integrates a pre-trained photolithography simulator into the model-based optical design loop. Leveraging a blend of physics-informed modeling and data-driven training using experimentally collected datasets, our photolithography simulator serves as a regularizer on fabrication feasibility during design, compensating for structure discrepancies introduced in the lithography process. We demonstrate the effectiveness of our approach through two typical tasks in computational optics, where we design and fabricate a holographic optical element (HOE) and a multi-level diffractive lens (MDL) using a two-photon lithography system, showcasing improved optical performance on the task-specific metrics. The source code for this work is available on the project page: https://neural-litho.github.io.

Graded index (GRIN) lens endoscopy has broadly benefited biomedical microscopic imaging by enabling accessibility to sites not reachable by traditional benchtop microscopes. It is a long-held notion that GRIN lenses can only be used as rigid probes, which may limit their potential for certain applications. Here, we describe bendable and long-range GRIN microimaging probes for a variety of potential micro-endoscopic biomedical applications. Using a two-photon fluorescence imaging system, we have experimentally demonstrated the feasibility of three-dimensional imaging through a 500-µm-diameter and ∼11 cm long GRIN lens subject to a cantilever beam-like deflection with a minimum bend radius of ∼25 cm. Bend-induced perturbation to the field of view and resolution has also been investigated quantitatively. Our development alters the conventional notion of GRIN lenses and enables a range of innovative applications. For example, the demonstrated flexibility is highly desirable for implementation into current and emerging minimally invasive clinical procedures, including a pioneering microdevice for high-throughput cancer drug selection.

El objetivo de este proyecto se basa en el desarrollo de las funciones de inicio de sesión y registro en una aplicación Android, implementadas en la plataforma oficial Android Studio. El objetivo primordial de esta investigación es desarrollar, diseñar e implementar un sistema que permita a los usuarios poder acceder y registrarse de manera segura y eficiente en la aplicación, aprovechando así, los beneficios proporcionados por Android Studio. En ella, se implementan las medidas necesarias de seguridad como pueden ser el cifrado de contraseñas y la autenticación de usuario. Asimismo, se ha integrado el inicio de sesión mediante terceros, en este caso, la plataforma de Google. Esta plataforma ha permitido a los usuarios poder utilizar sus propias credenciales registradas en esta plataforma para poder acceder a la aplicación, agilizando así el proceso de registro e inicio de sesión. El desarrollo de estos procesos se realizó siguiendo las mejoras de Android Studio, aprovechando los recursos y herramientas facilitados. Para ello, se han realizado pruebas extensas para garantizar la compatibilidad con los sistemas operativos de Android, donde se asegura la seguridad y la eficacia de las funcionalidades. Este proyecto se ha desarrollado en el lenguaje de programación Java para crear un sistema de inicio de sesión y registro en una aplicación Android, integrando la opción de poder iniciar sesión en plataformas externas, subrayando la capacidad de adaptar las preferencias del usuario y asegurando la seguridad de los datos personales. Abstract: The goal of this project is to develop the login and registration functions in an Android application, implemented on the official Android Studio platform. The primary goal of this research is to develop, design, and implement a system that allows users to securely and efficiently access and register in the application, thus leveraging the benefits provided by Android Studio. In this project, necessary security measures such as passw

The topological aspects of Einstein gravity suggest that topological invariance could be a more profound principle in understanding quantum gravity. In this work, we explore a topological supergravity action that initially describes a universe without Riemann curvature, which seems trivial. However, we made a surprising discovery by introducing a small deformation parameter $\lambda$, which can be regarded as an AdS generalization of supersymmetry (SUSY). We find that the deformed topological quantum field theory (TQFT) becomes unstable at low energy, resulting in the emergence of a classical metric, whose dynamics are controlled by the Einstein equation. Our findings suggest that a quantum theory of gravity could be governed by a UV fixed point of a SUSY TQFT, and classical spacetime ceases to exist beyond the Planck scale., Comment: 12 pages, no figure

The novel concept of entanglement renormalization and its corresponding tensor network renormalization technique have been highly successful in developing a controlled real space renormalization group (RG) scheme. Numerically approximate fixed-point (FP) tensors are widely used to extract the conformal data of the underlying conformal field theory (CFT) describing critical phenomena. In this paper, we present an explicit analytical construction of the FP tensor for 2D rational CFT. We define it as a correlation function between the "boundary-changing operators" on triangles. Our construction fully captures all the real-space RG conditions. We also provide a concrete example using the Ising model to compute the scaling dimensions explicitly based on the corresponding FP tensor. Interestingly, our construction of FP tensors is closely related to a strange correlator, where the holographic picture naturally emerges. Our results also open a new door towards understanding CFT in higher dimensions., Comment: 12 pages, 13 figures, 4 tables; typos corrected, references added, more data included in Appendix

Motivated by the novel phenomena observed in the layered material $\rm SrCu_2(BO_3)_2$, the Shastry-Sutherland model (SSM) has been extensively studied as the minimal model for $\rm SrCu_2(BO_3)_2$. However, the nature of its quantum phase transition from the plaquette valence-bond solid (PVBS) to antiferromagnetic (AFM) phase is under fierce debate, posing a challenge to understand the underlying quantum criticality. Via the state-of-the-art tensor network simulations, we study the ground state of the SSM on large-scale size up to $20 \times 20$ sites. We identify the continuous transition nature accompanied by an emergent O(4) symmetry between the PVBS and AFM phase, which strongly suggests a deconfined quantum critical point (DQCP). Furthermore, we map out the phase diagram of an extended SSM that can be continuously tuned to the SSM, which demonstrates the same DQCP phenomena along a whole critical line. Our results indicate a compelling scenario for understanding the origin of the proposed proximate DQCP in recent experiments of $\rm SrCu_2(BO_3)_2$., Comment: 5+6 pages; 4+5 figures; 3 tables

[Background] Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown. [Result] We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age. [Conclusion] DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.

Spatial cables in suspension bridges may twist between two adjacent suspension points due to transverse tension. To determine the spatial torsion angle of the cables, a scale-reduced suspender bridge model was fabricated and tested in this study. The cable torsion angles of the bridge model under different loading cases were obtained and presented. Theoretical methods (e.g., the coordinate method and the angle method) to calculate the spatial cable torsion angle in the suspender bridge were developed by analyzing the deforming characteristics of the suspender cables. The feasibility and predictive accuracy of the proposed methods were assessed using the experimental results. Results show that the proposed coordinate and angle methods produced similar torsion angles for the main cables, showing a maximum relative error of 10.4 %. Compared to the coordinate method's complex data transformation and measurement procedures, the angle method is more convenient, efficient, and easier to calculate the torsion angle of the main cable. Despite the capability of calculating the spatial cable torsion angles, the coordinate method also exhibits advanced capability in determining the geometric curves of the main cable under different spatial angles.

Space-based gravitational wave detectors such as TianQin, LISA, and TaiJi have the potential to outperform themselves through joint observation. To achieve this, it is desirable to practice joint data analysis in advance on simulated data that encodes the intrinsic correlation among the signals found in different detectors that operate simultaneously. In this paper, we introduce \texttt{GWSpace}, a package that can simulate the joint detection data from TianQin, LISA, and TaiJi. The software is not a groundbreaking work that starts from scratch. Rather, we use as many open-source resources as possible, tailoring them to the needs of simulating the multi-mission science data and putting everything into a ready-to-go and easy-to-use package. We shall describe the main components, the construction, and a few examples of application of the package. A common coordinate system, namely the Solar System Barycenter (SSB) coordinate system, is utilized to calculate spacecraft orbits for all three missions. The paper also provides a brief derivation of the detection process and outlines the general waveform of sources detectable by these detectors., Comment: 24 pages, 13 figures, GWSpace will be uploaded at https://github.com/TianQinSYSU/GWSpace

TianQin and LISA are space-based laser interferometer gravitational wave (GW) detectors planned to be launched in the mid-2030s. Both detectors will detect low-frequency GWs around $10^{-2}\,{\rm Hz}$, however, TianQin is more sensitive to frequencies above this common sweet-spot while LISA is more sensitive to frequencies below $10^{-2}\,{\rm Hz}$. Therefore, TianQin and LISA will be able to detect the same sources but with different accuracy depending on the source and its parameters. We consider some of the most important astrophysical sources -- massive black hole binaries, stellar-mass black hole binaries, double white dwarfs, extreme mass ratio inspirals, light and heavy intermediate mass ratio inspirals, as well as the stochastic gravitational background of astrophysical origin -- that TianQin and LISA will be able to detect. For each of these sources, we analyze how far they can be detected (detection distance) and how well their parameters can be measured (detection accuracy) using a Fisher Matrix analysis. We compare the results obtained by the three detection scenarios (TianQin alone, LISA alone, and joint detection by LISA and TianQin) highlighting the gains from joint detection as well as the contribution of TianQin and LISA to a combined study of astrophysical sources. In particular, we consider the different orientations, lifetimes, and duty cycles of the two detectors to explore how they can give a more complete picture when working together., Comment: 49 pages, 63 figures

This paper explores the detection capability of space-borne detectors to the anisotropic stochastic gravitational-wave background (SGWB) without relying on the low-frequency approximation. To assess the detection performance, we calculate the power-law integrated sensitivity (PLIS) curve. Our results demonstrate that a single detector has limited capabilities in detecting multipole moments beyond the monopole ($l=0$), quadrupole ($l=2$), and hexadecapole ($l=4$). However, when multiple detectors are combined, the presence of multiple pointing directions and the separation between detectors significantly enhance the detection capabilities for the other multipole moments. For instance, when considering the dipole ($l=1$), combining TianQin with TianQin II and LISA with TianQin significantly improves the detection sensitivity by 2-3 orders of magnitude, compared with using a single TianQin and a single LISA, respectively., Comment: 12 pages, 8 figures

We introduce neural lithography to address the 'design-to-manufacturing' gap in computational optics. Computational optics with large design degrees of freedom enable advanced functionalities and performance beyond traditional optics. However, the existing design approaches often overlook the numerical modeling of the manufacturing process, which can result in significant performance deviation between the design and the fabricated optics. To bridge this gap, we, for the first time, propose a fully differentiable design framework that integrates a pre-trained photolithography simulator into the model-based optical design loop. Leveraging a blend of physics-informed modeling and data-driven training using experimentally collected datasets, our photolithography simulator serves as a regularizer on fabrication feasibility during design, compensating for structure discrepancies introduced in the lithography process. We demonstrate the effectiveness of our approach through two typical tasks in computational optics, where we design and fabricate a holographic optical element (HOE) and a multi-level diffractive lens (MDL) using a two-photon lithography system, showcasing improved optical performance on the task-specific metrics., Comment: The paper, titled "Close the Design-to-Manufacturing Gap in Computational Optics with a 'Real2Sim' Learned Two-Photon Neural Lithography Simulator," has been accepted for presentation at SIGGRAPH Asia 2023. This version offers a more comprehensive and accessible read. Project page: https://neural-litho.github.io

Manually reading and logging gauge data is time inefficient, and the effort increases according to the number of gauges available. We present a computer vision pipeline that automates the reading of analog gauges. We propose a two-stage CNN pipeline that identifies the key structural components of an analog gauge and outputs an angular reading. To facilitate the training of our approach, a synthetic dataset is generated thus obtaining a set of realistic analog gauges with their corresponding annotation. To validate our proposal, an additional real-world dataset was collected with 4.813 manually curated images. When compared against state-of-the-art methodologies, our method shows a significant improvement of 4.55 in the average error, which is a 52% relative improvement. The resources for this project will be made available at: https://github.com/fuankarion/automatic-gauge-reading.

Nonlinear optical microscopy has enabled in vivo deep tissue imaging on the millimeter scale. A key unmet challenge is its limited throughput especially compared to rapid wide-field modalities that are used ubiquitously in thin specimens. Wide-field imaging methods in tissue specimens have found successes in optically cleared tissues and at shallower depths, but the scattering of emission photons in thick turbid samples severely degrades image quality at the camera. To address this challenge, we introduce a novel technique called De-scattering with Excitation Patterning or "DEEP," which uses patterned nonlinear excitation followed by computational imaging-assisted wide-field detection. Multiphoton temporal focusing allows high-resolution excitation patterns to be projected deep inside specimen at multiple scattering lengths due to the use of long wavelength light. Computational reconstruction allows high-resolution structural features to be reconstructed from tens to hundreds of DEEP images instead of millions of point-scanning measurements.

Nonlinear optical microscopy has enabled in vivo deep tissue imaging on the millimeter scale. A key unmet challenge is its limited throughput especially compared to rapid wide-field modalities that are used ubiquitously in thin specimens. Wide-field imaging methods in tissue specimens have found successes in optically cleared tissues and at shallower depths, but the scattering of emission photons in thick turbid samples severely degrades image quality at the camera. To address this challenge, we introduce a novel technique called De-scattering with Excitation Patterning or "DEEP," which uses patterned nonlinear excitation followed by computational imaging-assisted wide-field detection. Multiphoton temporal focusing allows high-resolution excitation patterns to be projected deep inside specimen at multiple scattering lengths due to the use of long wavelength light. Computational reconstruction allows high-resolution structural features to be reconstructed from tens to hundreds of DEEP images instead of millions of point-scanning measurements.

ObjectivesCommunity popular opinion leaders have played a critical role in HIV prevention interventions. However, it is often difficult to identify these 'HIV influencers' who are qualified and willing to promote HIV campaigns, especially online, because social media influencers change frequently. We sought to use an iterative deep learning framework to automatically discover HIV-related online social influencers.Design and methodOut of 1.15 million Twitter users' data from March 2018 to March 2020, we extracted tweets from 1099 Twitter users who had mentioned the keywords 'HIV' or 'AIDS'. Two Twitter users determined to be 'online HIV influencers' based on their conversation topics and engagement were hand-picked by domain experts and used as a seed training dataset. We modelled social influence and discovered new potential influencers based on these seeds using a graph neural network model. We tested the model's precision and recall compared with other baseline model approaches. We validated the results through manual verification.ResultsThe model identified 23 new (manually verified) HIV-related influencers, including health and research organizations and local HIV advocates across the United States. Our proposed model achieved the highest accuracy/recall, with an average improvement of 38.5% over the other baseline models.ConclusionResults suggest that iterative deep learning models can be used to automatically identify new and changing key HIV-related influencers online. We discuss the implications and potential of HIV researchers/departments applying this approach across online big data (e.g. hundreds of millions of social media posts per day) to help promote HIV prevention campaigns to affected communities.

Graph representation learning serves as the core of many important tasks on graphs, ranging from friendship recommendation, name disambiguation, drug discovery, and fraud detection. Recently, deep learning has revolutionized various domains such as computer vision, natural language processing, speech recognition, etc. Inspired by the success of deep neural networks, there has been an increasing interest to learn graph representations with deep learning models such as autoencoders, convolutional neural networks, etc. However, graphs in real-life applications usually have complex structures such as sparse connections, task-irrelevant information, and rapidly evolving structures. The complexity poses great challenges to the existing frameworks, such as network embedding models with random walks and graph neural networks based on the neighborhood aggregation. In this dissertation, we propose several deep learning frameworks to tackle the aforementioned problems of graph representation learning on complex graphs. We propose a novel model to learn network representations with adversarially regularized autoencoders to overcome the sparse sampling issue of random walks on graphs. To resolve the task-irrelevant noise, we propose a general framework that is trained to simultaneously select task-relevant edges and learn graph representations by the feedback signals from downstream tasks. To learn from the dynamic evolving graphs, we propose to extract local features by performing convolutions in nodes' neighborhoods defined in joint temporal-structural space. The methodologies presented in these frameworks span different research areas, including deep network embedding, graph representation learning, temporal graph modeling, and node classification on graphs. As a result, these methodologies not only tackle specific challenges in the graph learning tasks mentioned above but also shed light on other applications like social network analysis.

Graph representation learning serves as the core of many important tasks on graphs, ranging from friendship recommendation, name disambiguation, drug discovery, and fraud detection. Recently, deep learning has revolutionized various domains such as computer vision, natural language processing, speech recognition, etc. Inspired by the success of deep neural networks, there has been an increasing interest to learn graph representations with deep learning models such as autoencoders, convolutional neural networks, etc. However, graphs in real-life applications usually have complex structures such as sparse connections, task-irrelevant information, and rapidly evolving structures. The complexity poses great challenges to the existing frameworks, such as network embedding models with random walks and graph neural networks based on the neighborhood aggregation. In this dissertation, we propose several deep learning frameworks to tackle the aforementioned problems of graph representation learning on complex graphs. We propose a novel model to learn network representations with adversarially regularized autoencoders to overcome the sparse sampling issue of random walks on graphs. To resolve the task-irrelevant noise, we propose a general framework that is trained to simultaneously select task-relevant edges and learn graph representations by the feedback signals from downstream tasks. To learn from the dynamic evolving graphs, we propose to extract local features by performing convolutions in nodes' neighborhoods defined in joint temporal-structural space. The methodologies presented in these frameworks span different research areas, including deep network embedding, graph representation learning, temporal graph modeling, and node classification on graphs. As a result, these methodologies not only tackle specific challenges in the graph learning tasks mentioned above but also shed light on other applications like social network analysis.

Influence measurement in social networks is vital to various real-world applications, such as online marketing and political campaigns. In this paper, we investigate the problem of measuring time-sensitive and topic-specific influence based on streaming texts and dynamic social networks. A user's influence can change rapidly in response to a new event and vary on different topics. For example, the political influence of Douglas Jones increased dramatically after winning the Alabama special election, and then rapidly decreased after the election week. During the same period, however, Douglas Jones' influence on sports remained low. Most existing approaches can only model the influence based on static social network structures and topic distributions. Furthermore, as popular social networking services embody many features to connect their users, multi-typed interactions make it hard to learn the roles that different interactions play when propagating information. To address these challenges, we propose a Time-sensitive and Topic-specific Influence Measurement (TTIM) method, to jointly model the streaming texts and dynamic social networks. We simulate the influence propagation process with a self-attention mechanism to learn the contributions of different interactions and track the influence dynamics with a matrix-adaptive long short-term memory. To the best of our knowledge, this is the first attempt to measure time-sensitive and topic-specific influence. Furthermore, the TTIM model can be easily adapted to supporting online learning which consumes constant training time on newly arrived data for each timestamp. We comprehensively evaluate the proposed TTIM model on five datasets from Twitter and Reddit. The experimental results demonstrate promising performance compared to the state-of-the-art social influence analysis models and the potential of TTIM in visualizing influence dynamics and topic distribution.

The natural existence of crystalline symmetry in real materials manifests the importance of understanding crystalline symmetry-protected topological (SPT) phases, especially for interacting systems. In this paper, we systematically construct and classify all the crystalline topological superconductors and insulators in three-dimensional (3D) interacting fermion systems using the novel concept of topological crystal. The corresponding higher-order topological surface theory can also be systematically studied via higher-order bulk-boundary correspondence. In particular, we discover an intriguing fact that almost all topological crystals with nontrivial 2D block states are intrinsically interacting topological phases that cannot be realized in any free-fermion systems. Moreover, the crystalline equivalence principle for 3D interacting fermionic systems is also verified, with an additional subtle "twist" on the spin of fermions., Comment: 52+33 pages, 30+26 figures, comments are welcome

Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology.

The emergence of exotic quantum phenomena in frustrated magnets is rapidly driving the development of quantum many-body physics, raising fundamental questions on the nature of quantum phase transitions. Here we unveil the behaviour of emergent symmetry involving two extraordinarily representative phenomena, i.e., the deconfined quantum critical point (DQCP) and the quantum spin liquid (QSL) state. Via large-scale tensor network simulations, we study a spatially anisotropic spin-1/2 square-lattice frustrated antiferromagnetic (AFM) model, namely the $J_{1x}$-$J_{1y}$-$J_2$ model, which contains anisotropic nearest-neighbor couplings $J_{1x}$, $J_{1y}$ and the next nearest neighbor coupling $J_2$. For small $J_{1y}/J_{1x}$, by tuning $J_2$, a direct continuous transition between the AFM and valence bond solid phase is observed.(Of course, the possibility of weakly first order transition can not be fully excluded.) With growing $J_{1y}/J_{1x}$, a gapless QSL phase gradually emerges between the AFM and VBS phases. We observe an emergent O(4) symmetry along the AFM--VBS transition line, which is consistent with the prediction of DQCP theory. Most surprisingly, we find that such an emergent O(4) symmetry holds for the whole QSL--VBS transition line as well. These findings reveal the intrinsic relationship between the QSL and DQCP from categorical symmetry point of view, and strongly constrain the quantum field theory description of the QSL phase. The phase diagram and critical exponents presented in this paper are of direct relevance to future experiments on frustrated magnets and cold atom systems., Comment: 5+7 pages, 4+11 figures

Limited throughput is a key challenge in in-vivo deep-tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the wide-field imaging modalities used for optically cleared or thin specimens. We recently introduced 'De-scattering with Excitation Patterning or DEEP', as a widefield alternative to point-scanning geometries. Using patterned multiphoton excitation, DEEP encodes spatial information inside tissue before scattering. However, to de-scatter at typical depths, hundreds of such patterned excitations are needed. In this work, we present DEEP$^2$, a deep learning based model, that can de-scatter images from just tens of patterned excitations instead of hundreds. Consequently, we improve DEEP's throughput by almost an order of magnitude. We demonstrate our method in multiple numerical and physical experiments including in-vivo cortical vasculature imaging up to four scattering lengths deep, in alive mice.

We propose a novel scene flow method that captures 3D motions from point clouds without relying on ground-truth scene flow annotations. Due to the irregularity and sparsity of point clouds, it is expensive and time-consuming to acquire ground-truth scene flow annotations. Some state-of-the-art approaches train scene flow networks in a self-supervised learning manner via approximating pseudo scene flow labels from point clouds. However, these methods fail to achieve the performance level of fully supervised methods, due to the limitations of point cloud such as sparsity and lacking color information. To provide an alternative, we propose a novel approach that utilizes monocular RGB images and point clouds to generate pseudo scene flow labels for training scene flow networks. Our pseudo label generation module infers pseudo scene labels for point clouds by jointly leveraging rich appearance information in monocular images and geometric information of point clouds. To further reduce the negative effect of noisy pseudo labels on the training, we propose a noisy-label-aware training scheme by exploiting the geometric relations of points. Experiment results show that our method not only outperforms state-of-the-art self-supervised approaches, but also outperforms some supervised approaches that use accurate ground-truth flows.

The method of time delay interferometry (TDI) is proposed to cancel the laser noise in space-borne gravitational-wave detectors. Among all different TDI combinations, the most commonly used ones are the orthogonal channels A, E and T, where A and E are signal-sensitive and T is signal-insensitive. Meanwhile, for the detection of stochastic gravitational-wave background, one needs to introduce the overlap reduction function to characterize the correlation between channels. For the calculation of overlap reduction function, it is often convenient to work in the low-frequency approximation, and assuming the equal-arm Michelson channels. However, if one wishes to work on the overlap reduction function of $\rm A/E$ channels, then the low-frequency approximation fails. We derive the exact form of overlap reduction function for $\rm A/E$ channels. Based on the overlap reduction function, we calculate the sensitivity curves of TianQin, TianQin I+II and TianQin + LISA. We conclude that the detection sensitivity calculated with $\rm A/E$ channels is mostly consistent with that obtained from the equal-arm Michelson channels., Comment: 18 pages,10 figures

The detection of stochastic gravitational wave background (SGWB) is among the leading scientific goals of the space-borne gravitational wave observatory, which would have significant impact on astrophysics and fundamental physics. In this work, we developed a data analysis software, \texttt{TQSGWB}, which can extract isotropic SGWB using the Bayes analysis method based on the TianQin detector. We find that for the noise cross spectrum, there are imaginary components and they play an important role in breaking the degeneracy of the position noise in the common laser link. When the imaginary corrections are considered, the credible regions of the position noise parameters are reduced by two orders of magnitude. We demonstrate that the parameters of various signals and instrumental noise could be estimated directly in the absence of a Galactic confusion foreground through Markov chain Monte Carlo sampling. With only a three-month observation, we find that TianQin could be able to confidently detect SGWBs with energy density as low as $\Omega_{\rm PL} = 1.3 \times 10^{-12}$, $\Omega_{\rm Flat} = 6.0 \times 10^{-12}$, and $\Omega_{\rm SP} = 9.0 \times 10^{-12}$, for power-law, flat, and single-peak models respectively., Comment: 28 pages, 8 figures

A new optical microscopy technique, termed high spatial and temporal resolution synthetic aperture phase microscopy (HISTR-SAPM), is proposed to improve the lateral resolution of wide-field coherent imaging. Under plane wave illumination, the resolution is increased by twofold to around 260 nm, while achieving millisecond-level temporal resolution. In HISTR-SAPM, digital micromirror devices are used to actively change the sample illumination beam angle at high speed with high stability. An off-axis interferometer is used to measure the sample scattered complex fields, which are then processed to reconstruct high-resolution phase images. Using HISTR-SAPM, we are able to map the height profiles of subwavelength photonic structures and resolve the period structures that have 198 nm linewidth and 132 nm gap (i.e., a full pitch of 330 nm). As the reconstruction averages out laser speckle noise while maintaining high temporal resolution, HISTR-SAPM further enables imaging and quantification of nanoscale dynamics of live cells, such as red blood cell membrane fluctuations and subcellular structure dynamics within nucleated cells. We envision that HISTR-SAPM will broadly benefit research in material science and biology.

The genus Miscanthus is a promising lignocellulosic feedstock crop for the production of bioenergy. Miscanthus is drawing attention because it can be grown on marginal land and avoid competition with food crops. Therefore, as measures of performance, biomass yield and quality of two parent species (Miscanthus sinensis and Miscanthus lutarioriparius) and 15 of their interspecific hybrids were evaluated in a saline–alkaline soil of the Yellow River delta in China. Ethanol and biogas production potentials were also estimated. A field trial with a randomised block design with five replicates of each genotype was conducted in 2014. Biomass yield, plant height, tiller number, stem diameter, and tiller mass of each parent and hybrid were measured at the end of the growing season in 2015, 2016, and 2017. Quality traits, including cellulose, hemicellulose, lignin and ash contents, were measured. There was large genotype variation in biomass yield and in most of the biometric parameters, and cellulose (range: 0.33 to 0.45 g/g), hemicellulose (0.32 to 0.43 g/g), lignin (0.07–0.13 g/g) and ash (0.02–0.06 g/g) contents were significantly different among the hybrids. The ash content of all hybrids was significantly lower than that of M. sinensis. The two-year (2016 and 2017) average biomass yield of the 15 hybrids ranged from 9.87 to 23.11 t/ha, which was significantly higher than that of either parent (M. sinensis: 1.20 t/ha; M. lutarioriparius: 3.87 t/ha). Two hybrids, SL8 and SL15, were identified as potential genotypes suitable for the saline–alkaline soils of the Yellow River delta. These results demonstrate that interspecific hybridisation is crucial to improve Miscanthus biomass yield and quality under marginal conditions.

The genus Miscanthus is a promising lignocellulosic feedstock crop for the production of bioenergy. Miscanthus is drawing attention because it can be grown on marginal land and avoid competition with food crops. Therefore, as measures of performance, biomass yield and quality of two parent species (Miscanthus sinensis and Miscanthus lutarioriparius) and 15 of their interspecific hybrids were evaluated in a saline–alkaline soil of the Yellow River delta in China. Ethanol and biogas production potentials were also estimated. A field trial with a randomised block design with five replicates of each genotype was conducted in 2014. Biomass yield, plant height, tiller number, stem diameter, and tiller mass of each parent and hybrid were measured at the end of the growing season in 2015, 2016, and 2017. Quality traits, including cellulose, hemicellulose, lignin and ash contents, were measured. There was large genotype variation in biomass yield and in most of the biometric parameters, and cellulose (range: 0.33 to 0.45 g/g), hemicellulose (0.32 to 0.43 g/g), lignin (0.07–0.13 g/g) and ash (0.02–0.06 g/g) contents were significantly different among the hybrids. The ash content of all hybrids was significantly lower than that of M. sinensis. The two-year (2016 and 2017) average biomass yield of the 15 hybrids ranged from 9.87 to 23.11 t/ha, which was significantly higher than that of either parent (M. sinensis: 1.20 t/ha; M. lutarioriparius: 3.87 t/ha). Two hybrids, SL8 and SL15, were identified as potential genotypes suitable for the saline–alkaline soils of the Yellow River delta. These results demonstrate that interspecific hybridisation is crucial to improve Miscanthus biomass yield and quality under marginal conditions.

In the past decade, tremendous efforts have been made towards understanding fermionic symmetry protected topological (FSPT) phases in interacting systems. Nevertheless, for systems with continuum symmetry, e.g., electronic insulators, it is still unclear how to construct an exactly solvable model with a finite dimensional Hilbert space in general. In this paper, we give a lattice model construction and classification for 2D interacting electronic insulators. Based on the physical picture of $\mathrm{U(1)}_f$-charge decorations, we illustrate the key idea by considering the well known 2D interacting topological insulator. Then we generalize our construction to an arbitrary 2D interacting electronic insulator with symmetry $G_f=\mathrm{U(1)}_f \rtimes_{\rho_1,\omega_2} G$, where $\mathrm{U(1)}_f$ is the charge conservation symmetry and $\rho_1, \omega_2$ are additional data which fully characterize the group structure of $G_f$. Finally we study more examples, including the full interacting classification of 2D crystalline topological insulators., Comment: 6+11 pages, 2 figures, 3 tables

A quantum spin liquid (QSL) is a novel phase of matter with long-range entanglement where localized spins are highly correlated with the vanishing of magnetic order. Such exotic quantum states provide the opportunities to develop new theoretical frameworks for many-body physics and have the potential application in realizing robust quantum computations. Here we show that a gapless QSL can naturally emerge from a deconfined quantum critical point (DQCP), which is originally proposed to describe Landau forbidden continuous phase transition between antiferromagnetic (AFM) and valence-bond solid (VBS) phases. Via large-scale tensor network simulations of a square-lattice spin-1/2 frustrated Heisenberg model, both QSL state and DQCP-type AFM-VBS transition are observed. With tuning coupling constants, the AFM-VBS transition vanishes and instead, a gapless QSL phase gradually develops in between. Remarkably, along the phase boundaries of AFM-QSL and QSL-VBS transitions, we always observe the same correlation length exponents $\nu\approx 1.0$, which is intrinsically different from the one of the DQCP-type transition, indicating new types of universality classes. Our results explicitly demonstrate a new scenario for understanding the emergence of gapless QSL from an underlying DQCP. The discovered QSL phase survives in a large region of tuning parameters and we expect its experimental realizations in solid state materials or quantum simulators.

It is well known that two-dimensional fermionic systems with a nonzero Chern number must break the time reversal symmetry, manifested by the appearance of chiral edge modes on an open boundary. Such an incompatibility between topology and symmetry can occur more generally. We will refer to this phenomenon as enforced symmetry breaking by topological orders. In this work, we systematically study enforced breaking of a general finite group $G_f$ by a class of topological orders, namely 0D, 1D and 2D fermionic invertible topological orders. Mathematically, the symmetry group $G_f$ is a central extension of a bosonic group $G$ by the fermion parity group $Z_2^f$, characterized by a 2-cocycle $\lambda$ $\in H^2(G,Z_2)$. With some minor assumptions and for given $G$ and $\lambda$, we are able to obtain a series of criteria on the existence or non-existence of enforced symmetry breaking by the fermionic invertible topological orders. Using these criteria, we discover many examples that are not known previously. For 2D systems, we define the physical quantities to describe symmetry-enriched invertible topological orders and derive some obstruction functions using both fermionic and bosonic languages. In the latter case which is done via gauging the fermion parity, we find that some obstruction functions are consequences of conditional anomalies of the bosonic symmetry-enriched topological states, with the conditions inherited from the original fermionic system. We also study enforced breaking of the continuous group $SU_f(N)$ by 2D invertible topological orders through a different argument., Comment: 29 pages, 4 figures, 6 tables, comment and suggestion are welcome

In this work, we study the prospect of detecting the stochastic gravitational-wave background with the TianQin Observatory. We consider sources of both astrophysical-origin and cosmological-origin, including stellar-mass binary black holes, binary neutron stars, Galactic white dwarves, inflation, first-order phase transitions, and cosmic defects. For the detector configurations, we consider TianQin, TianQin I+II, and TianQin + LISA. We study the detectability of stochastic gravitational-wave backgrounds with both the cross correlation and null channel methods, and present the corresponding power-law integrated sensitivity curves. We introduce the definition of the "joint foreground" with a network of detectors. With the joint foreground, the number of resolved double white dwarves in the Galaxy will be increased by 5$-$22\% compared with a simple combination of individual detectors. The astrophysical background is expected to be detectable with a signal-to-noise ratio of 100 after 5 years of operation and dominated by the extragalactic double white dwarves. On the other hand, due to the uncertain nature of underlying models, we can only estimate the detection capability of the cosmological background for specific cases., Comment: 20 pages, 10 figures, update to match published version

A quantum spin liquid (QSL) is a novel phase of matter with long-range entanglement where localized spins are highly correlated with the vanishing of magnetic order. Such exotic quantum states provide the opportunities to develop new theoretical frameworks for many-body physics and have the potential application in realizing robust quantum computations. Here we show that a gapless QSL can naturally emerge from a deconfined quantum critical point (DQCP), which is originally proposed to describe Landau forbidden continuous phase transition between antiferromagnetic (AFM) and valence-bond solid (VBS) phases. Via large-scale tensor network simulations of a square-lattice spin-1/2 frustrated Heisenberg model, both QSL state and DQCP-type AFM-VBS transition are observed. With tuning coupling constants, the AFM-VBS transition vanishes and instead, a gapless QSL phase gradually develops in between. Remarkably, along the phase boundaries of AFM-QSL and QSL-VBS transitions, we always observe the same correlation length exponents $\nu\approx 1.0$, which is intrinsically different from the one of the DQCP-type transition, indicating new types of universality classes. Our results explicitly demonstrate a new scenario for understanding the emergence of gapless QSL from an underlying DQCP. The discovered QSL phase survives in a large region of tuning parameters and we expect its experimental realizations in solid state materials or quantum simulators.

Test on a small scaled model is an effective approach to predict the dynamic response of full scale structure under blast loadings. However, the geometric dimensions of specimens cannot simply comply with complete geometrical similarity due to manufacture or test restrictions. It would result in the difference structural performance between the full and small scaled models. This paper proposed a corrected similarity relationship of the dynamic behaviour between prototype and replica of stiffened plates subjected to blast load, in which both the thickness of the plate and the configuration (cross-sectional shape) of stiffeners are distortedly scaled-down (double distorted geometric scaling factors). Firstly, based on the mesh convergence study and comparing with results from experimental tests, a numerical method in predicting the confined blast load and dynamic response of structure was verified, which provides a reliable means to determine the dynamic behaviour of stiffened plate designed by the corrected similarity criterion of this paper. Then, the influence of altering the stiffener configuration on the dynamic response of stiffened plates was analysed and on the basis of it, a criterion for scaling the stiffener is proposed to help design a stiffener-distorted model from prototype structure. In addition, a method for scaling the double-parameter distortedly small scaled model is proposed to predict the dynamic response of the prototype. Finally, two sets of examples of both the small size and prototype stiffened structures subjected to blast load were analysed by using the presented method. It is shown that the replica developed by applying the present method is able to accurately predict the behaviour of the full-size stiffened plates, even when the thickness of the plate and the configuration of the stiffeners are distortedly scaling down with different factors., Accepted Author Manuscript, Marine and Transport Technology

In the past decade, tremendous efforts have been made towards understanding fermionic symmetry protected topological (FSPT) phases in interacting systems. Nevertheless, for systems with continuum symmetry, e.g., electronic insulators, it is still unclear how to construct an exactly solvable model with a finite dimensional Hilbert space in general. In this paper, we give a lattice model construction and classification for 2D interacting electronic insulators. Based on the physical picture of $\mathrm{U(1)}_f$-charge decorations, we illustrate the key idea by considering the well known 2D interacting topological insulator. Then we generalize our construction to an arbitrary 2D interacting electronic insulator with symmetry $G_f=\mathrm{U(1)}_f \rtimes_{\rho_1,\omega_2} G$, where $\mathrm{U(1)}_f$ is the charge conservation symmetry and $\rho_1, \omega_2$ are additional data which fully characterize the group structure of $G_f$. Finally we study more examples, including the full interacting classification of 2D crystalline topological insulators., Comment: 6+11 pages, 2 figures, 3 tables

It is well known that two-dimensional fermionic systems with a nonzero Chern number must break the time reversal symmetry, manifested by the appearance of chiral edge modes on an open boundary. Such an incompatibility between topology and symmetry can occur more generally. We will refer to this phenomenon as enforced symmetry breaking by topological orders. In this work, we systematically study enforced breaking of a general finite group $G_f$ by a class of topological orders, namely 0D, 1D and 2D fermionic invertible topological orders. Mathematically, the symmetry group $G_f$ is a central extension of a bosonic group $G$ by the fermion parity group $Z_2^f$, characterized by a 2-cocycle $\lambda$ $\in H^2(G,Z_2)$. With some minor assumptions and for given $G$ and $\lambda$, we are able to obtain a series of criteria on the existence or non-existence of enforced symmetry breaking by the fermionic invertible topological orders. Using these criteria, we discover many examples that are not known previously. For 2D systems, we define the physical quantities to describe symmetry-enriched invertible topological orders and derive some obstruction functions using both fermionic and bosonic languages. In the latter case which is done via gauging the fermion parity, we find that some obstruction functions are consequences of conditional anomalies of the bosonic symmetry-enriched topological states, with the conditions inherited from the original fermionic system. We also study enforced breaking of the continuous group $SU_f(N)$ by 2D invertible topological orders through a different argument., Comment: 29 pages, 4 figures, 6 tables, comment and suggestion are welcome

In this work, we study the prospect of detecting the stochastic gravitational-wave background with the TianQin Observatory. We consider sources of both astrophysical-origin and cosmological-origin, including stellar-mass binary black holes, binary neutron stars, Galactic white dwarves, inflation, first-order phase transitions, and cosmic defects. For the detector configurations, we consider TianQin, TianQin I+II, and TianQin + LISA. We study the detectability of stochastic gravitational-wave backgrounds with both the cross correlation and null channel methods, and present the corresponding power-law integrated sensitivity curves. We introduce the definition of the "joint foreground" with a network of detectors. With the joint foreground, the number of resolved double white dwarves in the Galaxy will be increased by 5$-$22\% compared with a simple combination of individual detectors. The astrophysical background is expected to be detectable with a signal-to-noise ratio of 100 after 5 years of operation and dominated by the extragalactic double white dwarves. On the other hand, due to the uncertain nature of underlying models, we can only estimate the detection capability of the cosmological background for specific cases., Comment: 20 pages, 10 figures, update to match published version

A quantum spin liquid (QSL) is a novel phase of matter with long-range entanglement where localized spins are highly correlated with the vanishing of magnetic order. Such exotic quantum states provide the opportunities to develop new theoretical frameworks for many-body physics and have the potential application in realizing robust quantum computations. Here we show that a gapless QSL can naturally emerge from a deconfined quantum critical point (DQCP), which is originally proposed to describe Landau forbidden continuous phase transition between antiferromagnetic (AFM) and valence-bond solid (VBS) phases. Via large-scale tensor network simulations of a square-lattice spin-1/2 frustrated Heisenberg model, both QSL state and DQCP-type AFM-VBS transition are observed. With tuning coupling constants, the AFM-VBS transition vanishes and instead, a gapless QSL phase gradually develops in between. Remarkably, along the phase boundaries of AFM-QSL and QSL-VBS transitions, we always observe the same correlation length exponents $\nu\approx 1.0$, which is intrinsically different from the one of the DQCP-type transition, indicating new types of universality classes. Our results explicitly demonstrate a new scenario for understanding the emergence of gapless QSL from an underlying DQCP. The discovered QSL phase survives in a large region of tuning parameters and we expect its experimental realizations in solid state materials or quantum simulators.

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article.

Fengqi Liu,1,2,&ast; Li Zhong,1,2,&ast; Feifei Zhou,3,&ast; Cheng Zheng,1,4 Kai Zhang,1 Jiachang Cai,5 Hongwei Zhou,5 Kankai Tang,2 Zhaohui Dong,2 Wei Cui,1 Gensheng Zhang1 1Department of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, People’s Republic of China; 2Department of Critical Care Medicine, First Affiliated Hospital, Huzhou Teachers College, The First People’s Hospital of Huzhou, Huzhou, 313000, Zhejiang, People’s Republic of China; 3Department of Critical Care Medicine, Ningbo Medical Center, Li Huili Hospital, Ningbo, Zhejiang, 315040, People’s Republic of China; 4Department of Critical Care Medicine, Taizhou Municipal Hospital, Taizhou, Zhejiang, 318000, People’s Republic of China; 5Department of Clinical Microbiology Laboratory, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Gensheng Zhang; Wei CuiDepartment of Critical Care Medicine, Second Affiliated Hospital, Zhejiang University School of Medicine, cc88 Jiefang Road, Hangzhou, 310009, People’s Republic of ChinaTel +86-571-8778-3636; +86-571-8778-3551Fax +86-571- 87022776Email genshengzhang@zju.edu.cn; zricu@zju.edu.cnPurpose: Candida albicans (C. albicans) candidemia has been well reported in previous studies, while research on non-albicans Candida (NAC) bloodstream infections remains poorly explored. Therefore, the present study aimed to investigate the clinical characteristics and outcomes of patients with NAC candidemia.Patients and Methods: We recruited inpatients with candidemia from January 2013 to June 2020 in a tertiary hospital for this retrospective observational study.Results: A total of 301 patients with candidemia were recruited in the current study, including 161 (53.5%) patients with NAC candidemia. The main pathogens in NAC candidemia were Candida tropi

Los brotes por Gripe Aviar, concretamente las variaciones de alta patogenicidad de este virus son un problema que afecta frecuentemente a toda Europa, especialmente en países ubicados al norte donde las temperaturas son más bajas lo que les convierte en entornos ideales para la supervivencia del virus. Durante este trabajo se ha desarrollado una herramienta para poder predecir en qué comarcas ganaderas de España hay un mayor riesgo de que ocurra un brote, teniendo en cuenta brotes de Europa, tomados de la plataforma de Empres-i y proporcionados por la organización de WAHIS, rutas migratorias de diferentes especies de aves que suelen realizar trayectos entre partes de Europa y España, proporcionadas por la Sociedad Española de Ornitología (SEO) y previsiones de la temperatura para cada comarca ganadera. La herramienta además generará reportes con información pertinente a las posibles alertas de cada comarca ganadera en formato PDF y CSV, que se enviarán auna unidad en Google Drive para su almacenamiento. Todas estas tareas se realizarán con una frecuencia semanal y además se actualizarán los brotes que vayan reportándose en Empres-i, con la misma frecuencia., Outbreaks due to Avian Influenza, specifically the highly pathogenic variations of this virus, are a problem that frequently affects all of Europe, especially in countries located to the north where temperatures are lower, which makes these environments ideal for the survival of the virus. During this project, a tool has been developed to be able to predict in which livestock regions of Spain there is a greater risk of an outbreak occurring, considering outbreaks in Europe, downloaded from the Empres-i platform, and provided by the WAHIS organization, migratory routes of different species of birds that usually travel between parts of Europe and Spain, provided by the Spanish Ornithological Society (SEO) and temperature forecasts for each livestock region. The tool will also generate reports with information relevant to the possible alerts of each livestock region in .pdf and .csv format, which will be sent to a Google drive unit for storage. All these tasks will be carried out on a weekly basis and the outbreaks that are reported in Empres-i will also be updated with the same frequency.

A quantum spin liquid (QSL) is a novel phase of matter with long-range entanglement where localized spins are highly correlated with the vanishing of magnetic order. Such exotic quantum states provide the opportunities to develop new theoretical frameworks for many-body physics and have the potential application in realizing robust quantum computations. Here we show that a gapless QSL can naturally emerge from a deconfined quantum critical point (DQCP), which is originally proposed to describe Landau forbidden continuous phase transition between antiferromagnetic (AFM) and valence-bond solid (VBS) phases. Via large-scale tensor network simulations of a square-lattice spin-1/2 frustrated Heisenberg model, both QSL state and DQCP-type AFM-VBS transition are observed. With tuning coupling constants, the AFM-VBS transition vanishes and instead, a gapless QSL phase gradually develops in between. Remarkably, along the phase boundaries of AFM-QSL and QSL-VBS transitions, we always observe the same correlation length exponents $\nu\approx 1.0$, which is intrinsically different from the one of the DQCP-type transition, indicating new types of universality classes. Our results explicitly demonstrate a new scenario for understanding the emergence of gapless QSL from an underlying DQCP. The discovered QSL phase survives in a large region of tuning parameters and we expect its experimental realizations in solid state materials or quantum simulators.

Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges). However, such projections often miss important and rich information. For example, in graph representations used in missing value imputation, items - represented as nodes - may contain rich textual information. However, when processing graphs with graph neural networks (GNN), such information is either ignored or summarized into a single vector representation used to initialize the GNN. Towards addressing this, we present CoRGi, a GNN that considers the rich data within nodes in the context of their neighbors. This is achieved by endowing CoRGi's message passing with a personalized attention mechanism over the content of each node. This way, CoRGi assigns user-item-specific attention scores with respect to the words that appear in an item's content. We evaluate CoRGi on two edge-value prediction tasks and show that CoRGi is better at making edge-value predictions over existing methods, especially on sparse regions of the graph.

Nonlinear optical microscopy has enabled in vivo deep tissue imaging on the millimeter scale. A key unmet challenge is its limited throughput especially compared to rapid wide-field modalities that are used ubiquitously in thin specimens. Wide-field imaging methods in tissue specimens have found successes in optically cleared tissues and at shallower depths, but the scattering of emission photons in thick turbid samples severely degrades image quality at the camera. To address this challenge, we introduce a novel technique called De-scattering with Excitation Patterning or "DEEP," which uses patterned nonlinear excitation followed by computational imaging-assisted wide-field detection. Multiphoton temporal focusing allows high-resolution excitation patterns to be projected deep inside specimen at multiple scattering lengths due to the use of long wavelength light. Computational reconstruction allows high-resolution structural features to be reconstructed from tens to hundreds of DEEP images instead of millions of point-scanning measurements.