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Yixuan Zhang,1,* Min Wang,2,* Ting Zhang,3 Honghua Wang,2 Ying Chen,1 Tao Zhou,1 Rui Yang1 1Research Institute for Reproductive Medicine and Genetic Diseases, Wuxi Maternity and Child Health Care Hospital, Wuxi, 214002, People’s Republic of China; 2Center for Reproductive Medicine, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, 214002, People’s Republic of China; 3Department of Laboratory Medicine, Wuxi Maternity and Child Health Care Hospital, Jiangnan University, Wuxi, 214002, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tao Zhou; Rui Yang, Email zhoutao@njmu.edu.cn; ruiyang826@163.comAbstract: Sperm quality is declining dramatically during the past decades. Male infertility has been a serious health and social problem. The sperm cell driven biohybrid nanorobot opens a new era for automated and precise assisted reproduction. Therefore, it is urgent and necessary to conduct an updated review and perspective from the viewpoints of the researchers and clinicians in the field of reproductive medicine. In the present review, we first update the current classification, design, control and applications of various spermbots. Then, by a comprehensive summary of the functional features of sperm cells, the journey of sperms to the oocyte, and sperm-related dysfunctions, we provide a systematic guidance to further improve the design of spermbots. Focusing on the translation of spermbots into clinical practice, we point out that the main challenges are biocompatibility, effectiveness, and ethical issues. Considering the special requirements of assisted reproduction, we also propose the three laws for the clinical usage of spermbots: good genetics, gentle operation and no contamination. Finally, a three-step roadmap is proposed to achieve the goal of clinical translation. We believe that spermbot-based treatments can be validated and approved for in vitro

The manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing paradigms. Achieving this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ experiments. Lorentz transmission electron microscopy (LTEM) enables real-space imaging of spin textures at the nanoscale, but quantitative characterization of in situ data is extremely challenging. Here, we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forward model for LTEM. Our approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing methods. Furthermore, our method is capable of isolating sample heterogeneities from magnetic contrast, as shown by application to simulated and experimental data. This approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.

The inorganic perovskite CsPbI3 shows promising photophysical properties for a range of potential optoelectronic applications but is metastable at room temperature. To address this, Br can be alloyed into the X-site to create compositions such as CsPbI2Br that are stable at room temperature but have bandgaps >1.9 eV - severely limiting solar applications. Herein, in an effort to achieve phase stable films with bandgaps <1.85 eV, we investigate alloying chlorine into iodine-rich triple-halide CsPb(I0.8Br0.2-x Cl x )3 with 0 < x < 0.1. We show that partial substitution of iodine with bromine and chlorine provides a path to maintain broadband terrestrial absorption while improving upon the perovskite phase stability due to chlorines smaller size and larger ionization potential than bromine. At moderate Cl loading up to ≈5%, X-ray diffraction reveals an increasingly smaller orthorhombic unit cell, suggesting chlorine incorporation into the lattice. Most notably, this Cl incorporation is accompanied by a significant enhancement over Cl-free controls in the duration of black-phase stability of up to 7× at elevated temperatures. Additionally, we observe up to 5× increased steady state photoluminescence intensity (PL), along with a small blue-shift. In contrast, at high loading (≈10%), Cl accumulates in a second phase that is visible at grain boundaries via synchrotron fluorescence microscopy and negatively impacts the perovskite phase stability. Thus, replacing small fractions of bromine for chlorine in the iodine-rich inorganic perovskite lattice results in distinct improvement thermal stability and optoelectronic quality while minimally impacting the bandgap.

Understanding the microscopic origin of the superior electromechanical response in relaxor ferroelectrics requires knowledge not only of the atomic-scale formation of polar nanodomains (PNDs) but also the rules governing the arrangements and stimulated response of PNDs over longer distances. Using x-ray coherent nanodiffraction, we show the staggered self-assembly of PNDs into unidirectional mesostructures that we refer to as polar laminates in the relaxor ferroelectric 0.68PbMg1/3Nb2/3O3-0.32PbTiO3 (PMN-0.32PT). We reveal the highly heterogeneous electric-field-driven responses of intra- and interlaminate PNDs and establish their correlation with the local strain and the nature of the PND walls. Our observations highlight the critical role of hierarchical lattice organizations on macroscopic material properties and provide guiding principles for the understanding and design of relaxors and a wide range of quantum and functional materials.

The PETRA III Swedish Materials Science beamline is a high energy X-ray beamline, which was established for research and innovation in materials science and engineering. This beamline is tailored to particularly suit the research interests of the Swedish community. In-situ/-operando measurements are a key opportunity that the Swedish Materials Science beamline offers. In particular, this beamline’s combined SAXS and WAXS (Small- and Wide-Angle X-ray Scattering) instrument setup provides the possibility to capture quantitative information about materials that comprise of structures that a wide range of length scales. This SAXS/WAXS setup has been refined, resulting in an instrument with outstanding capability. The outstanding SAXS/WAXS capability can provide researchers with new insights into heat treatments because phase information and precipitate information can be captured simultaneously - at temporal resolutions down to 1 second. Thus enabling the kinetics of precipitation and phase transformations to be assessed in detail. For example, microstructural evolution can be quantitatively tracked. This report therefore aims to enabling more Swedish researchers to make the most of the heat treatment research opportunities that the combined SAXS/WAXS instrument setup offers.Using the heat treatment of austenitic steels that contain precipitates as an example, this report presents a description of how to streamline the research process - from preparing beamtime proposals at your home lab to analysing the beamtime data. Process descriptions are brought to life using information from the research case, with the case also exemplifying details of each stage of the research process.This report starts with an introduction to the case as well as the PETRA III Swedish Materials Science beamline, and its combined SAXS/WAXS instrument setup. The streamlined research process, and its various stages, are presented and exemplified. In th

Background: Tillage practices can substantially affect soil properties depending on crop stage. The interaction between tillage and crop growth on arbuscular mycorrhizal fungi (AMF) communities remains unclear. We investigated the interactions between four tillage treatments (CT: conventional tillage, RT: reduced tillage, NT: no tillage with mulch, and SS: subsoiling with mulch), maintained for 25 years, and two wheat growth stages (elongation stage and grain filling stage) on AMF diversity and community composition. Results: The AMF community composition strongly changed during wheat growth, mainly because of changes in the relative abundance of dominant genera Claroideoglomus, Funneliformi, Rhizophagu, Entrophospora, and Glomus. Co-occurrence network analysis revealed that the grain filling stage had a more complex network than the elongation stage. Redundancy analysis results showed that keystone genera respond mainly to changes in soil organic carbon during elongation stage, whereas the total nitrogen content affected the keystone genera during grain filling. Compared with CT, the treatments with mulch, i.e., NT and SS, significantly changed the AMF community composition. The change of AMF communities under different tillage practices depended on wheat biomass and soil nutrients. NT significantly increased the relative abundances of Glomus and Septoglomus, while RT significantly increased the relative abundance of Claroideoglomus. Conclusion: Our findings indicate that the relative abundance of dominant genera changed during wheat growth stages. Proper tillage practices (e.g., NT and SS) benefit the long-term sustainable development of the Loess Plateau cropping systems.

The PETRA III Swedish Materials Science beamline is a high energy X-ray beamline, which was established for research and innovation in materials science and engineering. This beamline is tailored to particularly suit the research interests of the Swedish community. In-situ/-operando measurements are a key opportunity that the Swedish Materials Science beamline offers. In particular, this beamline’s combined SAXS and WAXS (Small- and Wide-Angle X-ray Scattering) instrument setup provides the possibility to capture quantitative information about materials that comprise of structures that a wide range of length scales. This SAXS/WAXS setup has been refined, resulting in an instrument with outstanding capability. The outstanding SAXS/WAXS capability can provide researchers with new insights into heat treatments because phase information and precipitate information can be captured simultaneously - at temporal resolutions down to 1 second. Thus enabling the kinetics of precipitation and phase transformations to be assessed in detail. For example, microstructural evolution can be quantitatively tracked. This report therefore aims to enabling more Swedish researchers to make the most of the heat treatment research opportunities that the combined SAXS/WAXS instrument setup offers.Using the heat treatment of austenitic steels that contain precipitates as an example, this report presents a description of how to streamline the research process - from preparing beamtime proposals at your home lab to analysing the beamtime data. Process descriptions are brought to life using information from the research case, with the case also exemplifying details of each stage of the research process.This report starts with an introduction to the case as well as the PETRA III Swedish Materials Science beamline, and its combined SAXS/WAXS instrument setup. The streamlined research process, and its various stages, are presented and exemplified. In th

The PETRA III Swedish Materials Science beamline is a high energy X-ray beamline, which was established for research and innovation in materials science and engineering. This beamline is tailored to particularly suit the research interests of the Swedish community. In-situ/-operando measurements are a key opportunity that the Swedish Materials Science beamline offers. In particular, this beamline’s combined SAXS and WAXS (Small- and Wide-Angle X-ray Scattering) instrument setup provides the possibility to capture quantitative information about materials that comprise of structures that a wide range of length scales. This SAXS/WAXS setup has been refined, resulting in an instrument with outstanding capability. The outstanding SAXS/WAXS capability can provide researchers with new insights into heat treatments because phase information and precipitate information can be captured simultaneously - at temporal resolutions down to 1 second. Thus enabling the kinetics of precipitation and phase transformations to be assessed in detail. For example, microstructural evolution can be quantitatively tracked. This report therefore aims to enabling more Swedish researchers to make the most of the heat treatment research opportunities that the combined SAXS/WAXS instrument setup offers.Using the heat treatment of austenitic steels that contain precipitates as an example, this report presents a description of how to streamline the research process - from preparing beamtime proposals at your home lab to analysing the beamtime data. Process descriptions are brought to life using information from the research case, with the case also exemplifying details of each stage of the research process.This report starts with an introduction to the case as well as the PETRA III Swedish Materials Science beamline, and its combined SAXS/WAXS instrument setup. The streamlined research process, and its various stages, are presented and exemplified. In th

The PETRA III Swedish Materials Science beamline is a high energy X-ray beamline, which was established for research and innovation in materials science and engineering. This beamline is tailored to particularly suit the research interests of the Swedish community. In-situ/-operando measurements are a key opportunity that the Swedish Materials Science beamline offers. In particular, this beamline’s combined SAXS and WAXS (Small- and Wide-Angle X-ray Scattering) instrument setup provides the possibility to capture quantitative information about materials that comprise of structures that a wide range of length scales. This SAXS/WAXS setup has been refined, resulting in an instrument with outstanding capability. The outstanding SAXS/WAXS capability can provide researchers with new insights into heat treatments because phase information and precipitate information can be captured simultaneously - at temporal resolutions down to 1 second. Thus enabling the kinetics of precipitation and phase transformations to be assessed in detail. For example, microstructural evolution can be quantitatively tracked. This report therefore aims to enabling more Swedish researchers to make the most of the heat treatment research opportunities that the combined SAXS/WAXS instrument setup offers.Using the heat treatment of austenitic steels that contain precipitates as an example, this report presents a description of how to streamline the research process - from preparing beamtime proposals at your home lab to analysing the beamtime data. Process descriptions are brought to life using information from the research case, with the case also exemplifying details of each stage of the research process.This report starts with an introduction to the case as well as the PETRA III Swedish Materials Science beamline, and its combined SAXS/WAXS instrument setup. The streamlined research process, and its various stages, are presented and exemplified. In th

Impact toughness is essential for evaluating the mechanical properties of ship hull steels. This study focused on understanding the embrittlement mechanism of NiAl precipitation-strengthened HSLA steels by integrating advanced characterization and atomic-scale calculations. The factors causing the deterioration of the impact toughness of NiAl precipitation-strengthened steels, which have been contentious, were identified. The embrittlement mechanism and crack propagation mode were revealed using first-principles calculations and 3D impact fracture morphology, respectively. The results suggested that the numerous homogeneous NiAl nanoparticles within the bcc-Fe matrix reduced the impact toughness of the HSLA steels. As the precipitate interparticle spacing (L) decreased, the impact toughness decreased until it attained a critical value (similar to 27 nm). This is interpreted effectively by the calculations indicating that the shear modulus (75 GPa) and fracture energy (4.5 J/m(2)) of the NiAl phase, particularly the NiAlMn phase (46 GPa, 4 J/m(2)), are significantly lower than those of bccFe (83 GPa, 5 J/m(2)). This induces the fracture of nanoparticles under rapid impact loading, which functions as numerous crack initiations before plastic deformation of the matrix. The small L achieved after peak-hardening aging can result in the interconnection of these crack sources and cause instantaneous cleavage fractures, similar to brittle materials., QC 20240620

To enhance the fundamental understanding for micromechanical lath martensite deformation, the microstructure as well as macro- and microscopic tensile properties of as -quenched 15-5 PH stainless steel are systematically analysed depending on the austenitisation temperature. Based on electron backscatter diffraction (EBSD) and backscattered electron (BSE) analysis, it is noted that the martensite morphology alters from a less defined to a more clearly defined parallel arrangement of the block and lath structure with increasing temperature. For an indepth quantification of the hierarchical boundary strengthening contributions in relation to local stress/strain heterogeneity, separate high-fidelity virtual microstructures are realised for the different scales (prior austenite grains, packets and blocks). This is consistent with the materials transformation process. The virtual microstructures are simulated employing the crystal plasticity finite element method (CPFEM) adapted for handling high dislocation density and encompassing all relevant strengthening mechanisms by boundaries, dislocations and solute atoms. While accurately capturing the measured size -dependent stress-strain behaviour, the simulations reveal in line with the experiments (Hall-Petch) that blocks are the most effective dislocation motion barrier, causing increased strain hardening and stress/strain heterogeneity. Furthermore, since strain localisation is predicted strongest in the distinct block structure, the experimentally observed early plastic material yielding is thought to be favoured here., QC 20240404

Spatial information on the soil carbon-to-nitrogen (C:N) ratio is essential for sustainable soil use and management. The unprecedented availability of Sentinel optical and radar data on cloud computing platforms, such as the Google Earth Engine (GEE), has created new possibilities for developing soil prediction models from the local scale to the planetary scale. However, there is a paucity of literature on the effects of Sentinel sensor selection and integration and radar data utilization strategies on mapping the C:N ratio. In this study, we explored the use of multiyear Sentinel-1 radar and Sentinel-2 optical data obtained from the GEE platform combined with the digital soil mapping (DSM) technique to map the soil C:N ratio at the European scale. The performance of soil prediction models, which were constructed using two modeling techniques (random forest and support vector machine), derived under multiple scenarios based on optical, radar and commonly used auxiliary data (climatic and topographic variables) combined with the LUCAS 2018 soil dataset, was evaluated by a cross-validation technique. The results showed that the modeling performance varied with the selection and integration of Sentinel observations, as well as the configuration of the radar data. Models based on single polarization performed the worst across all scenarios related to Sentinel-1, with cross-polarization performing better than copolarization. Models that utilized Sentinel-1 data from ascending orbits outperformed those that utilized data from descending orbits. The application of Sentinel-1 backscatter information derived from different orbits and polarization modes resulted in improved prediction accuracy. Our study also demonstrated the potential of integrating multiyear Sentinel satellite data via the GEE to map the continental-scale C:N ratio. The model based on Sentinel-1 data outperformed the one built on Sentinel-2 data, whereas combining Sentinel-2 optical data with Sentinel-1 rad

Background and Aim: Europe faces an elevated risk of nonalcoholic fatty liver disease (NAFLD) among people living with HIV (PLWH), contributing to the region’s highest global burden of NAFLD. However, the prevalence of NAFLD across various European countries and regions remains unclear. This study aims to investigate the prevalence and risk factors associated with NAFLD among PLWH across European countries. Methods: A systematic search was conducted across four databases: PubMed, Embase, Web of Science, and Cochrane Library. Data on the prevalence of NAFLD, nonalcoholic steatohepatitis (NASH), and fibrosis, as well as the associated risk factors, were collected among PLWH in Europe. Results: Thirty-six studies from 13 European nations were included. The prevalence of NAFLD, NASH, and fibrosis were 42% (95%CI 37–48), 35% (95%CI 21–50) and 13% (95%CI 10–15), respectively. Male gender, BMI, waist circumference, Diabetes, hypertension, metabolic syndrome, dyslipidemia, triglycerides, HDL, LDL, ALT, AST, and years on antiretroviral therapy (ART) were found to be risk factors for NAFLD. High BMI and triglycerides were associated with NASH. Patients with high BMI and triglycerides are at increased risk of significant liver fibrosis. Conclusion: The high prevalence of NAFLD, NASH, and fibrosis among PLWH in Europe highlights the need for early screening, intervention, and increased research focus on adolescents living with HIV. Furthermore, the significant variations observed between countries and regions underscore the influence of related risk factors.

The advent of cloud computing platforms (e.g., Google Earth Engine (GEE)) and the massive amounts of optical and radar Earth Observation (EO) data hosted by these platforms present new opportunities for mapping soil pH at large scales. However, existing studies generally lack consensus on the effects of satellite and radar sensor parameters on GEE-based soil pH prediction models. In this study, we assessed the suitability of long-term radar (C-band Sentinel-1 and L-band PALSAR-1/2) and optical (Sentinel-2) EO data on GEE for the digital mapping of soil pH on a continental (Europe) scale and determined the most appropriate radar sensor parameters. Thirteen scenarios with different data configurations were simulated and combined with the 2018 LUCAS soil database and two machine learners (boosted regression trees and extreme gradient boosting) to develop soil prediction models. Results showed that the selection of modeling techniques, satellite sensors and radar system parameters largely affected the model output. Models involving a single polarization mode of PALSAR-1/2 data performed the worst (RPD = 1.24). Models based on Sentinel-1 data performed better than those built using PALSAR-1/2 data. The model performance was improved when a model involved more polarization bands, orbital directions, and band frequencies. The multiband model built using the two radar datasets achieved a comparable accuracy to the model based on optical data. Moreover, the model that fused radar-optical data achieved better results, with RPD values of 1.56 and 1.46 for the models with and without radar data, respectively; its performance was comparable to that of models built with commonly used variables (topography and climate). The analysis of importance indicated that long-term optical and radar EO data on GEE were important in our model. The modelling of soil pH at the continental scale largely benefits from GEE. The predicted maps exhibited strong spatial heterogeneit

Jinyuan Ma,1,2,&ast; Zongguang Tai,1,2,&ast; Ying Li,1,2,&ast; Yang Li,3 Jiandong Wang,1,2 Tao Zhou,1,2 Min Shen,1,2 Rujuan Xin,1,2 Cuie Shen,1,2 Quangang Zhu,1,2 Zhongjian Chen1,2 1Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China; 2Shanghai Engineering Research Center for Topical Chinese Medicine, Shanghai, People’s Republic of China; 3CAS Key Laboratory of Design and Assembly of Functional Nanostructures, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Quangang Zhu; Zhongjian Chen, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, 1278 Baode Road, Shanghai, 200443, People’s Republic of China, Tel/Fax +86 21 36803155 ; +86 21 36803007, Email qgzhu@126.com; aajian818@163.comPurpose: Photodynamic therapy (PDT) has been an attractive strategy for skin tumor treatment. However, the hypoxic microenvironment of solid tumors and further O2 consumption during PDT would diminish its therapeutic effect. Herein, we developed a strategy using the combination of PDT and hypoxia-activated bioreductive drug tirapazamine (TPZ).Methods: TPZ was linked to DSPE-PEG-NHS forming DSPE-PEG-TPZ to solve leakage of water-soluble TPZ and serve as an antitumor agent and monomer molecule further forming the micellar. Chlorin e6 (Ce6) was loaded in DSPE-PEG-TPZ forming DSPE-PEG-TPZ@Ce6 (DPTC). To further improve tumor infiltration and accumulation, hyaluronic acid was adopted to make DPTC-containing microneedles (DPTC-MNs).Results: Both in vitro and in vivo studies consistently demonstrated the synergistic antitumor effect of photodynamic therapy and TPZ achieved by DPTC-MNs. With laser irradiation, overexpressions of PDT tolerance factors NQO1 and HIF-1α were inhibited by this PDT process.Conclusion: The synergistic effect of PDT and TPZ significantly i

Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images. These promptable models exhibit denoising abilities for imprecise prompt inputs, such as imprecise bounding boxes. In this paper, we explore the potential of applying SAM to track and segment objects in videos where we recognize the tracking task as a prompt denoising task. Specifically, we iteratively propagate the bounding box of each object's mask in the preceding frame as the prompt for the next frame. Furthermore, to enhance SAM's denoising capability against position and size variations, we propose a multi-prompt strategy where we provide multiple jittered and scaled box prompts for each object and preserve the mask prediction with the highest semantic similarity to the template mask. We also introduce a point-based refinement stage to handle occlusions and reduce cumulative errors. Without involving tracking modules, our approach demonstrates comparable performance in video object/instance segmentation tasks on three datasets: DAVIS2017, YouTubeVOS2018, and UVO, serving as a concise baseline and endowing SAM-based downstream applications with tracking capabilities.

Link prediction is a paradigmatic and challenging problem in network science, which aims to predict missing links, future links and temporal links based on known topology. Along with the increasing number of link prediction algorithms, a critical yet previously ignored risk is that the evaluation metrics for algorithm performance are usually chosen at will. This paper implements extensive experiments on hundreds of real networks and 25 well-known algorithms, revealing significant inconsistency among evaluation metrics, namely different metrics probably produce remarkably different rankings of algorithms. Therefore, we conclude that any single metric cannot comprehensively or credibly evaluate algorithm performance. Further analysis suggests the usage of at least two metrics: one is the area under the receiver operating characteristic curve (AUC), and the other is one of the following three candidates, say the area under the precision-recall curve (AUPR), the area under the precision curve (AUC-Precision), and the normalized discounted cumulative gain (NDCG). In addition, as we have proved the essential equivalence of threshold-dependent metrics, if in a link prediction task, some specific thresholds are meaningful, we can consider any one threshold-dependent metric with those thresholds. This work completes a missing part in the landscape of link prediction, and provides a starting point toward a well-accepted criterion or standard to select proper evaluation metrics for link prediction., Comment: 20 pages, 9 figures

Poker, also known as Texas Hold'em, has always been a typical research target within imperfect information games (IIGs). IIGs have long served as a measure of artificial intelligence (AI) development. Representative prior works, such as DeepStack and Libratus heavily rely on counterfactual regret minimization (CFR) to tackle heads-up no-limit Poker. However, it is challenging for subsequent researchers to learn CFR from previous models and apply it to other real-world applications due to the expensive computational cost of CFR iterations. Additionally, CFR is difficult to apply to multi-player games due to the exponential growth of the game tree size. In this work, we introduce PokerGPT, an end-to-end solver for playing Texas Hold'em with arbitrary number of players and gaining high win rates, established on a lightweight large language model (LLM). PokerGPT only requires simple textual information of Poker games for generating decision-making advice, thus guaranteeing the convenient interaction between AI and humans. We mainly transform a set of textual records acquired from real games into prompts, and use them to fine-tune a lightweight pre-trained LLM using reinforcement learning human feedback technique. To improve fine-tuning performance, we conduct prompt engineering on raw data, including filtering useful information, selecting behaviors of players with high win rates, and further processing them into textual instruction using multiple prompt engineering techniques. Through the experiments, we demonstrate that PokerGPT outperforms previous approaches in terms of win rate, model size, training time, and response speed, indicating the great potential of LLMs in solving IIGs.

Link prediction aims to predict the potential existence of links between two unconnected nodes within a network based on the known topological characteristics. Evaluation metrics are used to assess the effectiveness of algorithms in link prediction. The discriminating ability of these evaluation metrics is vitally important for accurately evaluating link prediction algorithms. In this study, we propose an artificial network model, based on which one can adjust a single parameter to monotonically and continuously turn the prediction accuracy of the specifically designed link prediction algorithm. Building upon this foundation, we show a framework to depict the effectiveness of evaluating metrics by focusing on their discriminating ability. Specifically, a quantitative comparison in the abilities of correctly discerning varying prediction accuracies was conducted encompassing nine evaluation metrics: Precision, Recall, F1-Measure, Matthews Correlation Coefficient (MCC), Balanced Precision (BP), the Area Under the receiver operating characteristic Curve (AUC), the Area Under the Precision-Recall curve (AUPR), Normalized Discounted Cumulative Gain (NDCG), and the Area Under the magnified ROC (AUC-mROC). The results indicate that the discriminating abilities of the three metrics, AUC, AUPR, and NDCG, are significantly higher than those of other metrics.

We introduce a novel sampler called the energy based diffusion generator for generating samples from arbitrary target distributions. The sampling model employs a structure similar to a variational autoencoder, utilizing a decoder to transform latent variables from a simple distribution into random variables approximating the target distribution, and we design an encoder based on the diffusion model. Leveraging the powerful modeling capacity of the diffusion model for complex distributions, we can obtain an accurate variational estimate of the Kullback-Leibler divergence between the distributions of the generated samples and the target. Moreover, we propose a decoder based on generalized Hamiltonian dynamics to further enhance sampling performance. Through empirical evaluation, we demonstrate the effectiveness of our method across various complex distribution functions, showcasing its superiority compared to existing methods.

Ptychography is a powerful imaging technique that is used in a variety of fields, including materials science, biology, and nanotechnology. However, the accuracy of the reconstructed ptychography image is highly dependent on the accuracy of the recorded probe positions which often contain errors. These errors are typically corrected jointly with phase retrieval through numerical optimization approaches. When the error accumulates along the scan path or when the error magnitude is large, these approaches may not converge with satisfactory result. We propose a fundamentally new approach for ptychography probe position prediction for data with large position errors, where a neural network is used to make single-shot phase retrieval on individual diffraction patterns, yielding the object image at each scan point. The pairwise offsets among these images are then found using a robust image registration method, and the results are combined to yield the complete scan path by constructing and solving a linear equation. We show that our method can achieve good position prediction accuracy for data with large and accumulating errors on the order of $10^2$ pixels, a magnitude that often makes optimization-based algorithms fail to converge. For ptychography instruments without sophisticated position control equipment such as interferometers, our method is of significant practical potential.

The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical images. In medical imaging contexts, it is not uncommon for human experts to rectify segmentations of specific test samples after SAM generates its segmentation predictions. These rectifications typically entail manual or semi-manual corrections employing state-of-the-art annotation tools. Motivated by this process, we introduce a novel approach that leverages the advantages of online machine learning to enhance Segment Anything (SA) during test time. We employ rectified annotations to perform online learning, with the aim of improving the segmentation quality of SA on medical images. To improve the effectiveness and efficiency of online learning when integrated with large-scale vision models like SAM, we propose a new method called Auxiliary Online Learning (AuxOL). AuxOL creates and applies a small auxiliary model (specialist) in conjunction with SAM (generalist), entails adaptive online-batch and adaptive segmentation fusion. Experiments conducted on eight datasets covering four medical imaging modalities validate the effectiveness of the proposed method. Our work proposes and validates a new, practical, and effective approach for enhancing SA on downstream segmentation tasks (e.g., medical image segmentation)., Comment: Project Link: https://sam-auxol.github.io/AuxOL

Current fundus image analysis models are predominantly built for specific tasks relying on individual datasets. The learning process is usually based on data-driven paradigm without prior knowledge, resulting in poor transferability and generalizability. To address this issue, we propose MM-Retinal, a multi-modal dataset that encompasses high-quality image-text pairs collected from professional fundus diagram books. Moreover, enabled by MM-Retinal, we present a novel Knowledge-enhanced foundational pretraining model which incorporates Fundus Image-Text expertise, called KeepFIT. It is designed with image similarity-guided text revision and mixed training strategy to infuse expert knowledge. Our proposed fundus foundation model achieves state-of-the-art performance across six unseen downstream tasks and holds excellent generalization ability in zero-shot and few-shot scenarios. MM-Retinal and KeepFIT are available at https://github.com/lxirich/MM-Retinal., Comment: Early Accepted by The International Conference on Medical Image Computing and Computer Assisted Intervention(MICCAI)2024

Comprehending the association between social capabilities and individual psychological traits is paramount for educational administrators. Presently, many studies heavily depend on online questionnaires and self-reported data, while analysis of the connection between offline social networks and mental health status remains scarce. By leveraging a public dataset encompassing on-campus dining activities over 21 weeks, we establish student co-occurrence networks and closely observe the changes in network topology over time. Empirical analysis shows that the node centralities of the student co-occurrence networks exhibit significantly positive correlation with the enhancement of the flourishing trait within the field of mental well-being. Our findings offer potential guidance for assisting students in maintaining a positive mental health status., Comment: 14 pages, 2 figures, 1 Table

The community plays a crucial role in understanding user behavior and network characteristics in social networks. Some users can use multiple social networks at once for a variety of objectives. These users are called overlapping users who bridge different social networks. Detecting communities across multiple social networks is vital for interaction mining, information diffusion, and behavior migration analysis among networks. This paper presents a community detection method based on nonnegative matrix tri-factorization for multiple heterogeneous social networks, which formulates a common consensus matrix to represent the global fused community. Specifically, the proposed method involves creating adjacency matrices based on network structure and content similarity, followed by alignment matrices which distinguish overlapping users in different social networks. With the generated alignment matrices, the method could enhance the fusion degree of the global community by detecting overlapping user communities across networks. The effectiveness of the proposed method is evaluated with new metrics on Twitter, Instagram, and Tumblr datasets. The results of the experiments demonstrate its superior performance in terms of community quality and community fusion., Comment: This paper was accepted by IEEE Transactions on Computational Social Systems(TCSS)

In this work, we propose a martingale based neural network, SOC-MartNet, for solving high-dimensional Hamilton-Jacobi-Bellman (HJB) equations where no explicit expression is needed for the Hamiltonian $\inf_{u \in U} H(t,x,u, z,p)$, and stochastic optimal control problems with controls on both drift and volatility. We reformulate the HJB equations into a stochastic neural network learning process, i.e., training a control network and a value network such that the associated Hamiltonian process is minimized and the cost process becomes a martingale.To enforce the martingale property for the cost process, we employ an adversarial network and construct a loss function based on the projection property of conditional expectations. Then, the control/value networks and the adversarial network are trained adversarially, such that the cost process is driven towards a martingale and the minimum principle is satisfied for the control.Numerical results show that the proposed SOC-MartNet is effective and efficient for solving HJB-type equations and SOCP with a dimension up to $500$ in a small number of training epochs.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causal agent for coronavirus disease 2019 (COVID-19). Although vaccines have helped to prevent uncontrolled viral spreading, our understanding of the fundamental biology of SARS-CoV-2 infection remains insufficient, which hinders effective therapeutic development. Here, we found that Apolipoprotein E (ApoE), a lipid binding protein, is hijacked by SARS-CoV-2 for infection. Preincubation of SARS-CoV-2 with a neutralizing antibody specific to ApoE led to inhibition of SARS-CoV-2 infection. The ApoE neutralizing antibody efficiently blocked SARS-CoV-2 infection of human iPSC-derived astrocytes and air-liquid interface organoid models in addition to human ACE2-expressing HEK293T cells and Calu-3 lung cells. ApoE mediates SARS-CoV-2 entry through binding to its cellular receptors such as the low density lipoprotein receptor (LDLR). LDLR knockout or ApoE mutations at the receptor binding domain or an ApoE mimetic peptide reduced SARS-CoV-2 infection. Furthermore, we detected strong membrane LDLR expression on SARS-CoV-2 Spike-positive cells in human lung tissues, whereas no or low ACE2 expression was detected. This study provides a new paradigm for SARS-CoV-2 cellular entry through binding of ApoE on the lipoviral particles to host cell receptor(s). Moreover, this study suggests that ApoE neutralizing antibodies are promising antiviral therapies for COVID-19 by blocking entry of both parental virus and variants of concern.

Enhancing industrial robot path following accuracy requires the real-time feedback of an external sensor. This study introduces a position-based visual servoing (PBVS) scheme to decrease path error by correcting the Cartesian pose in real-time. The vision system estimates the end effector pose from which the Cartesian pose offset is calculated. The robot’s internal control system treats the pose offset as a high-level control input and induces real-time modification of the robot’s intrinsic motion. A proportional-integral-derivative (PID) controller is utilized as the baseline control method. Due to the repetitiveness of robot tasks, the control performance undergoes iterative improvement via the supplementation of a reinforcement learning(RL)-based controller trained via a state-of-the-art actor-critic algorithm. The experimental platform comprises two commercial systems: the C-Track 780 dual camera sensor from Creaform and the M-20iA robot from FANUC. In a position-only line following experiment, the effect of RL-based controller supplementation significantly enhances path accuracy by attenuating overshoot. The mean absolute error (MAE) and the maximum error are reduced by 10% and 20%, respectively. In terms of the Euclidean norm, the maximum path error is 0.09 mm., Communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023.

Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects. Modern multi-object trackers commonly follow the tracking-by-detection strategy. Therefore, fooling detectors can be an effective solution but it usually requires attacks in multiple successive frames, resulting in low efficiency. Attacking association processes improves efficiency but may require model-specific design, leading to poor generalization. In this paper, we propose a novel False negative and False positive attack (F&F attack) mechanism: it perturbs the input image to erase original detections and to inject deceptive false alarms around original ones while integrating the association attack implicitly. The mechanism can produce effective identity switches against multi-object trackers by only fooling detectors in a few frames. To demonstrate the flexibility of the mechanism, we deploy it to three multi-object trackers (ByteTrack, SORT, and CenterTrack) which are enabled by two representative detectors (YOLOX and CenterNet). Comprehensive experiments on MOT17 and MOT20 datasets show that our method significantly outperforms existing attackers, revealing the vulnerability of the tracking-by-detection paradigm to detection attacks. © 2023 IEEE.

This technical report presents an overview of sample environments which are usable at the PETRA III Swedish Materials Science beamline (potentially requiring arrangement or development of the beamline layout). Alongside the description of each sample environment, illustrative materials science studies are presented that exemplify the use of these sample environments for in situ and/or in operando measurements. The sample environments are catalogued according to the research application areas, which are categorised as Thermal treatments, Electrochemistry, Catalysis, Thin films, Mechanical response of materials and Levitation. Such cataloguing means that researchers can now start their search for relevant sample environments by looking up a relevant research application area. Citations and links to specifications, published research cases and the organisation that is responsible for a given sample environment, are also provided as a basis for researchers to proceed with their research planning., QC 20230328

This technical report presents an overview of sample environments which are usable at the PETRA III Swedish Materials Science beamline (potentially requiring arrangement or development of the beamline layout). Alongside the description of each sample environment, illustrative materials science studies are presented that exemplify the use of these sample environments for in situ and/or in operando measurements. The sample environments are catalogued according to the research application areas, which are categorised as Thermal treatments, Electrochemistry, Catalysis, Thin films, Mechanical response of materials and Levitation. Such cataloguing means that researchers can now start their search for relevant sample environments by looking up a relevant research application area. Citations and links to specifications, published research cases and the organisation that is responsible for a given sample environment, are also provided as a basis for researchers to proceed with their research planning., QC 20230328

Targeting to obtain fine dispersions of nanoscale precipitates to enhance the mechanical properties of a highperformance tool steel, re-engineering of the alloy composition and heat treatment was guided by computational thermodynamics and kinetics. A prototype alloy was prepared using the designed chemistry and heat treatment. Thereafter, advanced microstructural characterization and mechanical testing confirmed the successful design to reach a high number density of (V, Mo)C precipitates with an average diameter of about 5 nm in the peak-hardened condition, after tempering the martensite at 600 degrees C for 2 h., QC 20230607

Objective: This study aimed to assess the efficacy of currently used anti-diabetic medications in the treatment of non-alcoholic fatty liver disease (NAFLD) without diabetes. DESIGN: The efficacy of various anti-diabetic medicines on non-alcoholic fatty liver disease in the absence of diabetes was evaluated by searching Pubmed, Embase, Cochrane Library, and Web of Science for randomized controlled trials (RCT) only. The methodological quality was evaluated using the Revised Cochrane risk-of-bias tool for randomized trials (RoB2), and the data were analyzed using Stata software (version 15.1). Results: All papers published between the time of the pooling and September 2022 were searched. There were a total of 18 randomized controlled studies with a total sample size of 1141 cases. The outcomes of interest included variations in alanine transaminase (ALT) and aspartate transaminase (AST). Rosiglitazone (SUCRA: 100%) and vildagliptin (SUCRA: 99.9%) were the best anti-diabetic medicines to improve ALT and AST, respectively, in patients with NAFLD without diabetes, according to the findings of this network meta-analysis. Conclusion: In accordance with the Network Ranking plot, Rosiglitazone was the best anti-diabetic medicine for improving ALT, and vildagliptin was the best for improving AST in patients with non-diabetic NAFLD.

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and “ASCENDING” orbits outperformed those involving copolarization, a single time period and “DESCENDING” orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical an

This technical report presents an overview of sample environments which are usable at the PETRA III Swedish Materials Science beamline (potentially requiring arrangement or development of the beamline layout). Alongside the description of each sample environment, illustrative materials science studies are presented that exemplify the use of these sample environments for in situ and/or in operando measurements. The sample environments are catalogued according to the research application areas, which are categorised as Thermal treatments, Electrochemistry, Catalysis, Thin films, Mechanical response of materials and Levitation. Such cataloguing means that researchers can now start their search for relevant sample environments by looking up a relevant research application area. Citations and links to specifications, published research cases and the organisation that is responsible for a given sample environment, are also provided as a basis for researchers to proceed with their research planning., QC 20230328

This technical report presents an overview of sample environments which are usable at the PETRA III Swedish Materials Science beamline (potentially requiring arrangement or development of the beamline layout). Alongside the description of each sample environment, illustrative materials science studies are presented that exemplify the use of these sample environments for in situ and/or in operando measurements. The sample environments are catalogued according to the research application areas, which are categorised as Thermal treatments, Electrochemistry, Catalysis, Thin films, Mechanical response of materials and Levitation. Such cataloguing means that researchers can now start their search for relevant sample environments by looking up a relevant research application area. Citations and links to specifications, published research cases and the organisation that is responsible for a given sample environment, are also provided as a basis for researchers to proceed with their research planning., QC 20230328

Jet precession has previously been proposed to explain the apparently repeating features in the light curves of a few gamma-ray bursts (GRBs). In this {\it Letter}, we further apply the precession model to a bright GRB 220408B by examining both its temporal and spectral consistency with the predictions of the model. As one of the recently confirmed GRBs observed by our GRID CubeSat mission, GRB 220408B is noteworthy as it exhibits three apparently similar emission episodes. Furthermore, the similarities are reinforced by their strong temporal correlations and similar features in terms of spectral evolution and spectral lags. Our analysis demonstrates that these features can be well explained by the modulated emission of a Fast-Rise-Exponential-Decay (FRED) shape light curve intrinsically produced by a precessing jet with a precession period of $18.4 \pm 0.2$ seconds, a nutation period of $11.1 \pm 0.2$ seconds and viewed off-axis. This study provides a straightforward explanation for the complex yet similar multi-episode GRB light curves., Comment: 11 pages, 3 tables, 8 figures

Serum hepatitis B virus (HBV) RNA is a new serological indicator reflecting viral replication with good clinical application prospects. This study aimed to clarify the dynamic changes of serum HBV RNA levels and the quasispecies of HBV RNA virus-like particles in nucleos(t)ide analogues (NAs)-experienced chronic hepatitis B (CHB) patients harboring NAs-resistant mutations and their identifiable effects on NAs resistance. We included CHB patients who were on long-term NAs treatment and with HBV DNA rebound. The longitudinally dynamics of serum HBV RNA levels were quantitatively detected, and the quasispecies differences between serum HBV DNA and serum HBV RNA were compared by high-throughput sequencing. The effect of NAs concentration pressure on altering the resistance mutations quasispecies proportion of HBV DNA and HBV RNA in cell supernatant was analyzed in vitro. A total of 447 serum samples from 36 CHB patients treated with NAs were collected. The median follow-up period was 47 months (about 4 years), and the longest follow-up period was 117 months (about 10 years). Our results showed that HBV RNA could reflect virological breakthrough in 23 (64%, 23/36) patients, and serum HBV RNA rebound earlier than HBV DNA in 12 (52%, 12/23) patients. However, serum HBV RNA remained at a consistently high level and did not fluctuate significantly with the HBV DNA rebound in 6 of 36 patients. In addition, serum HBV RNA was not consistently detectable in 7 of the 36 patients, and their serum HBV RNA was undetectable even after HBV DNA had rebounded. The proportion of drug-resistant mutations in HBV DNA was higher than that of HBV RNA by high-throughput sequencing. The results of in vitro experiments showed that the viral strains with drug-resistant mutation in HBV DNA in cell supernatants gradually become the dominant strains with the increase of NAs concentrations. Serum HBV RNA levels can reflect virological breakthrough in most NAs- treated CHB patients, but there are cert

Rechargeable zinc-air batteries (ZABs) hold great promise for energy storage and conversion due to their high theoretical energy density, cost-effectiveness, and inherent safety. However, progress is constrained by sluggish oxygen electrocatalysis and instability at the air cathode. To address such issues, we resort to a directly integrated pseudo-3D composite electrocatalyst based on carbon cloth, on which Fe/Fe3C- and N- co-doped carbon nanotubes are directly induced and then used to further intercalate NiFe hydroxide clusters, FexNC@NiFe(OH)(x). This hierarchical electrocatalyst shows enhanced oxygen electrocatalysis (Delta E is 636 mV), rendering high efficiency and durability of ZABs. Such improvement can be attributed to the rationally integrated pseudo-3D structure with high conductivity, high density of active sites, interconnected porosity, and well-bonded components for accelerating electron transfer and ion diffusion while ensuring structural integrity. Moreover, the hierarchical structure increases the electrochemical surface area with superior surface hydrophilicity. As a result, the composite electrocatalyst shows great potential as a binder-free air electrode, as demonstrated in a rechargeable ZAB of a high power density of 85.1 mW cm(-2) and a long period of operation beyond 2000 cycles (350 h) without notable degradation, outperforming noble metal electrodes.

Modeling accurate and detailed soil spatial information is essential for environmental modeling, precision soil management and decision-making. In this study, we integrated long-term optical (Sentinel-2) and radar (Sentinel-1) satellite observations via the Google Earth Engine (GEE) platform for high-resolution national-scale digital mapping of soil organic carbon (SOC) and total soil nitrogen (TSN) in Austria. Our soil predictive models based on boosted regression tree (BRT) and regression kriging (RK) methods were constructed from 449 soil samples (0–20 cm) covering the study area in the LUCAS soil database and Sentinel observations synthesized with different time intervals. The different input predictors of these soil predictive models resulted in seven modeling scenarios, and their prediction performance was evaluated by a cross-validation technique. Comparative analysis indicated that satellite sensors, modeling techniques, and SAR data acquisition configurations greatly affected the model outputs. Cross-polarization and co-polarization had similar performance in TSN and SOC predictions, and their combination improved the prediction accuracy. Predictive models based on Sentinel-1 with the “ASCENDING” orbits outperformed the models involving the “DESCENDING” orbits; the prediction accuracy of the former was comparable to models involving two orbital data. The models built by Sentinel-1 and Sentinel-2 performed similarly in predicting SOC (R2 = 0.51 vs. R2 = 0.52, respectively) and TSN (their R2 were both 0.42); their synergistic utilization improved the prediction results. Models involving more years of Sentinel observations on the GEE platform provided more accurate modeling results. The best soil predictive models explained 55% and 45% of soil variability for SOC and TSN, respectively, both constructed from long-term Sentinel-1/2 observations using the RK method. The overall trends of the mapping results of the mode

Domain adaptation tackles the challenge of generalizing knowledge acquired from a source domain to a target domain with different data distributions. Traditional domain adaptation methods presume that the classes in the source and target domains are identical, which is not always the case in real-world scenarios. Open-set domain adaptation (OSDA) addresses this limitation by allowing previously unseen classes in the target domain. Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples. We propose a novel framework based on self-paced learning to distinguish common and unknown class samples precisely, referred to as SPLOS (self-paced learning for open-set). To utilize unlabeled target samples for self-paced learning, we generate pseudo labels and design a cross-domain mixup method tailored for OSDA scenarios. This strategy minimizes the noise from pseudo labels and ensures our model progressively learns common class features of the target domain, beginning with simpler examples and advancing to more complex ones. Furthermore, unlike existing OSDA methods that require manual hyperparameter $threshold$ tuning to separate common and unknown classes, our approach self-tunes a suitable threshold, eliminating the need for empirical tuning during testing. Comprehensive experiments illustrate that our method consistently achieves superior performance on different benchmarks compared with various state-of-the-art methods.

We propose a novel framework for uncertainty quantification via information bottleneck (IB-UQ) for scientific machine learning tasks, including deep neural network (DNN) regression and neural operator learning (DeepONet). Specifically, we incorporate the bottleneck by a confidence-aware encoder, which encodes inputs into latent representations according to the confidence of the input data belonging to the region where training data is located, and utilize a Gaussian decoder to predict means and variances of outputs conditional on representation variables. Furthermore, we propose a data augmentation based information bottleneck objective which can enhance the quantification quality of the extrapolation uncertainty, and the encoder and decoder can be both trained by minimizing a tractable variational bound of the objective. In comparison to uncertainty quantification (UQ) methods for scientific learning tasks that rely on Bayesian neural networks with Hamiltonian Monte Carlo posterior estimators, the model we propose is computationally efficient, particularly when dealing with large-scale data sets. The effectiveness of the IB-UQ model has been demonstrated through several representative examples, such as regression for discontinuous functions, real-world data set regression, learning nonlinear operators for partial differential equations, and a large-scale climate model. The experimental results indicate that the IB-UQ model can handle noisy data, generate robust predictions, and provide confident uncertainty evaluation for out-of-distribution data., Comment: 27 pages, 22figures

This is the second part of our series works on failure-informed adaptive sampling for physic-informed neural networks (FI-PINNs). In our previous work \cite{gao2022failure}, we have presented an adaptive sampling framework by using the failure probability as the posterior error indicator, where the truncated Gaussian model has been adopted for estimating the indicator. In this work, we present two novel extensions to FI-PINNs. The first extension consist in combining with a re-sampling technique, so that the new algorithm can maintain a constant training size. This is achieved through a cosine-annealing, which gradually transforms the sampling of collocation points from uniform to adaptive via training progress. The second extension is to present the subset simulation algorithm as the posterior model (instead of the truncated Gaussian model) for estimating the error indicator, which can more effectively estimate the failure probability and generate new effective training points in the failure region. We investigate the performance of the new approach using several challenging problems, and numerical experiments demonstrate a significant improvement over the original algorithm.

Improving machine translation (MT) systems with translation memories (TMs) is of great interest to practitioners in the MT community. However, previous approaches require either a significant update of the model architecture and/or additional training efforts to make the models well-behaved when TMs are taken as additional input. In this paper, we present a simple but effective method to introduce TMs into neural machine translation (NMT) systems. Specifically, we treat TMs as prompts to the NMT model at test time, but leave the training process unchanged. The result is a slight update of an existing NMT system, which can be implemented in a few hours by anyone who is familiar with NMT. Experimental results on several datasets demonstrate that our system significantly outperforms strong baselines., Comment: Accepted to AAAI 2023

The carcinogenic and hard-to-dispose Cr(VI) in high-salinity industrial wastewater has caused severe environmental problems. Herein, a novel hollow spherical CuFe2O4 spinel oxide with concentrated confined oxygen vacancies (HS/CuFe2O4-OVs) was developed for Cr(VI) decontamination. Such a fancy material delivers a remarkable removal rate of 0.041 min−1, approximately-one order of magnitude higher than the commonly reported iron-based materials. The OVs in the confined space could exert dual effects on Cr(VI) removal from the aspects of enhanced stabilization via local electronic structure regulation and strengthened immobilization via electron conductivity modulation. Importantly, due to the highly sensitive response of Cr(VI) to the changed confined OVs content, the HS/CuFe2O4-OVs showed higher selectivity for Cr(VI) removal in the interference of high concentration salts than its nonhollow-structured counterpart and can successfully treat Cr(VI)-contaminated industrial wastewater with high salinity. This work opens up a new concept for rational design of robust spatial confined architectures for Cr(VI) treatment. © 2022 Elsevier B.V.

This technical report presents an overview of sample environments which are usable at the PETRA III Swedish Materials Science beamline (potentially requiring arrangement or development of the beamline layout). Alongside the description of each sample environment, illustrative materials science studies are presented that exemplify the use of these sample environments for in situ and/or in operando measurements. The sample environments are catalogued according to the research application areas, which are categorised as Thermal treatments, Electrochemistry, Catalysis, Thin films, Mechanical response of materials and Levitation. Such cataloguing means that researchers can now start their search for relevant sample environments by looking up a relevant research application area. Citations and links to specifications, published research cases and the organisation that is responsible for a given sample environment, are also provided as a basis for researchers to proceed with their research planning., QC 20230328

The existing collaborative recommendation models that use multi-modal information emphasize the representation of users' preferences but easily ignore the representation of users' dislikes. Nevertheless, modelling users' dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users' dislikes should be integrated into the user modelling when we construct a collaborative recommendation model. In this paper, we propose a novel Collaborative Recommendation Model based on Multi-modal multi-view Attention Network (CRMMAN), in which the users are represented from both preference and dislike views. Specifically, the users' historical interactions are divided into positive and negative interactions, used to model the user's preference and dislike views, respectively. Furthermore, the semantic and structural information extracted from the scene is employed to enrich the item representation. We validate CRMMAN by designing contrast experiments based on two benchmark MovieLens-1M and Book-Crossing datasets. Movielens-1m has about a million ratings, and Book-Crossing has about 300,000 ratings. Compared with the state-of-the-art knowledge-graph-based and multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by 2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled experiments to explore the effects of multi-modal information and multi-view mechanism. The experimental results show that both of them enhance the model's performance.

In this paper, we develop an invertible mapping, called B-KRnet, on a bounded domain and apply it to density estimation/approximation for data or the solutions of PDEs such as the Fokker-Planck equation and the Keller-Segel equation. Similar to KRnet, the structure of B-KRnet adapts the triangular form of the Knothe-Rosenblatt rearrangement into a normalizing flow model. The main difference between B-KRnet and KRnet is that B-KRnet is defined on a hypercube while KRnet is defined on the whole space, in other words, we introduce a new mechanism in B-KRnet to maintain the exact invertibility. Using B-KRnet as a transport map, we obtain an explicit probability density function (PDF) model that corresponds to the pushforward of a prior (uniform) distribution on the hypercube. To approximate PDFs defined on a bounded computational domain, B-KRnet is more effective than KRnet. By coupling KRnet and B-KRnet, we can also define a deep generative model on a high-dimensional domain where some dimensions are bounded and other dimensions are unbounded. A typical case is the solution of the stationary kinetic Fokker-Planck equation, which is a PDF of position and momentum. Based on B-KRnet, we develop an adaptive learning approach to approximate partial differential equations whose solutions are PDFs or can be regarded as a PDF. In addition, we apply B-KRnet to density estimation when only data are available. A variety of numerical experiments is presented to demonstrate the effectiveness of B-KRnet., Comment: 26 pages, 13 figures

The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target. This paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models. In particular, we demonstrate how to use SAM to augment image input for commonly-used medical image segmentation models (e.g., U-Net). Experiments on three segmentation tasks show the effectiveness of our proposed SAMAug method. The code is available at \url{https://github.com/yizhezhang2000/SAMAug}., Comment: GitHub: https://github.com/yizhezhang2000/SAMAug. Comments and questions are welcome

Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentation is a fundamental task in the medical imaging field, which plays a critical role in the diagnosis and cure of colorectal cancer. In particular, applying SAM to the polyp segmentation task is interesting. In this report, we evaluate the performance of SAM in segmenting polyps, in which SAM is under unprompted settings. We hope this report will provide insights to advance this polyp segmentation field and promote more interesting works in the future. This project is publicly at https://github.com/taozh2017/SAMPolyp., Comment: Technical Report