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Ischemic stroke is a devastative nervous system disease associated with high mortality and morbidity rates. Unfortunately, no clinically effective neuroprotective drugs are available now. In ischemic stroke, S100 calcium-binding protein b (S100b) binds to receptor for advanced glycation end products (Rage), leading to the neurological injury. Therefore, disruption of the interaction between S100B and Rage can rescue neuronal cells. Here, we designed a peptide, termed TAT-W61, derived from the V domain of Rage which can recognize S100b. Intriguingly, TAT-W61 can reduce the inflammatory caused by ischemic stroke through the direct binding to S100b. The further investigation demonstrated that TAT-W61 can improve pathological infarct volume and reduce the apoptotic rate. Particularly, TAT-W61 significantly improved the learning ability, memory, and motor dysfunction of the mouse in the ischemic stroke model. Our study provides a mechanistic insight into the abnormal expression of S100b and Rage in ischemic stroke and yields an invaluable candidate for the development of drugs in tackling ischemic stroke.

Huang Y, Chen J, Yang S, et al. Drug Des Devel Ther. 2020;14:4625–4637. We, the Editor and Publisher of the journal Drug Design, Development and Therapy, have retracted the following article. Following publication of the article, concerns were raised about the duplication of images from Figures 4 and 6. Specifically, Images for Figure 4F, MG63, control and 80 µM, have been duplicated. Images for Figure 4J, MG63, 12H, control and 100 µM, have been duplicated. Images for Figure 6D, Osteosarcoma, Blank (x 200) and 75 mg/kg (x 200) have been duplicated. Images for Figure 6E, p-Akt (Ser473), 50 mg/kg (x 400) and 75 mg/kg (x 400) have been duplicated. The authors responded to our queries but were unable to provide a satisfactory explanation for the duplicated images or provide satisfactory data for the study. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article and the authors were notified of this. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

A multitude of substances exist as mixtures comprising multiple chemical components in the natural world. These substances undergo morphological changes under external influences. the phase field model coupled with fluid flow, the dynamic movement and evolution of the phase interface intricately interact with the fluid motion. This article focuses on the N-component models that couple the conservative Allen-Cahn equation with two types of incompressible fluid flow systems: the Navier-Stokes equation and the Darcy equation. By utilizing the scalar auxiliary variable method and the projection method, we innovatively construct two types of structure-preserving weighted implicit-explicit schemes for the coupled models, resulting in fully decoupled linear systems and second-order accuracy in time. The schemes are proved to be mass-conservative. In addition, with the application of $G$-norm inspired by the idea of $G$-stability, we rigorously establish its unconditional energy stability. Finally, the performance of the proposed scheme is verified by some numerical simulations.

We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of processing lengthy text inputs with up to 8192 tokens, making them highly versatile for a range of natural language processing tasks such as text retrieval, clustering, and semantic textual similarity (STS) calculations. By focusing on bilingual models and introducing a unique multi-task learning objective, we have significantly improved the model performance on STS tasks, which outperforms the capabilities of existing multilingual models in both target language understanding and cross-lingual evaluation tasks. Moreover, our bilingual models are more efficient, requiring fewer parameters and less memory due to their smaller vocabulary needs. Furthermore, we have expanded the Massive Text Embedding Benchmark (MTEB) to include benchmarks for German and Spanish embedding models. This integration aims to stimulate further research and advancement in text embedding technologies for these languages.

Long-term fetal heart rate (FHR) monitoring during the antepartum period, increasingly popularized by electronic FHR monitoring, represents a growing approach in FHR monitoring. This kind of continuous monitoring, in contrast to the short-term one, collects an extended period of fetal heart data. This offers a more comprehensive understanding of fetus's conditions. However, the interpretation of long-term antenatal fetal heart monitoring is still in its early stages, lacking corresponding clinical standards. Furthermore, the substantial amount of data generated by continuous monitoring imposes a significant burden on clinical work when analyzed manually. To address above challenges, this study develops an automatic analysis system named LARA (Long-term Antepartum Risk Analysis system) for continuous FHR monitoring, combining deep learning and information fusion methods. LARA's core is a well-established convolutional neural network (CNN) model. It processes long-term FHR data as input and generates a Risk Distribution Map (RDM) and Risk Index (RI) as the analysis results. We evaluate LARA on inner test dataset, the performance metrics are as follows: AUC 0.872, accuracy 0.816, specificity 0.811, sensitivity 0.806, precision 0.271, and F1 score 0.415. In our study, we observe that long-term FHR monitoring data with higher RI is more likely to result in adverse outcomes (p=0.0021). In conclusion, this study introduces LARA, the first automated analysis system for long-term FHR monitoring, initiating the further explorations into its clinical value in the future.

Object detection in visible (RGB) and infrared (IR) images has been widely applied in recent years. Leveraging the complementary characteristics of RGB and IR images, the object detector provides reliable and robust object localization from day to night. Most existing fusion strategies directly input RGB and IR images into deep neural networks, leading to inferior detection performance. However, the RGB and IR features have modality-specific noise, these strategies will exacerbate the fused features along with the propagation. Inspired by the mechanism of the human brain processing multimodal information, in this paper, we introduce a new coarse-to-fine perspective to purify and fuse two modality features. Specifically, following this perspective, we design a Redundant Spectrum Removal module to coarsely remove interfering information within each modality and a Dynamic Feature Selection module to finely select the desired features for feature fusion. To verify the effectiveness of the coarse-to-fine fusion strategy, we construct a new object detector called the Removal and Selection Detector (RSDet). Extensive experiments on three RGB-IR object detection datasets verify the superior performance of our method., Comment: 11pages, 11figures

Heterogeneous graph few-shot learning (HGFL) has been developed to address the label sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and edges. The core concept of HGFL is to extract knowledge from rich-labeled classes in a source HG, transfer this knowledge to a target HG to facilitate learning new classes with few-labeled training data, and finally make predictions on unlabeled testing data. Existing methods typically assume that the source HG, training data, and testing data all share the same distribution. However, in practice, distribution shifts among these three types of data are inevitable due to two reasons: (1) the limited availability of the source HG that matches the target HG distribution, and (2) the unpredictable data generation mechanism of the target HG. Such distribution shifts result in ineffective knowledge transfer and poor learning performance in existing methods, thereby leading to a novel problem of out-of-distribution (OOD) generalization in HGFL. To address this challenging problem, we propose a novel Causal OOD Heterogeneous graph Few-shot learning model, namely COHF. In COHF, we first characterize distribution shifts in HGs with a structural causal model, establishing an invariance principle for OOD generalization in HGFL. Then, following this invariance principle, we propose a new variational autoencoder-based heterogeneous graph neural network to mitigate the impact of distribution shifts. Finally, by integrating this network with a novel meta-learning framework, COHF effectively transfers knowledge to the target HG to predict new classes with few-labeled data. Extensive experiments on seven real-world datasets have demonstrated the superior performance of COHF over the state-of-the-art methods.

Pure-quartic solitons (PQSs) have recently received increasing attention due to their energy-width scaling over the traditional soliton, which has expanded our understanding of soliton dynamics with high-order dispersion in nonlinear systems. Here, we numerically reveal the asynchronization and synchronization processes of the sub-pulse within the vector PQS molecule in a mode-locked fiber laser by solving the coupled Ginzburg-Landau equations. During the establishment of a vector PQS molecule, the repulsion, attraction, and finally stabilization processes have been observed. Specifically, sub-pulse disappearance, regeneration, and finally synchronization with the other pulses are also investigated. Our analysis of the pulse energy, time interval, and relative phase evolution dynamics with the round trip indicates that the asynchronization and synchronization within the vector PQS molecule associate tightly with the gain competition and the cross-phase modulation. Our findings provide insights into the internal mutual dynamics within the vector soliton molecule and offer guidance for the applications of PQS.

This paper focuses on the motion planning problem for the systems exhibiting both continuous and discrete behaviors, which we refer to as hybrid dynamical systems. Firstly, the motion planning problem for hybrid systems is formulated using the hybrid equation framework, which is general to capture most hybrid systems. Secondly, a propagation algorithm template is proposed that describes a general framework to solve the motion planning problem for hybrid systems. Thirdly, a rapidly-exploring random trees (RRT) implementation of the proposed algorithm template is designed to solve the motion planning problem for hybrid systems. At each iteration, the proposed algorithm, called HyRRT, randomly picks a state sample and extends the search tree by flow or jump, which is also chosen randomly when both regimes are possible. Through a definition of concatenation of functions defined on hybrid time domains, we show that HyRRT is probabilistically complete, namely, the probability of failing to find a motion plan approaches zero as the number of iterations of the algorithm increases. This property is guaranteed under mild conditions on the data defining the motion plan, which include a relaxation of the usual positive clearance assumption imposed in the literature of classical systems. The motion plan is computed through the solution of two optimization problems, one associated with the flow and the other with the jumps of the system. The proposed algorithm is applied to an actuated bouncing ball system and a walking robot system so as to highlight its generality and computational features., Comment: 33 pages, 8 figures. Submitted to IJRR. arXiv admin note: text overlap with arXiv:2210.15082

Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to learn global features within the graphs. Moreover, most contemporary methods are based on spatial domain and lack exploration of spectral characteristics. In this paper, we propose a multi-perspective hybrid graph-level anomaly detector namely GLADformer, consisting of two key modules. Specifically, we first design a Graph Transformer module with global spectrum enhancement, which ensures balanced and resilient parameter distributions by fusing global features and spectral distribution characteristics. Furthermore, to uncover local anomalous attributes, we customize a band-pass spectral GNN message passing module that further enhances the model's generalization capability. Through comprehensive experiments on ten real-world datasets from multiple domains, we validate the effectiveness and robustness of GLADformer. This demonstrates that GLADformer outperforms current state-of-the-art models in graph-level anomaly detection, particularly in effectively capturing global anomaly representations and spectral characteristics.

Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. However, CLIP models generally underperform in text-only tasks compared to specialized text models. This creates inefficiencies for information retrieval systems that keep separate embeddings and models for text-only and multimodal tasks. We propose a novel, multi-task contrastive training method to address this issue, which we use to train the jina-clip-v1 model to achieve the state-of-the-art performance on both text-image and text-text retrieval tasks., Comment: 4 pages, ICML2024 workshop submission

Advanced Persistent Threat (APT) is challenging to detect due to prolonged duration, infrequent occurrence, and adept concealment techniques. Existing approaches primarily concentrate on the observable traits of attack behaviors, neglecting the intricate relationships formed throughout the persistent attack lifecycle. Thus, we present an innovative APT detection framework named LTRDetector, implementing an end-to-end holistic operation. LTRDetector employs an innovative graph embedding technique to retain comprehensive contextual information, then derives long-term features from these embedded provenance graphs. During the process, we compress the data of the system provenance graph for effective feature learning. Furthermore, in order to detect attacks conducted by using zero-day exploits, we captured the system's regular behavior and detects abnormal activities without relying on predefined attack signatures. We also conducted extensive evaluations using five prominent datasets, the efficacy evaluation of which underscores the superiority of LTRDetector compared to existing state-of-the-art techniques.

This paper proposes a bidirectional rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. The proposed algorithm, called HyRRT-Connect, propagates in both forward and backward directions in hybrid time until an overlap between the forward and backward propagation results is detected. Then, HyRRT-Connect constructs a motion plan through the reversal and concatenation of functions defined on hybrid time domains, ensuring the motion plan thoroughly satisfies the given hybrid dynamics. To address the potential discontinuity along the flow caused by tolerating some distance between the forward and backward partial motion plans, we reconstruct the backward partial motion plan by a forward-in-hybrid-time simulation from the final state of the forward partial motion plan. By applying the reversed input of the backward partial motion plan, the reconstruction process effectively eliminates the discontinuity and ensures that as the tolerance distance decreases to zero, the distance between the endpoint of the reconstructed motion plan and the final state set approaches zero. The proposed algorithm is applied to an actuated bouncing ball example and a walking robot example so as to highlight its generality and computational improvement., Comment: Accepted by the 8th IFAC International Conference on Analysis and Design of Hybrid Systems (ADHS 2024)

In this paper, we innovatively develop uniform/variable-time-step weighted and shifted BDF2 (WSBDF2) methods for the anisotropic Cahn-Hilliard (CH) model, combining the scalar auxiliary variable (SAV) approach with two types of stabilized techniques. Using the concept of $G$-stability, the uniform-time-step WSBDF2 method is theoretically proved to be energy-stable. Due to the inapplicability of the relevant G-stability properties, another technique is adopted in this work to demonstrate the energy stability of the variable-time-step WSBDF2 method. In addition, the two numerical schemes are all mass-conservative.Finally, numerous numerical simulations are presented to demonstrate the stability and accuracy of these schemes.

PurposeTo develop a 3D multitasking multi-echo (MT-ME) technique for the comprehensive characterization of liver tissues with 5-min free-breathing acquisition; whole-liver coverage; a spatial resolution of 1.5 × 1.5 × 6 mm3 ; and simultaneous quantification of T1 , water-specific T1 (T1w ), proton density fat fraction (PDFF), and R2∗ .MethodsSix-echo bipolar spoiled gradient echo readouts following inversion recovery preparation was performed to generate T1 , water/fat, and R2∗ contrast. MR multitasking was used to reconstruct the MT-ME images with 3 spatial dimensions: 1 T1 recovery dimension, 1 multi-echo dimension, and 1 respiratory dimension. A basis function-based approach was developed for T1w quantification, followed by the estimation of R2∗ and T1 -corrected PDFF. The intrasession repeatability and agreement against references of MT-ME measurements were tested on a phantom and 15 clinically healthy subjects. In addition, 4 patients with confirmed liver diseases were recruited, and the agreement between MT-ME measurements and references was assessed.ResultsMT-ME produced high-quality, coregistered T1 , T1w , PDFF, and R2∗ maps with good intrasession repeatability and substantial agreement with references on phantom and human studies. The intra-class coefficients of T1 , T1w , PDFF, and R2∗ from the repeat MT-ME measurements on clinically healthy subjects were 0.989, 0.990, 0.999, and 0.988, respectively. The intra-class coefficients of T1 , PDFF, and R2∗ between the MT-ME and reference measurements were 0.924, 0.987, and 0.975 in healthy subjects and 0.980, 0.999, and 0.998 in patients. The T1w was independent to PDFF (R = -0.029, P = .904).ConclusionThe proposed MT-ME technique quantifies T1 , T1w , PDFF, and R2∗ simultaneously and is clinically promising for the comprehensive characterization of liver tissue properties.

Background and aimsAccurate differentiation of chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) is an area of unmet clinical need. In this study, a novel Multitasking dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) technique was used to quantitatively evaluate the microcirculation properties of pancreas in CP and PDAC and differentiate between them.MethodsThe Multitasking DCE technique was able to acquire one 3D image per second during the passage of MRI contrast agent, allowing the quantitative estimation of microcirculation properties of tissue, including blood flow Fp, plasma volume fraction vp, transfer constant Ktrans, and extravascular extracellular volume fraction ve. Receiver operating characteristic (ROC) analysis was performed to differentiate the CP pancreas, PDAC pancreas, normal control pancreas, PDAC tumor, PDAC upstream, and PDAC downstream. ROCs from quantitative analysis and conventional analysis were compared.ResultsFourteen PDAC patients, 8 CP patients and 20 healthy subjects were prospectively recruited. The combination of Fp, vp, Ktrans, and ve can differentiate CP versus PDAC pancreas with good AUC (AUC [95% CI] = 0.821 [0.654 - 0.988]), CP versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), PDAC pancreas versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC tumor with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC downstream with excellent AUC (0.917 [0.795 - 1.000]), and CP versus PDAC upstream with fair AUC (0.722 [0.465 - 0.980]). This quantitative analysis outperformed conventional analysis in differentiation of each pair.ConclusionMultitasking DCE MRI is a promising clinical tool that is capable of unbiased quantitative differentiation between CP from PDAC.

Ruixian Zhang,1 Sha Luo,1 Nan Wang,1 Hongying Zhang,2 Xuping Wu1 1The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210003, People’s Republic of China; 2Nanjing Center for Disease Control and Prevention Affiliated to Nanjing Medical University, Nanjing, Jiangsu, 210008, People’s Republic of ChinaCorrespondence: Xuping Wu, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210003, People’s Republic of China, Email xuping.wu@njucm.edu.cn Hongying Zhang, Email xiao99now@aliyun.comBackground: Nontuberculous mycobacteria (NTM) are easily misdiagnosed as multidrug-resistant tuberculosis (MDR-TB), and treatment drugs are very limited. The main objective of our study was to evaluate the minimal inhibitory concentration (MIC) in vitro of bedaquiline (BDQ), clofazimine (CFZ), linezolid (LZD), delamanid (DLM), and pretomanid (PA-824) for treatment of M. abscessus and M. intracellulare. Furthermore, we determined whether MAB_1448, MAB_4384, MAB_2299c, MAB_1483, MAB_0540, rplD, rplC, and rrl were related to drug resistance to provide an experimental basis for the use of these five drugs in the treatment of NTM.Methods: We identified sample characteristics of epidemics in 550 patients with suspected NTM infection in Nanjing from 2019 to 2021 using the PCR-reverse spot hybrid method. Furthermore, we evaluated the MIC of BDQ, CFZ, DLM, LZD, and PA-824 against 155 clinical isolates of NTM using the microbroth dilution method. The resistant isolates were sequenced using Sanger sequencing.Results: The top three dominant species of NTM distributed in Nanjing were M. intracellulare, M. avium, and M. abscessus. Notably, the proportion of M. abscessus infections increased. The proportion of M. abscessus increased from 12% in 2019 to 18% in 2021. Demographic analysis showed that female infection rates were substantialy greater than male for M. abscessus (P=0.017, < 0.05). Our results demonstrate that NT

CO2 separation plays a vital role in reducing CO2 emissions to combat climate change, while the available technologies still face challenges, including high costs and intensive energy usage. It is still critical to develop an energy-efficient and cost-effective technology for CO2 separation. Ionic liquid/deep eutectic solvent (IL/DES) has drawn significant attention in CO2 separation because they are tunable and exhibit high thermal stability and high CO2 solubility. The technologies based on IL/DES show their potential to reduce cost and energy usage. However, their viscosity is relatively high. In our work, adding cosolvents was used to adjust the high viscosity of IL/DES. A universal framework of IL/DES-based hybrid solvents for CO2 separation, from properties determination, thermodynamic modeling, to process design and evaluation, has been built up. Several DESs/ILs interacted with CO2 chemically and/or physically were investigated, aiming for the gas streams with different CO2 concentrations (biogas, flue gas). A detailed comparison of the performance of IL/DES-based hybrid solvents in terms of energy usage and cost with respect to conventional solvents was provided to study their feasibility. Our research shows that IL/DES-based hybrid solvents are promising for CO2 separation for different gas streams to reach the aim of lowing energy usage and cost, while further improvement is still needed to reach a technological breakthrough., Godkänd;2023;Nivå 0;2023-08-15 (joosat);Konferensartikel i tidskrift;Funder: National Natural Science Foundation of ChinaLicens fulltext: CC BY LicensePart of special issue: ISPT 2022: International Congress on Separation and Purification Technology. Edited by Shouliang Yi

T1 mapping is becoming a staple magnetic resonance imaging method for diagnosing myocardial diseases such as ischemic cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and more. Clinically, most T1 mapping sequences acquire a single slice at a single cardiac phase across a 10 to 15-heartbeat breath-hold, with one to three slices acquired in total. This leaves opportunities for improving patient comfort and information density by acquiring data across multiple cardiac phases in free-running acquisitions and across multiple respiratory phases in free-breathing acquisitions. Scanning in the presence of cardiac and respiratory motion requires more complex motion characterization and compensation. Most clinical mapping sequences use 2D single-slice acquisitions; however newer techniques allow for motion-compensated reconstructions in three dimensions and beyond. To further address confounding factors and improve measurement accuracy, T1 maps can be acquired jointly with other quantitative parameters such as T2, T2∗, fat fraction, and more. These multiparametric acquisitions allow for constrained reconstruction approaches that isolate contributions to T1 from other motion and relaxation mechanisms. In this review, we examine the state of the literature in motion-corrected and motion-resolved T1 mapping, with potential future directions for further technical development and clinical translation.

Introduction: Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated. Method: Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well. Results: The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant Ktrans, fractional extravascular extracellular volume ve, and rate constant kep) achieved intraclass correlation coefficient and R2 between 0.84–0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas. Discussion: Retrospective quantification (RoQ) of clinical multi-pha

CO2 separation plays a vital role in reducing CO2 emissions to combat climate change, while the available technologies still face challenges, including high costs and intensive energy usage. It is still critical to develop an energy-efficient and cost-effective technology for CO2 separation. Ionic liquid/deep eutectic solvent (IL/DES) has drawn significant attention in CO2 separation because they are tunable and exhibit high thermal stability and high CO2 solubility. The technologies based on IL/DES show their potential to reduce cost and energy usage. However, their viscosity is relatively high. In our work, adding cosolvents was used to adjust the high viscosity of IL/DES. A universal framework of IL/DES-based hybrid solvents for CO2 separation, from properties determination, thermodynamic modeling, to process design and evaluation, has been built up. Several DESs/ILs interacted with CO2 chemically and/or physically were investigated, aiming for the gas streams with different CO2 concentrations (biogas, flue gas). A detailed comparison of the performance of IL/DES-based hybrid solvents in terms of energy usage and cost with respect to conventional solvents was provided to study their feasibility. Our research shows that IL/DES-based hybrid solvents are promising for CO2 separation for different gas streams to reach the aim of lowing energy usage and cost, while further improvement is still needed to reach a technological breakthrough., Godkänd;2023;Nivå 0;2023-08-15 (joosat);Konferensartikel i tidskrift;Funder: National Natural Science Foundation of ChinaLicens fulltext: CC BY LicensePart of special issue: ISPT 2022: International Congress on Separation and Purification Technology. Edited by Shouliang Yi

PurposeTo develop an MR multitasking-based dynamic imaging for cerebrovascular evaluation (MT-DICE) technique for simultaneous quantification of permeability and leakage-insensitive perfusion with a single-dose contrast injection.MethodsMT-DICE builds on a saturation-recovery prepared multi-echo fast low-angle shot sequence. The k-space is randomly sampled for 7.6 min, with single-dose contrast agent injected 1.5 min into the scan. MR multitasking is used to model the data into six dimensions, including three spatial dimensions for whole-brain coverage, a saturation-recovery time dimension, and a TE dimension for dynamic T1$$ {\mathrm{T}}_1 $$ and T2*$$ {\mathrm{T}}_2^{\ast } $$ quantification, respectively, and a contrast dynamics dimension for capturing contrast kinetics. The derived pixel-wise T1/T2*$$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ time series are converted into contrast concentration-time curves for calculation of kinetic metrics. The technique was assessed for its agreement with reference methods in T1$$ {\mathrm{T}}_1 $$ and T2*$$ {\mathrm{T}}_2^{\ast } $$ measurements in eight healthy subjects and, in three of them, inter-session repeatability of permeability and leakage-insensitive perfusion parameters. Its feasibility was also demonstrated in four patients with brain tumors.ResultsMT-DICE T1/T2*$$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ values of normal gray matter and white matter were in excellent agreement with reference values (intraclass correlation coefficients = 0.860/0.962 for gray matter and 0.925/0.975 for white matter ). Both permeability and perfusion parameters demonstrated good to excellent intersession agreement with the lowest intraclass correlation coefficients at 0.694. Contrast kinetic parameters in all healthy subjects and patients were within the literature range.ConclusionBased on dynamic T1/T2*$$ {\mathrm{T}}_1/{\mathrm{T}}_2^{\ast } $$ mapping, MT-DICE allows for simultaneous quantification of permeabi

Zhaojun Liang,1,2 Nan Wang,1,2 Chunxue Fan,1,2 Lili Shang,1,2 Yaping Zhang,1,2 Chong Gao,3 Jing Luo1,2 1Division of Rheumatology, Department of Medicine, the Second Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China; 2Shanxi Key Laboratory for Immunomicroecology, the Second Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China; 3Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USACorrespondence: Jing Luo, Email ljty966@hotmail.comIntroduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to disorders of immune function and a decrease in the diversity of intestinal flora. We aimed to explore the changes of circulating immune cell subsets and the plasma level of intestinal short-chain fatty acids (SCFAs) in patients with Coronavirus disease 2019 (COVID-19), further understanding the pathogenesis of COVID-19.Methods: The study included 83 newly diagnosed COVID-19 patients and 39 non-COVID-19 controls. All have completed a full course of vaccination against SARS-CoV-2. The levels of peripheral lymphocyte subsets and plasma cytokines were detected by flow cytometry. Targeted metabolomics was used to explore the level of SCFAs in plasma.Results: Compared with the non-COVID-19 group, COVID-19 patients showed a decrease in CD19+B cells, CD4+T cells, CD8+T cells, NK cells, CD4+CD8+T cells and CD4–CD8–T cells (all p< 0.001) and concomitantly an increase in sIL-2R, IL-6 and IL-10 (all p< 0.005). These alterations were more pronounced in those critical patients. In addition, COVID-19 patients had lower levels of propanoic acid (PA), butyric acid (BA), isobutyric acid (IBA) and isohexanoic acid (ICA) (all p< 0.01). Among them, the level of ICA is positively correlated with the absolute number of immune cells.Conclusion: Our study suggests the immune cell subsets in COVID-19 patients who had completed vaccination were still severely disturbed and concomitantly lower

Microsatellite repeat expansions within genes contribute to a number of neurological diseases1,2. The accumulation of toxic proteins and RNA molecules with repetitive sequences, and/or sequestration of RNA-binding proteins by RNA molecules containing expanded repeats are thought to be important contributors to disease aetiology3-9. Here we reveal that the adenosine in CAG repeat RNA can be methylated to N1-methyladenosine (m1A) by TRMT61A, and that m1A can be demethylated by ALKBH3. We also observed that the m1A/adenosine ratio in CAG repeat RNA increases with repeat length, which is attributed to diminished expression of ALKBH3 elicited by the repeat RNA. Additionally, TDP-43 binds directly and strongly with m1A in RNA, which stimulates the cytoplasmic mis-localization and formation of gel-like aggregates of TDP-43, resembling the observations made for the protein in neurological diseases. Moreover, m1A in CAG repeat RNA contributes to CAG repeat expansion-induced neurodegeneration in Caenorhabditis elegans and Drosophila. In sum, our study offers a new paradigm of the mechanism through which nucleotide repeat expansion contributes to neurological diseases and reveals a novel pathological function of m1A in RNA. These findings may provide an important mechanistic basis for therapeutic intervention in neurodegenerative diseases emanating from CAG repeat expansion.

Eco-innovation is the necessary element for the production process and quality and the organization’s success that needs researchers’ and policymakers’ emphasis. Hence, the current article investigates the impact of product eco-innovation, process eco-innovation, and organizational eco-innovation on the food production quality in the food industry in China. The present study also investigates the mediating impact of environmental awareness among the association of product eco-innovation, process eco-innovation, organizational eco-innovation, and quality of food production in the food industry in China. The article followed the survey questionnaires to gather the primary data. The study used the PLS-SEM to check the relationships among the constructs using SPSS-AMOS. The findings indicated that product eco-innovation, process eco-innovation, and organizational ecoinnovation have a positive association with food production quality in the food industry in China. The outcomes also revealed that environmental awareness significantly mediates among product eco-innovation, process eco-innovation, organizational eco-innovation, and food production quality in the food industry in China. This study guides the policymakers in developing policies related to food production quality using eco-innovation and environmental awareness in the complete business process.

Zhixin Liu,1,2,&ast; Huanyu Zhang,1 Nan Wang,1,&ast; Yajie Feng,1,&ast; Junping Liu,1 Lin Wu,1 Zhaoyue Liu,1 Xinru Liu,1 Libo Liang,1 Jie Liu,3 Qunhong Wu,1 Chaojie Liu4 1Department of Social Medicine, School of Health Management, Harbin Medical University, Harbin, People’s Republic of China; 2Department of Health Policy and Management, School of Public Health, Peking University, Beijing, People’s Republic of China; 3Intensive Care Unit, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 4Department of Public Health, School of Psychology and Public Health, La Trobe University, Melbourne, VIC, Australia&ast;These authors contributed equally to this workCorrespondence: Libo Liang; Jie Liu, Email llbhit@163.com; liujie21814@126.comBackground: This study aimed to test the mediating role of anxiety and insomnia in the association between fear of infection and fatigue.Methods: A cross-sectional questionnaire survey was conducted on the nurses deployed to Heihe. A serial multiple mediation model was established to determine the role of anxiety and insomnia in the association between fear of infection and fatigue.Findings: Over half (53.0%) of the study participants reported experiencing fear of infection despite stringent personal protection measures. The scores of anxiety (11.87± 5.19), insomnia (16.33± 5.95), and fatigue (45.94± 12.93) were moderately correlated, with a Pearson correlation coefficient ranging from 0.501 to 0.579. Anxiety, either alone or in combination with insomnia, mediated the association between fear of infection and fatigue.Conclusion: The findings suggest that anxiety and insomnia play a mediating role in the relationship between fear of infection and fatigue. These results emphasize the importance of implementing targeted mental health interventions and work arrangements to address the well-being of healthcare professionals.Keywords: fear of infection, anxiety, insomnia, fatigue

Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.

The mitotic quiescence of prospermatogonia is the event known to occur during genesis of the male germline and is tied to the development of the spermatogenic lineage. The regulatory mechanisms and the functional importance of this process have been demonstrated in mice; however, regulation of this process in human and domestic animal is still largely unknown. In this study, we employed single-cell RNA sequencing to identify transcriptional signatures of prospermatogonia and major somatic cell types in testes of goats at E85, E105, and E125. We identified both common and specific Gene Ontology categories, transcription factor regulatory networks, and cell–cell interactions in cell types from goat testis. We also analyzed the transcriptional dynamic changes in prospermatogonia, Sertoli cells, Leydig cells, and interstitial cells. Our datasets provide a useful resource for the study of domestic animal germline development. © 2023 Federation of American Societies for Experimental Biology.

This study demonstrates the linkages between green technological innovations, sustainable tourism, financial development, economic growth, and ecological sustainability using China’s regional data from 2000 to 2019. The study applies the novel estimation technique, Quantile Autoregressive Distributive Lag (QARDL) approach to examine long-run and short-run relationships between the stated variables. The initial findings confirm non-linearity in the data verified through J-B test statistics. It approves the implication of QARDL estimation for exploring ecological sustainability trends over the study period. The study outcomes confirm that tourism and green technology innovation assists in reducing ecological footprints in China in the long run. Moreover, financial development and economic growth reflect a direct role towards more ecological footprints; therefore, the sustainability dimension has been missing both in financial development and growth. Furthermore, the results in the short run cover the same phenomenon and confirm that ecological innovations and tourism would help in sustaining the natural environment. The study outcomes demonstrate that government officials in China should specifically implement long-term policies to support the natural environment from adverse shocks of more financial development and economic growth.

Acclimation of root growth is vital for plants to survive salt stress. Halophytes are great examples of plants that thrive even under severe salinity, but their salt tolerance mechanisms, especially those mediated by root responses, are still largely unknown. We compared root growth responses of the halophyte Schrenkiella parvula with its glycophytic relative species Arabidopsis thaliana under salt stress and performed transcriptomic analysis of S. parvula roots to identify possible gene regulatory networks underlying their physiological responses. Schrenkiella parvula roots do not avoid salt and experience less growth inhibition under salt stress. Salt-induced abscisic acid levels were higher in S. parvula roots compared with Arabidopsis. Root transcriptomic analysis of S. parvula revealed the induction of sugar transporters and genes regulating cell expansion and suberization under salt stress. 14C-labeled carbon partitioning analyses showed that S. parvula continued allocating carbon to roots from shoots under salt stress while carbon barely allocated to Arabidopsis roots. Further physiological investigation revealed that S. parvula roots maintained root cell expansion and enhanced suberization under severe salt stress. In summary, roots of S. parvula deploy multiple physiological and developmental adjustments under salt stress to maintain growth, providing new avenues to improve salt tolerance of plants using root-specific strategies.

Flavonoids are the most abundant class of secondary metabolites that are ubiquitously involved in plant development and resistance to biotic and abiotic stresses. Flavonoid biosynthesis involves multiple channels of orchestrated molecular regulatory factors. Methyl jasmonate (MeJA) has been demonstrated to enhance flavonoid accumulation in numerous plant species; however, the underlying molecular mechanism of MeJA-induced flavonoid biosynthesis in safflower is still not evident. In the present study, we revealed the underlying molecular basis of a putative F3'5'H gene from safflower imparting MeJA-induced flavonoid accumulation in transgenic plants. The constitutive expression of the CtF3'5'H1 gene was validated at different flowering stages, indicating their diverse transcriptional regulation through flower development in safflower. Similarly, the CtF3'5'H1-overexpressed Arabidopsis plants exhibit a higher expression level, with significantly increased anthocyanins and flavonoid content, but less proanthocyanidins than wild-type plants. In addition, transgenic plants treated with exogenous MeJA revealed the up-regulation of CtF3'5'H1 expression over different time points with significantly enhanced anthocyanin and flavonoid content as confirmed by HPLC analysis. Moreover, CtF3'5'H1- overexpressed Arabidopsis plants under methyl violet and UV-B irradiation also indicated significant increase in the expression level of CtF3'5'H1 with improved anthocyanin and flavonoid content, respectively. Noticeably, the virus-induced gene silencing (VIGS) assay of CtF3'5'H1 in safflower leaves also confirmed reduced anthocyanin accumulation. However, the CtF3'5'H1 suppression in safflower leaves under MeJA elicitation demonstrated significant increase in total flavonoid content. Together, our findings confirmed that CtF3'5'H1 is likely mediating methyl jasmonate-induced flavonoid biosynthesis in transgenic plants via enhanced anthocyanin accumulation.

We propose a novel smearing scheme which gives a Gaussian-like profile and is more efficient than the traditional Gaussian smearing in terms of computer time consumption. We also carry out a detailed analysis of the profiles, smearing sizes, and the behaviors of hadron effective masses of different smearing schemes, and point out that having a sufficient number of gauge paths in a smearing scheme is essential to produce strong smearing effects. For a moderate smearing size $\bar{r}\sim 10a$, the time cost for the novel smearing is less than $1/8$ of that for the traditional Gaussian smearing. In practical lattice calculations with larger smearing sizes or finer lattice spacings the improvement will be more substantial.

Tropospheric ozone threatens human health and crop yields, exacerbates global warming, and fundamentally changes atmospheric chemistry. Evidence has pointed toward widespread ozone increases in the troposphere, and particularly surface ozone is chemically complex and difficult to abate. Despite past successes in some regions, a solution to new challenges of ozone pollution in a warming climate remains unexplored. In this perspective, by compiling surface measurements at ∼4,300 sites worldwide between 2014 and 2019, we show the emerging global challenge of ozone pollution, featuring the unintentional rise in ozone due to the uncoordinated emissions reduction and increasing climate penalty. On the basis of shared emission sources, interactive chemical mechanisms, and synergistic health effects between ozone pollution and climate warming, we propose a synergistic ozone-climate control strategy incorporating joint control of ozone and fine particulate matter. This new solution presents an opportunity to alleviate tropospheric ozone pollution in the forthcoming low-carbon transition.

Objective: The generalizability of clinical large language models is usually ignored during the model development process. This study evaluated the generalizability of BERT-based clinical NLP models across different clinical settings through a breast cancer phenotype extraction task. Materials and Methods: Two clinical corpora of breast cancer patients were collected from the electronic health records from the University of Minnesota and the Mayo Clinic, and annotated following the same guideline. We developed three types of NLP models (i.e., conditional random field, bi-directional long short-term memory and CancerBERT) to extract cancer phenotypes from clinical texts. The models were evaluated for their generalizability on different test sets with different learning strategies (model transfer vs. locally trained). The entity coverage score was assessed with their association with the model performances. Results: We manually annotated 200 and 161 clinical documents at UMN and MC, respectively. The corpora of the two institutes were found to have higher similarity between the target entities than the overall corpora. The CancerBERT models obtained the best performances among the independent test sets from two clinical institutes and the permutation test set. The CancerBERT model developed in one institute and further fine-tuned in another institute achieved reasonable performance compared to the model developed on local data (micro-F1: 0.925 vs 0.932). Conclusions: The results indicate the CancerBERT model has the best learning ability and generalizability among the three types of clinical NLP models. The generalizability of the models was found to be correlated with the similarity of the target entities between the corpora., Comment: 16 pages, 3 figures

Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy. However, the current FL algorithms face the challenges of non-independent and identically distributed (non-IID) data, which causes high communication costs and model accuracy declines. To address the statistical imbalances in FL, we propose a clustered data sharing framework which spares the partial data from cluster heads to credible associates through device-to-device (D2D) communication. Moreover, aiming at diluting the data skew on nodes, we formulate the joint clustering and data sharing problem based on the privacy-preserving constrained graph. To tackle the serious coupling of decisions on the graph, we devise a distribution-based adaptive clustering algorithm (DACA) basing on three deductive cluster-forming conditions, which ensures the maximum yield of data sharing. The experiments show that the proposed framework facilitates FL on non-IID datasets with better convergence and model accuracy under a limited communication environment.

In this paper, we propose a practically efficient model for securely computing rank-based statistics, e.g., median, percentiles and quartiles, over distributed datasets in the malicious setting without leaking individual data privacy. Based on the binary search technique of Aggarwal et al. (EUROCRYPT \textquotesingle 04), we respectively present an interactive protocol and a non-interactive protocol, involving at most $\log ||R||$ rounds, where $||R||$ is the range size of the dataset elements. Besides, we introduce a series of optimisation techniques to reduce the round complexity. Our computing model is modular and can be instantiated with either homomorphic encryption or secret-sharing schemes. Compared to the state-of-the-art solutions, it provides stronger security and privacy while maintaining high efficiency and accuracy. Unlike differential-privacy-based solutions, it does not suffer a trade-off between accuracy and privacy. On the other hand, it only involves $O(N \log ||R||)$ time complexity, which is far more efficient than those bitwise-comparison-based solutions with $O(N^2\log ||R||)$ time complexity, where $N$ is the dataset size. Finally, we provide a UC-secure instantiation with the threshold Paillier cryptosystem and $\Sigma$-protocol zero-knowledge proofs of knowledge.

Tan T, Chen J, Hu Y, et al. Onco Targets Ther. 2019;12:5111–5122. The Editor and Publisher of OncoTargets and Therapy are retracting the published article. Concerns were raised regarding image duplication in Figure 2. Specifically, The image for Figure 2E, 143B, Control, has been duplicated with the same image for Figure 2E, 143B, 0µM. The image for Figure 2I, 143B, Control, has been duplicated with the same image for Figure 2I, 143B, 0µM. The authors cooperated with the investigation and provided data associated with the reported study. However, despite the authors’ assistance, a satisfactory explanation for how the images came to be duplicated could not be provided, and the validity of the published work could not be verified. The decision was made to retract the article and the authors do not agree with this. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

This retrospective cohort study aimed to assess the systemic effects of three commonly available anti-vascular endothelial growth factor intravitreal injections in patients with diabetes, using data taken from a multi-institutional database in Taiwan. Patient data were sourced from the multi-institutional Chang Gung Research Database. Participants were divided into groups based on treatment with bevacizumab, ranibizumab, or aflibercept. Baseline characteristics were matched among the groups by the inverse probability of treatment weighting. The incidence rate of outcome events was calculated as the number of events divided by 100 person-years of follow-up. The cumulative incidence function was used to estimate the incidence rate of the outcome events among groups. The incidence of ischemic stroke was higher in the ranibizumab group than the bevacizumab and aflibercept groups (1.65, 0.92, and 0.61 per 100 person-years, respectively). The incidence of major adverse lower-limb events was higher in the bevacizumab group (2.95), followed by ranibizumab (2.00) and aflibercept (0.74). Major bleeding was relatively higher in bevacizumab (12.1) compared to ranibizumab (4.3) and aflibercept (3.8). All-cause death was higher for both bevacizumab (3.26) and aflibercept (2.61) when compared to ranibizumab (0.55), and all-cause admission was found to be highest with bevacizumab (58.6), followed by aflibercept (30.2), and ranibizumab (27.6). The bevacizumab group demonstrated a greater decrease in glycated hemoglobin compared to the baseline level (−0.33%). However, a few differences in the clinical condition between the groups were still observed after matching. In conclusion, this study suggests that different anti-vascular endothelial growth factor agents may be associated with various and differing systemic adverse events. The differences might also be attributed to differences in patient characteristics and clinical status.

Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel in various tasks, such as arithmetic reasoning, question answering, summarization, relation extraction, machine translation, and sentiment analysis. Researchers have been actively exploring different prompt engineering strategies, such as Chain of Thought (CoT), Zero-CoT, and In-context learning. However, an unresolved problem arises from the fact that current approaches lack a solid mathematical solution for determining optimal prompts. To address this issue in prompt engineering, we propose a new and effective approach called Prompt Space. Our methodology utilizes text embeddings to obtain basis vectors by matrix decomposition, and then constructs a space for representing all prompts. Prompt Space significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. Notably, without the help of the CoT method and the prompt "Let's think step by step", Prompt Space shows superior performance over the few-shot method. Overall, our approach provides a robust and effective mathematical framework for selecting simple and effective prompts. This advancement marks a significant step towards improving prompt engineering for a wide variety of applications in LLMs. Our code is publicly available at \textcolor{blue}{\url{https://github.com/YouBLEI/Prompt-Space}}, Comment: Natural language processing (NLP)

Anomaly detection aims to detect data that do not conform to regular patterns, and such data is also called outliers. The anomalies to be detected are often tiny in proportion, containing crucial information, and are suitable for application scenes like intrusion detection, fraud detection, fault diagnosis, e-commerce platforms, et al. However, in many realistic scenarios, only the samples following normal behavior are observed, while we can hardly obtain any anomaly information. To address such problem, we propose an anomaly detection method GALDetector which is combined of global and local information based on observed normal samples. The proposed method can be divided into a three-stage method. Firstly, the global similar normal scores and the local sparsity scores of unlabeled samples are computed separately. Secondly, potential anomaly samples are separated from the unlabeled samples corresponding to these two scores and corresponding weights are assigned to the selected samples. Finally, a weighted anomaly detector is trained by loads of samples, then the detector is utilized to identify else anomalies. To evaluate the effectiveness of the proposed method, we conducted experiments on three categories of real-world datasets from diverse domains, and experimental results show that our method achieves better performance when compared with other state-of-the-art methods., Comment: 6 pages, 1 figures

This paper proposes a stable sparse rapidly-exploring random trees (SST) algorithm to solve the optimal motion planning problem for hybrid systems. At each iteration, the proposed algorithm, called HySST, selects a vertex with the lowest cost among all the vertices within the neighborhood of a randomly selected sample and then extends the search tree by flow or jump, which is also chosen randomly when both regimes are possible. In addition, HySST maintains a static set of witness points such that all the vertices within the neighborhood of each witness are pruned except the vertex with the lowest cost. Through a definition of concatenation of functions defined on hybrid time domains, we show that HySST is asymptotically near optimal, namely, the probability of failing to find a motion plan such that its cost is close to the optimal cost approaches zero as the number of iterations of the algorithm increases to infinity. This property is guaranteed under mild conditions on the data defining the motion plan, which include a relaxation of the usual positive clearance assumption imposed in the literature of classical systems. The proposed algorithm is applied to an actuated bouncing ball system and a collision-resilient tensegrity multicopter system so as to highlight its generality and computational features., Comment: This paper has been submitted to the 2023 Conference of Decision and Control (CDC). arXiv admin note: substantial text overlap with arXiv:2210.15082

Deep learning methods have advanced quickly in brain imaging analysis over the past few years, but they are usually restricted by the limited labeled data. Pre-trained model on unlabeled data has presented promising improvement in feature learning in many domains, including natural language processing and computer vision. However, this technique is under-explored in brain network analysis. In this paper, we focused on pre-training methods with Transformer networks to leverage existing unlabeled data for brain functional network classification. First, we proposed a Transformer-based neural network, named as BrainNPT, for brain functional network classification. The proposed method leveraged token as a classification embedding vector for the Transformer model to effectively capture the representation of brain network. Second, we proposed a pre-training framework for BrainNPT model to leverage unlabeled brain network data to learn the structure information of brain networks. The results of classification experiments demonstrated the BrainNPT model without pre-training achieved the best performance with the state-of-the-art models, and the BrainNPT model with pre-training strongly outperformed the state-of-the-art models. The pre-training BrainNPT model improved 8.75% of accuracy compared with the model without pre-training. We further compared the pre-training strategies, analyzed the influence of the parameters of the model, and interpreted the trained model., Comment: Prepared to Submit

APT detection is difficult to detect due to the long-term latency, covert and slow multistage attack patterns of Advanced Persistent Threat (APT). To tackle these issues, we propose TBDetector, a transformer-based advanced persistent threat detection method for APT attack detection. Considering that provenance graphs provide rich historical information and have the powerful attacks historic correlation ability to identify anomalous activities, TBDetector employs provenance analysis for APT detection, which summarizes long-running system execution with space efficiency and utilizes transformer with self-attention based encoder-decoder to extract long-term contextual features of system states to detect slow-acting attacks. Furthermore, we further introduce anomaly scores to investigate the anomaly of different system states, where each state is calculated with an anomaly score corresponding to its similarity score and isolation score. To evaluate the effectiveness of the proposed method, we have conducted experiments on five public datasets, i.e., streamspot, cadets, shellshock, clearscope, and wget_baseline. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method., Comment: 10 pages, 7 figures

Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely applied to GFD, characterizing the anomalous possibility of a node by aggregating neighbor information. However, fraud graphs are inherently heterophilic, thus most of GNNs perform poorly due to their assumption of homophily. In addition, due to the existence of heterophily and class imbalance problem, the existing models do not fully utilize the precious node label information. To address the above issues, this paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector includes a hybrid filtering module and a local environmental constraint module, the two modules are utilized to solve heterophily and label utilization problem respectively. The first module starts from the perspective of the spectral domain, and solves the heterophily problem to a certain extent. Specifically, it divides the spectrum into various mixed-frequency bands based on the correlation between spectrum energy distribution and heterophily. Then in order to make full use of the node label information, a local environmental constraint module is adaptively designed. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors. We release our code at https://github.com/Sunxkissed/SEC-GFD.

Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in various domains of information security, including network intrusion, financial fraud, and malicious comments, et al. Existing methods are primarily developed in an unsupervised manner due to the challenge in obtaining labeled data. For lack of guidance from prior knowledge in unsupervised manner, the identified anomalies may prove to be data noise or individual data instances. In real-world scenarios, a limited batch of labeled anomalies can be captured, making it crucial to investigate the few-shot problem in graph anomaly detection. Taking advantage of this potential, we propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a self-supervised contrastive learning strategy within and across views to capture intrinsic and transferable structural representations. Furthermore, we propose the Deep-GNN message-enhanced reconstruction module, which extensively exploits the few-shot label information and enables long-range propagation to disseminate supervision signals to deeper unlabeled nodes. This module in turn assists in the training of self-supervised contrastive learning. Comprehensive experimental results on six real-world datasets demonstrate that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies.

Conversational Recommender Systems (CRS) actively elicit user preferences to generate adaptive recommendations. Mainstream reinforcement learning-based CRS solutions heavily rely on handcrafted reward functions, which may not be aligned with user intent in CRS tasks. Therefore, the design of task-specific rewards is critical to facilitate CRS policy learning, which remains largely under-explored in the literature. In this work, we propose a novel approach to address this challenge by learning intrinsic rewards from interactions with users. Specifically, we formulate intrinsic reward learning as a multi-objective bi-level optimization problem. The inner level optimizes the CRS policy augmented by the learned intrinsic rewards, while the outer level drives the intrinsic rewards to optimize two CRS-specific objectives: maximizing the success rate and minimizing the number of turns to reach a successful recommendation in conversations. To evaluate the effectiveness of our approach, we conduct extensive experiments on three public CRS benchmarks. The results show that our algorithm significantly improves CRS performance by exploiting informative learned intrinsic rewards., Comment: 11 pages

Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information. While these models are essential for tasks like information retrieval, semantic clustering, and text re-ranking, most existing open-source models, especially those built on architectures like BERT, struggle to represent lengthy documents and often resort to truncation. One common approach to mitigate this challenge involves splitting documents into smaller paragraphs for embedding. However, this strategy results in a much larger set of vectors, consequently leading to increased memory consumption and computationally intensive vector searches with elevated latency. To address these challenges, we introduce Jina Embeddings 2, an open-source text embedding model capable of accommodating up to 8192 tokens. This model is designed to transcend the conventional 512-token limit and adeptly process long documents. Jina Embeddings 2 not only achieves state-of-the-art performance on a range of embedding-related tasks in the MTEB benchmark but also matches the performance of OpenAI's proprietary ada-002 model. Additionally, our experiments indicate that an extended context can enhance performance in tasks such as NarrativeQA., Comment: 14 pages

It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. Firstly, we propose an IMU-PARSAC algorithm which can robustly detect and match keypoints in a two-stage process. In the first state, landmarks are matched with new keypoints using visual and IMU measurements. We collect statistical information from the matching and then guide the intra-keypoint matching in the second stage. Secondly, to handle the problem of pure rotation, we detect the motion type and adapt the deferred-triangulation technique during the data-association process. We make the pure-rotational frames into the special subframes. When solving the visual-inertial bundle adjustment, they provide additional constraints to the pure-rotational motion. We evaluate the proposed VIO system on public datasets and online comparison. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments. The source code is available at: \href{https://github.com/openxrlab/xrslam}{{\fontfamily{pcr}\selectfont https://github.com/openxrlab/xrslam}}.

Dense SLAM based on monocular cameras does indeed have immense application value in the field of AR/VR, especially when it is performed on a mobile device. In this paper, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone. Specifically, we present a specifically optimized multi-basis depth completion network, called BBC-Net, tailored to the characteristics of traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases and a confidence map from a monocular image with sparse points generated by off-the-shelf keypoint-based SLAM systems. The final depth is a linear combination of predicted depth bases that can be optimized by tuning the corresponding weights. To seamlessly incorporate the weights into traditional SLAM optimization and ensure efficiency and robustness, we design a set of depth weight factors, which makes our network a versatile plug-in module, facilitating easy integration into various existing sparse SLAM systems and significantly enhancing global depth consistency through bundle adjustment. To verify the portability of our method, we integrate BBC-Net into two representative SLAM systems. The experimental results on various datasets show that the proposed method achieves better performance in monocular dense mapping than the state-of-the-art methods. We provide an online demo running on a mobile phone, which verifies the efficiency and mapping quality of the proposed method in real-world scenarios.

The introduction and advancements in Local Differential Privacy (LDP) variants have become a cornerstone in addressing the privacy concerns associated with the vast data produced by smart devices, which forms the foundation for data-driven decision-making in crowdsensing. While harnessing the power of these immense data sets can offer valuable insights, it simultaneously poses significant privacy risks for the users involved. LDP, a distinguished privacy model with a decentralized architecture, stands out for its capability to offer robust privacy assurances for individual users during data collection and analysis. The essence of LDP is its method of locally perturbing each user's data on the client-side before transmission to the server-side, safeguarding against potential privacy breaches at both ends. This article offers an in-depth exploration of LDP, emphasizing its models, its myriad variants, and the foundational structure of LDP algorithms.