Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation

Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models. A major limitation of current DNN-aided designs stems from the need to train them to filter data originating from a specific distribution and underlying SS model. Consequently, changes in the model parameters may require lengthy retraining. While the KF adapts through parameter tuning, the black-box nature of DNNs makes identifying tunable components difficult. Hence, we propose Adaptive KalmanNet (AKNet), a DNN-aided KF that can adapt to changes in the SS model without retraining. Inspired by recent advances in large language model fine-tuning paradigms, AKNet uses a compact hypernetwork to generate context-dependent modulation weights. Numerical evaluation shows that AKNet provides consistent state estimation performance across a continuous range of noise distributions, even when trained using data from limited noise settings

KalmanNet:Neural Network Aided Kalman Filtering for Partially Known Dynamics

Real-time state estimation of dynamical systems is a fundamental task in signal processing and control. For systems that are well-represented by a fully known linear Gaussian state space (SS) model, the celebrated Kalman filter (KF) is a low complexity optimal solution. However, both linearity of the underlying SS model and accurate knowledge of it are often not encountered in practice. Here, we present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics with partial information. By incorporating the structural SS model with a dedicated recurrent neural network module in the flow of the KF, we retain data efficiency and interpretability of the classic algorithm while implicitly learning complex dynamics from data. We numerically demonstrate that KalmanNet overcomes nonlinearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.</p

Angiopoietin-2 impairs collateral artery growth associated with the suppression of the infiltration of macrophages in mouse hindlimb ischaemia

Abstract Background Angiopoietin-2 (Ang-2), a ligand of the Tie-2 receptor, plays an important role in maintaining endothelial cells and in destabilizing blood vessels. Collateral artery growth (arteriogenesis) is a key adaptive response to arterial occlusion. It is unknown whether the destabilization of blood vessels by Ang-2 can affect arteriogenesis and modulate mononuclear cell function. This study aimed to investigate the effects of Ang-2 on collateral artery growth. Methods Hindlimb ischaemia model was produced in C57BL/6 mice by femoral artery ligation. Blood flow perfusion was measured using a laser Doppler perfusion imager quantitative RT-PCR analysis was applied to identify the level of angiogenic factors. Results After the induction of hindlimb ischaemia, blood flow recovery was impaired in mice treated with recombinant Ang-2 protein; this was accompanied by a reduction of peri-collateral macrophage infiltration. In addition, quantitative RT-PCR analysis revealed that Ang-2 treatment decreased monocyte chemotactic protein-1 (MCP-1), platelet-derived growth factor-BB (PDGF-BB) mRNA levels in ischaemic adductor muscles. Ang-2 can lead to macrophage M1/M2 polarization shift inhibition in the ischaemic muscles. Furthermore, Ang-2 reduced the in vitro inflammatory response in macrophages and vascular cells involved in arteriogenesis. Conclusions Our results demonstrate that Ang-2 is essential for efficient arteriogenesis, which controls macrophage infiltration

The Potential Role of ORM2 in the Development of Colorectal Cancer

Colorectal cancer (CRC) is the third most common malignancy in the world. The risk of death is closely correlated to the stage of CRC at the time of primary diagnosis. Therefore, there is a compelling need for the identification of blood biomarkers that can enable early detection of CRC. We used a quantitative proteomic approach with isobaric labeling (iTRAQ) to examine changes in the plasma proteome of 10 patients with CRC compared to healthy volunteers. Enzyme-Linked Immunosorbnent Assay (ELISA) and Western blot were used for further validation. In our quantitative proteomics analysis, we detected 75 human plasma proteins with more than 95% confidence using iTRAQ labeling in conjunction with microQ-TOF MS. 9 up-regulated and 4 down-regulated proteins were observed in the CRC group. The ORM2 level in plasma was confirmed to be significantly elevated in patients suffering from CRC compared with the controls. ORM2 expression in CRC tissues was significantly increased compared with that in corresponding adjacent normal mucous tissues (P<0.001). ITRAQ together with Q-TOF/MS is a sensitive and reproducible technique of quantitative proteomics. Alteration in expression of ORM2 suggests that ORM2 could be used as a potential biomarker in the diagnosis of CRC

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Synergistic Influence of Rainstorm and Waterlogging on Drivers&rsquo; Driving Behavior&mdash;An Experimental Study Based on High-Fidelity Driving Simulator

Drivers are vulnerable to inclement weather such as heavy rainstorms and severe rain-induced waterlogging. A thorough investigation of drivers&rsquo; driving behavior during rainstorms and waterlogging is a strong basis on which characteristics of traffic flow in such circumstances could be thoroughly studied; however, relevant studies from the individual perspective are very rare. In this paper, an experiment based on a driving simulator investigates the synergistic influence of rainstorm and waterlogging on drivers&rsquo; driving behavior, where a total of 47 drivers are recruited, and 30 circumstances with diverse rainfall intensities or water depth are included. The dataset of drivers&rsquo; driving behavior obtained from the experiment is furtherly analyzed using two-way analysis of variance (two-way ANOVA) and statistical analysis. The results show that rainfall, waterlogging, and their synergistic influence significantly reduce vehicles&rsquo; speed, acceleration, and deceleration, and increase headway distance generally. This indicates that the drivers tend to adopt a more conservative driving strategy when encountering stronger rainfall and more severe waterlogging. Moreover, waterlogging was found to have a much more significant impact on vehicles&rsquo; motion parameters than rainfall, and should be viewed with more importance. This study quantitatively analyzes drivers&rsquo; driving behavior from the individual perspective in circumstances of rainstorm and waterlogging, and the findings in turn strengthen our understanding of the impacts on driving behavior

An Enhanced Replica Selection Approach Based on Distance Constraint in ICN

Fifth generation (5G) networks have a high requirement for low latency of data delivery. Information-centric networking (ICN) adopts the paradigm of separation of the identifier and locator. It is efficient in content distribution by supporting in-network caching and has the potential to satisfy the low latency requirement in 5G. Replica selection is a key problem to retrieving content in ICN. Prior research usually utilizes the nearest replica. However, using the nearest replica cannot guarantee the smallest content download delay. To exploit in-network caching better, we propose an enhanced replica selection approach, called ERS. ERS first uses a distance-constrained-based name resolution system to discover the nearby replicas. Then, the most appropriate replica is chosen according to a local state table that maintains the state of replica nodes within a limited domain. In addition to network distance and replica node load, ERS innovatively introduces the path congestion degree between requester and replica nodes to assist replica selection. With extensive simulations, the proposed approach shows better performance than the state-of-the-art methods in terms of average content download delay. Finally, the overhead of the proposed method is analyzed

An Effective Transmission Scheme Based on Early Congestion Detection for Information-Centric Network

As one of the candidates for future network architecture, Information-Centric Networking (ICN) has revolutionized the manner of content retrieval by transforming the communication mode from host-centric to information-centric. Unlike a traditional TCP/IP network, ICN uses a location-independent name to identify content and takes a receiver-driven model to retrieve the content. Moreover, ICN routers not only perform a forwarding function but also act as content providers due to pervasive in-network caching. The network traffic is more complicated and routers are more prone to congestion. These distinguished characteristics pose new challenges to ICN transmission control mechanism. In this paper, we propose an effective transmission scheme by combining the receiver-driven transport protocol and the router-driven congestion detection mechanism. We first outline the process of content retrieval and transmission in an IP-compatible ICN architecture and propose a practical receiver-driven transport protocol. Then, we present an early congestion detection mechanism applied on ICN routers based on an improved Active Queue Management (AQM) algorithm and design a receiver-driven congestion control algorithm. Finally, experiment results show that the proposed transmission scheme can maintain high bandwidth utilization and significantly reduce transmission delay and packet loss rate