Transformers have emerged as a transformative tool in various computer vision tasks, excelling at capturing long-range dependencies. Their potential applicability and scalability in the interpretation of high-resolution remote sensing images (HRRSIs) have thus garnered substantial interest. However, unlike natural images, HRRSIs present intricate scenes characterized by scale variations and diverse appearances. These challenges underscore the importance of enabling networks to effectively assimilate both local intricacies and global context. In this letter, we introduce LETFormer, a semantic segmentation transformer. LETFormer balances capturing longrange dependencies with preserving local details through its unique LETFormer block, featuring an anchor token. This token aggregates localized contextual information within a designated window and promotes meaningful interactions among anchor tokens. With a mask transformer decoder, LETFormer gains ample contextual cues for precise semantic mask prediction. Empirical findings based on evaluations using the ISPRS Potsdam and LoveDA benchmarks unequivocally establish LETFormer’s superiority over state-of-the-art models. Additionally, we analyze the parameter size and floating-point operations per second (FLOPs) of LETFormer.

The rapid advancements in remote sensing technology have enabled the widespread availability of fine-resolution remote sensing images (RSIs), offering rich spatial details and semantics. Despite the applicability and scalability of transformers in semantic segmentation of RSIs by learning pairwise contextual affinity, they inevitably introduce irrelevant context, hindering accurate inference of patch semantics. To address this, we propose a novel multi-head attention-attended module (AAM) that refines the multi-head self-attention mechanism. The AAM filters out irrelevant context while highlighting informative ones by considering the relevance between self-attention maps and the query vector. The AAM generates an attention gate to complement contextual affinity and emphasize the useful ones with a higher weight simultaneously. Leveraging multi-head AAM as the core unit, we construct a lightweight attention-attended transformer block (ATB). Subsequently, we devise AAFormer, a pure transformer with a mask transformer decoder, for achieving semantic segmentation of RSIs. We extensively evaluate our approach on the ISPRS Potsdam and LoveDA datasets, demonstrating compelling performance compared to mainstream methods. Additionally, we conduct evaluations to analyze the effects of AAM.

Supervised based deep learning methods have achieved great success in medical image analysis domain. Essentially, most of them could be further improved by exploring and embedding context knowledge for accuracy boosting. Moreover, they generally suffer from slow convergency and high computing cost, which prevents their usage in a practical scenario. To tackle these problems, we present CT-CAD, context-aware transformers for end-to-end chest abnormality detection on X-Ray images. The proposed method firstly constructs a context-aware feature extractor, which enlarges receptive fields to encode multi-scale context information via an iterative feature fusion scheme and dilated context encoding blocks. Afterwards, deformable transformer detector are built for category classification and location regression, where their deformable attention block attend to a small set of key sampling points, thus allowing the transformer to focus on feature subspace and accelerate convergence speed. Through comparative experiments on Vinbig Chest and Chest Det10 Datasets, the proposed CT-CAD demonstrates its effectiveness and outperforms the existing methods in mAP and training epoches.

Clinical trials are the gold standard for generating reliable medical evidence. The biggest bottleneck in clinical trials is recruitment. To facilitate recruitment, tools for patient search of relevant clinical trials have been developed, but users often suffer from information overload. With nearly 700 coronavirus disease 2019 (COVID-19) trials conducted in the United States as of August 2020, it is imperative to enable rapid recruitment to these studies. The COVID-19 Trial Finder was designed to facilitate patient-centered search of COVID-19 trials, first by location and radius distance from trial sites, and then by brief, dynamically generated medical questions to allow users to prescreen their eligibility for nearby COVID-19 trials with minimum human computer interaction. A simulation study using 20 publicly available patient case reports demonstrates its precision and effectiveness.

OBJECTIVE: Evidence is scarce regarding the safety of long-term drug use, especially for drugs treating chronic diseases. To bridge this knowledge gap, this research investigated the differences in drug exposure between clinical trials and clinical practice. MATERIALS AND METHODS: We extracted drug follow-up times from clinical trials in ClinicalTrials.gov and compared the difference between clinical trials and real-world usage data for 914 drugs taken by 96 645 927 patients. RESULTS: A total of 17.5% of drugs had longer median exposure in practice than in trials, 6% of patients had extended exposure to at least 1 drug, and drugs treating nervous system disorders and cardiovascular diseases were the most common among drugs with high rates of extended exposure. CONCLUSIONS: For most of patients, the drug use length is shorter than the tested length in clinical trials. Still, a remarkable number of patients experienced extended drug exposure, particularly for drugs treating nervous system disorders or cardiovascular disorders.

The current maritime applications have yielded strong demands for the development of advanced unmanned surface vehicles (USVs) with more reliable path following capabilities to greatly extend mission durations and enhance accommodative capabilities of USVs to more hazardous and dynamic environments. This paper presents an adaptive path following control method using a retrofit adaptive tracking control technique with application to a USV with consideration of environmental disturbances (like winds, waves, and currents), while taking into account of the system constraints of USVs, including both turning features (turning rate limit and turning dynamics) and rudder operation constraints (rudder deflection and rate saturation, and its dynamics). In order to guarantee the satisfactory performance of the USV operating in a calm environment, a baseline state feedback tracking controller considering the characteristics of yaw rate and rudder operations, and USV steering and actuator dynamics is first designed. In the presence of time-varying environmental disturbances, a retrofit adaptive disturbance compensating control mechanism is then developed based on the disturbance amplitude estimated from an indirect adaptive disturbance estimator. Finally, a reconfigurable adaptive path following controller is synthesized by combining the baseline controller and the adaptive disturbance compensating mechanism for the proper operation of the USV in the presence of environmental disturbances, while the desired path is successfully followed by the USV within an acceptable deviation boundary and without violating constraints of turning rates as well as amplitude and rate of rudder deflections. To evaluate the effectiveness of the proposed path following control methodology, both numerical simulations on a nonlinear USV model and field experiments on a real-size USV are conducted.

Selfish herd optimizer (SHO) is a new optimization algorithm. However, its optimization performance is not satisfactory. The main reason for this phenomenon is the weak global search ability of SHO. In this paper, in order to increase the global search ability of SHO, we add Levy-flight distribution strategy. To verify the performance of the proposed algorithm, we use 10 benchmark functions as test cases. Experiment results show that our algorithm is more competitive.

OBJECTIVE: Coordination ellipsis is a linguistic phenomenon abound in medical text and is challenging for concept normalization because of difficulty in recognizing elliptical expressions referencing 2 or more entities accurately. To resolve this bottleneck, we aim to contribute a generalizable method to reconstruct concepts from medical coordinated elliptical expressions in a variety of biomedical corpora. MATERIALS AND METHODS: We proposed a graph-based representation model and built a pipeline to reconstruct concepts from coordinated elliptical expressions in medical text (RECEEM). There are 4 modules: (1) identify all possible candidate conjunct pairs from original coordinated elliptical expressions, (2) calculate coefficients for candidate conjuncts using the embedding model, (3) select the most appropriate decompositions by global optimization, and (4) rebuild concepts based on a pathfinding algorithm. We evaluated the pipeline's performance on 2658 coordinated elliptical expressions from 3 different medical corpora (ie, biomedical literature, clinical narratives, and eligibility criteria from clinical trials). Precision, recall, and F1 score were calculated. RESULTS: The F1 scores for biomedical publications, clinical narratives, and research eligibility criteria were 0.862, 0.721, and 0.870, respectively. RECEEM outperformed 2 previously released methods. By incorporating RECEEM into 2 existing NLP tools, the F1 scores increased from 0.248 to 0.460 and from 0.287 to 0.630 on concept mapping of 1125 coordination ellipses. CONCLUSIONS: RECEEM improves concept normalization for medical coordinated elliptical expressions in a variety of biomedical corpora. It outperformed existing methods and significantly enhanced the performance of 2 notable NLP systems for mapping coordination ellipses in the evaluation. The algorithm is open sourced online (https://github.com/chiyuan1126/RECEEM).

OBJECTIVE: Information overload remains a challenge for patients seeking clinical trials. We present a novel system (DQueST) that reduces information overload for trial seekers using dynamic questionnaires. MATERIALS AND METHODS: DQueST first performs information extraction and criteria library curation. DQueST transforms criteria narratives in the ClinicalTrials.gov repository into a structured format, normalizes clinical entities using standard concepts, clusters related criteria, and stores the resulting curated library. DQueST then implements a real-time dynamic question generation algorithm. During user interaction, the initial search is similar to a standard search engine, and then DQueST performs real-time dynamic question generation to select criteria from the library 1 at a time by maximizing its relevance score that reflects its ability to rule out ineligible trials. DQueST dynamically updates the remaining trial set by removing ineligible trials based on user responses to corresponding questions. The process iterates until users decide to stop and begin manually reviewing the remaining trials. RESULTS: In simulation experiments initiated by 10 diseases, DQueST reduced information overload by filtering out 60%-80% of initial trials after 50 questions. Reviewing the generated questions against previous answers, on average, 79.7% of the questions were relevant to the queried conditions. By examining the eligibility of random samples of trials ruled out by DQueST, we estimate the accuracy of the filtering procedure is 63.7%. In a study using 5 mock patient profiles, DQueST on average retrieved trials with a 1.465 times higher density of eligible trials than an existing search engine. In a patient-centered usability evaluation, patients found DQueST useful, easy to use, and returning relevant results. CONCLUSION: DQueST contributes a novel framework for transforming free-text eligibility criteria to questions and filtering out clinical trials based on user answers to questions dynamically. It promises to augment keyword-based methods to improve clinical trial search.

OBJECTIVE: Cohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases. MATERIALS AND METHODS: Criteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, transforms it first into a structured criteria representation and next into sharable and executable clinical data queries represented as SQL queries conforming to the OMOP Common Data Model. Users can interactively review, refine, and execute queries in the ATLAS web application. To test effectiveness, we evaluated 125 criteria across different disease domains from ClinicalTrials.gov and 52 user-entered criteria. We evaluated F1 score and accuracy against 2 domain experts and calculated the average computation time for fully automated query formulation. We conducted an anonymous survey evaluating usability. RESULTS: Criteria2Query achieved 0.795 and 0.805 F1 score for entity recognition and relation extraction, respectively. Accuracies for negation detection, logic detection, entity normalization, and attribute normalization were 0.984, 0.864, 0.514 and 0.793, respectively. Fully automatic query formulation took 1.22 seconds/criterion. More than 80% (11+ of 13) of users would use Criteria2Query in their future cohort definition tasks. CONCLUSIONS: We contribute a novel natural language interface to clinical databases. It is open source and supports fully automated and interactive modes for autonomous data-driven cohort definition by researchers with minimal human effort. We demonstrate its promising user friendliness and usability.

The sensors in wireless sensor network (WSN) are vulnerable to malicious attacks due to the transmission nature of wireless media. Secure and authenticated message delivery with low energy consumption is one of the major aims in WSN. The identity‐based key authentication scheme is more suitable for the WSN. In this paper, the Hierarchical Matrix Decomposition‐based Signcryption (HMDS) algorithm was proposed, which is a kind of identity‐based authentication scheme. In HMDS scheme, three‐layer architecture, base station (BS), cluster head, and intracluster, is employed to adapt to the common structure of WSN. As the key generation center (KGC), the BS adopts matrix decomposition to generate the identification information and public key for cluster head, which not only reduces the cost of calculation and storage but also avoids the collusion attack. Experiments show that the HMDS algorithm has more advantages over other algorithms and is very suitable for the large‐scale WSN.

The field of biomarkers in cancer research has recently gained widespread interest, for its potential to improve diagnosis accuracy, prognosis, and make cancer treatments to be more personalized. However, the detection of multi-tumor markers method still has many problems, such as limited applicability, need to different model for different tumor markers, a simple series or parallel method cannot effectively take advantage of different tumor markers. This paper proposed a data fusion method for multi-tumor markers, which can be adapted to different scene. It can effectively use the different markers to give an adjuvant diagnosis. With the new markers continue to be found, we can provide guidance for the combined detection.

Underwater Wireless Sensor Networks (UWSNs) are vulnerable to attack because of the broadcast nature of the transmission. The sensor nodes in UWSN are highly constrained in terms of computational capabilities and communication bandwidth. Authentication schemes for ground WSNs might not be applicable for UWSNs due to their less computation and communication capacity. Thus, it is necessary to design special schemes tailored to underwater environments. In this paper, a low computational complexity authentication scheme is proposed. By using Vandermonde matrix, we replace the matrix multiplication by matrix addition to greatly reduce the computation overhead. Moreover, our scheme is self-correctable and irreversible which further enhances the security of the UWSNs. Experiment results indicate our algorithm has advantages in energy and time consumption over traditional RSA and Blom's scheme.

In metropolitan area networks (MANs), fiber optic rings are widely deployed. These rings are currently using architectures that are neither optimized nor scalable to the network demands. Therefore, some emerging technologies are being pushed to replace the traditional architecture. Optical burst switching (OBS) is an effective and promising optical switching technology among these emerging technologies. In this paper, a new architecture, called dual-fiber-link OBS and a related media access control protocol, are proposed for MANs. The proposed architecture, which uses dual-fiber link to ease burst contention, has the similar functions of fiber delay lines, partial wavelength conversion, and deflection routing. A theoretical model is developed for evaluating the performance of the proposed architecture. Furthermore, the dual-fiber-link is compared with the single-fiber link and the simple two-fiber link schemes by introduced them into ring and mesh MANs through simulations. Both analysis and simulation results show that the DFL OBS MANs is feasible for carrying out with existing commercial devices and available dark fibers. It is commercially viable in that the DFL can exponentially decrease the burst dropping probability in the ring and mesh MANs.