Adaptive Recognition Research Articles

MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing

MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing

Physiological Time-Series Fusion With Hybrid Attention for Adaptive Recognition of Pain.

Automatic pain assessment is an application in healthcare serving personalized pain care, and patients cannot self-report pain. Pain at the present is inferred from physiological dynamics at the present and in the near past. However, heterogeneous pain responses cross-subject and cross-type hinder accurate recognition of pain. This work solves the adaptive pain recognition problem across pain types. We concrete the adaptivity problem into recognizing both phasic/short and tonic/long pain from the physiological sequences of the same length. The adaptivity of the proposed solution (TCAtt-PainNet) was ensured by hybrid temporal-channel attention when fusing multivariate time-series of electrocardiogram (ECG) and galvanic skin response (GSR) features. The attention was obtained by learning the dependencies between the point at present and the sequence in the near past, where sequence point temporal attention was constructed via modified self-attention, and the following feature channel attention was constructed by squeeze-and-excitation temporal attention weighted deep feature sequence. The proposed solution successfully enhanced recognition adaptivity by addressing relevant information only from long input sequences when testing with tonic and phasic pain databases, making progress towards automatic pain assessment for real application scenarios with attributes unknown pain.

Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

Intelligent Adaptive Real-Time Monitoring and Recognition System for Human Activities

Ensembled Pretrained Model-Based Adaptive Sequential Trace Recognition for Moving Objects

Ensembled Pretrained Model-Based Adaptive Sequential Trace Recognition for Moving Objects

Genetic and functional diversity of allorecognition receptors in the urochordate, Botryllus schlosseri.

Allorecognition in Botryllus schlosseri is controlled by a highly polymorphic locus (the fuhc ), and functionally similar to missing-self recognition utilized by Natural Killer cells-compatibility is determined by sharing a self-allele, and integration of activating and inhibitory signals determines outcome. We had found these signals were generated by two fuhc -encoded receptors, called fester and uncle fester . Here we show that fester genes are members of an extended family consisting of >37 loci, and co-expressed with an even more diverse gene family-the fester co-receptors ( FcoR ). The FcoRs are membrane proteins related to fester , but include conserved tyrosine motifs, including ITIMs and hemITAMs. Both genes are encoded in highly polymorphic haplotypes on multiple chromosomes, revealing an unparalleled level of diversity of innate receptors. Our results also suggest that ITAM/ITIM signal integration is a deeply conserved mechanism that has allowed convergent evolution of innate and adaptive cell-based recognition systems.

Evaluation of immune sensor responses to a viral small noncoding RNA.

Gammaherpesviruses are widespread pathogens causing persistent infections linked to the development of numerous types of lymphomas in humans. During latency, most of the viral protein-coding genes are suppressed, facilitating evasion of adaptive immune recognition of protein antigens. In contrast, many noncoding RNA (ncRNA) molecules are expressed in infected cells and can regulate key cellular pathways while simultaneously evading adaptive immune recognition. To counteract this, many cells express internal pattern recognition receptors that can intrinsically sense ongoing infections and initiate cellular defenses. Murine gammaherpesvirus 68 (MHV68) is a valuable model to study in vivo aspects of gammaherpesvirus pathogenesis. The MHV68 ncRNA TMER4 (tRNA-miRNA-encoding RNA 4) promotes lymph node egress of infected B cells: in the absence of TMER4, MHV68-infected B cells accumulate in the lymph node in a manner similar to B cells activated through specific antigen encounter. We hypothesized that TMER4 may alter intrinsic immune activation. In research described here, we aimed to explore the immunomodulatory functions of TMER4 by evaluating its impact on signaling through the critical immune sensors Toll-like receptor 4 (TLR4), TLR3, TLR7, and retinoic acid-inducible gene I (RIG-I). To accomplish this, we developed a system to test noncoding RNAs using commercially available reporter cell lines. We optimized the experimental procedure to ensure ncRNA expression and to quantify immune sensory molecule induction or inhibition by the expressed ncRNA. Expression of TMER4 RNAs from plasmid constructs did not alter TLR or RIG-I signaling. This study provides a clear experimental framework that can be applied to test other small ncRNAs for their impact on various innate immune sensor proteins.

Adaptive boundary-aware artificial immune recognition system for data classification

In this paper, we introduce AIBARS (Adaptive Immune Boundary-Aware Recognition System), a novel learning algorithm designed to improve upon the traditional Artificial Immune Recognition System (AIRS). AIBARS addresses key limitations of AIRS by integrating a boundary-aware stimulation measure, an antigen-specific stopping criterion, and a more directed mutation approach. These innovations enable AIBARS to effectively navigate complex decision boundaries and reduce time complexity, particularly in high-dimensional datasets. Experimental results show that AIBARS achieves a 5-10% improvement in classification accuracy compared to AIRS. Additionally, AIBARS reduces memory cells by 20-30%, leading to more efficient data representation and a 15-25% reduction in computational costs. The algorithm also reduces processing time by 10-20%, making it a more efficient approach overall. Evaluations were conducted using a variety of datasets, including public domain datasets from the UCI Machine Learning Repository and synthetic datasets. The results indicate that AIBARS offers significant performance and efficiency gains over AIRS, while substantially reducing computational expenses.

A framework of insole blanking robot based on adaptive edge detection and FSPS-BIT* path planning

Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.

Whence and wherefore IgE?

Despite the near ubiquitous presence of Ig-based antibodies in vertebrates, IgE is unique to mammals. How and why it emerged remains mysterious. IgE expression is greatly constrained compared to other IgH isotypes. While other IgH isotypes are relatively abundant, soluble IgE has a truncated half-life, and IgE plasma cells are mostly short-lived. Despite its rarity, IgE is consequential and can trigger life-threatening anaphylaxis. IgE production reflects a dynamic steady state with IgG memory B cells feeding short-lived IgE production. Emerging evidence suggests that IgE may also potentially be produced in longer-lived plasma cells as well, perhaps as an aberrancy stemming from its evolutionary roots from an antibody isotype that likely functioned more like IgG. As a late derivative of an ancient systemic antibody system, the benefits of IgE in mammals likely stems from the antibody system's adaptive recognition and response capability. However, the tendency for massive, systemic, and long-lived production, common to IgH isotypes like IgG, were likely not a good fit for IgE. The evolutionary derivation of IgE from an antibody system that for millions of years was good at antigen de-sensitization to now functioning as a highly specialized antigen-sensitization function required heavy restrictions on antibody production-insufficiency of which may contribute to allergic disease.

DualFaceNet: augmentation consistency for optimal facial landmark detection and face mask classification

<p>In an era where face masks are commonplace, facial recognition faces new challenges and opportunities. This study introduces DualFaceNet (DFN), a cutting-edge neural network that efficiently combines facial landmark detection with mask classification. Benefiting from multi-task learning (MTL) and enhanced with a unique consistency loss, DFN outperforms traditional single-task models. Tests using the reputable 300W dataset and a face mask dataset showcase DFN’s strengths: a significant reduction in landmark error to 5.42 and an increase in mask classification accuracy to 92.59%. These results highlight the potential of integrating MTL and custom loss functions in facial recognition. As face masks continue to be globally essential, DFN’s integrated approach offers a fresh perspective in facial recognition studies. Furthermore, DFN paves the way for adaptive facial recognition systems, emphasizing the adaptability needed in our current era.</p>

The branched receptor-binding complex of Ackermannviridae phages promotes adaptive host recognition

Bacteriophages can encode multiple receptor-binding proteins, allowing them to recognize diverse receptors for infecting different strains. Ackermannviridae phages recognize various polysaccharides as receptors by encoding multiple tail spike proteins (TSPs), forming a branched complex. We aimed to mimic the evolution of the TSP complex by studying the acquisition of TSPs without disrupting the complex's functionality. Using kuttervirus S117 as a backbone, we demonstrated that acquiring tsp genes from Kuttervirus and Agtrevirus phages within the Ackermannviridae family led to altered host recognition. A fifth TSP was designed to interact with the branched complex and expand host recognition even further. Interestingly, the acquisition of tsp5 resulted in a recombination event between tsp4 and tsp5 or deletion of tsp3 and truncation of tsp4 genes. Our study provides insight into the development of the branched TSP complex, enabling Ackermannviridae phages to adapt to different hosts.

Motor fault diagnosis based on multisensor-driven visual information fusion

To need of accurate motor fault diagnosis in industrial system, we propose a fault diagnosis framework that utilizes motor current and electromagnetic signals, combining them with a self-attention-enhanced capsule network for enhanced signal analysis and accuracy. Firstly, the original signal extracted by multiple sensors is constructed into a symmetric point mode (SDP) image, and the visual fault information of different sensors and fusion signals of different motion health states are obtained by the proposed multi-channel image fusion method. Then, the capsule network, combined with self-attention, extracts spatial features from the high-dimensional tensor of the multi-channel fused image for adaptive recognition and extraction. Subsequently, advanced feature vector information is obtained through softmax for diagnosis. Diagnosis results of several datasets indicate that the developed diagnosis framework with compressed image information can availably identify 8 kinds of motor fault states under various loads, and the fault diagnosis rate is as high as 99.95 %, it is helpful for low cost and high-speed diagnosis of motors. In addition, by learning multiple sensor signals in the same state, it obtains stronger robustness and effectiveness than a single signal model.

Expandable rotational origami nano-boxes made by giant shape amphiphile

Dynamic cavity is widespread in living matter and supramolecular scaffolds. Generally speaking, flexible building block as a segment is used to realize dynamic cavities building in artificial molecules and materials. However, the incorporation of rigid synthetic supramolecular scaffolds with flexible cavity motion remains rare. Inspired by rotational origami patterns, giant shape amphiphile (GSA), POSS-NDI-POSS (PNP), assembled into an origami nano-box with feature of rotational expansion. The open/close of cavity and size control of PNP host can be adjusted by rotational offset between adjacent PNP in supramolecule entities. The rotational offset of 150°, 130° and 0° were observed when the host nano-box presented close, half-open and fully-open state, respectively. The rotational expansion of PNP trigged by gaseous arenes makes PNP as a novel nonporous crystal. Compared with conformation change in flexible building segment, rotational expansion in acyclic host (PNP) provides a new strategy for the development of rigid segment to build adaptive host-guest recognition.

A step length estimation method based on frequency domain feature analysis and gait recognition for pedestrian dead reckoning

Purpose Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait. Design/methodology/approach The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds. Findings Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning. Originality/value A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

A hierarchical progressive recognition network for building change detection in high‐resolution remote sensing images

AbstractBuilding change detection (BCD) plays a crucial role in urban planning and development. However, several pressing issues remain unresolved in this field, including false detections of buildings in complex backgrounds, the occurrence of jagged edges in segmentation results, and detection blind spots in densely built‐up areas. To address these challenges, this study innovatively proposes a Hierarchical Adaptive Gradual Recognition Network (HAGR‐Net) to improve the accuracy and robustness of BCD. Additionally, this research is the first to employ the Reinforcement Learning Optimization Algorithm Based on Particle Swarm (ROPS) to optimize the training process of HAGR‐Net, thereby accelerating the training process and reducing memory overhead. Experimental results indicate that the optimized HAGR‐Net outperforms state‐of‐the‐art methods on the WHU_CD, Google_CD, and LEVIR_CD data sets, achieving F1 scores of 93.13%, 85.31%, and 91.72%, and mean intersection over union (mIoU) scores of 91.20%, 85.99%, and 90.01%, respectively.

N-terminal domain of polypyrimidine-tract binding protein is a dynamic folding platform for adaptive RNA recognition.

The N-terminal RNA recognition motif domain (RRM1) of polypyrimidine tract binding protein (PTB) forms an additional C-terminal helix α3, which docks to one edge of the β-sheet upon binding to a stem-loop RNA containing a UCUUU pentaloop. Importantly, α3 does not contact the RNA. The α3 helix therefore represents an allosteric means to regulate the conformation of adjacent domains in PTB upon binding structured RNAs. Here we investigate the process of dynamic adaptation by stem-loop RNA and RRM1 using NMR and MD in order to obtain mechanistic insights on how this allostery is achieved. Relaxation data and NMR structure determination of the free protein show that α3 is partially ordered and interacts with the domain transiently. Stem-loop RNA binding quenches fast time scale dynamics and α3 becomes ordered, however microsecond dynamics at the protein-RNA interface is observed. MD shows how RRM1 binding to the stem-loop RNA is coupled to the stabilization of the C-terminal helix and helps to transduce differences in RNA loop sequence into changes in α3 length and order. IRES assays of full length PTB and a mutant with altered dynamics in the α3 region show that this dynamic allostery influences PTB function in cultured HEK293T cells.

Incremental Learning for LiDAR Attack Recognition Framework in Intelligent Driving Using Gaussian Processes

The perception system plays a crucial role by integrating LiDAR and various sensors to perform localization and object detection, which ensures the security of intelligent driving. However, existing research indicates that LiDAR is vulnerable to sensor attacks, which lead to inappropriate driving strategies and need effective attack recognition methods. Previous LiDAR attack recognition methods rely on fixed anomaly thresholds obtained from depth map data distributions in specific scenarios as static anomaly boundaries, which lead to reduced accuracy, increased false alarm rates, and a lack of performance stability. To address these problems, we propose an adaptive LiDAR attack recognition framework capable of adjusting to different driving scenarios. This framework initially models the perception system by integrating the vehicle dynamics model and object tracking algorithms to extract data features, subsequently employing Gaussian Processes for the probabilistic modeling of these features. Finally, the framework employs sparsification computing techniques and a sliding window strategy to continuously update the Gaussian Process model with window data, which achieves incremental learning that generates uncertainty estimates as dynamic anomaly boundaries to recognize attacks. The performance of the proposed framework has been evaluated extensively using the real-world KITTI dataset covering four driving scenarios. Compared to previous methods, our framework achieves a 100% accuracy rate and a 0% false positive rate in the localization system, and an average increase of 3.43% in detection accuracy in the detection system across the four scenarios, which demonstrates superior adaptive capabilities.

Thermal preference prediction through infrared thermography technology: Recognizing adaptive behaviors

The recognition of thermal adaptation behaviors is a crucial and noninvasive method for accurately and swiftly predicting the thermal preferences of occupants. This is instrumental in enhancing the energy efficiency, indoor personnel comfort, and overall productivity of office buildings. This study employed deep-learning models to analyze a thermal adaptive behavior video dataset captured by Infrared thermography (IRT) cameras to predict the thermal preferences of occupants. A large-scale infrared thermography dataset was developed for recognizing thermal adaptive behaviors using 9650 video samples. Two adaptive behavior recognition models for infrared videos were developed by leveraging neural network models: Two-Stream Inflated 3D ConvNet (I3D) and SlowFast (SF) networks. These models achieved the average prediction accuracies of 83.53 % and 88.56 %, respectively. Notably, both models exhibited a recognition time of milliseconds, facilitating the real-time recognition of thermal adaptive behaviors. This study offers vital technical and theoretical underpinnings for developing decision-making solutions for smart buildings based on noninvasive thermal preference prediction.

BcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data.

B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow.

Adaptive attack recognition method based on probability model for autonomous vehicle

Adaptive attack recognition method based on probability model for autonomous vehicle