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Artykuły w czasopismach na temat „Encodage joint”

Autor: Grafiati

Data publikacji: 30 listopada 2024

Data aktualizacji: 31 lipca 2025

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1

Wu, Jiahua, and Hyo-Jong Lee. "A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network." Applied Sciences 11, no. 9 (2021): 4241. http://dx.doi.org/10.3390/app11094241.

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In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s j

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2

Solère, Jérémie, Jean-Yves Meunier, Jean-Pierre Hébrard, et al. "Faune entomologique du site de la Feuillane, Fos-sur-Mer (Bouches-du-Rhône, France)." Bulletin de la Société linnéenne de Provence 73 (July 6, 2023): 129–51. https://doi.org/10.5281/zenodo.8119288.

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Version en ligne des données présentées et discutées dans Solere J., Meunier J.-Y., Hebrard J.-P., Grima L., Schurr L., Claude J., Lebard T., Gachet S. et Neve G. 2022 Faune entomologique du site de la Feuillanne, Fos-sur-Mer (Bouches-du-Rhône) Bulletin de la Socété Linnéenne de Provence, 73 : 129–151. avec correction dans : Neve, G., Hebrard J.-P. et Soldati, F. 2023. Première mention de Microrhagus pyrenaeus Bonvouloir, 1872 en région Provence-Alpes-Côte-d’Azur (Coleoptera, Eucnemidae). Bulletin de la

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3

Vlutters, Mark, Edwin H. F. van Asseldonk, and Herman van der Kooij. "Ankle muscle responses during perturbed walking with blocked ankle joints." Journal of Neurophysiology 121, no. 5 (2019): 1711–17. http://dx.doi.org/10.1152/jn.00752.2018.

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The ankle joint muscles can contribute to balance during walking by modulating the center of pressure and ground reaction forces through an ankle moment. This is especially effective in the sagittal plane through ankle plantar- or dorsiflexion. If the ankle joints were to be physically blocked to make an ankle strategy ineffective, there would be no functional contribution of these muscles to balance during walking, nor would these muscles generate afferent output regarding ankle joint rotation. Consequently, ankle muscle activation for the purpose of balance control would be expected to disap

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Wen, Yu-Hui, Lin Gao, Hongbo Fu, Fang-Lue Zhang, and Shihong Xia. "Graph CNNs with Motif and Variable Temporal Block for Skeleton-Based Action Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8989–96. http://dx.doi.org/10.1609/aaai.v33i01.33018989.

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Hierarchical structure and different semantic roles of joints in human skeleton convey important information for action recognition. Conventional graph convolution methods for modeling skeleton structure consider only physically connected neighbors of each joint, and the joints of the same type, thus failing to capture highorder information. In this work, we propose a novel model with motif-based graph convolution to encode hierarchical spatial structure, and a variable temporal dense block to exploit local temporal information over different ranges of human skeleton sequences. Moreover, we em

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5

Bosco, G., R. E. Poppele, and J. Eian. "Reference Frames for Spinal Proprioception: Limb Endpoint Based or Joint-Level Based?" Journal of Neurophysiology 83, no. 5 (2000): 2931–45. http://dx.doi.org/10.1152/jn.2000.83.5.2931.

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Many sensorimotor neurons in the CNS encode global parameters of limb movement and posture rather than specific muscle or joint parameters. Our investigations of spinocerebellar activity have demonstrated that these second-order spinal neurons also may encode proprioceptive information in a limb-based rather than joint-based reference frame. However, our finding that each foot position was determined by a unique combination of joint angles in the passive limb made it difficult to distinguish unequivocally between a limb-based and a joint-based representation. In this study, we decoupled foot p

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6

Day, James, Leah R. Bent, Ingvars Birznieks, Vaughan G. Macefield, and Andrew G. Cresswell. "Muscle spindles in human tibialis anterior encode muscle fascicle length changes." Journal of Neurophysiology 117, no. 4 (2017): 1489–98. http://dx.doi.org/10.1152/jn.00374.2016.

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Muscle spindles provide exquisitely sensitive proprioceptive information regarding joint position and movement. Through passively driven length changes in the muscle-tendon unit (MTU), muscle spindles detect joint rotations because of their in-parallel mechanical linkage to muscle fascicles. In human microneurography studies, muscle fascicles are assumed to follow the MTU and, as such, fascicle length is not measured in such studies. However, under certain mechanical conditions, compliant structures can act to decouple the fascicles, and, therefore, the spindles, from the MTU. Such decoupling

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7

Zhang, Fenghao, Lin Zhao, Shengling Li, Wanjuan Su, Liman Liu, and Wenbing Tao. "3D hand pose and shape estimation from monocular RGB via efficient 2D cues." Computational Visual Media 10, no. 1 (2023): 79–96. http://dx.doi.org/10.1007/s41095-023-0346-4.

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AbstractEstimating 3D hand shape from a single-view RGB image is important for many applications. However, the diversity of hand shapes and postures, depth ambiguity, and occlusion may result in pose errors and noisy hand meshes. Making full use of 2D cues such as 2D pose can effectively improve the quality of 3D human hand shape estimation. In this paper, we use 2D joint heatmaps to obtain spatial details for robust pose estimation. We also introduce a depth-independent 2D mesh to avoid depth ambiguity in mesh regression for efficient hand-image alignment. Our method has four cascaded stages:

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8

Zill, S. N. "Plasticity and proprioception in insects. I. Responses and cellular properties of individual receptors of the locust metathoracic femoral chordotonal organ." Journal of Experimental Biology 116, no. 1 (1985): 435–61. http://dx.doi.org/10.1242/jeb.116.1.435.

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The metathoracic femoral chordotonal organ is a joint angle receptor of the locust hindleg. It consists of 45–55 bipolar sensory neurones located distally in the femur and mechanically coupled to the tibia. Responses of receptors of the organ were examined by extracellular and intracellular recording. The organ as a whole encodes the angle of the femorotibial joint but shows substantial hysteresis. Tonic activity is greatest at the extremes of joint position. The organ possesses no direct linkage to tibial muscle fibres and shows no response to resisted muscle contractions in most ranges of jo

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9

Ajemian, Robert, Daniel Bullock, and Stephen Grossberg. "Kinematic Coordinates In Which Motor Cortical Cells Encode Movement Direction." Journal of Neurophysiology 84, no. 5 (2000): 2191–203. http://dx.doi.org/10.1152/jn.2000.84.5.2191.

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During goal-directed reaching in primates, a sensorimotor transformation generates a dynamical pattern of muscle activation. Within the context of this sensorimotor transformation, a fundamental question concerns the coordinate systems in which individual cells in the primary motor cortex (MI) encode movement direction. This article develops a mathematical framework that computes, as a function of the coordinate system in which an individual cell is hypothesized to operate, the spatial preferred direction (pd) of that cell as the arm configuration and hand location vary. Three coordinate syste

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10

BRINGMANN, KATHRIN. "CONGRUENCES FOR DYSON'S RANKS." International Journal of Number Theory 05, no. 04 (2009): 573–84. http://dx.doi.org/10.1142/s1793042109002262.

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In this paper, we prove infinite families of congruences for coefficients of harmonic Maass forms whose coefficients encode Dyson's rank. This generalizes the earlier joint work of the author with Ken Ono.

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Xu, Guoyan, Qirui Zhang, Du Yu, Sijun Lu, and Yuwei Lu. "JKRL: Joint Knowledge Representation Learning of Text Description and Knowledge Graph." Symmetry 15, no. 5 (2023): 1056. http://dx.doi.org/10.3390/sym15051056.

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The purpose of knowledge representation learning is to learn the vector representation of research objects projected by a matrix in low-dimensional vector space and explore the relationship between embedded objects in low-dimensional space. However, most methods only consider the triple structure in the knowledge graph and ignore the additional information related to the triple, especially the text description information. In this paper, we propose a knowledge graph representation model with a symmetric architecture called Joint Knowledge Representation Learning of Text Description and Knowled

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12

Okada, J., and Y. Toh. "Peripheral representation of antennal orientation by the scapal hair plate of the cockroach Periplaneta americana." Journal of Experimental Biology 204, no. 24 (2001): 4301–9. http://dx.doi.org/10.1242/jeb.204.24.4301.

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SUMMARY Arthropods have hair plates that are clusters of mechanosensitive hairs, usually positioned close to joints, which function as proprioceptors for joint movement. We investigated how angular movements of the antenna of the cockroach (Periplaneta americana) are coded by antennal hair plates. A particular hair plate on the basal segment of the antenna, the scapal hair plate, can be divided into three subgroups: dorsal, lateral and medial. The dorsal group is adapted to encode the vertical component of antennal direction, while the lateral and medial groups are specialized for encoding the

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13

Nan, Jiang, Xiao Xinglei, Liu Shanglun, Chen Guoqing, and Chen Hongwei. "Mechano Growth Factor E Peptide Improves Degenerative Meniscus by Promoting the Proliferation and Migration of Meniscus Cells." Current Topics in Nutraceutical Research 22, no. 2 (2024): 716–22. http://dx.doi.org/10.37290/ctnr2641-452x.22:716-722.

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This study aimed to examine the effect of the Mechano growth factor E peptide on meniscus repair, which is critical for restoring knee joint function. Meniscal cells harvested from the inner and outer regions of knee joints from patients with osteoarthritis were subjected to Mechano growth factor E peptide treatment to investigate alterations in cell morphology and their effect on cell colony formation. The effect of Mechano growth factor E peptide treatment on cell migration was evaluated using a transwell system, while polymerase chain reaction was utilized to assess the effect of Mechano gr

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14

Gunti, Nethra, Sathyanarayanan Ramamoorthy, Parth Patwa, and Amitava Das. "Memotion Analysis through the Lens of Joint Embedding (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12959–60. http://dx.doi.org/10.1609/aaai.v36i11.21616.

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Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Resul

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15

Durrett, Greg, and Dan Klein. "A Joint Model for Entity Analysis: Coreference, Typing, and Linking." Transactions of the Association for Computational Linguistics 2 (December 2014): 477–90. http://dx.doi.org/10.1162/tacl_a_00197.

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We present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia entities). Our model is formally a structured conditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for al

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Bosco, G., A. Rankin, and R. Poppele. "Representation of passive hindlimb postures in cat spinocerebellar activity." Journal of Neurophysiology 76, no. 2 (1996): 715–26. http://dx.doi.org/10.1152/jn.1996.76.2.715.

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1. We report here about the modulation of dorsal spinocerebellar tract (DSCT) activity by limb posture. In principle, DSCT activity could represent limb position in one of several ways. According to a classical notion of DSCT function, DSCT activity might be expected to correlate with changes in individual joint angles. However, given the evidence for extensive polysynaptic convergence onto DSCT units, it is reasonable to propose that DSCT activity represents more global variables such as the orientation of limb segments or the length and orientation of the whole limb. 2. In six anesthetized c

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17

Hartman, A. B., C. P. Mallett, S. Sheriff, and S. J. Smith-Gill. "Unusual joining sites in the H and L chains of an anti-lysozyme antibody." Journal of Immunology 141, no. 3 (1988): 932–36. http://dx.doi.org/10.4049/jimmunol.141.3.932.

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Abstract Nucleotide sequences of HyHEL-5, an antibody specific for chicken lysozyme (HEL), indicated unusual joins in the third complementarity-determining region of both the H and L chains. The VK-JK recombination site is unusual in that codon 96, normally derived from the JK gene segment, is deleted entirely, making the L3 one amino acid shorter than normal. Examination of the HyHEL-5 Fab-HEL x-ray structure suggests that the conformation of L3 is clearly important for Ag specificity. A comparison of the HyHEL-5 L3 with that of the structurally related antibody J539 indicates that the delete

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18

de Rooy, Diederik P. C., Roula Tsonaka, Maria L. E. Andersson, et al. "Genetic Factors for the Severity of ACPA-negative Rheumatoid Arthritis in 2 Cohorts of Early Disease: A Genome-wide Study." Journal of Rheumatology 42, no. 8 (2015): 1383–91. http://dx.doi.org/10.3899/jrheum.140741.

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Objective.Rheumatoid arthritis (RA) that is negative for anticitrullinated protein antibodies (ACPA) is a subentity of RA, characterized by less severe disease. At the individual level, however, considerable differences in the severity of joint destruction occur. We performed a study on genetic factors underlying the differences in joint destruction in ACPA-negative patients.Methods.A genome-wide association study was done with 262 ACPA-negative patients with early RA included in the Leiden Early Arthritis Clinic and related to radiographic joint destruction over 7 years. Significant single-nu

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19

Liu, Chen, Feng Li, Xian Sun, and Hongzhe Han. "Attention-Based Joint Entity Linking with Entity Embedding." Information 10, no. 2 (2019): 46. http://dx.doi.org/10.3390/info10020046.

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Entity linking (also called entity disambiguation) aims to map the mentions in a given document to their corresponding entities in a target knowledge base. In order to build a high-quality entity linking system, efforts are made in three parts: Encoding of the entity, encoding of the mention context, and modeling the coherence among mentions. For the encoding of entity, we use long short term memory (LSTM) and a convolutional neural network (CNN) to encode the entity context and entity description, respectively. Then, we design a function to combine all the different entity information aspects

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Gu, Junhua, Chuanxin Lan, Wenbai Chen, and Hu Han. "Joint Pedestrian and Body Part Detection via Semantic Relationship Learning." Applied Sciences 9, no. 4 (2019): 752. http://dx.doi.org/10.3390/app9040752.

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While remarkable progress has been made to pedestrian detection in recent years, robust pedestrian detection in the wild e.g., under surveillance scenarios with occlusions, remains a challenging problem. In this paper, we present a novel approach for joint pedestrian and body part detection via semantic relationship learning under unconstrained scenarios. Specifically, we propose a Body Part Indexed Feature (BPIF) representation to encode the semantic relationship between individual body parts (i.e., head, head-shoulder, upper body, and whole body) and highlight per body part features, providi

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Xie, Zhongwei, Ling Liu, Yanzhao Wu, Luo Zhong, and Lin Li. "Learning Text-image Joint Embedding for Efficient Cross-modal Retrieval with Deep Feature Engineering." ACM Transactions on Information Systems 40, no. 4 (2022): 1–27. http://dx.doi.org/10.1145/3490519.

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This article introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineering in data preprocessing from training the text-image joint embedding model. We use the Recipe1M dataset for the technical description and empirical validation. In preprocessing, we perform deep feature engineering by combining deep feature engineering with semantic context features derived from raw text-image input data. We leverage LSTM to identify key terms, deep NLP models from the BERT family, TextRank, or TF

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Planas Rego, Mónica, Paulette Laniado Abud, Miguel Ángel Martínez Camacho, Natalie Fastag Weber, Paloma Gómez Llano, and Irlanda Olvera Gómez. "Pediatric physiotherapeutic intervention in Classical Ehlers-Danlos." Proceedings of Scientific Research Universidad Anáhuac. Multidisciplinary Journal of Healthcare 4, no. 8 (2024): 93–100. https://doi.org/10.36105/psrua.2024v4n8.05.

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Ehlers-Danlos Syndrome (EDS) is a group of genetic connective tissue disorders characterized by mutations in the genes that encode collagen, collagen-modifying enzymes, and other extracellular matrix proteins. These alterations generate clinical manifestations such as joint hypermobility, skin fragility and musculoskeletal complications, which significantly impact the quality of life of patients. Although scientific evidence on the physiotherapy management of EDS is limited, it has been observed that specialized interventions can improve joint stability, muscle strength and functionality, espe

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Chen, Yan, Vitaliy Marchenko, and Robert F. Rogers. "Joint Probability-Based Neuronal Spike Train Classification." Computational and Mathematical Methods in Medicine 10, no. 3 (2009): 229–39. http://dx.doi.org/10.1080/17486700802448615.

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Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs) have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM) to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs). The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and

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Pynadath, D. V., and M. Tambe. "The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models." Journal of Artificial Intelligence Research 16 (June 1, 2002): 389–423. http://dx.doi.org/10.1613/jair.1024.

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Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP mo

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Wen, Liqun, Donglin Li, Xinglong Pei, Yan Zhang, and Jianhui Wang. "Continuous Estimation of Upper Limb Joint Angle Based on Stacked Denoising Autoencoder." Journal of Physics: Conference Series 2402, no. 1 (2022): 012043. http://dx.doi.org/10.1088/1742-6596/2402/1/012043.

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Abstract In the human-robot interaction system of the rehabilitation robot for stroke rehabilitation, surface electromyography (sEMG) signal-based continuous joint angle estimation has essential significance and implementation value. However, the existing intra-subject mode is time-consuming and lacks generality, while the adoption of the new inter-subject mode tests the model’s generalization ability; at the same time, the often-adopted multi-feature fusion strategy makes the feature dimensionality too high and increases the computational pressure of the system. In this regard, firstly, four

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Shin, Jaehun, Wonkee Lee, Byung-Hyun Go, Baikjin Jung, Youngkil Kim, and Jong-Hyeok Lee. "Exploration of Effective Attention Strategies for Neural Automatic Post-editing with Transformer." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (2021): 1–17. http://dx.doi.org/10.1145/3465383.

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Automatic post-editing (APE) is the study of correcting translation errors in the output of an unknown machine translation (MT) system and has been considered as a method of improving translation quality without any modification to conventional MT systems. Recently, several variants of Transformer that take both the MT output and its corresponding source sentence as inputs have been proposed for APE; and models introducing an additional attention layer into the encoder to jointly encode the MT output with its source sentence recorded a high-rank in the WMT19 APE shared task. We examine the eff

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Cunningham, Ryan J., and Ian D. Loram. "Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks." Journal of The Royal Society Interface 17, no. 162 (2020): 20190715. http://dx.doi.org/10.1098/rsif.2019.0715.

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The objective is to test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal muscle states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction or generalization of independently varying active and passive states. We use deep learning to investigate the generalizable content of two-dimensional (2D) US muscle images. US d

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Ciszkiewicz, Adam. "Analyzing Uncertainty of an Ankle Joint Model with Genetic Algorithm." Materials 13, no. 5 (2020): 1175. http://dx.doi.org/10.3390/ma13051175.

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Recent studies in biomechanical modeling suggest a paradigm shift, in which the parameters of biomechanical models would no longer treated as fixed values but as random variables with, often unknown, distributions. In turn, novel and efficient numerical methods will be required to handle such complicated modeling problems. The main aim of this study was to introduce and verify genetic algorithm for analyzing uncertainty in biomechanical modeling. The idea of the method was to encode two adversarial models within one decision variable vector. These structures would then be concurrently optimize

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Ding, Guanwen, Xizhe Zang, Xuehe Zhang, Changle Li, Yanhe Zhu, and Jie Zhao. "A Human–Robot Skill Transfer Strategy with Task-Constrained Optimization and Real-Time Whole-Body Adaptation." Applied Sciences 15, no. 6 (2025): 3171. https://doi.org/10.3390/app15063171.

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Human–robot skill transfer enables robots to learn skills from humans and adapt to new task-constrained scenarios. During task execution, robots are expected to react in real-time to unforeseen dynamic obstacles. This paper proposes an integrated human–robot skill transfer strategy with offline task-constrained optimization and real-time whole-body adaptation. Specifically, we develop the via-point trajectory generalization method to learn from only one human demonstration. To incrementally incorporate multiple human skill variations, we encode initial distributions for each skill with Joint P

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Labandeira-Rey, Maria, and Jonathan T. Skare. "Decreased Infectivity in Borrelia burgdorferi Strain B31 Is Associated with Loss of Linear Plasmid 25 or 28-1." Infection and Immunity 69, no. 1 (2001): 446–55. http://dx.doi.org/10.1128/iai.69.1.446-455.2001.

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ABSTRACT Previous reports indicated a correlation between loss of plasmids and decreased infectivity of Borrelia burgdorferi strain B31, suggesting that plasmids may encode proteins that are required for pathogenesis. In this study, we expand on this correlation. Using theB. burgdorferi genomic sequence, we designed primers specific for each plasmid, and by using PCR we catalogued 11 linear and 2 circular plasmids from 49 clonal isolates of a mid-passage B. burgdorferi strain B31, initially derived from infected mouse skin, and 20 clones obtained from mouse skin infected with a low-passage iso

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Wu, Huiyan, and Jun Huang. "Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information." Applied Sciences 12, no. 12 (2022): 6231. http://dx.doi.org/10.3390/app12126231.

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The main purpose of the joint entity and relation extraction is to extract entities from unstructured texts and extract the relation between labeled entities at the same time. At present, most existing joint entity and relation extraction networks ignore the utilization of explicit semantic information and explore implicit semantic information insufficiently. In this paper, we propose Joint Entity and Relation Extraction Network with Enhanced Explicit and Implicit Semantic Information (EINET). First, on the premise of using the pre-trained model, we introduce explicit semantics from Semantic R

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Peng, Yongfang, Shengwei Tian, Long Yu, Yalong Lv, and Ruijin Wang. "A Joint Approach to Detect Malicious URL Based on Attention Mechanism." International Journal of Computational Intelligence and Applications 18, no. 03 (2019): 1950021. http://dx.doi.org/10.1142/s1469026819500214.

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To improve the accuracy and automation of malware Uniform Resource Locator (URL) recognition, a joint approach of Convolutional neural network (CNN) and Long-short term memory (LSTM) based on the Attention mechanism (JCLA) is proposed to identify and detect malicious URL. Firstly, the URL features including texture information, lexical information and host information are extracted and filtered, and pre-processed with encode. Then, the feature matrix more relevant to the output are chose according to the weight of the attention mechanism and input to the constructed parallel processing model c

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Shunsheng, Zhang, Du Long, and Wang Wenqin. "LPI radar waveform design based on complementary phase and discrete chaotic frequency joint coding." Journal of Applied Artificial Intelligence 1, no. 1 (2024): 293–312. http://dx.doi.org/10.59782/aai.v1i1.264.

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In order to reduce the probability of radar radiation signals being detected by enemy passive detection systems, this paper proposes a phase and frequency joint coding low intercept Radar waveform design method. Based on the linear frequency modulation signal, this method uses complementary binary code and chaotic sequence to phase encode the intra-pulse modulation. Code and frequency coding. The numerical simulation results show that the designed waveform exhibits pseudo-random characteristics in the time-frequency domain, and the low recognition performance is improved; The signal has an ext

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Kalaska, John F. "The representation of arm movements in postcentral and parietal cortex." Canadian Journal of Physiology and Pharmacology 66, no. 4 (1988): 455–63. http://dx.doi.org/10.1139/y88-075.

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Considerable experimental evidence supports the hypothesis that the neocortical processes underlying kinesthetic sensation form a hierarchical series of cells signalling increasingly complex patterns of movement of the body. However, this view has been criticized and the data lack quantitative verification under controlled conditions. These studies have also typically used one-dimensional (reciprocal) movements, even of multiple degree-of-freedom joints such as the wrist or shoulder, and have been restricted to passive movements. This latter limitation is particularly critical, since the respo

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Ding, Mingyu, Zhe Wang, Bolei Zhou, Jianping Shi, Zhiwu Lu, and Ping Luo. "Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 10713–20. http://dx.doi.org/10.1609/aaai.v34i07.6699.

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A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised methods fail to utilize information from the rest of the frames. To exploit the spatio-temporal information in videos, many previous works use pre-computed optical flows, which encode the temporal consistency to improve the video segmentation. However, the video segmentation and optical flow estimation are still considered as two separate tasks. In this paper, we propose a novel framework for joint video semantic seg

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Purcell, P., A. Jheon, M. P. Vivero, H. Rahimi, A. Joo, and O. D. Klein. "Spry1 and Spry2 Are Essential for Development of the Temporomandibular Joint." Journal of Dental Research 91, no. 4 (2012): 387–93. http://dx.doi.org/10.1177/0022034512438401.

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The temporomandibular joint (TMJ) is a specialized synovial joint essential for the function of the mammalian jaw. The main components of the TMJ are the mandibular condyle, the glenoid fossa of the temporal bone, and a fibrocartilagenous disc interposed between them. The genetic program for the development of the TMJ remains poorly understood. Here we show the crucial role of sprouty ( Spry) genes in TMJ development. Sprouty genes encode intracellular inhibitors of receptor tyrosine kinase (RTK) signaling pathways, including those triggered by fibroblast growth factors (Fgfs). Using in situ h

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Li, Lin, Jia-Ning Guo, Qi Wu, and Jian Zhang. "Dimming Control Scheme of Visible Light Communication Based on Joint Multilevel Time-Shifted Coding." Electronics 11, no. 10 (2022): 1602. http://dx.doi.org/10.3390/electronics11101602.

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Dimming control is an essential objection in the signal designing of visible light communication (VLC), which requires improving the communication performance of the system as much as possible while considering the illumination quality. Here, we studied the problem of high-efficiency transmission in an indoor VLC multi-core light-emitting diode (LED) communication model while considering dimming constraints, and propose a dimming method based on joint multilevel multi-LED time-shifted coding (ML-MTSC). The scheme utilizes the code structure of time-shifted space–time codes to encode and uses p

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Cao, Pengcheng, Weiwei Liu, Guangjie Liu, et al. "Design a Wireless Covert Channel Based on Dither Analog Chaotic Code." International Journal of Digital Crime and Forensics 13, no. 2 (2021): 115–33. http://dx.doi.org/10.4018/ijdcf.2021030108.

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To conceal the very existence of communication, the noise-based wireless covert channel modulates secret messages into artificial noise, which is added to the normal wireless signal. Although the state-of-the-art work based on constellation modulation has made the composite and legitimate signal undistinguishable, there exists an imperfection on reliability due to the dense distribution of covert constellation points. In this study, the authors design a wireless covert channel based on dither analog chaotic code to improve the reliability without damaging the undetectability. The dither analog

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Jun, Kooksung, Keunhan Lee, Sanghyub Lee, Hwanho Lee, and Mun Sang Kim. "Hybrid Deep Neural Network Framework Combining Skeleton and Gait Features for Pathological Gait Recognition." Bioengineering 10, no. 10 (2023): 1133. http://dx.doi.org/10.3390/bioengineering10101133.

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Human skeleton data obtained using a depth camera have been used for pathological gait recognition to support doctor or physician diagnosis decisions. Most studies for skeleton-based pathological gait recognition have used either raw skeleton sequences directly or gait features, such as gait parameters and joint angles, extracted from raw skeleton sequences. We hypothesize that using skeleton, joint angles, and gait parameters together can improve recognition performance. This study aims to develop a deep neural network model that effectively combines different types of input data. We propose

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Tang, Pan, Shiqi Shao, Dapeng Zhou, and Huihua Hu. "Understanding the Collaborative Process and Its Effects on Perceived Outcomes during Emergency Response in China: From Perspectives of Local Government Sectors." Sustainability 13, no. 14 (2021): 7605. http://dx.doi.org/10.3390/su13147605.

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In contemporary China, the rapidly urbanized cities are exposed to a broad range of natural and human-made emergencies, such as COVID-19. Responding to emergencies successfully requires widespread participation of local government sectors that engages in diversified collaboration behaviors across organizational boundaries for achieving sustainability. However, the multi-organizational collaborative process is highly dynamic and complex, as well as its outcomes are uncertain underlying the emergency response network. Examining characteristics of the collaborative process and exploring how colla

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Smolyanskaya, Alexandra, Douglas A. Ruff, and Richard T. Born. "Joint tuning for direction of motion and binocular disparity in macaque MT is largely separable." Journal of Neurophysiology 110, no. 12 (2013): 2806–16. http://dx.doi.org/10.1152/jn.00573.2013.

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Neurons in sensory cortical areas are tuned to multiple dimensions, or features, of their sensory space. Understanding how single neurons represent multiple features is of great interest for determining the informative dimensions of the neurons' response, the decoding algorithms appropriate for extracting this information from the neuronal population, and for determining where specific transformations occur along the visual hierarchy. Despite the established role of cortical area MT in judgments of motion and depth, it is not known how individual neurons jointly encode the two dimensions. We i

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Kim, Yeonggwang, Giwon Ku, Chulseung Yang, Jeonggi Lee, and Jinsul Kim. "Lightweight Three-Dimensional Pose and Joint Center Estimation Model for Rehabilitation Therapy." Electronics 12, no. 20 (2023): 4273. http://dx.doi.org/10.3390/electronics12204273.

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In this study, we proposed a novel transformer-based model with independent tokens for estimating three-dimensional (3D) human pose and shape from monocular videos, specifically focusing on its application in rehabilitation therapy. The main objective is to recover pixel-aligned rehabilitation-customized 3D human poses and body shapes directly from monocular images or videos, which is a challenging task owing to inherent ambiguity. Existing human pose estimation methods heavily rely on the initialized mean pose and shape as prior estimates and employ parameter regression with iterative error f

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Xu, Zhe, Ivan Gavran, Yousef Ahmad, et al. "Joint Inference of Reward Machines and Policies for Reinforcement Learning." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 590–98. http://dx.doi.org/10.1609/icaps.v30i1.6756.

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Incorporating high-level knowledge is an effective way to expedite reinforcement learning (RL), especially for complex tasks with sparse rewards. We investigate an RL problem where the high-level knowledge is in the form of reward machines, a type of Mealy machines that encode non-Markovian reward functions. We focus on a setting in which this knowledge is a priori not available to the learning agent. We develop an iterative algorithm that performs joint inference of reward machines and policies for RL (more specifically, q-learning). In each iteration, the algorithm maintains a hypothesis rew

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Ben Tamou, Abdelouahid, Lahoucine Ballihi, and Driss Aboutajdine. "Automatic Learning of Articulated Skeletons Based on Mean of 3D Joints for Efficient Action Recognition." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (2017): 1750008. http://dx.doi.org/10.1142/s0218001417500082.

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In this paper, we present a new approach for human action recognition using [Formula: see text] skeleton joints recovered from RGB-D cameras. We propose a descriptor based on differences of skeleton joints. This descriptor combines two characteristics including static posture and overall dynamics that encode spatial and temporal aspects. Then, we apply the mean function on these characteristics in order to form the feature vector, used as an input to Random Forest classifier for action classification. The experimental results on both datasets: MSR Action 3D dataset and MSR Daily Activity 3D da

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Jones, Heath G., Andrew D. Brown, Kanthaiah Koka, Jennifer L. Thornton, and Daniel J. Tollin. "Sound frequency-invariant neural coding of a frequency-dependent cue to sound source location." Journal of Neurophysiology 114, no. 1 (2015): 531–39. http://dx.doi.org/10.1152/jn.00062.2015.

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The century-old duplex theory of sound localization posits that low- and high-frequency sounds are localized with two different acoustical cues, interaural time and level differences (ITDs and ILDs), respectively. While behavioral studies in humans and behavioral and neurophysiological studies in a variety of animal models have largely supported the duplex theory, behavioral sensitivity to ILD is curiously invariant across the audible spectrum. Here we demonstrate that auditory midbrain neurons in the chinchilla ( Chinchilla lanigera) also encode ILDs in a frequency-invariant manner, efficient

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Zhai, Hao, Xin Pan, You Yang, Jinyuan Jiang, and Qing Li. "Two-Stage Focus Measurement Network with Joint Boundary Refinement for Multifocus Image Fusion." International Journal of Intelligent Systems 2023 (August 31, 2023): 1–16. http://dx.doi.org/10.1155/2023/4155948.

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Focus measurement, one of the key tasks in multifocus image fusion (MFIF) frameworks, identifies the clearer parts of multifocus images pairs. Most of the existing methods aim to achieve disposable pixel-level focus measurement. However, the lack of sufficient accuracy often gives rise to misjudgments in the results. To this end, a novel two-stage focus measurement with joint boundary refinement network is proposed for MFIF. In this work, we adopt a coarse-to-fine strategy to gradually achieve block-level and pixel-level focus measurement for producing more fine-grained focus probability maps,

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Arnoux, Léo, Sebastien Fromentin, Dario Farotto, Mathieu Beraneck, Joseph McIntyre, and Michele Tagliabue. "The visual encoding of purely proprioceptive intermanual tasks is due to the need of transforming joint signals, not to their interhemispheric transfer." Journal of Neurophysiology 118, no. 3 (2017): 1598–608. http://dx.doi.org/10.1152/jn.00140.2017.

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To perform goal-oriented hand movement, humans combine multiple sensory signals (e.g., vision and proprioception) that can be encoded in various reference frames (body centered and/or exo-centered). In a previous study (Tagliabue M, McIntyre J. PLoS One 8: e68438, 2013), we showed that, when aligning a hand to a remembered target orientation, the brain encodes both target and response in visual space when the target is sensed by one hand and the response is performed by the other, even though both are sensed only through proprioception. Here we ask whether such visual encoding is due 1) to the

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Geigle, Gregor, Jonas Pfeiffer, Nils Reimers, Ivan Vulić, and Iryna Gurevych. "Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval." Transactions of the Association for Computational Linguistics 10 (2022): 503–21. http://dx.doi.org/10.1162/tacl_a_00473.

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Abstract Current state-of-the-art approaches to cross- modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross- modal retrieval, we propose a novel fine-tuni

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Giarmatzis, Georgios, Evangelia I. Zacharaki, and Konstantinos Moustakas. "Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning." Sensors 20, no. 23 (2020): 6933. http://dx.doi.org/10.3390/s20236933.

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Conventional biomechanical modelling approaches involve the solution of large systems of equations that encode the complex mathematical representation of human motion and skeletal structure. To improve stability and computational speed, being a common bottleneck in current approaches, we apply machine learning to train surrogate models and to predict in near real-time, previously calculated medial and lateral knee contact forces (KCFs) of 54 young and elderly participants during treadmill walking in a speed range of 3 to 7 km/h. Predictions are obtained by fusing optical motion capture and mus

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Xie, Cunxiang, Limin Zhang, and Zhaogen Zhong. "Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations." Applied Sciences 13, no. 9 (2023): 5748. http://dx.doi.org/10.3390/app13095748.

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Entity alignment helps discover and link entities from different knowledge graphs (KGs) that refer to the same real-world entity, making it a critical technique for KG fusion. Most entity alignment methods are based on knowledge representation learning, which uses a mapping function to project entities from different KGs into a unified vector space and align them based on calculated similarities. However, this process requires sufficient pre-aligned entity pairs. To address this problem, this study proposes an entity alignment method based on joint learning of entity and attribute representati

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