Academic literature on the topic 'Encodage joint'
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Journal articles on the topic "Encodage joint"
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Wu, Jiahua, and Hyo-Jong Lee. "A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network." Applied Sciences 11, no. 9 (May 7, 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 joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.
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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 (May 1, 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 disappear. We have performed an experiment in which subjects received anteroposterior pelvis perturbations during walking while their ankle joints could not contribute to the balance recovery. The latter was realized by physically blocking the ankle joints through a pair of modified ankle-foot orthoses. In this article we present the lower limb muscle activity responses in reaction to these perturbations. Of particular interest are the tibialis anterior and gastrocnemius medialis muscles, which could not contribute to the balance recovery through the ankle joint or encode muscle length changes caused by ankle joint rotation. Yet, these muscles showed long-latency responses, ~100 ms after perturbation onset. The response amplitudes were dependent on the perturbation magnitude and direction, as well as the state of the leg. The results imply that ankle muscle responses can be evoked without changes in proprioceptive information of those muscles through ankle rotation. This suggest a more centralized regulation of balance control, not strictly related to the ankle joint kinematics. NEW & NOTEWORTHY Walking human subjects received forward-backward perturbations at the pelvis while wearing “pin-shoes,” a pair of modified ankle-foot orthoses that physically blocked ankle joint movement and reduced the base of support of each foot to a single point. The lower leg muscles showed long-latency perturbation-dependent activity changes, despite having no functional contributions to balance control through the ankle joint and not having been subjected to muscle length changes through ankle joint rotation.
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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 employ a non-local block to capture global dependencies of temporal domain in an attention mechanism. Our model achieves improvements over the stateof-the-art methods on two large-scale datasets.
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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 (May 1, 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 position from limb geometry by applying mechanical constraints to individual hindlimb joints in anesthetized cats. We quantified the effect of the joint constraints on limb geometry by analyzing joint-angle covariance in the free and constrained conditions. One type of constraint, a rigid constraint of the knee angle, both changed the covariance pattern and significantly reduced the strength of joint-angle covariance. The other type, an elastic constraint of the ankle angle, changed only the covariance pattern and not its overall strength. We studied the effect of these constraints on the activity in 70 dorsal spinocerebellar tract (DSCT) neurons using a multivariate regression model, with limb axis length and orientation as predictors of neuronal activity. This model also included an experimental condition indicator variable that allowed significant intercept or slope changes in the relationships between foot position parameters and neuronal activity to be determined across conditions. The result of this analysis was that the spatial tuning of 37/70 neurons (53%) was unaffected by the constraints, suggesting that they were somehow able to signal foot position independently from the specific joint angles. We also investigated the extent to which cell activity represented individual joint angles by means of a regression model based on a linear combination of joint angles. A backward elimination of the insignificant predictors determined the set of independent joint angles that best described the neuronal activity for each experimental condition. Finally, by comparing the results of these two approaches, we could determine whether a DSCT neuron represented foot position, specific joint angles, or none of these variables consistently. We found that 10/70 neurons (14%) represented one or more specific joint-angles. The activity of another 27 neurons (39%) was significantly affected by limb geometry changes, but 33 neurons (47%) consistently elaborated a foot position representation in the coordinates of the limb axis.
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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 (April 1, 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 may reduce the fidelity by which muscle spindles encode joint position and movement. The aim of the present study was to measure, for the first time, both the changes in firing of single muscle spindle afferents and changes in muscle fascicle length in vivo from the tibialis anterior muscle (TA) during passive rotations about the ankle. Unitary recordings were made from 15 muscle spindle afferents supplying TA via a microelectrode inserted into the common peroneal nerve. Ultrasonography was used to measure the length of an individual fascicle of TA. We saw a strong correlation between fascicle length and firing rate during passive ankle rotations of varying rates (0.1–0.5 Hz) and amplitudes (1–9°). In particular, we saw responses observed at relatively small changes in muscle length that highlight the sensitivity of the TA muscle to small length changes. This study is the first to measure spindle firing and fascicle dynamics in vivo and provides an experimental basis for further understanding the link between fascicle length, MTU length, and spindle firing patterns. NEW & NOTEWORTHY Muscle spindles are exquisitely sensitive to changes in muscle length, but recordings from human muscle spindle afferents are usually correlated with joint angle rather than muscle fascicle length. In this study, we monitored both muscle fascicle length and spindle firing from the human tibialis anterior muscle in vivo. Our findings are the first to measure these signals in vivo and provide an experimental basis for exploring this link further.
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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 (February 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: 2D cue extraction, pose feature encoding, initial reconstruction, and reconstruction refinement. Specifically, we first encode the image to determine semantic features during 2D cue extraction; this is also used to predict hand joints and for segmentation. Then, during the pose feature encoding stage, we use a hand joints encoder to learn spatial information from the joint heatmaps. Next, a coarse 3D hand mesh and 2D mesh are obtained in the initial reconstruction step; a mesh squeeze-and-excitation block is used to fuse different hand features to enhance perception of 3D hand structures. Finally, a global mesh refinement stage learns non-local relations between vertices of the hand mesh from the predicted 2D mesh, to predict an offset hand mesh to fine-tune the reconstruction results. Quantitative and qualitative results on the FreiHAND benchmark dataset demonstrate that our approach achieves state-of-the-art performance.
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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 (May 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 joint angle. However, responses to extensor muscle contractions are obtained when the tibia is held in full flexion due to specializations of the femoro-tibial joint. These responses could be of importance in signalling preparedness for a jump. Intracellular soma recordings of activity in individual receptors indicate that the organ contains two types of receptors: phasic units that respond to joint movement and tonic units that encode joint position and also show some response to movement. All units are directionally sensitive and respond only in limited ranges of joint angle. Some phasic units increase firing frequency with increasing rate of movement and thus encode joint velocity. Other phasic units fire only single action potentials and can encode only the occurrence and direction of joint movement. All tonic units increase activity in the extremes of joint position and show substantial hysteresis upon return to more median positions. Direct soma depolarization produces different responses in different types of units: phasic receptors show only transient discharges to current injection; tonic receptors exhibit sustained increases in activity that are followed by periods of inhibition of background firing upon cessation of current injection. Receptors of the chordotonal organ are separable into two major groups, based upon their response characteristics, soma location and dendritic orientation: a dorsal group of receptors contains tonic units that respond in ranges of joint flexion (joint angle 0–80 degrees) and phasic units that respond to flexion movements; a ventral group of sensilla contains tonic units active in ranges of joint extension (joint angle 80–170 degrees) and phasic receptors that respond to extension movements. The response properties of these receptors are discussed with reference to the potential functions of the chordotonal organ in the locust's behavioural repertoire.
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Ajemian, Robert, Daniel Bullock, and Stephen Grossberg. "Kinematic Coordinates In Which Motor Cortical Cells Encode Movement Direction." Journal of Neurophysiology 84, no. 5 (November 1, 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 systems are explicitly modeled: Cartesian spatial, shoulder-centered, and joint angle. The computed patterns of spatial pds are distinct for each of these three coordinate systems, and experimental approaches are described that can capitalize on these differences to compare the empirical adequacy of each coordinate hypothesis. One particular experiment involving curved motion was analyzed from this perspective. Out of the three coordinate systems tested, the assumption of joint angle coordinates best explained the observed cellular response properties. The mathematical framework developed in this paper can also be used to design new experiments that are capable of disambiguating between a given set of specified coordinate hypotheses.
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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 (May 10, 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 Knowledge Graph (JKRL), which models the entity description and relationship description of the triple structure for joint representation learning of knowledge and balances the contribution of the triple structure and text description in the process of vector learning. First, we adopt the TransE model to learn the structural vector representations of entities and relations, and then use a CNN model to encode the entity description to obtain the text representation of the entity. To semantically encode the relation descriptions, we designed an Attention-Bi-LSTM text encoder, which introduces an attention mechanism into the Bi-LSTM model to calculate the semantic relevance between each word in the sentence and different relations. In addition, we also introduce position features into word features in order to better encode word order information. Finally, we define a joint evaluation function to learn the joint representation of structural and textual representations. The experiments show that compared with the baseline methods, our model achieves the best performance on both Mean Rank and Hits@10 metrics. The accuracy of the triple classification task on the FB15K dataset reached 93.2%.
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BRINGMANN, KATHRIN. "CONGRUENCES FOR DYSON'S RANKS." International Journal of Number Theory 05, no. 04 (June 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.
Dissertations / Theses on the topic "Encodage joint"
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Sadikine, Mohamed Amine. "Deep vascular segmentation with geometric and topological constraints." Electronic Thesis or Diss., Brest, 2024. http://www.theses.fr/2024BRES0042.
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Dans le domaine de l’analyse d’images médicales, la segmentation des vaisseaux sanguins joue un rôle clé dans l’amélioration du diagnostic assisté par ordinateur et de la planification chirurgicale. Ce travail introduit trois contributions innovantes en segmentation automatique de structures vasculaires. Premièrement, nous présentons une méthodologie qui améliore les architectures inspirées de U-Net avec un auto-encodeur convolutif semi-surcomplet qui intègre des a priori de forme pour améliorer la délimitation des vaisseaux, avec un focus particulier sur la caractérisation des structures fines. Ensuite, notre recherche se prolonge pour affiner la segmentation à travers un mécanisme d’encodage préalable conjoint qui fusionne des contraintes géométriques et topologiques, fournissant un espace latent unifié qui capture les informations contextuelles et la connectivité des vaisseaux et répondant ainsi aux défis posés par leur variabilité anatomique. Enfin, nous présentons une approche supervisée multi-tâches incorporant des tâches auxiliaires spécifiques à l’échelle et un apprentissage contrastif. Ces avancées représentent un pas en avant dans la segmentation vasculaire automatisée, offrant ainsi le potentiel d’améliorer les résultats cliniques dans une large gamme d’applicationsIn the evolving field of medical image analysis, blood vessel segmentation plays a key role in improving computer-aided diagnosis and surgical planning. This work combines three innovative contributions to advance the automatic segmentation of vascular structures. Firstly, we introduce a novel methodology that enhances U-Net inspired architectures with a semi-overcomplete convolutional auto-encoder that integrates shape priors to improve the delineation of intricate vascular systems, with a specific emphasis on characterizing fine structures. Subsequently, our research delves into enhancing vessel delineation through a novel joint prior encoding mechanism that combines geometric and topological constraints, providing a unified latent space that captures contextual information and connectivity of blood vessels, thereby addressing the challenges posed by their anatomical variability. Finally, we present a novel clustering technique for scale decomposition, along with a multi-task supervised approach that incorporates scale-specific auxiliary tasks and contrastive learning. These advances represent a step forward in reliable automated vascular segmentation, offering the potential to enhance clinical outcomes in a wide range of applications in clinical routine
Books on the topic "Encodage joint"
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McDougall, Jason J., and Joel A. Vilensky. The innervation of the joint and its role in osteoarthritis pain. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0007.
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Diarthrodial joints possess an extensive network of sensory and sympathetic nerve fibres whose physiological functions are varied and complex. Nerves are primarily located in the synovium but also innervate the subchondral bone, the outer third of menisci, and the superficial surface of tendons and ligaments. Large-diameter, myelinated neurons are involved in joint position sense while small-diameter neurons with thin or no myelin typically sense pain. The small-diameter nerves in conjunction with sympathetic fibres control synovial blood flow and maintain joint homeostasis. In patients with osteoarthritis (OA), the sensory nerves become sensitized and increase their firing rate in response to normal movement. This peripheral sensitization is mediated by numerous algogenic agents released into the OA knee including neuropeptides, eicosanoids, and proteinases. A portion of joint afferents fire in the absence of mechanical stimuli and encode pain at rest. Interestingly, the firing rate of joint afferents does not correlate with OA severity, indicating that pain is a poor predictor of joint pathology. Evidence is accumulating to suggest that a subpopulation of OA patients who are unresponsive to classical non-steroidal anti-inflammatory drugs may be suffering from neuropathic pain in which there is damage to the joint nerves themselves. Better understanding of the biology of joint nerves could help in the development of patient-targeted therapies to alleviate OA pain and inflammation.
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Hakim, Alan J., and Rodney Grahame. Hypermobility syndromes. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0159.
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Hypermobility-related syndromes constitute a family of heritable disorders of connective tissue (HDCT) that derive from abnormalities affecting genes that encode for the connective tissue matrix proteins such as collagen, fibrillin, and tenascin. They range from such commonplace though poorly recognized conditions such as the joint hypermobility syndrome (JHS) to the better-known, if more rare, eponymous syndromes such as Marfan's syndrome (MFS) and the different types of the Ehlers-Danlos syndrome (EDS). The more common presentations are with skin pathology (bruising, scaring), joint or spinal and/or muscle pain and instability with vulnerability to injury and chronic widespread pain, cardiac valve pathologies, and in MFS and vascular EDS, arterial dilatation with the risk of dissection and rupture. JHS (widely considered synonymous with the EDS hypermobility type) is further complicated by cardiovascular autonomic dysfunction such as orthostatic intolerance, palpitations, and syncope, and the recently described and commonly encountered pangastrointestinal dysmotility. The latter can manifest as gastro-oesophageal reflux, gastroparesis, slow-transit constipation, or rectal evacuatory dysfunction with rectal intussusception. In addition, HDCT are associated with bladder and uterine problems as a consequence of pelvic floor weakness. Such multisystemic conditions need to be managed by a multidisciplinary team able to draw on medical, surgical, physical, and psychological interventions by appropriately experienced specialists and therapists. This chapter introduces the reader to the epidemiology, genetics, classification, and clinical presentation of JHS, EDS, and MFS. It also describes the key investigations required to support a diagnosis and assess complications of an HDCT, as well as the multidisciplinary approach to management.
Book chapters on the topic "Encodage joint"
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Cai, Hua, Qing Xu, and Weilin Shen. "Complex Relative Position Encoding for Improving Joint Extraction of Entities and Relations." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 644–55. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_66.
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AbstractRelative position encoding (RPE) is important for transformer based pretrained language model to capture sequence ordering of input tokens. Transformer based model can detect entity pairs along with their relation for joint extraction of entities and relations. However, prior works suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first use BERT with complex relative position encoding (cRPE) to encode the input text information, then decompose the joint extraction task into two interrelated subtasks, namely head entity extraction and tail entity relation extraction. Owing to the excellent feature representation and reasonable decomposition strategy, our model can fully capture the semantic interdependence between different steps, as well as reduce noise from irrelevant entity pairs. Experimental results show that the F1 score of our method outperforms previous baseline work, achieving a better result on NYT-multi dataset with F1 score of 0.935.
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Jothimurugan, Kishor, Suguman Bansal, Osbert Bastani, and Rajeev Alur. "Specification-Guided Learning of Nash Equilibria with High Social Welfare." In Computer Aided Verification, 343–63. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_17.
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AbstractReinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning framework for training joint policies that form a Nash equilibrium. In our approach, rather than providing low-level reward functions, the user provides high-level specifications that encode the objective of each agent. Then, guided by the structure of the specifications, our algorithm searches over policies to identify one that provably forms an $$\epsilon $$ ϵ -Nash equilibrium (with high probability). Importantly, it prioritizes policies in a way that maximizes social welfare across all agents. Our empirical evaluation demonstrates that our algorithm computes equilibrium policies with high social welfare, whereas state-of-the-art baselines either fail to compute Nash equilibria or compute ones with comparatively lower social welfare.
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Singer, Wolf. "Time as Coding Space in the Cerebral Cortex." In Functional Neuroimaging of Visual Cognition, 99–123. Oxford University PressOxford, 2004. http://dx.doi.org/10.1093/oso/9780198528456.003.0005.
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Abstract Evidence is reviewed which suggests that the cerebral cortex applies, in parallel, complementary strategies to enhance the saliency of neuronal responses selected for further joint processing and to encode relations among the activities of distributed neurons. Saliency is enhanced by increasing discharge rate and/or by synchronizing the discharges of neurons. Relations between distributed neurons are encoded by integrating their responses in conjunction with specific neurons or by associating the neurons in functionally coherent cell assemblies that are distinguished by synchronous firing. It is proposed that selecting responses and encoding relations through synchronization is particularly well suited for the processing of rare or novel relations for which no conjunction-specific neurons have been implemented.
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"Review of Probability Elicitation and Examination of Approaches for Large Bayesian Networks." In Adaptive Security and Cyber Assurance for Risk-Based Decision Making, 178–214. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7766-3.ch009.
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Probability elicitation is the process of formulating a person's knowledge and beliefs about one or more uncertain quantities into a joint probability distribution for those quantities. The important point is that the goal of elicitation is to capture a person's knowledge and beliefs based upon their current state of information. Consequently, the results of elicitation need be only good enough to make reasoned decisions or reasonable inferences. This chapter identifies how to elicit probabilities for large conditional probability tables in Bayesian networks. This chapter looks at Bayesian networks which are statistical models to describe and visualize in a compact graphical form the probabilistic relationships between variables of interest; the nodes of a graphical structure correspond to the variables, while directed edges between the nodes encode conditional independence relationships between them.
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Alonso-Barba, Juan I., Jens D. Nielsen, Luis de la Ossa, and Jose M. Puerta. "Learning Probabilistic Graphical Models." In Medical Applications of Intelligent Data Analysis, 223–36. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1803-9.ch015.
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Probabilistic Graphical Models (PGM) are a class of statistical models that use a graph structure over a set of variables to encode independence relations between those variables. By augmenting the graph by local parameters, a PGM allows for a compact representation of a joint probability distribution over the variables of the graph, which allows for efficient inference algorithms. PGMs are often used for modeling physical and biological systems, and such models are then in turn used to both answer probabilistic queries concerning the variables and to represent certain causal and/or statistical relations in the domain. In this chapter, the authors give an overview of common techniques used for automatic construction of such models from a dataset of observations (usually referred to as learning), and they also review some important applications. The chapter guides the reader to the relevant literature for further study.
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Hakim, Alan J., and Rodney Grahame. "Hypermobility syndromes." In Oxford Textbook of Rheumatology, 1362–72. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0159_update_003.
Full textAbstract:
Hypermobility-related syndromes constitute a family of heritable disorders of connective tissue (HDCT) that derive from abnormalities affecting genes that encode for the connective tissue matrix proteins such as collagen, fibrillin, and tenascin. They range from such commonplace though poorly recognized conditions such as the joint hypermobility syndrome (JHS) to the better-known, if rarer, eponymous syndromes such as the Marfan syndrome (MFS) and the different types of the Ehlers-Danlos syndrome (EDS). The more common presentations are with skin pathology (bruising, scaring), joint or spinal and/or muscle pain and instability with vulnerability to injury and chronic widespread pain, cardiac valve pathologies, and in MFS and vascular EDS, arterial dilatation with the risk of dissection and rupture. JHS (widely considered synonymous with the EDS hypermobility type) is further complicated by cardiovascular autonomic dysfunction such as orthostatic intolerance, palpitations, and syncope, and the recently described and commonly encountered pan-gastrointestinal dysmotility. The latter can manifest as gastro-oesophageal reflux, gastroparesis, slow-transit constipation, or rectal evacuatory dysfunction with rectal intussusception. In addition, HDCT are associated with bladder and uterine problems as a consequence of pelvic floor weakness. Such multisystemic conditions need to be managed by a multidisciplinary team able to draw on medical, surgical, physical, and psychological interventions by appropriately experienced specialists and therapists. This chapter introduces the reader to the epidemiology, genetics, classification, and clinical presentation of JHS, EDS, and MFS. It also describes the key investigations required to support a diagnosis and assess complications of an HDCT, as well as the multidisciplinary approach to management.
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Hakim, Alan J., and Rodney Grahame. "Hypermobility syndromes." In Oxford Textbook of Rheumatology, 1362–72. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0159_update_004.
Full textAbstract:
Hypermobility-related syndromes constitute a family of heritable disorders of connective tissue (HDCT) that derive from abnormalities affecting genes that encode for the connective tissue matrix proteins such as collagen, fibrillin, and tenascin. They range from such commonplace though poorly recognized conditions such as hypermobility spectrum disorder (HSD), formerly within the joint hypermobility syndrome (JHS), to the better-known, if rarer, eponymous syndromes such as the Marfan syndrome (MFS), Loeys–Dietz syndrome, and the different types of the Ehlers-Danlos syndrome (EDS). The more common presentations are with skin pathology (bruising, scaring), joint or spinal and/or muscle pain and instability with vulnerability to injury and chronic widespread pain, cardiac valve pathologies, and in MFS and vascular EDS, in particular, arterial dilatation with the risk of dissection and rupture. The hypermobile variant of EDS and HSD are further complicated by cardiovascular autonomic dysfunction such as orthostatic intolerance, palpitations, and syncope, and the recently described and commonly encountered pan-gastrointestinal dysmotility. The latter can manifest as gastro-oesophageal reflux, gastroparesis, slow-transit constipation, or rectal evacuatory dysfunction with rectal intussusception. In addition, HDCT are associated with bladder and uterine problems as a consequence of pelvic floor weakness. Such multisystemic conditions need to be managed by a multidisciplinary team able to draw on medical, surgical, physical, and psychological interventions by appropriately experienced specialists and therapists. This chapter introduces the reader to the epidemiology, genetics, classification, and clinical presentation of HSD, EDS, and MFS. It also describes the key investigations required to support a diagnosis and assess complications of an HDCT, as well as the multidisciplinary approach to management.
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Gao, Tianyu. "A Joint Extraction Method of Entity Relationship in Tourism Domain Based on Multi-Dimensional Features and Mixed Time Series." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230894.
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Aiming at the problems of entity nesting and relationship overlap in the process of tourism domain information extraction, a joint entity relationship extraction method based on multi-dimensional features and mixed time series was proposed. The relationship extraction model based on multi-dimensional features and mixed time series is constructed by coding layer, entity extraction layer and relationship extraction layer. In the coding layer, BiLSTM can better capture the bidirectional semantic dependence, and use the bidirectional long and short memory network to encode sentences in combination with word context information. Entity extraction layer uses CNN to classify annotated text data, relationship extraction layer integrates the representation ability of different dimensions for relationship extraction, and uses weighted dot product to calculate the loss of relationship extraction to improve the effect of relationship extraction. The experimental results show that the recall rate of the model in this paper is 97.46%, the accuracy is 97.18%, and the F1 value is close to 1. When the threshold is 0.41, the F1 value is 0.805, and the curve reaches the inflection point, then the model has the best effect. The problem of entity nesting and relationship overlap is solved effectively.
Conference papers on the topic "Encodage joint"
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Yan, Eddie, and Yanping Huang. "Do CNNs Encode Data Augmentations?" In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534219.
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Yadav, Nishant, Nicholas Monath, Rico Angell, and Andrew McCallum. "Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions." In Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.crac-1.11.
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Agarwal, Oshin, Funda Durupınar, Norman I. Badler, and Ani Nenkova. "Word Embeddings (Also) Encode Human Personality Stereotypes." In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/s19-1023.
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Zhou, Lei, Ning Wu, and Fen Ge. "A Joint-Coding Scheme With Crosstalk Avoidance." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48912.
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The reliable transfer in Network on Chip can be guaranteed by crosstalk avoidance and error detection code. In this paper, we propose a joint coding scheme combined with crosstalk avoidance coding with error control coding. The Fibonacci numeral system is applied to satisfy the requirement of crosstalk avoidance coding, and the error detection is achieved by adding parity bits. We also implement the codec in register transfer level. Furthermore, the schemes of codec applying to fault-tolerant router are analyzed. The experimental result shows that “once encode, multiple decode” scheme outperforms other schemes in trade-off of delay, area and power.
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Mirus, Florian, Terrence C. Stewart, and Jorg Conradt. "Analyzing the Capacity of Distributed Vector Representations to Encode Spatial Information." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207137.
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Li, Junyou, Gong Cheng, Qingxia Liu, Wen Zhang, Evgeny Kharlamov, Kalpa Gunaratna, and Huajun Chen. "Neural Entity Summarization with Joint Encoding and Weak Supervision." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/228.
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In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions, called entity summaries. Existing solutions to entity summarization are mainly unsupervised. In this paper, we present a supervised approach NEST that is based on our novel neural model to jointly encode graph structure and text in KGs and generate high-quality diversified summaries. Since it is costly to obtain manually labeled summaries for training, our supervision is weak as we train with programmatically labeled data which may contain noise but is free of manual work. Evaluation results show that our approach significantly outperforms the state of the art on two public benchmarks.
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Malmi, Eric, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, and Aliaksei Severyn. "Encode, Tag, Realize: High-Precision Text Editing." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1510.
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Yang, Zheng, Bing Han, Weiming Chen, and Xinbo Gao. "Learn to Encode Heterogeneous Data: A Heterogeneous Aware Network for Multi-Future Trajectory Prediction." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191508.
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Xia, Rui, Mengran Zhang, and Zixiang Ding. "RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/734.
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The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document. Techniques including rule-based methods, traditional machine learning methods and deep neural networks have been proposed to solve this task. However, most of the previous work considered ECE as a set of independent clause classification problems and ignored the relations between multiple clauses in a document. In this work, we propose a joint emotion cause extraction framework, named RNN-Transformer Hierarchical Network (RTHN), to encode and classify multiple clauses synchronously. RTHN is composed of a lower word-level encoder based on RNNs to encode multiple words in each clause, and an upper clause-level encoder based on Transformer to learn the correlation between multiple clauses in a document. We furthermore propose ways to encode the relative position and global predication information into Transformer that can capture the causality between clauses and make RTHN more efficient. We finally achieve the best performance among 12 compared systems and improve the F1 score of the state-of-the-art from 72.69% to 76.77%.
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Anastasyev, D. G. "EXPLORING PRETRAINED MODELS FOR JOINT MORPHO-SYNTACTIC PARSING OF RUSSIAN." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-1-12.
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In this paper, we build a joint morpho-syntactic parser for Russian. We describe a method to train a joint model which is significantly faster and as accurate as a traditional pipeline of models. We explore various ways to encode the word-level information and how they can affect the parser’s performance. To this end, we utilize learned from scratch character-level word embeddings and grammeme embeddings that have shown state-of-theart results for similar tasks for Russian in the past. We compare them with the pretrained contextualized word embeddings, such as ELMo and BERT, known to lead to the breakthrough in miscellaneous tasks in English. As a result, we prove that their usage can significantly improve parsing quality.
Reports on the topic "Encodage joint"
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Mawassi, Munir, and Valerian V. Dolja. Role of the viral AlkB homologs in RNA repair. United States Department of Agriculture, June 2014. http://dx.doi.org/10.32747/2014.7594396.bard.
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AlkB proteins that repair DNA via reversing methylation damage are conserved in a broad range of prokaryotes and eukaryotes including plants. Surprisingly, AlkB-domains were discovered in the genomes of numerous plant positive-strand RNA viruses, majority of which belong to the family Flexiviridae. The major goal of this research was to reveal the AlkB functions in the viral infection cycle using a range of complementary genetic and biochemical approaches. Our hypotheses was that AlkB is required for efficient replication and genetic stability of viral RNA genomes The major objectives of the research were to identify the functions of GVA AlkB domain throughout the virus infection cycle in N. benthamiana and grapevine, to investigate possible RNA silencing suppression activity of the viral AlkBs, and to characterize the RNA demethylation activity of the mutated GVA AlkBs in vitro and in vivo to determine methylation status of the viral RNA. Over the duration of project, we have made a very substantial progress with the first two objectives. Because of the extreme low titer of the virus particles in plants infected with the AlkB mutant viruses, we were unable to analyze RNA demethylation activity and therefore had to abandon third objective. The major achievements with our objectives were demonstration of the AlkB function in virus spread and accumulation in both experimental and natural hosts of GVA, discovery of the functional cooperation and physical interaction between AlkB and p10 AlkB in suppression of plant RNA silencing response, developing a powerful virus vector technology for grapevine using GLRaV-2-derived vectors for functional genomics and pathogen control in grapevine, and in addition we used massive parallel sequencing of siRNAs to conduct comparative analysis of the siRNA populations in grape plants infected with AlkB-containing GLRaV-3 versus GLRaV-2 that does not encode AlkB. This analysis revealed dramatically reduced levels of virus-specific siRNAs in plants infected with GLRaV-3 compared to that in GLRaV-2 infection implicating AlkB in suppression of siRNA formation. We are pleased to report that BARD funding resulted in 5 publications directly supported by BARD, one US patent, and 9 more publications also relevant to project. Moreover, two joint manuscripts that summarize work on GVA AlkB (led by Israeli PI) and on viral siRNAs in grapevine (led by US PI in collaboration with University of Basel) are in preparation.