Performance Analysis of the Decentralized Eigendecomposition and ESPRIT Algorithm

In this paper, we consider performance analysis of the decentralized power method for the eigendecomposition of the sample covariance matrix based on the averaging consensus protocol. An analytical expression of the second order statistics of the eigenvectors obtained from the decentralized power method which is required for computing the mean square error (MSE) of subspace-based estimators is presented. We show that the decentralized power method is not an asymptotically consistent estimator of the eigenvectors of the true measurement covariance matrix unless the averaging consensus protocol is carried out over an infinitely large number of iterations. Moreover, we introduce the decentralized ESPRIT algorithm which yields fully decentralized direction-of-arrival (DOA) estimates. Based on the performance analysis of the decentralized power method, we derive an analytical expression of the MSE of DOA estimators using the decentralized ESPRIT algorithm. The validity of our asymptotic results is demonstrated by simulations.Comment: 18 pages, 5 figures, submitted for publication in IEEE Transactions on Signal Processin

Intermittent alien hand syndrome and callosal apraxia in multiple sclerosis: implications for interhemispheric communication

We report a case of a 47-year-old woman with 35-year history of multiple sclerosis, who showed alien hand signs, a rare behavioural disorder that involves unilateral goal-directed movements that are contrary to the individual\u2019s intention. Alien hand syndrome has been described in multiple sclerosis (MS) only occasionally and is generally suggestive of callosal disconnection. The patient presented also with bilateral limb apraxia and left hand agraphia, raising the possibility of cortical dysfunction or disconnection, in addition to corpus callosum and white matter involvement. Her specific pattern of symptoms supports the role of the corpus callosum in interhemispheric communication for complex as well as fine motor activities and may indicate that it can serve as both an inhibitory and excitatory function depending on task demands

Numerical simulation of forerunning fracture in saturated porous solids with hybrid FEM/Peridynamic model

In this paper, a novel hybrid FEM and Peridynamic modeling approach proposed in Ni et al. (2020) is used to predict the dynamic solution of hydro-mechanical coupled problems. A modified staggered solution algorithm is adopted to solve the coupled system. A one-dimensional dynamic consolidation problem is solved first to validate the hybrid modeling approach, and both -convergence and -convergence studies are carried out to determine appropriate discretization parameters for the hybrid model. Thereafter, dynamic fracturing in a rectangular dry/fully saturated structure with a central initial crack is simulated both under mechanical loading and fluid-driven conditions. In the mechanical loading fracture case, fixed surface pressure is applied on the upper and lower surfaces of the initial crack near the central position to force its opening. In the fluid-driven fracture case, the fluid injection is operated at the centre of the initial crack with a fixed rate. Under the action of the applied external force and fluid injection, forerunning fracture behavior is observed both in the dry and saturated conditions.Comment: arXiv admin note: text overlap with arXiv:2307.1092

Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and failing to estimate displacement correctly results in failure in generating strain images. As conventional ultrasound elastography techniques suffer from decorrelation noise, they are prone to fail in estimating displacement between echo signals obtained during tissue distortions. This study proposes a novel elastography technique which addresses the decorrelation in estimating displacement field. We call our method GLUENet (GLobal Ultrasound Elastography Network) which uses deep Convolutional Neural Network (CNN) to get a coarse time-delay estimation between two ultrasound images. This displacement is later used for formulating a nonlinear cost function which incorporates similarity of RF data intensity and prior information of estimated displacement. By optimizing this cost function, we calculate the finer displacement by exploiting all the information of all the samples of RF data simultaneously. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain images from our technique is very much close to that of strain images from GLUE. While most elastography algorithms are sensitive to parameter tuning, our robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type late

Hybrid FEM and peridynamic simulation of hydraulic fracture propagation in saturated porous media

This paper presents a hybrid modeling approach for simulating hydraulic fracture propagation in saturated porous media: ordinary state-based peridynamics is used to describe the behavior of the solid phase, including the deformation and crack propagation, while FEM is used to describe the fluid flow and to evaluate the pore pressure. Classical Biot poroelasticity theory is adopted. The proposed approach is first verified by comparing its results with the exact solutions of two examples. Subsequently, a series of pressure- and fluid-driven crack propagation examples are solved and presented. The phenomenon of fluid pressure oscillation is observed in the fluid-driven crack propagation examples, which is consistent with previous experimental and numerical evidences. All the presented examples demonstrate the capability of the proposed approach in solving problems of hydraulic fracture propagation in saturated porous media

Selective imitation impairments differentially interact with language processing

Whether motor and linguistic representations of actions share common neural structures has recently been the focus of an animated debate in cognitive neuroscience. Group studies with brain-damaged patients reported association patterns of praxic and linguistic deficits whereas single case studies documented double dissociations between the correct execution of gestures and their comprehension in verbal contexts. When the relationship between language and imitation was investigated, each ability was analysed as a unique process without distinguishing between possible subprocesses. However, recent cognitive models can be successfully used to account for these inconsistencies in the extant literature. In the present study, in 57 patients with left brain damage, we tested whether a deficit at imitating either meaningful or meaningless gestures differentially impinges on three distinct linguistic abilities (comprehension, naming and repetition). Based on the dual-pathway models, we predicted that praxic and linguistic performance would be associated when meaningful gestures are processed, and would dissociate for meaningless gestures. We used partial correlations to assess the association between patients' scores while accounting for potential confounding effects of aspecific factors such age, education and lesion size. We found that imitation of meaningful gestures significantly correlated with patients' performance on naming and repetition (but not on comprehension). This was not the case for the imitation of meaningless gestures. Moreover, voxel-based lesion-symptom mapping analysis revealed that damage to the angular gyrus specifically affected imitation of meaningless gestures, independent of patients' performance on linguistic tests. Instead, damage to the supramarginal gyrus affected not only imitation of meaningful gestures, but also patients' performance on naming and repetition. Our findings clarify the apparent conflict between associations and dissociations patterns previously observed in neuropsychological studies, and suggest that motor experience and language can interact when the two domains conceptually overla

The Role of Amygdala in Self-Conscious Emotions in a Patient With Acquired Bilateral Damage

Shame plays a fundamental role in the regulation of our social behavior. One intriguing question is whether amygdala might play a role in processing this emotion. In the present single-case study, we tested a patient with acquired damage of bilateral amygdalae and surrounding areas as well as healthy controls on shame processing and other social cognitive tasks. Results revealed that the patient\u2019s subjective experience of shame, but not of guilt, was more reduced than in controls, only when social standards were violated, while it was not different than controls in case of moral violations. The impairment in discriminating between normal social situations and violations also emerged. Taken together, these findings suggest that the role of the amygdala in processing shame might reflect its relevance in resolving ambiguity and uncertainty, in order to correctly detect social violations and to generate shame feelings

Targeted online liquid chromatography electron capture dissociation mass spectrometry for the localization of sites of in vivo phosphorylation in human Sprouty2

We demonstrate a strategy employing collision-induced dissociation for phosphopeptide discovery, followed by targeted electron capture dissociation (ECD) for site localization. The high mass accuracy and low background noise of the ECD mass spectra allow facile sequencing of coeluting isobaric phosphopeptides, with up to two isobaric phosphopeptides sequenced from a single mass spectrum. In contrast to the previously described neutral loss of dependent ECD method, targeted ECD allows analysis of both phosphotyrosine peptides and lower abundance phosphopeptides. The approach was applied to phosphorylation analysis of human Sprouty2, a regulator of receptor tyrosine kinase signaling. Fifteen sites of phosphorylation were identified, 11 of which are novel