Geometrically-exact time-integration mesh-free schemes for advection-diffusion problems derived from optimal transportation theory and their connection with particle methods

We develop an Optimal Transportation Meshfree (OTM) particle method for advection-diffusion in which the concentration or density of the diffusive species is approximated by Dirac measures. We resort to an incremental variational principle for purposes of time discretization of the diffusive step. This principle characterizes the evolution of the density as a competition between the Wasserstein distance between two consecutive densities and entropy. Exploiting the structure of the Euler-Lagrange equations, we approximate the density as a collection of Diracs. The interpolation of the incremental transport map is effected through mesh-free max-ent interpolation. Remarkably, the resulting update is geometrically exact with respect to advection and volume. We present three-dimensional examples of application that illustrate the scope and robustness of the method.Comment: 19 pages, 8 figure

Ultrasound-Guided Glenohumeral Joint Injection Using the Posterior Approach

Injection treatment to the glenohumeral joint is often needed to treat shoulder problems such as adhesive capsulitis. This can be done through blind palpation technique and fluoroscopic or musculoskeletal ultrasound guidance. In recent years, ultrasound has been proven to increase the accuracy of needle placement into the glenohumeral joint. Ultrasound is radiation free and offers real-time images in performing needle-guided injection procedures. Glenohumeral joint injection can be done using the anterior rotator interval approach or the posterior approach technique. Both techniques are generally well tolerated by the patients. However, it was shown that the posterior injection technique offers an easier and a more effective approach to the glenohumeral joint with less extravasation rate as compared with the anterior approach. The posterior approach also avoids the potential risk of accidental puncture or injection into the axillary neurovascular structures. A linear transducer of 5–12 MHz is usually used. This technique is often applied to inject corticosteroid for the treatment of frozen shoulder or contrast medium for computed tomography or magnetic resonance shoulder arthrography

Integrating Information Theory and Adversarial Learning for Cross-modal Retrieval

Accurately matching visual and textual data in cross-modal retrieval has been widely studied in the multimedia community. To address these challenges posited by the heterogeneity gap and the semantic gap, we propose integrating Shannon information theory and adversarial learning. In terms of the heterogeneity gap, we integrate modality classification and information entropy maximization adversarially. For this purpose, a modality classifier (as a discriminator) is built to distinguish the text and image modalities according to their different statistical properties. This discriminator uses its output probabilities to compute Shannon information entropy, which measures the uncertainty of the modality classification it performs. Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy. Thus, maximizing information entropy gradually reduces the distribution discrepancy of cross-modal features, thereby achieving a domain confusion state where the discriminator cannot classify two modalities confidently. To reduce the semantic gap, Kullback-Leibler (KL) divergence and bi-directional triplet loss are used to associate the intra- and inter-modality similarity between features in the shared space. Furthermore, a regularization term based on KL-divergence with temperature scaling is used to calibrate the biased label classifier caused by the data imbalance issue. Extensive experiments with four deep models on four benchmarks are conducted to demonstrate the effectiveness of the proposed approach.Comment: Accepted by Pattern Recognitio

Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks

Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to address these issues, researchers develop weight regularization methods and activation normalization methods. In this work we propose a weight soft-regularization method based on the Oblique manifold. The proposed method uses a loss function which pushes each weight vector to have a norm close to one, i.e. the weight matrix is smoothly steered toward the so-called Oblique manifold. We evaluate our method on the very popular CIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art architectures, namely the ResNet and wide-ResNet. Our method introduces negligible computational overhead and the results show that it is competitive to the state-of-the-art and in some cases superior to it. Additionally, the results are less sensitive to hyperparameter settings such as batch size and regularization factor

Exploring emergent properties in cellular homeostasis using OnGuard to model K+ and other ion transport in guard cells

It is widely recognized that the nature and characteristics of transport across eukaryotic membranes are so complex as to defy intuitive understanding. In these circumstances, quantitative mathematical modeling is an essential tool, both to integrate detailed knowledge of individual transporters and to extract the properties emergent from their interactions. As the first, fully integrated and quantitative modeling environment for the study of ion transport dynamics in a plant cell, OnGuard offers a unique tool for exploring homeostatic properties emerging from the interactions of ion transport, both at the plasma membrane and tonoplast in the guard cell. OnGuard has already yielded detail sufficient to guide phenotypic and mutational studies, and it represents a key step toward ‘reverse engineering’ of stomatal guard cell physiology, based on rational design and testing in simulation, to improve water use efficiency and carbon assimilation. Its construction from the HoTSig libraries enables translation of the software to other cell types, including growing root hairs and pollen. The problems inherent to transport are nonetheless challenging, and are compounded for those unfamiliar with conceptual ‘mindset’ of the modeler. Here we set out guidelines for the use of OnGuard and outline a standardized approach that will enable users to advance quickly to its application both in the classroom and laboratory. We also highlight the uncanny and emergent property of OnGuard models to reproduce the ‘communication’ evident between the plasma membrane and tonoplast of the guard cell

Integrin-mediated membrane blebbing is dependent on the NHE1 and NCX1 activities.

Integrin-mediated signal transduction and membrane blebbing have been well studied to modulate cell adhesion, spreading and migration^1-6^. However, the relationship between membrane blebbing and integrin signaling has not been explored. Here we show that integrin-ligand interaction induces membrane blebbing and membrane permeability change. We found that sodium-proton exchanger 1 (NHE1) and sodium-calcium exchanger 1 (NCX1) are located in the membrane blebbing sites and inhibition of NHE1 disrupts membrane blebbing and decreases membrane permeability change. However, inhibition of NCX1 enhances cell blebbing to cause cell swelling which is correlated with an intracellular sodium accumulation induced by NHE17. These data suggest that sodium influx induced by NHE1 is a driving force for membrane blebbing growth, while sodium efflux induced by NCX1 in a reverse mode causes membrane blebbing retraction. Together, these data reveal a novel function of NHE1 and NCX1 in membrane permeability change and blebbing and provide the link for integrin signaling and membrane blebbing

Lifelong Person Re-Identification via Adaptive Knowledge Accumulation

Person ReID methods always learn through a stationary domain that is fixed by the choice of a given dataset. In many contexts (e.g., lifelong learning), those methods are ineffective because the domain is continually changing in which case incremental learning over multiple domains is required potentially. In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains. Following the cognitive processes in the human brain, we design an Adaptive Knowledge Accumulation (AKA) framework that is endowed with two crucial abilities: knowledge representation and knowledge operation. Our method alleviates catastrophic forgetting on seen domains and demonstrates the ability to generalize to unseen domains. Correspondingly, we also provide a new and large-scale benchmark for LReID. Extensive experiments demonstrate our method outperforms other competitors by a margin of 5.8% mAP in generalising evaluation.Comment: 10 pages, 5 figures, Accepted by CVPR202

Inhibitory GEF Phosphorylation Provides Negative Feedback in the Yeast Polarity Circuit

Cell polarity is critical for the form and function of many celltypes. During polarity establishment, cells define a cortical‘‘front’’ that behaves differently from the rest of the cortex.The front accumulates high levels of the active form ofa polarity-determining Rho-family GTPase (Cdc42, Rac, orRop) that then orients cytoskeletal elements through variouseffectors to generate the polarized morphology appropriateto the particular cell type [1, 2]. GTPase accumulation isthought to involve positive feedback, such that activeGTPase promotes further delivery and/or activation ofmore GTPase in its vicinity [3]. Recent studies suggest thatonce a front forms, the concentration of polarity factors atthe front can increase and decrease periodically, first clus-tering the factors at the cortex and then dispersing themback to the cytoplasm [4–7]. Such oscillatory behaviorimplies the presence of negative feedback in the polaritycircuit [8], but the mechanism of negative feedback wasnot known. Here we show that, in the budding yeastSaccha-romyces cerevisiae, the catalytic activity of the Cdc42-directed GEF is inhibited by Cdc42-stimulated effectorkinases, thus providing negative feedback. We furthershow that replacing the GEF with a phosphosite mutantGEF abolishes oscillations and leads to the accumulationof excess GTP-Cdc42 and other polarity factors at the front.These findings reveal a mechanism for negative feedbackand suggest that the function of negative feedback via GEFinhibition is to buffer the level of Cdc42 at the polarity site

Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging and essential task in night-time intelligent surveillance systems. Except for the intra-modality variance that RGB-RGB person re-identification mainly overcomes, VI-ReID suffers from additional inter-modality variance caused by the inherent heterogeneous gap. To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively. Disentangling cross-modality identity-discriminable features leads to more robust retrieval for VI-ReID. To achieve efficient optimization like conventional VAE, we theoretically derive two variational inference terms for the MoG prior under the supervised setting, which not only restricts the identity-discriminable subspace so that the model explicitly handles the cross-modality intra-identity variance, but also enables the MoG distribution to avoid posterior collapse. Furthermore, we propose a triplet swap reconstruction (TSR) strategy to promote the above disentangling process. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on two VI-ReID datasets.Comment: Accepted by ACM MM 2020 poster. 12 pages, 10 appendixe