Dynamical self-assembly of dipolar active Brownian particles in two dimensions

Based on Brownian Dynamics (BD) simulations, we study the dynamical self-assembly of active Brownian particles with dipole–dipole interactions, stemming from a permanent point dipole at the particle center. The propulsion direction of each particle is chosen to be parallel to its dipole moment. We explore a wide range of motilities and dipolar coupling strengths and characterize the corresponding behavior based on several order parameters. At low densities and low motilities, the most important structural phenomenon is the aggregation of the dipolar particles into chains. Upon increasing the particle motility, these chain-like structures break, and the system transforms into a weakly correlated isotropic fluid. At high densities, we observe that the motility-induced phase separation is strongly suppressed by the dipolar coupling. Once the dipolar coupling dominates the thermal energy, the phase separation disappears, and the system rather displays a flocking state, where particles form giant clusters and move collective along one direction. We provide arguments for the emergence of the flocking behavior, which is absent in the passive dipolar system.TU Berlin, Open-Access-Mittel - 2020DFG, 65143814, GRK 1524: Self-Assembled Soft-Matter Nanostructures at Interface

Nonlinear Hodge maps

We consider maps between Riemannian manifolds in which the map is a stationary point of the nonlinear Hodge energy. The variational equations of this functional form a quasilinear, nondiagonal, nonuniformly elliptic system which models certain kinds of compressible flow. Conditions are found under which singular sets of prescribed dimension cannot occur. Various degrees of smoothness are proven for the sonic limit, high-dimensional flow, and flow having nonzero vorticity. The gradient flow of solutions is estimated. Implications for other quasilinear field theories are suggested.Comment: Slightly modified and updated version; tcilatex, 32 page

The Convergent Generalized Central Paths for Linearly Constrained Convex Programming

The convergence of central paths has been a focal point of research on interior point methods. Quite detailed analyses have been made for the linear case. However, when it comes to the convex case, even if the constraints remain linear, the problem is unsettled. In [Math. Program., 103 (2005), pp. 63–94], Gilbert, Gonzaga, and Karas presented some examples in convex optimization, where the central path fails to converge. In this paper, we aim at finding some continuous trajectories which can converge for all linearly constrained convex optimization problems under some mild assumptions. We design and analyze a class of continuous trajectories, which are the solutions of certain ordinary differential equation (ODE) systems for solving linearly constrained smooth convex programming. The solutions of these ODE systems are named generalized central paths. By only assuming the existence of a finite optimal solution, we are able to show that, starting from any interior feasible point, (i) all of the generalized central paths are convergent, and (ii) the limit point(s) are indeed the optimal solution(s) of the original optimization problem. Furthermore, we illustrate that for the key example of Gilbert, Gonzaga, and Karas, our generalized central paths converge to the optimal solutions

Disparity Estimation with Scene Depth Cues

The cost volume plays a pivotal role in stereo matching, usually working as an optimization object. However, we find it also can provide effective scene prior to guide the disparity learning, as it reflects well the depth relationship between scenario objects. Inspired by this new perspective, we propose the CSA module, which consists of a new correlation and selection (CS) layer and a new aggregation layer. The CS layer can regulate the matching costs and re-encode the feature information into the correlation volume. The aggregation layer can preserve better the depth cues of the refined cost volume, through a convolution network and a unimodalization operation. The proposed module can be trained in a supervised manner, making the extraction of scene depth cues more accurate. Extensive experiments on the Sceneflow and KITTI datasets have demonstrated that with our module embedded, SOTA networks can achieve substantially better performance

Bromelain and Cardiovascular Risk Factors in Diabetes: An Exploratory Randomized, Placebo Controlled, Double Blind Clinical Trial

Objective: The objective of this trial was to assess whether the dietary supplement (bromelain) had the potential to reduce plasma fibrinogen and other Cardiovascular Disease (CVD) risk factors in patients with diabetes. Methods: This randomized placebo controlled, double blind, parallel design, efficacy study was carried out in China and investigated the effect of 12 weeks of bromelain (1.05g/day) on plasma fibrinogen . This randomized controlled trial (RCT) recruited 68 Chinese diabetic patients (32 males and 36 females; Han origin, mean age of 61.26 years (Standard Deviation, 12.62 years)) with at least one CVD risk factor. Patients were randomized into either bromelain or placebo group. While bromelain group received bromelain capsule, the placebo group received placebo capsule which consisted inert ingredient and has no treatment effect. Patients and researcher were blinded and did not know whether they received bromelain or placebo capsules. Plasma fibrinogen, CVD risk factors and anthropometric indicators were determined at baseline and at 12 weeks. Results: The change in the fibrinogen level in the placebo group at the end of the study showed a mean reduction of 0.36g/L (Standard Deviation (SD) 0.96g/L) compared with the mean reduction of 0.13g/L (SD 0.86g/L) for the bromelain group. However, there was no significant difference in the mean change in fibrinogen between the placebo and bromelain groups (mean difference=0.23g/L (SD 0.22g/L), p=0.291). Similarly, the difference in mean change in other CVD risk factors (blood lipids, blood pressure), blood glucose, C - reactive protein (CRP) and anthropometric measures between the bromelain and placebo groups was also not statistically significant. Conclusions: This RCT failed to show a beneficial effect in reducing fibrinogen or influencing other selected CVD risk factors but suggests other avenues for subsEquent research on bromelain

Better Stereo Matching From Simple Yet Effective Wrangling of Deep Features

Cost volume plays a pivotal role in stereo matching. Most recent works focused on deep feature extraction and cost refinement for a more accurate cost volume. Unlike them, we probe from a different perspective: feature wrangling. We find that simple wrangling of deep features can effectively improve the construction of cost volume and thus the performance of stereo matching. Specifically, we develop two simple yet effective wrangling techniques of deep features, spatially a differentiable feature transformation and channel-wise a memory-economical feature expansion, for better cost construction. Exploiting the local ordering information provided by a differentiable rank transform, we achieve an enhancement of the search for correspondence; with the help of disparity division, our feature expansion allows for more features into the cost volume with no extra memory required. Equipped with these two feature wrangling techniques, our simple network can perform outstandingly on the widely used KITTI and Sceneflow datasets