arXiv subjects

Philip Lenz

Publications and source records attributed to Philip Lenz.

3 recordsLinked to original sources

arXiv2025

Colossal Effect of Nanopore Surface Ionic Charge on the Dynamics of Confined Water

Interfacial interactions significantly alter the fundamental properties of water confined in mesoporous structures, with crucial implications for geological, physicochemical, and biological processes. Herein, we focused on the effect of changing the surface ionic charge of nanopores with comparable pore size (3.5-3.8 nm) on the dynamics of confined liquid water. The control of the pore surface ionicity was achieved by using two periodic mesoporous organosilicas (PMOs) containing either neutral or charged forms of a chemically similar bridging unit. The effect on the dynamics of water at the nanoscale was investigated in the temperature range of 245 -300 K, encompassing the glass transition by incoherent quasielastic neutron scattering (QENS), For both types of PMOs, the water dynamics revealed two distinct types of molecular motions: rapid local movements and translational jump diffusion. While the neutral PMO induces a moderate confinement effect, we show that the charged PMO drastically slows down water dynamics, reducing translational diffusion by a factor of four and increasing residence time by an order of magnitude. Notably, by changing the pore filling values, we demonstrate that for charged pore this effect extends beyond the interfacial layer of surface-bound water molecules to encompass the entire pore volume. Thus, our observation indicates a dramatic change in the long-range character of the interaction of water confined in nanopores with surface ionic charge compared to a simple change in hydrophilicity. This is relevant for the understanding of a broad variety of applications in (nano)technological phenomena and processes, such as nanofiltration and membrane design.

physics.chem-ph↗

arXiv2018

CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation

State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images. However, gathering such a corpus is expensive, due to human annotation effort, in contrast to gathering unlabeled data. We propose an active learning-based strategy, called CEREALS, in which a human only has to hand-label a few, automatically selected, regions within an unlabeled image corpus. This minimizes human annotation effort while maximizing the performance of a semantic image segmentation method. The automatic selection procedure is achieved by: a) using a suitable information measure combined with an estimate about human annotation effort, which is inferred from a learned cost model, and b) exploiting the spatial coherency of an image. The performance of CEREALS is demonstrated on Cityscapes, where we are able to reduce the annotation effort to 17%, while keeping 95% of the mean Intersection over Union (mIoU) of a model that was trained with the fully annotated training set of Cityscapes.

cs.CV↗

arXiv2014

FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in significantly faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrarily length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.

cs.CV↗