Integrated circuit flat-pack lead bender

Tool bends leads quickly and accurately for mounting on printed circuit boards. It has grooves and bend-angles aligned for particular circuit board applications

Towards experimental therapies for retinal degenerative diseases

This thesis describes part of the preclinical road that is essential in developing experimental therapies for retinal degenerative diseases such as age-related macular degeneration (AMD) and a specific type of retinitis pigmentosa (RPE-RP). The retinal pigment epithelium (RPE) plays a significant role in the pathology of both diseases. Indeed, patients of all ages can be affected by conditions involving (primarily) the RPE. This thesis is focused on RPE disease pathology, illustrated by the complex retinal disease AMD and a specific genetic form of the monogenic disorder RP. Many experimental therapeutic strategies are being developed to treat AMD and RPE-RP; however, gene therapy and cell-replacement therapy can be considered important strategies for these diseases, especially because of the curative nature of these two treatment modalities. In this thesis, we first used a systematic approach to identify and analyze all preclinical studies that have been published regarding RPE cell-replacement strategy to treat retinal degenerative diseases (Chapter 2). We next used a genome-editing technique to create a new animal model for an RPE-RP subtype and characterized the model in-depth (Chapter 3). Additionally, we describe an induced preclinical model for AMD and its in-depth characterization (Chapter 4). As a final step, we describe the generation of a 3D-bio-printed tissue recapitulating the RPE and underlying tissues and its transplantation and integration into rat eyes (Chapter 5)

Pre-processing for Triangulation of Probabilistic Networks

The currently most efficient algorithm for inference with a probabilistic network builds upon a triangulation of a network's graph. In this paper, we show that pre-processing can help in finding good triangulations forprobabilistic networks, that is, triangulations with a minimal maximum clique size. We provide a set of rules for stepwise reducing a graph, without losing optimality. This reduction allows us to solve the triangulation problem on a smaller graph. From the smaller graph's triangulation, a triangulation of the original graph is obtained by reversing the reduction steps. Our experimental results show that the graphs of some well-known real-life probabilistic networks can be triangulated optimally just by preprocessing; for other networks, huge reductions in their graph's size are obtained.Comment: Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001

Nanomechanical optical devices fabricated with aligned wafer bonding

This paper reports on a new method for making some types of integrated optical nanomechanical devices. Intensity modulators as well as phase modulators were fabricated using several silicon micromachining techniques, including chemical mechanical polishing and aligned wafer bonding. This new method enables batch fabrication of the nanomechanical optical devices, and enhances their performance

A Backtracking-Based Algorithm for Computing Hypertree-Decompositions

Hypertree decompositions of hypergraphs are a generalization of tree decompositions of graphs. The corresponding hypertree-width is a measure for the cyclicity and therefore tractability of the encoded computation problem. Many NP-hard decision and computation problems are known to be tractable on instances whose structure corresponds to hypergraphs of bounded hypertree-width. Intuitively, the smaller the hypertree-width, the faster the computation problem can be solved. In this paper, we present the new backtracking-based algorithm det-k-decomp for computing hypertree decompositions of small width. Our benchmark evaluations have shown that det-k-decomp significantly outperforms opt-k-decomp, the only exact hypertree decomposition algorithm so far. Even compared to the best heuristic algorithm, we obtained competitive results as long as the hypergraphs are not too large.Comment: 19 pages, 6 figures, 3 table

Electronic coupling between Bi nanolines and the Si(001) substrate: An experimental and theoretical study

Atomic nanolines are one dimensional systems realized by assembling many atoms on a substrate into long arrays. The electronic properties of the nanolines depend on those of the substrate. Here, we demonstrate that to fully understand the electronic properties of Bi nanolines on clean Si(001) several different contributions must be accounted for. Scanning tunneling microscopy reveals a variety of different patterns along the nanolines as the imaging bias is varied. We observe an electronic phase shift of the Bi dimers, associated with imaging atomic p-orbitals, and an electronic coupling between the Bi nanoline and neighbouring Si dimers, which influences the appearance of both. Understanding the interplay between the Bi nanolines and Si substrate could open a novel route to modifying the electronic properties of the nanolines.Comment: 6 pages (main), 2 pages (SI), accepted by Phys. Rev.

Robust Flows over Time: Models and Complexity Results

We study dynamic network flows with uncertain input data under a robust optimization perspective. In the dynamic maximum flow problem, the goal is to maximize the flow reaching the sink within a given time horizon $T$, while flow requires a certain travel time to traverse an edge. In our setting, we account for uncertain travel times of flow. We investigate maximum flows over time under the assumption that at most $\Gamma$ travel times may be prolonged simultaneously due to delay. We develop and study a mathematical model for this problem. As the dynamic robust flow problem generalizes the static version, it is NP-hard to compute an optimal flow. However, our dynamic version is considerably more complex than the static version. We show that it is NP-hard to verify feasibility of a given candidate solution. Furthermore, we investigate temporally repeated flows and show that in contrast to the non-robust case (that is, without uncertainties) they no longer provide optimal solutions for the robust problem, but rather yield a worst case optimality gap of at least $T$. We finally show that the optimality gap is at most $O(\eta k \log T)$, where $\eta$ and $k$ are newly introduced instance characteristics and provide a matching lower bound instance with optimality gap $\Omega(\log T)$ and $\eta = k = 1$. The results obtained in this paper yield a first step towards understanding robust dynamic flow problems with uncertain travel times