TY - THES
AB - We present solutions to several problems originating from geometry and discrete mathematics: existence of equipartitions, maps without Tverberg multiple points, and inscribing quadrilaterals. Equivariant obstruction theory is the natural topological approach to these type of questions. However, for the specific problems we consider it had yielded only partial or no results. We get our results by complementing equivariant obstruction theory with other techniques from topology and geometry.
AU - Avvakumov, Sergey
ID - 8156
TI - Topological methods in geometry and discrete mathematics
ER -
TY - THES
AB - Designing and verifying concurrent programs is a notoriously challenging, time consuming, and error prone task, even for experts. This is due to the sheer number of possible interleavings of a concurrent program, all of which have to be tracked and accounted for in a formal proof. Inventing an inductive invariant that captures all interleavings of a low-level implementation is theoretically possible, but practically intractable. We develop a refinement-based verification framework that provides mechanisms to simplify proof construction by decomposing the verification task into smaller subtasks.
In a first line of work, we present a foundation for refinement reasoning over structured concurrent programs. We introduce layered concurrent programs as a compact notation to represent multi-layer refinement proofs. A layered concurrent program specifies a sequence of connected concurrent programs, from most concrete to most abstract, such that common parts of different programs are written exactly once. Each program in this sequence is expressed as structured concurrent program, i.e., a program over (potentially recursive) procedures, imperative control flow, gated atomic actions, structured parallelism, and asynchronous concurrency. This is in contrast to existing refinement-based verifiers, which represent concurrent systems as flat transition relations. We present a powerful refinement proof rule that decomposes refinement checking over structured programs into modular verification conditions. Refinement checking is supported by a new form of modular, parameterized invariants, called yield invariants, and a linear permission system to enhance local reasoning.
In a second line of work, we present two new reduction-based program transformations that target asynchronous programs. These transformations reduce the number of interleavings that need to be considered, thus reducing the complexity of invariants. Synchronization simplifies the verification of asynchronous programs by introducing the fiction, for proof purposes, that asynchronous operations complete synchronously. Synchronization summarizes an asynchronous computation as immediate atomic effect. Inductive sequentialization establishes sequential reductions that captures every behavior of the original program up to reordering of coarse-grained commutative actions. A sequential reduction of a concurrent program is easy to reason about since it corresponds to a simple execution of the program in an idealized synchronous environment, where processes act in a fixed order and at the same speed.
Our approach is implemented the CIVL verifier, which has been successfully used for the verification of several complex concurrent programs. In our methodology, the overall correctness of a program is established piecemeal by focusing on the invariant required for each refinement step separately. While the programmer does the creative work of specifying the chain of programs and the inductive invariant justifying each link in the chain, the tool automatically constructs the verification conditions underlying each refinement step.
AU - Kragl, Bernhard
ID - 8332
SN - 2663-337X
TI - Verifying concurrent programs: Refinement, synchronization, sequentialization
ER -
TY - THES
AB - One of the most striking hallmarks of the eukaryotic cell is the presence of intracellular vesicles and organelles. Each of these membrane-enclosed compartments has a distinct composition of lipids and proteins, which is essential for accurate membrane traffic and homeostasis. Interestingly, their biochemical identities are achieved with the help
of small GTPases of the Rab family, which cycle between GDP- and GTP-bound forms on the selected membrane surface. While this activity switch is well understood for an individual protein, how Rab GTPases collectively transition between states to generate decisive signal propagation in space and time is unclear. In my PhD thesis, I present
in vitro reconstitution experiments with theoretical modeling to systematically study a minimal Rab5 activation network from bottom-up. We find that positive feedback based on known molecular interactions gives rise to bistable GTPase activity switching on system’s scale. Furthermore, we determine that collective transition near the critical
point is intrinsically stochastic and provide evidence that the inactive Rab5 abundance on the membrane can shape the network response. Finally, we demonstrate that collective switching can spread on the lipid bilayer as a traveling activation wave, representing a possible emergent activity pattern in endosomal maturation. Together, our
findings reveal new insights into the self-organization properties of signaling networks away from chemical equilibrium. Our work highlights the importance of systematic characterization of biochemical systems in well-defined physiological conditions. This way, we were able to answer long-standing open questions in the field and close the gap between regulatory processes on a molecular scale and emergent responses on system’s level.
AU - Bezeljak, Urban
ID - 8341
TI - In vitro reconstitution of a Rab activation switch
ER -
TY - THES
AB - Fabrication of curved shells plays an important role in modern design, industry, and science. Among their remarkable properties are, for example, aesthetics of organic shapes, ability to evenly distribute loads, or efficient flow separation. They find applications across vast length scales ranging from sky-scraper architecture to microscopic devices. But, at
the same time, the design of curved shells and their manufacturing process pose a variety of challenges. In this thesis, they are addressed from several perspectives. In particular, this thesis presents approaches based on the transformation of initially flat sheets into the target curved surfaces. This involves problems of interactive design of shells with nontrivial mechanical constraints, inverse design of complex structural materials, and data-driven modeling of delicate and time-dependent physical properties. At the same time, two newly-developed self-morphing mechanisms targeting flat-to-curved transformation are presented.
In architecture, doubly curved surfaces can be realized as cold bent glass panelizations. Originally flat glass panels are bent into frames and remain stressed. This is a cost-efficient fabrication approach compared to hot bending, when glass panels are shaped plastically. However such constructions are prone to breaking during bending, and it is highly
nontrivial to navigate the design space, keeping the panels fabricable and aesthetically pleasing at the same time. We introduce an interactive design system for cold bent glass façades, while previously even offline optimization for such scenarios has not been sufficiently developed. Our method is based on a deep learning approach providing quick
and high precision estimation of glass panel shape and stress while handling the shape
multimodality.
Fabrication of smaller objects of scales below 1 m, can also greatly benefit from shaping originally flat sheets. In this respect, we designed new self-morphing shell mechanisms transforming from an initial flat state to a doubly curved state with high precision and detail. Our so-called CurveUps demonstrate the encodement of the geometric information
into the shell. Furthermore, we explored the frontiers of programmable materials and showed how temporal information can additionally be encoded into a flat shell. This allows prescribing deformation sequences for doubly curved surfaces and, thus, facilitates self-collision avoidance enabling complex shapes and functionalities otherwise impossible.
Both of these methods include inverse design tools keeping the user in the design loop.
AU - Guseinov, Ruslan
ID - 8366
KW - computer-aided design
KW - shape modeling
KW - self-morphing
KW - mechanical engineering
SN - 2663-337X
TI - Computational design of curved thin shells: From glass façades to programmable matter
ER -
TY - THES
AB - Deep neural networks have established a new standard for data-dependent feature extraction pipelines in the Computer Vision literature. Despite their remarkable performance in the standard supervised learning scenario, i.e. when models are trained with labeled data and tested on samples that follow a similar distribution, neural networks have been shown to struggle with more advanced generalization abilities, such as transferring knowledge across visually different domains, or generalizing to new unseen combinations of known concepts. In this thesis we argue that, in contrast to the usual black-box behavior of neural networks, leveraging more structured internal representations is a promising direction
for tackling such problems. In particular, we focus on two forms of structure. First, we tackle modularity: We show that (i) compositional architectures are a natural tool for modeling reasoning tasks, in that they efficiently capture their combinatorial nature, which is key for generalizing beyond the compositions seen during training. We investigate how to to learn such models, both formally and experimentally, for the task of abstract visual reasoning. Then, we show that (ii) in some settings, modularity allows us to efficiently break down complex tasks into smaller, easier, modules, thereby improving computational efficiency; We study this behavior in the context of generative models for colorization, as well as for small objects detection. Secondly, we investigate the inherently layered structure of representations learned by neural networks, and analyze its role in the context of transfer learning and domain adaptation across visually
dissimilar domains.
AU - Royer, Amélie
ID - 8390
SN - 978-3-99078-007-7
TI - Leveraging structure in Computer Vision tasks for flexible Deep Learning models
ER -