Amorphous Placement and Informed Diffusion for Timely Monitoring by Autonomous, Resource-Constrained, Mobile Sensors

Personal communication devices are increasingly equipped with sensors for passive monitoring of encounters and surroundings. We envision the emergence of services that enable a community of mobile users carrying such resource-limited devices to query such information at remote locations in the field in which they collectively roam. One approach to implement such a service is directed placement and retrieval (DPR), whereby readings/queries about a specific location are routed to a node responsible for that location. In a mobile, potentially sparse setting, where end-to-end paths are unavailable, DPR is not an attractive solution as it would require the use of delay-tolerant (flooding-based store-carry-forward) routing of both readings and queries, which is inappropriate for applications with data freshness constraints, and which is incompatible with stringent device power/memory constraints. Alternatively, we propose the use of amorphous placement and retrieval (APR), in which routing and field monitoring are integrated through the use of a cache management scheme coupled with an informed exchange of cached samples to diffuse sensory data throughout the network, in such a way that a query answer is likely to be found close to the query origin. We argue that knowledge of the distribution of query targets could be used effectively by an informed cache management policy to maximize the utility of collective storage of all devices. Using a simple analytical model, we show that the use of informed cache management is particularly important when the mobility model results in a non-uniform distribution of users over the field. We present results from extensive simulations which show that in sparsely-connected networks, APR is more cost-effective than DPR, that it provides extra resilience to node failure and packet losses, and that its use of informed cache management yields superior performance

Conformational equilibria and spectroscopy of gas-phase homologous peptides from first principles

Peptides and proteins fulfil crucial tasks enabling and maintaining life. Their function is directly correlated with their three-dimensional structure, which is in turn determined by their chemical composition, the amino-acid sequence. Predicting the structure of a peptide based only on its sequence information is of fundamental interest. A fully first-principles treatment free of empirical parameters would be ideal. However, this presents an ongoing challenge, due to the large system size and conformational space of most peptides. In the present work, we address this challenge concentrating on the example of polyalanine-based peptides in the gas phase. Such studies under isolated conditions follow a bottom-up approach that allows one to investigate the intramolecular interactions important for secondary structure separate from environmental effects. Furthermore, direct benchmarks of theoretical structure predictions against experiment are facilitated. The peptide series Ac-Alan-Lys(H+), (n > 6), forms α-helices in the gas phase due to a favorable interaction of the helix dipole with the positive charge at the C-terminal lysine residue. Using this design principle as a template, we explore the impact of increased structural flexibility on the conformational space due to (i) sequence length [Ac-Alan-Lys(H+), n = 19], (ii) charge placement [Ac-Ala19-Lys(H+) versus Ac-Lys(H+)-Ala19], and (iii) backbone elongation of the monomer units as represented by β-amino acids [Ac-β2hAla6-Lys(H+)]. To address the large conformational space, we develop a three-step structure-search strategy employing an unprecedented first-principles screening effort. After pre-sampling of the conformational space using a force field, thousands of structures are optimized employing density-functional theory (DFT). For this, the PBE functional is used, coupled with a pairwise correction for van der Waals interactions. For the best few structure candidates, ab initio replica-exchange molecular-dynamics simulations are performed in order to refine the local structural environment. It is shown that these can yield lower-energy conformations and lead to rearrangements of the hydrogen-bonding network. In order to connect to experiment, collision cross sections are calculated that link to ion mobility-mass spectrometry. Furthermore, infrared spectra are derived from ab initio Born-Oppenheimer molecular-dynamics simulations accounting for anharmonicities within the classical-nuclei approximation. As expected, the 20-residue peptide Ac-Ala19-Lys(H+) forms helical structures. In contrast, placing the charge at the N-terminus [Ac-Lys(H+)-Ala19], leads to several different compact structures, which are close in energy. Such small energy differences present a challenge to the theoretical approach. Incorporating exact exchange and many-body van der Waals effects predicts the presence of only one dominant conformer, which is compatible with both experimental datasets. In comparison to Ac-Ala6-Lys(H+), the β-peptide Ac-β2hAla6-Lys(H+) exhibits increased conformational flexibility due to an extended monomer backbone. Out of the almost 15,000 structures optimized with DFT, no helical conformers are found in the low-energy regime. This is changed when considering vibrational free energy (300K, harmonic approximation), which strongly favors helical conformations due to softer vibrational modes. One possible structure candidate is the H16-helix, which is compatible with both experiments. It is a unique structure as it exhibits a hydrogen-bonding pattern equivalent to the helix of natural peptides. The systems considered here highlight the advances of current DFT functionals to address the large conformational space of peptides, but also the need for further development

Modeling structural and electronic properties of nano-scale systems

Computergestütze Modellierung von organischen elektronsichen Materialien durch gezielte Untersuchung mikroskopischer Prozesse und Berechnung molekülspezifischer Materialparameter ermöglicht die effiziente Entwicklung langlebiger, effizienter Bauteile. In dieser Arbeit werden die strukturellen und elektronischen Eigenschaften organischer und metall-organischer Schichten untersucht, sowie effiziente Simulationsmethoden (weiter-)entwickelt

Computational approaches to complex biological networks

The need of understanding and modeling the biological networks is one of the raisons d'\ueatre and of the driving forces behind the emergence of Systems Biology. Because of its holistic approach and because of the widely different level of complexity of the networks, different mathematical methods have been developed during the years. Some of these computational methods are used in this thesis in order to investigate various properties of different biological systems. The first part deals with the prediction of the perturbation of cellular metabolism induced by drugs. Using Flux Balance Analysis to describe the reconstructed genome-wide metabolic networks, we consider the problem of identifying the most selective drug synergisms for given therapeutic targets. The second part of this thesis considers gene regulatory and large social networks as signed graphs (activation/deactivation or friendship/hostility are rephrased as positive/negative coupling between spins). Using the analogy with an Ising spin glass an analysis of the energy landscape and of the content of \u201cdisorder\u201d 'is carried out. Finally, the last part concerns the study of the spatial heterogeneity of the signaling pathway of rod photoreceptors. The electrophysiological data produced by our collaborators in the Neurobiology laboratory have been analyzed with various dynamical systems giving an insight into the process of ageing of photoreceptors and into the role diffusion in the pathway

Native like helices in a specially designed β peptide in the gas phase

In the natural peptides, helices are stabilized by hydrogen bonds that point backward along the sequence direction. Until now, there is only little evidence for the existence of analogous structures in oligomers of conformationally unrestricted β amino acids. We specifically designed the β peptide Ac-(β2hAla)6-LysH+ to form native like helical structures in the gas phase. The design follows the known properties of the peptide Ac-Ala6-LysH+ that forms a α helix in isolation. We perform ion-mobility mass-spectrometry and vibrational spectroscopy in the gas phase, combined with state-of-the-art density-functional theory simulations of these molecular systems in order to characterize their structure. We can show that the straightforward exchange of alanine residues for the homologous β amino acids generates a system that is generally capable of adopting native like helices with backward oriented H-bonds. By pushing the limits of theory and experiments, we show that one cannot assign a single preferred structure type due to the densely populated energy landscape and present an interpretation of the data that suggests an equilibrium of three helical structures

Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior

We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. Our framework invokes low-level controllers - either learned or implicit in position-command control - to stabilize imitation around expert demonstrations. We show that with (a) a suitable low-level stability guarantee and (b) a powerful enough generative model as our imitation learner, pure supervised behavior cloning can generate trajectories matching the per-time step distribution of essentially arbitrary expert trajectories in an optimal transport cost. Our analysis relies on a stochastic continuity property of the learned policy we call "total variation continuity" (TVC). We then show that TVC can be ensured with minimal degradation of accuracy by combining a popular data-augmentation regimen with a novel algorithmic trick: adding augmentation noise at execution time. We instantiate our guarantees for policies parameterized by diffusion models and prove that if the learner accurately estimates the score of the (noise-augmented) expert policy, then the distribution of imitator trajectories is close to the demonstrator distribution in a natural optimal transport distance. Our analysis constructs intricate couplings between noise-augmented trajectories, a technique that may be of independent interest. We conclude by empirically validating our algorithmic recommendations, and discussing implications for future research directions for better behavior cloning with generative modeling.Comment: updated figures, minor notational change for readabilit