Strongly Interacting Holes in Ge/Si Nanowires

We consider holes confined to Ge/Si core/shell nanowires subject to strong Rashba spin-orbit interaction and screened Coulomb interaction. Such wires can, for instance, serve as host systems for Majorana bound states. Starting from a microscopic model, we find that the Coulomb interaction strongly influences the properties of experimentally realistic wires. To show this, a Luttinger liquid description is derived based on a renormalization group analysis. This description in turn allows to calculate the scaling exponents of various correlation functions as a function of the microscopic system parameters. It furthermore permits to investigate the effect of Coulomb interaction on a small magnetic field, which opens a strongly anisotropic partial gap

Scalable Hash Tables

The term scalability with regards to this dissertation has two meanings: It means taking the best possible advantage of the provided resources (both computational and memory resources) and it also means scaling data structures in the literal sense, i.e., growing the capacity, by “rescaling” the table. Scaling well to computational resources implies constructing the fastest best per- forming algorithms and data structures. On today’s many-core machines the best performance is immediately associated with parallelism. Since CPU frequencies have stopped growing about 10-15 years ago, parallelism is the only way to take ad- vantage of growing computational resources. But for data structures in general and hash tables in particular performance is not only linked to faster computations. The most execution time is actually spent waiting for memory. Thus optimizing data structures to reduce the amount of memory accesses or to take better advantage of the memory hierarchy especially through predictable access patterns and prefetch- ing is just as important. In terms of scaling the size of hash tables we have identified three domains where scaling hash-based data structures have been lacking previously, i.e., space effi- cient growing, concurrent hash tables, and Approximate Membership Query data structures (AMQ-filter). Throughout this dissertation, we describe the problems in these areas and develop efficient solutions. We highlight three different libraries that we have developed over the course of this dissertation, each containing mul- tiple implementations that have shown throughout our testing to be among the best implementations in their respective domains. In this composition they offer a comprehensive toolbox that can be used to solve many kinds of hashing related problems or to develop individual solutions for further ones. DySECT is a library for space efficient hash tables specifically growing space effi- cient hash tables that scale with their input size. It contains the namesake DySECT data structure in addition to a number of different probing and cuckoo based im- plementations. Growt is a library for highly efficient concurrent hash tables. It contains a very fast base table and a number of extensions to adapt this table to match any purpose. All extension can be combined to create a variety of different interfaces. In our extensive experimental evaluation, each adaptation has shown to be among the best hash tables for their specific purpose. Lpqfilter is a library for concurrent approximate membership query (AMQ) data structures. It contains some original data structures, like the linear probing quotient filter, as well as some novel approaches to dynamically sized quotient filters

Proteome-Wide Analyis of Chaperonin-Dependent Protein Folding in Escherichia coli

In Escherichia coli, the cylindrical chaperonin GroEL and its cofactor GroES promote the folding of a fraction of newly synthesized polypeptide chains by acting as an Anfinsen cage. GroEL recognizes substrate proteins with its apical domains of the tetradecameric structure. Exposed hydrophobic side chains in non-native proteins interact with GroEL and bound substrates are subsequently encapsulated under the GroES lid, where they can fold in a protected environment. Despite the detailed knowledge about structural and mechanistic features of GroEL and GroES, little is known about its genuine in vivo substrate proteins. Here, the nearly complete set of GroEL interacting proteins in vivo was identified and quantified by an approach using affinity chromatography for the isolation of GroEL/GroES/substrate complexes and subsequent analysis by mass spectrometric methods. GroEL substrate proteins were analyzed with respect to their fold types and functional classes, revealing a preference for proteins which fold into the versatile TIM barrel fold to interact with GroEL. Further in vivo and in vitro experiments with individual proteins identified as GroEL substrates verified the data obtained by the proteomic approach and allowed conclusions on the usage of the other main chaperone system in E. coli: DnaK/DnaJ/GrpE. Taken together, the results culminated in the classification of GroEL interacting proteins according to their dependence on chaperones for folding. Class I proteins are largely independent of chaperones but their folding yield can be increased by chaperone interaction. Class II proteins do not refold efficiently in the absence of chaperones in vitro, but can utilize either the DnaK or the GroEL/GroES systems for folding. Class III substrates are fully dependent on GroEL. DnaK can bind class III proteins and thus prevent their aggregation, but folding is achieved only upon transfer to GroEL

Evolvability of Chaperonin Substrate Proteins

Molecular chaperones ensure that their substrate proteins reach the functional native state, and prevent their aggregation. Recently, an additional function was proposed for molecular chaperones: they serve as buffers (_capacitors_) for evolution by permitting their substrate proteins to mutate and at the same time still allowing them to fold productively.

Using pairwise alignments of _E. coli_ genes with genes from other gamma-proteobacteria, we showed that the described buffering effect cannot be observed among substrate proteins of GroEL, an essential chaperone in _E. coli_. Instead, we find that GroEL substrate proteins evolve less than other soluble _E. coli_ proteins. We analyzed several specific structural and biophysical properties of proteins to assess their influence on protein evolution and to find out why specifically GroEL substrates do not show the expected higher divergence from their orthologs.

Our results culminate in four main findings: *1.* We find little evidence that GroEL in _E. coli_ acts as a capacitor for evolution _in vivo_. *2.* GroEL substrates evolved less than other _E. coli_ proteins. *3.* Predominantly structural features appear to be a strong determinant of evolutionary rate. *4.* Besides size, hydrophobicity is a criterion for exclusion for a protein as a chaperonin substrate

Dynamic Space Efficient Hashing

We consider space efficient hash tables that can grow and shrink dynamically and are always highly space efficient, i.e., their space consumption is always close to the lower bound even while growing and when taking into account storage that is only needed temporarily. None of the traditionally used hash tables have this property. We show how known approaches like linear probing and bucket cuckoo hashing can be adapted to this scenario by subdividing them into many subtables or using virtual memory overcommitting. However, these rather straightforward solutions suffer from slow amortized insertion times due to frequent reallocation in small increments. Our main result is DySECT (Dynamic Space Efficient Cuckoo Table) which avoids these problems. DySECT consists of many subtables which grow by doubling their size. The resulting inhomogeneity in subtable sizes is equalized by the flexibility available in bucket cuckoo hashing where each element can go to several buckets each of which containing several cells. Experiments indicate that DySECT works well with load factors up to 98%. With up to 2.7 times better performance than the next best solution

Deep Denoising for Hearing Aid Applications

Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain. We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.Comment: submitted to IWAENC 201

Modeling of the Thermomechanical Process Effects on Machine Tool Structures

AbstractThermally induced deviations are a key issue in the development of machine tools, especially when considering the actual trends of high performance and dry cutting. The interactions between the cutting process and the machine tool structure are significant boundary conditions for the numerical prediction of the thermomechanical machine behavior. Within this paper an approach for the holistic modeling of process effects is presented, including process heat, cutting forces and increased load on feed and main drives. The modeling approach is supported by experimental investigations on a lathe to provide empiric data for the link between cutting forces and active drive power