Deterministic Distributed Edge-Coloring via Hypergraph Maximal Matching

We present a deterministic distributed algorithm that computes a $(2\Delta-1)$-edge-coloring, or even list-edge-coloring, in any $n$-node graph with maximum degree $\Delta$, in $O(\log^7 \Delta \log n)$ rounds. This answers one of the long-standing open questions of \emph{distributed graph algorithms} from the late 1980s, which asked for a polylogarithmic-time algorithm. See, e.g., Open Problem 4 in the Distributed Graph Coloring book of Barenboim and Elkin. The previous best round complexities were $2^{O(\sqrt{\log n})}$ by Panconesi and Srinivasan [STOC'92] and $\tilde{O}(\sqrt{\Delta}) + O(\log^* n)$ by Fraigniaud, Heinrich, and Kosowski [FOCS'16]. A corollary of our deterministic list-edge-coloring also improves the randomized complexity of $(2\Delta-1)$-edge-coloring to poly$(\log\log n)$ rounds. The key technical ingredient is a deterministic distributed algorithm for \emph{hypergraph maximal matching}, which we believe will be of interest beyond this result. In any hypergraph of rank $r$ --- where each hyperedge has at most $r$ vertices --- with $n$ nodes and maximum degree $\Delta$, this algorithm computes a maximal matching in $O(r^5 \log^{6+\log r } \Delta \log n)$ rounds. This hypergraph matching algorithm and its extensions lead to a number of other results. In particular, a polylogarithmic-time deterministic distributed maximal independent set algorithm for graphs with bounded neighborhood independence, hence answering Open Problem 5 of Barenboim and Elkin's book, a $((\log \Delta/\varepsilon)^{O(\log (1/\varepsilon))})$-round deterministic algorithm for $(1+\varepsilon)$-approximation of maximum matching, and a quasi-polylogarithmic-time deterministic distributed algorithm for orienting $\lambda$-arboricity graphs with out-degree at most $(1+\varepsilon)\lambda$, for any constant $\varepsilon>0$, hence partially answering Open Problem 10 of Barenboim and Elkin's book

Human CLPP reverts the longevity phenotype of a fungal ClpP deletion strain

Mitochondrial maintenance crucially depends on the quality control of proteins by various chaperones, proteases and repair enzymes. While most of the involved components have been studied in some detail, little is known on the biological role of the CLPXP protease complex located in the mitochondrial matrix. Here we show that deletion of PaClpP, encoding the CLP protease proteolytic subunit CLPP, leads to an unexpected healthy phenotype and increased lifespan of the fungal ageing model organism Podospora anserina. This phenotype can be reverted by expression of human ClpP in the fungal deletion background, demonstrating functional conservation of human and fungal CLPP. Our results show that the biological role of eukaryotic CLP proteases can be studied in an experimentally accessible model organism

Sublinear-Time Distributed Algorithms for Detecting Small Cliques and Even Cycles

In this paper we give sublinear-time distributed algorithms in the CONGEST model for subgraph detection for two classes of graphs: cliques and even-length cycles. We show for the first time that all copies of 4-cliques and 5-cliques in the network graph can be listed in sublinear time, O(n^{5/6+o(1)}) rounds and O(n^{21/22+o(1)}) rounds, respectively. Prior to our work, it was not known whether it was possible to even check if the network contains a 4-clique or a 5-clique in sublinear time. For even-length cycles, C_{2k}, we give an improved sublinear-time algorithm, which exploits a new connection to extremal combinatorics. For example, for 6-cycles we improve the running time from O~(n^{5/6}) to O~(n^{3/4}) rounds. We also show two obstacles on proving lower bounds for C_{2k}-freeness: First, we use the new connection to extremal combinatorics to show that the current lower bound of Omega~(sqrt{n}) rounds for 6-cycle freeness cannot be improved using partition-based reductions from 2-party communication complexity, the technique by which all known lower bounds on subgraph detection have been proven to date. Second, we show that there is some fixed constant delta in (0,1/2) such that for any k, a Omega(n^{1/2+delta}) lower bound on C_{2k}-freeness implies new lower bounds in circuit complexity. For general subgraphs, it was shown in [Orr Fischer et al., 2018] that for any fixed k, there exists a subgraph H of size k such that H-freeness requires Omega~(n^{2-Theta(1/k)}) rounds. It was left as an open problem whether this is tight, or whether some constant-sized subgraph requires truly quadratic time to detect. We show that in fact, for any subgraph H of constant size k, the H-freeness problem can be solved in O(n^{2 - Theta(1/k)}) rounds, nearly matching the lower bound of [Orr Fischer et al., 2018]

Biodiesel via in situ wet microalgae biotransformation: Zwitter-type ionic liquid supported extraction and transesterification

The production of biodiesel derived from microalgae is among the most forthcoming technologies that provide an ecologic alternative to fossil fuels. Herein, a method was developed that enables the direct extraction and conversion of algal oil to biodiesel without prior isolation. The reaction occurs in aqueous media catalyzed by immobilized Candida antarctica lipase B (Novozyme 435). Zwitter-type ionic liquids were used as cocatalyst to improve the selectivity and reactivity of the enzyme. In a model reaction with sunflower oil, 64% biodiesel was obtained. Applying this method to a slurry of whole-cell Chlorella zof ingiensis in water resulted in 74.8% of lipid extraction, with 27.7% biotransformation products and up to 16% biodiesel. Factors that reduced the lipase activity with whole-cell algae were subsequently probed and discussed. This "in situ" method shows an improvement to existing methods, since it integrates the oil extraction and conversion into an one-pot procedure in aqueous conditions. The extraction is nondisruptive, and is a model for a greener algae to biodiesel process

Evaluation of activated alginate-GO beads for removal of pharmaceuticals from water: bachelor's thesis : diploma 2016

The aim of this project was to produce calcium alginate beads with graphene oxide incorporated into the structure, in order to enhance the adsorption properties. The effects of initial concentration, adsorbent dose, pH, temperature, contact time were investigated. Adsorption kinetics and isotherms were evaluated with different models such as pseudo-first-order, pseudo-second-order, intraparticle diffusion, Langmuir and Freundlich models. In addition, thermodynamic and desorption studies were performed. Thus, CaOAlg2/GO beads were assessed as potential adsorbent for three micropollutants

Enzyme catalysis with small ionic liquid quantities

Enzyme catalysis with minimal ionic liquid quantities improves reaction rates, stereoselectivity and enables solvent-free processing. In particular the widely used lipases combine well with many ionic liquids. Demonstrated applications are racemate separation, esterification and glycerolysis. Minimal solvent processing is also an alternative to sluggish solvent-free catalysis. The method allows simplified down-stream processing, as only traces of ionic liquids have to be remove

MPRAnalyze: statistical framework for massively parallel reporter assays.

Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences' activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods

CpCo(III) Precatalysts for [2+2+2] Cycloadditions

Catalysts applied in cobalt-catalyzed cyclotrimerizations reactions in general rely on the use of Co(I) precatalysts or the in situ generation of Co(I) catalysts from Co(II) sources by reduction in the presence of steering ligands, often by addition of less noble metals. In this paper, we report the synthesis and properties of novel stable CpCo(III) complexes as precatalysts and their exemplary evaluation for application in catalytic [2+2+2] cycloadditions. The role of phosphite neutral ligands, as well as iodide and cyanide as anionic ligands, on the reactivity of the complexes was evaluated. A modified one-pot approach to the synthesis of Cp ring-functionalized Cp’Co(III) complexes was developed. The investigations demonstrated that CpCo(III) complexes can be directly applied as catalysts in catalytic cyclotrimerizations of triynes without reducing agents as additives. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Boosting Neural Image Compression for Machines Using Latent Space Masking

Today, many image coding scenarios do not have a human as final intended user, but rather a machine fulfilling computer vision tasks on the decoded image. Thereby, the primary goal is not to keep visual quality but maintain the task accuracy of the machine for a given bitrate. Due to the tremendous progress of deep neural networks setting benchmarking results, mostly neural networks are employed to solve the analysis tasks at the decoder side. Moreover, neural networks have also found their way into the field of image compression recently. These two developments allow for an end-to-end training of the neural compression network for an analysis network as information sink. Therefore, we first roll out such a training with a task-specific loss to enhance the coding performance of neural compression networks. Compared to the standard VVC, 41.4% of bitrate are saved by this method for Mask R-CNN as analysis network on the uncompressed Cityscapes dataset. As a main contribution, we propose LSMnet, a network that runs in parallel to the encoder network and masks out elements of the latent space that are presumably not required for the analysis network. By this approach, additional 27.3% of bitrate are saved compared to the basic neural compression network optimized with the task loss. In addition, we are the first to utilize a feature-based distortion in the training loss within the context of machine-to-machine communication, which allows for a training without annotated data. We provide extensive analyses on the Cityscapes dataset including cross-evaluation with different analysis networks and present exemplary visual results. Inference code and pre-trained models are published at https://github.com/FAU-LMS/NCN_for_M2M.Comment: 12 pages, 9 figures, 3 tables; This work has been accepted for IEEE T-CSVT special issue "Learned Visual Data Compression for both Human and Machine". Copyright may be transferred without notice, after which this version may no longer be accessibl

Entwicklung und technischer Einsatz eines Photokatalysators am Beispiel der Totaloxidation von Ammoniak mit Sauerstoff

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