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The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: \textit{de novo} molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at \url{https://github.com/pluskal-lab/MassSpecGym}.

We investigate the stellar Mass-Metallicity Relation (MZR) using a sample of 637 quiescent galaxies with 10.4 <= log(M*/M ) < 11.7 selected from the LEGA-C survey at 0.6 <= z <= 1. We derive mass-weighted stellar metallicities using full-spectral fitting. We find that while lower-mass galaxies are both metal -rich and -poor, there are no metal-poor galaxies at high masses, and that metallicity is bounded at low values by a mass-dependent lower limit. This lower limit increases with mass, empirically defining a MEtallicity-Mass Exclusion (MEME) zone. We find that the spectral index MgFe = \sqrt{Mgb \times Fe4383}, a proxy for the stellar metallicity, also shows a mass-dependent lower limit resembling the MEME relation. Crucially, MgFe is independent of stellar population models and fitting methods. By constructing the Metallicity Enrichment Histories, we find that, after the first Gyr, the Star Formation History of galaxies has a mild impact on the observed metallicity distribution. Finally, from the average formation times, we find that galaxies populate differently the metallicity-mass plane at different cosmic times, and that the MEME limit is recovered by galaxies that formed at z >= 3. Our work suggests that the stellar metallicity of quiescent galaxies is bounded by a lower limit which increases with the stellar mass. On the other hand, low-mass galaxies can have metallicities as high as galaxies ~1 dex more massive. This suggests that, at log(M*/M ) >= 10.4, rather than lower-mass galaxies being systematically less metallic, the observed MZR might be a consequence of the lack of massive, metal-poor galaxies., Comment: Accepted for publication in A&A

The hierarchical model of galaxy evolution suggests that the impact of mergers is substantial on the intricate processes that drive stellar assembly within a galaxy. However, accurately measuring the contribution of accretion to a galaxy's total stellar mass and its balance with in-situ star formation poses a persistent challenge, as it is neither directly observable nor easily inferred from observational properties. Here, we present theory-motivated predictions for the fraction of stellar mass originating from mergers in a statistically significant sample of nearby galaxies, using data from MaNGA. Employing a robust machine learning model trained on mock MaNGA analogs (MaNGIA) in turn obtained from a cosmological simulation (TNG50), we unveil that in-situ stellar mass dominates almost across the entire stellar mass spectrum (1e9Msun < M* < 1e12Msun). Only in more massive galaxies (M* > 1e11Msun) does accreted mass become a substantial contributor, reaching up to 35-40% of the total stellar mass. Notably, the ex-situ stellar mass in the nearby universe exhibits significant dependence on galaxy characteristics, with higher accreted fractions favored by elliptical, quenched galaxies and slow rotators, as well as galaxies at the center of more massive dark matter halos., Comment: 16 pages, 7 Figures. Accepted for publication

Observations of nearby massive galaxies have revealed that they are older and richer in metals and magnesium than their low-mass counterparts. In particular, the overabundance of magnesium compared to iron, [Mg/Fe], is interpreted to reflect the short star formation history that the current massive galaxies underwent early in the Universe. We present a systematic revision of the [Mg/Fe] - velocity dispersion ($\sigma$) relation based on stacked spectra of early-type galaxies with a high signal-to-noise ratio from the Sloan Digital Sky Survey (SDSS). Using the penalized pixel-fitting (pPXF) method of Cappellari & Emsellem 2004 and the Vazdekis et al. 2015 MILES single stellar population (SSP) models, we fit a wide optical wavelength range to measure the net $\alpha$-abundance. The combination of pPXF and $\alpha$-enhanced MILES models incorrectly leads to an apparently decreasing trend of [$\alpha$/Fe] with velocity dispersion. We interpret this result as a consequence of variations in the individual abundances of the different $\alpha$-elements. This warrants caution for a na\"ive use of full spectral fitting algorithms paired with stellar population models that do not take individual elemental abundance variations into account, especially when deriving averaged quantities such as the mean [$\alpha$/Fe] of a stellar population. In addition, and based on line-strength measurements, we quantify the impact of a non-universal initial mass function on the recovered abundance pattern of galaxies. In particular, we find that a simultaneous fit of the slope of the initial mass function and the [Mg/Fe] results in a shallower [Mg/Fe]-$\sigma$ relation. Therefore, our results suggest that star formation in massive galaxies lasted longer than what has been reported previously, although it still occurred significantly faster than in the solar neighbourhood., Comment: 7 pages, 5 figures, accepted for publication in A&A

This manuscript aims to formalize and conclude the discussions initiated during the PriTEM workshop 22-23 March 2023. We present important ideas and discussion topics in the context of transactive energy systems. Moreover, the conclusions from the discussions articulate potential aspects to be explored in future studies on transactive energy management. Particularly, these conclusions cover research topics in energy technology and energy informatics, energy law, data law, energy market and socio-psychology that are relevant to the seamless integration of renewable energy resources and the transactive energy systems-in smart microgrids-focusing on distributed frameworks such as peer-to-peer (P2P) energy trading. We clarify issues, identify barriers, and suggest possible solutions to open questions in diversified topics, such as block-chain interoperability, consumer privacy and data sharing, and participation incentivization. Furthermore, we also elaborate challenges associated with cross-disciplinary collaboration and coordination for transactive energy systems, and enumerate the lessons learned from our work so far.

The 6G standard aims to be an integral part of the future economy by providing high-performance communication and sensing services. At terahertz (THz) frequencies, indoor campus networks can offer the highest sensing quality. Health monitoring in hospitals is expected to be an application site for these. This work outlines a monostatic phase-based system for breathing rate monitoring. Our feasibility study observes motion measurement accuracy down to the micrometer level. However, we also find that the patient's pose needs to be considered for generalized applicability. Thus, a solution that leverages multiple propagation paths and beam orientations is proposed., Comment: 5 pages, 10 figures. Accepted for presentation in: 2023 6th IEEE International Workshop on Mobile Terahertz Systems (IWMTS), Bonn, Germany, July 2023

We investigate the origin of stars in the innermost $500\,\mathrm{pc}$ of galaxies spanning stellar masses of $5\times10^{8-12}\,\mathrm{M}_{\odot}$ at $\mathrm{z=0}$ using the cosmological magnetohydrodynamical TNG50 simulation. Three different origins of stars comprise galactic centers: 1) in-situ (born in the center), 2) migrated (born elsewhere in the galaxy and ultimately moved to the center), 3) ex-situ (accreted from other galaxies). In-situ and migrated stars dominate the central stellar mass budget on average with 73% and 23% respectively. The ex-situ fraction rises above 1% for galaxies $\gtrsim10^{11}\,\mathrm{M}_{\odot}$. Yet, only 9% of all galaxies exhibit no ex-situ stars in their centers and the scatter of ex-situ mass is significant ($4-6\,\mathrm{dex}$). Migrated stars predominantly originate closely from the center ($1-2\,\mathrm{kpc}$), but if they travelled together in clumps distances reach $\sim10\,\mathrm{kpc}$. Central and satellite galaxies possess similar amounts and origins of central stars. Star forming galaxies ($\gtrsim10^{10}\,\mathrm{M}_{\odot}$) have on average more ex-situ mass in their centers than quenched ones. We predict readily observable stellar population and dynamical properties: 1) migrated stars are distinctly young ($\sim2\,\mathrm{Gyr}$) and rotationally supported, especially for Milky Way mass galaxies, 2) in-situ stars are most metal-rich and older than migrated stars, 3) ex-situ stars are on random motion dominated orbits and typically the oldest, most metal-poor and $\alpha$-enhanced population. We demonstrate that the interaction history with other galaxies leads to diverse pathways of building up galaxy centers in a $\Lambda$CDM universe. Our work highlights the necessity for cosmological context in formation scenarios of central galactic components and the potential to use galaxy centers as tracers of overall galaxy assembly., Comment: 24 pages, 13 Figures, published in MNRAS

Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems., Comment: 8 pages, 9 figures, PrePrint, Technical Report, German Rescue Robotic Center

Modern astronomical observations give unprecedented access to the physical properties of nearby galaxies, including spatially resolved stellar populations. However, observations can only give a present-day view of the Universe, whereas cosmological simulations give access to the past record of the processes that galaxies have experienced in their evolution. To connect the events that happened in the past with galactic properties as seen today, simulations must be taken to a common ground before being compared to observations. We emulate data from the MaNGA survey, which is the largest integral field spectroscopic galaxy survey to date with its 10,000 nearby galaxies of all types. For this, we use the cosmological simulations TNG50 to generate MaNGIA (Mapping Nearby Galaxies with IllustrisTNG Astrophysics), a mock MaNGA sample of similar size that emulates observations of galaxies for stellar population analysis. We choose TNG galaxies to match the MaNGA sample selection to limit the impact of selection effects. We produce MaNGA-like datacubes from all simulated galaxies, and process these with the pyPipe3D analysis code. This allows us to extract spatially resolved stellar maps. This first paper presents the approach to generate the mock sample and provides an initial exploration of its properties. We show that the stellar populations and kinematics of the simulated MaNGIA galaxies are overall in good agreement with observations. Specific discrepancies, especially in the age and metallicity gradients in low- to intermediate-mass regimes and in massive galaxies' kinematics, require further investigation. We compare our results to other attempts to mock similar observations, all of smaller data sets. Our final dataset will be released with the publication, consisting of >10,000 post-processed data-cubes analysed with pyPipe3D, along with the codes developed to create it., Comment: Submitted to A&A, 21 pages, 17 figures, 1 table

Context. Blob detection is a common problem in astronomy. One example is in stellar population modelling, where the distribution of stellar ages and metallicities in a galaxy is inferred from observations. In this context, blobs may correspond to stars born in-situ versus those accreted from satellites, and the task of blob detection is to disentangle these components. A difficulty arises when the distributions come with significant uncertainties, as is the case for stellar population recoveries inferred from modelling spectra of unresolved stellar systems. There is currently no satisfactory method for blob detection with uncertainties. Aims. We introduce a method for uncertainty-aware blob detection developed in the context of stellar population modelling of integrated-light spectra of stellar systems. Methods. We develop theory and computational tools for an uncertainty-aware version of the classic Laplacian-of-Gaussians method for blob detection, which we call ULoG. This identifies significant blobs considering a variety of scales. As a prerequisite to apply ULoG to stellar population modelling, we introduce a method for efficient computation of uncertainties for spectral modelling. This method is based on the truncated Singular Value Decomposition and Markov Chain Monte Carlo sampling (SVD-MCMC). Results. We apply the methods to data of the star cluster M54. We show that the SVD-MCMC inferences match those from standard MCMC, but are a factor 5-10 faster to compute. We apply ULoG to the inferred M54 age/metallicity distributions, identifying between 2 or 3 significant, distinct populations amongst its stars., Comment: Revision submitted to A&A, comments welcome

Galaxy mergers are instrumental in dictating the final mass, structure, stellar populations, and kinematics of galaxies. Cosmological galaxy simulations indicate that the most massive galaxies at z=0 are dominated by high fractions of `ex-situ' stars, which formed first in distinct independent galaxies, and then subsequently merged into the host galaxy. Using spatially resolved MUSE spectroscopy we quantify and map the ex-situ stars in thirteen massive Early Type galaxies. We use full spectral fitting together with semi-analytic galaxy evolution models to isolate the signatures in the galaxies' light which are indicative of ex-situ populations. Using the large MUSE field of view we find that all galaxies display an increase in ex-situ fraction with radius, with massive and more extended galaxies showing a more rapid increase in radial ex-situ fraction, (reaching values between 30% to 100% at 2 effective radii) compared to less massive and more compact sources (reaching between 5% to 40% ex-situ fraction within the same radius). These results are in line with predictions from theory and simulations which suggest ex-situ fractions should increase significantly with radius at fixed mass for the most massive galaxies., Comment: Accepted for publication in MNRAS

Interdisciplinary application fields, such as automotive, industrial applications or field robotics show an increasing need for reliable and resilient wireless communication even under high load conditions. These mission-critical applications require dependable service quality characteristics in terms of latency and especially stability. Current deployments often use either wired links that lack the flexibility to accommodate them, or wireless technologies that are susceptible to interference. Depending on the application and the surrounding environments, different technologies can meet the associated requirements and have to be tested deliberately to prevent unexpected system failure. To stress test these infrastructures in a reproducible and application-aware manner, we propose STING, a spatially distributed traffic and interference generation framework. STING is evaluated in a remote control test case of an Unmanned Ground Vehicle that serves as a scout in Search and Rescue missions. A significant impact of interference on the remote control quality of experience is shown in tests with different operators, which result in an 80% increase in completion time in our test scenario with high interference on the radio channel. With this case study, we have proven STING to be a reliable and reproducible way to asses resilience against interference of wireless machine-type communication use cases. Our concept can find use for any type of wireless technology, in unlicensed (e.g. Wi-Fi) as well as licensed bands (e.g. 5G).

With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly. Whenever critical temperatures cannot be measured economically on a sensor base, a thermal model lends itself to estimate those unknown quantities. Thermal models for electric power systems are usually required to be both, real-time capable and of high estimation accuracy. Moreover, ease of implementation and time to production play an increasingly important role. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based lumped-parameter models, and data-driven nonlinear function approximation with supervised machine learning. A quasi-linear parameter-varying system is identified solely from empirical data, where relationships between scheduling variables and system matrices are inferred statistically and automatically. At the same time, a TNN has physically interpretable states through its state-space representation, is end-to-end trainable -- similar to deep learning models -- with automatic differentiation, and requires no material, geometry, nor expert knowledge for its design. Experiments on an electric motor data set show that a TNN achieves higher temperature estimation accuracies than previous white-/grey- or black-box models with a mean squared error of $3.18~\text{K}^2$ and a worst-case error of $5.84~\text{K}$ at 64 model parameters., Comment: Preprint; Fix typos, streamline math. notation; 10 pages

Current instruments and spectral analysis programs are now able to decompose the integrated spectrum of a stellar system into distributions of ages and metallicities. The reliability of these methods have rarely been tested on nearby systems with resolved stellar ages and metallicities. Here we derive the age-metallicity distribution of M54, the nucleus of the Sagittarius dwarf spheroidal galaxy, from its integrated MUSE spectrum. We find a dominant old (8-14 Gyr), metal-poor (-1.5 dex) and a young (1 Gyr), metal-rich (+0.25 dex) component - consistent with the complex stellar populations measured from individual stars in the same MUSE data set. There is excellent agreement between the (mass-weighted) average age and metallicity of the resolved and integrated analyses. Differences are only 3% in age and 0.2 dex metallicitiy. By co-adding individual stars to create M54's integrated spectrum, we show that the recovered age-metallicity distribution is insensitive to the magnitude limit of the stars or the contribution of blue horizontal branch stars - even when including additional blue wavelength coverage from the WAGGS survey. However, we find that the brightest stars can induce the spurious recovery of an old ($>8$ Gyr), metal-rich (+0.25 dex) stellar population, which is otherwise not expected from our understanding of chemical enrichment in M54. The overall derived stellar mass-to-light ratio of M54 is M/L$_{\mathrm{V}}=1.46$ with a scatter of 0.22 across the field-of-view, which we attribute to the stochastic contribution of a young, metal-rich component. These findings provide strong evidence that complex stellar population distributions can be reliably recovered from integrated spectra of extragalactic systems., Comment: 28 pages, 15 figures incl. appendix, accepted for publication in ApJ

The society as a whole, and many vertical sectors in particular, is becoming increasingly digitalized. Machine Type Communication (MTC), encompassing its massive and critical aspects, and ubiquitous wireless connectivity are among the main enablers of such digitization at large. The recently introduced 5G New Radio is natively designed to support both aspects of MTC to promote the digital transformation of the society. However, it is evident that some of the more demanding requirements cannot be fully supported by 5G networks. Alongside, further development of the society towards 2030 will give rise to new and more stringent requirements on wireless connectivity in general, and MTC in particular. Driven by the societal trends towards 2030, the next generation (6G) will be an agile and efficient convergent network serving a set of diverse service classes and a wide range of key performance indicators (KPI). This white paper explores the main drivers and requirements of an MTC-optimized 6G network, and discusses the following six key research questions: - Will the main KPIs of 5G continue to be the dominant KPIs in 6G; or will there emerge new key metrics? - How to deliver different E2E service mandates with different KPI requirements considering joint-optimization at the physical up to the application layer? - What are the key enablers towards designing ultra-low power receivers and highly efficient sleep modes? - How to tackle a disruptive rather than incremental joint design of a massively scalable waveform and medium access policy for global MTC connectivity? - How to support new service classes characterizing mission-critical and dependable MTC in 6G? - What are the potential enablers of long term, lightweight and flexible privacy and security schemes considering MTC device requirements?, Comment: White paper by http://www.6GFlagship.com

Two of the most important aspects of electric vehicles are their efficiency or achievable range. In order to achieve high efficiency and thus a long range, it is essential to avoid over-dimensioning the drive train. Therefore, the drive train has to be kept as lightweight as possible while at the same time being utilized to the best possible extent. This can only be achieved if the dynamic behavior of the drive train is accurately known by the controller. The task of the controller is to achieve a desired torque at the wheels of the car by controlling the currents of the electric motor. With machine learning modeling techniques, accurate models describing the behavior can be extracted from measurement data and then used by the controller. For the comparison of the different modeling approaches, a data set consisting of about 40 million data points was recorded at a test bench for electric drive trains. The data set is published on Kaggle, an online community of data scientists.

A data set was recorded to evaluate different methods for extracting mathematical models for a three-phase permanent magnet synchronous motor (PMSM) and a two-level IGBT inverter from measurement data. It consists of approximately 40 million multidimensional samples from a defined operating range of the drive. This document describes how to use the published data set \cite{Dataset} and how to extract models using introductory examples. The examples are based on known ordinary differential equations, the least squares method or on (deep) machine learning methods. The extracted models are used for the prediction of system states in a model predictive control (MPC) environment of the drive. In case of model deviations, the performance utilizing MPC remains below its potential. This is the case for state-of-the-art white-box models that are based only on nominal drive parameters and are valid in only limited operation regions. Moreover, many parasitic effects (e.g. from the feeding inverter) are normally not covered in white-box models. In order to achieve a high control performance, it is necessary to use models that cover the motor behavior in all operating points sufficiently well.

Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or sensor-based methods still prove unfeasible in a commercial context. Overheating results in severe motor deterioration and is thus of high concern for the machine's control strategy and its design. Lack of precise temperature estimations leads to lesser device utilization and higher material cost. In this work, several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles. The range of selected algorithms covers as diverse approaches as possible with ordinary and weighted least squares, support vector regression, $k$-nearest neighbors, randomized trees and neural networks. Having test bench data available, it is shown that ML approaches relying merely on collected data meet the estimation performance of classical thermal models built on thermodynamic theory, yet not all kinds of models render efficient use of large datasets or sufficient modeling capacities. Especially linear regression and simple feed-forward neural networks with optimized hyperparameters mark strong predictive quality at low to moderate model sizes., Comment: preprint for TII: SS on Applications of Artificial Intelligence in Industrial Power Electronics and Systems

The performance of model predictive controllers (MPC) strongly depends on the model quality. In the field of electric drive control, white-box (WB) modeling approaches derived from first-order physical principles are most common. This procedure typically does not cover parasitic effects and parameter deviations are frequent. These issues are particularly crucial in the domain of self-commissioning drives when a hand-tailored, accurate WB plant model is not available. In order to compensate for such modeling errors and, therefore, to improve the control performance during transients and steady-state, this paper proposes a data-driven, real-time capable recursive least squares (RLS) estimation method for the current control of a permanent magnet synchronous motor (PMSM). The effect of the flux linkage and voltage harmonics due to the winding scheme can also be taken into account. Moreover, a compensating scheme for the interlocking time of the inverter is proposed. The proposed identification algorithm is investigated using the well-known finite-control-set MPC (FCS-MPC) in the rotor-oriented coordinate system. The extensive experimental results show the superior performance of the presented scheme compared to a FCS-MPC-based on a state-of-the-art WB motor model using look-up tables for adressing (cross-)saturation.

We present a new method of quantifying a galaxy's accretion history from its integrated spectrum alone. Using full spectral fitting and calibrated regularization techniques we show how we can accurately derive a galaxy's mass distribution in age-metallicity space and further separate this into stellar populations from different chemical enrichment histories. By exploiting the fact that accreted lower mass galaxies will exhibit an offset to lower metallicities at fixed age compared to the in-situ stellar population, we quantify the fraction of light that comes from past merger events, that are long since mixed in phase-space and otherwise indistinguishable. Empirical age-metallicity relations (AMRs) parameterized for different galaxy masses are used to identify the accreted stellar populations and link them back to the progenitor galaxy's stellar mass. This allows us to not only measure the host galaxy's total ex-situ mass fraction ($f_{acc}$), but also quantify the relative amount of accreted material deposited by satellite galaxies of different masses, i.e. the accreted satellite mass function in analogy to the subhalo mass function. Using mock spectra of simulated, present-day galaxies from the EAGLE suite we demonstrate that our method can recover the total accreted fraction to within $\approx 12 \%$, the stellar mass of the most massive accreted subhalo to within $\approx 26 \%$ and the slope of the accreted satellite mass function to within $\approx 16 \%$ of the true values from the EAGLE merger trees. Future application of this method to observations could potentially provide us accretion histories of hundreds of individual galaxies, for which deep integrated light spectroscopy is available., Comment: 10 pages, 6 figures, submitted to MNRAS on 20th December 2018

Predictive control of power electronic systems always requires a suitable model of the plant. Using typical physics-based white box models, a trade-off between model complexity (i.e. accuracy) and computational burden has to be made. This is a challenging task with a lot of constraints, since the model order is directly linked to the number of system states. Even though white-box models show suitable performance in most cases, parasitic real-world effects often cannot be modeled satisfactorily with an expedient computational load. Hence, a Koopman operator-based model reduction technique is presented which directly links the control action to the system's outputs in a black-box fashion. The Koopman operator is a linear but infinite-dimensional operator describing the dynamics of observables of nonlinear autonomous dynamical systems which can be nicely applied to the switching principle of power electronic devices. Following this data-driven approach, the model order and the number of system states are decoupled which allows us to consider more complex systems. Extensive experimental tests with an automotive-type permanent magnet synchronous motor fed by an IGBT 2-level inverter prove the feasibility of the proposed modeling technique in a finite-set model predictive control application.

The analysis of a protein-expression pattern from tissue microarray (TMA) data will not immediately give an answer on synergistic or antagonistic effects between the expression of the observed proteins. But contrary to apparent first impression, it is possible to reveal those cooperative phenomena from TMA data. We present here a largely assumption-free combinatorial analysis, related to correlation networks but with much less arbitrary constraints. A strong focus was put on the analysis of the basic data to analyze how the cooperative phenomena might be imprinted in the TMA data structure. The study design was based on two independent panels of 589 and 366 invasive breast cancer cases from different institutions, assembled on tissue microarrays. The combinatorial analysis generates an optimal rank ordering of protein-expression coherence. The outcome of the analysis corresponds to all the single observations scattered over several publications and integrates them in one context. This means all these scattered observations can also be deduced from one TMA experiment. A comprehensive statistical meta-analysis of the TMA data suggests the existence of a superposition of three basic coherence situations, and offers the opportunity to analyze these data properties with additional real-world data and synthetic data in more detail. The presented algorithm gives molecular pathologists a tool to extract dependency information from TMA data. Beyond this practical benefit, some light was shed on how dependency aspects might be imprinted into expression data. This will certainly foster the refinement of algorithms to reconstruct dependency networks. The implementation of the algorithm is at the moment not end-user suitable, but available on request., Comment: The DOVE Press Journal 'Open Access Bioinformatics' ceased publishing in May 2016

In metabolomics, small molecules are structurally elucidated using tandem mass spectrometry (MS/MS); this resulted in the computational Maximum Colorful Subtree problem, which is NP-hard. Unfortunately, data from a single metabolite requires us to solve hundreds or thousands of instances of this problem; and in a single Liquid Chromatography MS/MS run, hundreds or thousands of metabolites are measured. Here, we comprehensively evaluate the performance of several heuristic algorithms for the problem against an exact algorithm. We put particular emphasis on whether a heuristic is able to rank candidates such that the correct solution is ranked highly. We propose this "intermediate" evaluation because evaluating the approximating quality of heuristics is misleading: Even a slightly suboptimal solution can be structurally very different from the true solution. On the other hand, we cannot structurally evaluate against the ground truth, as this is unknown. We find that one particular heuristic consistently ranks the correct solution in a top position, allowing us to speed up computations about 100-fold. We also find that scores of the best heuristic solutions are very close to the optimal score; in contrast, the structure of the solutions can deviate significantly from the optimal structures.

We present a dynamic programming algorithm for optimally solving the Cograph Editing problem on an $n$-vertex graph that runs in $O(3^n n)$ time and uses $O(2^n)$ space. In this problem, we are given a graph $G = (V, E)$ and the task is to find a smallest possible set $F \subseteq V \times V$ of vertex pairs such that $(V, E \bigtriangleup F)$ is a cograph (or $P_4$-free graph), where $\bigtriangleup$ represents the symmetric difference operator. We also describe a technique for speeding up the performance of the algorithm in practice. Additionally, we present a heuristic for solving the Cograph Editing problem which produces good results on small to medium datasets. In application it is much more important to find the ground truth, not some optimal solution. For the first time, we evaluate whether the cograph property is strict enough to recover the true graph from data to which noise has been added., Comment: 18 pages, 6 figures

Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually relies on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. Fragmentation trees have become a powerful tool for the interpretation of tandem mass spectrometry data of small molecules. These trees are found by combinatorial optimization, and aim at explaining the experimental data via fragmentation cascades. To obtain biochemically meaningful results requires an elaborate optimization function. We present a new scoring for computing fragmentation trees, transforming the combinatorial optimization into a maximum a posteriori estimator. We demonstrate the superiority of the new scoring for two tasks: Both for the de novo identification of molecular formulas of unknown compounds, and for searching a database for structurally similar compounds, our methods performs significantly better than the previous scoring, as well as other methods for this task. Our method can expedite the workflow for untargeted metabolomics, allowing researchers to investigate unknowns using automated computational methods., Comment: different dataset

The Robinson-Foulds (RF) metric is arguably the most widely used measure of phylogenetic tree similarity, despite its well-known shortcomings: For example, moving a single taxon in a tree can result in a tree that has maximum distance to the original one; but the two trees are identical if we remove the single taxon. To this end, we propose a natural extension of the RF metric that does not simply count identical clades but instead, also takes similar clades into consideration. In contrast to previous approaches, our model requires the matching between clades to respect the structure of the two trees, a property that the classical RF metric exhibits, too. We show that computing this generalized RF metric is, unfortunately, NP-hard. We then present a simple Integer Linear Program for its computation, and evaluate it by an all-against-all comparison of 100 trees from a benchmark data set. We find that matchings that respect the tree structure differ significantly from those that do not, underlining the importance of this natural condition., Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013)

Metabolomics complements investigation of the genome, transcriptome, and proteome of an organism. Today, the vast majority of metabolites remain unknown, in particular for non-model organisms. Mass spectrometry is one of the predominant techniques for analyzing small molecules such as metabolites. A fundamental step for identifying a small molecule is to determine its molecular formula. Here, we present and evaluate three algorithm engineering techniques that speed up the molecular formula determination. For that, we modify an existing algorithm for decomposing the monoisotopic mass of a molecule. These techniques lead to a four-fold reduction of running times, and reduce memory consumption by up to 94%. In comparison to the classical search tree algorithm, our algorithm reaches a 1000-fold speedup., Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013)

We consider the following simple game: We are given a table with ten slots indexed one to ten. In each of the ten rounds of the game, three dice are rolled and the numbers are added. We then put this number into any free slot. For each slot, we multiply the slot index with the number in this slot, and add up the products. The goal of the game is to maximize this score. In more detail, we play the game many times, and try to maximize the sum of scores or, equivalently, the expected score. We present a strategy to optimally play this game with respect to the expected score. We then modify our strategy so that we need only polynomial time and space. Finally, we show that knowing all ten rolls in advance, results in a relatively small increase in score. Although the game has a random component and requires a non-trivial strategy to be solved optimally, this strategy needs only polynomial time and space., Comment: 10 pages

Computing supertrees is a central problem in phylogenetics. The supertree method that is by far the most widely used today was introduced in 1992 and is called Matrix Representation with Parsimony analysis (MRP). Matrix Representation using Flipping (MRF)}, which was introduced in 2002, is an interesting variant of MRP: MRF is arguably more relevant that MRP and various efficient implementations of MRF have been presented. From a theoretical point of view, implementing MRF or MRP is solving NP-hard optimization problems. The aim of this paper is to study the approximability and the fixed-parameter tractability of the optimization problem corresponding to MRF, namely Minimum-Flip Supertree. We prove strongly negative results., Comment: To be submitted

Define Minimum Soapy Union (MinSU) as the following optimization problem: given a $k$-tuple $(X_1, X_2,..., X_k)$ of finite integer sets, find a $k$-tuple $(t_1, t_2,..., t_k)$ of integers that minimizes the cardinality of $(X_1 + t_1) \cup (X_2 + t_2) \cup ... \cup (X_n + t_k)$. We show that MinSU is NP-complete, APX-hard, and polynomial for fixed $k$. MinSU appears naturally in the context of protein shotgun sequencing: Here, the protein is cleaved into short and overlapping peptides, which are then analyzed by tandem mass spectrometry. To improve the quality of such spectra, one then asks for the mass of the unknown prefix (the shift) of the spectrum, such that the resulting shifted spectra show a maximum agreement. For real-world data the problem is even more complicated than our definition of MinSU; but our intractability results clearly indicate that it is unlikely to find a polynomial time algorithm for shotgun protein sequencing., Comment: 10 pages, submitted

In computational phylogenetics, the problem of constructing a supertree of a given set of rooted input trees can be formalized in different ways, to cope with contradictory information in the input. We consider the Minimum Flip Supertree problem, where the input trees are transformed into a 0/1/?-matrix, such that each row represents a taxon, and each column represents an inner node of one of the input trees. Our goal is to find a perfect phylogeny for the input matrix requiring a minimum number of 0/1-flips, that is, corrections of 0/1-entries in the matrix. The problem is known to be NP-complete. Here, we present a parameterized data reduction with polynomial running time. The data reduction guarantees that the reduced instance has a solution if and only if the original instance has a solution. We then make our data reduction parameter-independent by using upper bounds. This allows us to preprocess an instance, and to solve the reduced instance with an arbitrary method. Different from an existing data reduction for the consensus tree problem, our reduction allows us to draw conclusions about certain entries in the matrix. We have implemented and evaluated our data reduction. Unfortunately, we find that the Minimum Flip Supertree problem is also hard in practice: The amount of information that can be derived during data reduction diminishes as instances get more "complicated", and running times for "complicated" instances quickly become prohibitive. Still, our method offers another route of attack for this relevant phylogenetic problem.

We re-examine the possibility of an astrophysically allowed KSVZ-type axion that has a strongly suppressed coupling to photons. We then investigate the impact of such ''hadronic axions'' on two classes of astrophysical objects: black hole accretion disc (BHAD) based gamma-ray bursts and isolated neutron-stars. Although our results are sensitive to details of the underlying models, we show that hadronic axions could in principle play an important role in the evolution of gamma-ray bursts and in the cooling behaviour of neutron stars., Comment: Masters Thesis (TU Munich), 63 pp