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Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades., Comment: 7 pages, 6 figures, accepted to the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)

Accurate modeling of astrophysical jets is critical for understanding accretion systems and their impact on the interstellar medium. While astronomical observations can validate models, they have limitations. Controlled laboratory experiments offer a complementary approach for qualitative and quantitative demonstration. Laser experiments offer a complementary approach. This article introduces a new platform on the OMEGA laser facility for high-velocity (1500 km/s), high-aspect-ratio ($\sim$36) jet creation with strong cylindrical symmetry. This platform s capabilities bridge observational gaps, enabling controlled initial conditions and direct measurements, Comment: 9 pages, 7 figures, 2 tables, submitted to Physics of Plasma

Adversarially robust streaming algorithms are required to process a stream of elements and produce correct outputs, even when each stream element can be chosen as a function of earlier algorithm outputs. As with classic streaming algorithms, which must only be correct for the worst-case fixed stream, adversarially robust algorithms with access to randomness can use significantly less space than deterministic algorithms. We prove that for the Missing Item Finding problem in streaming, the space complexity also significantly depends on how adversarially robust algorithms are permitted to use randomness. (In contrast, the space complexity of classic streaming algorithms does not depend as strongly on the way randomness is used.) For Missing Item Finding on streams of length $\ell$ with elements in $\{1,\ldots,n\}$, and $\le 1/\text{poly}(\ell)$ error, we show that when $\ell = O(2^{\sqrt{\log n}})$, "random seed" adversarially robust algorithms, which only use randomness at initialization, require $\ell^{\Omega(1)}$ bits of space, while "random tape" adversarially robust algorithms, which may make random decisions at any time, may use $O(\text{polylog}(\ell))$ space. When $\ell$ is between $n^{\Omega(1)}$ and $O(\sqrt{n})$, "random tape" adversarially robust algorithms need $\ell^{\Omega(1)}$ space, while "random oracle" adversarially robust algorithms, which can read from a long random string for free, may use $O(\text{polylog}(\ell))$ space. The space lower bound for the "random seed" case follows, by a reduction given in prior work, from a lower bound for pseudo-deterministic streaming algorithms given in this paper., Comment: 46 pages, 3 figures; since v1, rewrote Sec 2, simplified proofs in Sec 4, clarified Sec 6, changed title from "When a random tape is not enough: lower bounds for a problem in adversarially robust streaming", and made many minor improvements

The low-voltage ride-through service is carried out in this paper according to the voltage profile described by the IEC 61400-21 European normative when short-duration voltage sags happen, and some instantaneous reactive power is delivered to the grid in accordance with the Spanish grid code; the mandatory limitation of the amplitude of the three-phase inverter currents to its nominal value is carried out with a novel control strategy, in which a certain amount of instantaneous constant active power can also be delivered to the grid when small or moderate voltage sags happen. A Multiple second order generalized integrator frequency-locked loop synchronization algorithm is employed in order to estimate the system frequency without harmonic distortions, as well as to output the positive- and the negative- sequence of the {\alpha}\b{eta} quantities of the three-phase grid voltages when balanced and unbalanced voltage sags happen in a frequency-adaptive scheme. The current control is carried out in the stationary reference frame, which guarantees the cancellation of the harmonic distortions in the utility grid currents using a Harmonic compensation structure, and the implementation of a constant active power control in order to protect the DC link capacitor from thermal stresses avoiding the appearance of large harmonic distortions at twice the fundamental frequency in the DC link voltage. A case study of a three-phase single-stage grid-connected PV system with a maximum apparent power about 500 kVA is tested with several simulations using MATLAB/SIMULINK firstly, and secondly, with some experiments using the Controller hardware-in-the-loop (CHIL) simulation technique for several types of voltage sags in order to do the final validation of the control algorithms.

We provide a systematic enumerative and combinatorial study of geometric cellular diagonals on the permutahedra. In the first part of the paper, we study the combinatorics of certain hyperplane arrangements obtained as the union of $\ell$ generically translated copies of the classical braid arrangement. Based on Zaslavsky's theory, we derive enumerative results on the faces of these arrangements involving combinatorial objects named partition forests and rainbow forests. This yields in particular nice formulas for the number of regions and bounded regions in terms of exponentials of generating functions of Fuss-Catalan numbers. By duality, the specialization of these results to the case $\ell = 2$ gives the enumeration of any geometric diagonal of the permutahedron. In the second part of the paper, we study diagonals which respect the operadic structure on the family of permutahedra. We show that there are exactly two such diagonals, which are moreover isomorphic. We describe their facets by a simple rule on paths in partition trees, and their vertices as pattern-avoiding pairs of permutations. We show that one of these diagonals is a topological enhancement of the Sanbeblidze-Umble diagonal, and unravel a natural lattice structure on their sets of facets. In the third part of the paper, we use the preceding results to show that there are precisely two isomorphic topological cellular operadic structures on the families of operahedra and multiplihedra, and exactly two infinity-isomorphic geometric universal tensor products of homotopy operads and A-infinity morphisms., Comment: 71 pages, 28 figures, 8 tables

In this paper, we construct groupoid coloured operads governing props and wheeled props, and show they are Koszul. This is accomplished by new biased definitions for (wheeled) props, and an extension of the theory of Groebner bases for operads to apply to groupoid coloured operads. Using the Koszul machine, we define homotopy (wheeled) props, and show they are not formed by polytope based models. Finally, using homotopy transfer theory, we construct Massey products for (wheeled) props, show these products characterise the formality of these structures, and re-obtain a theorem of Mac Lane on the existence of higher homotopies of (co)commutative Hopf algebras., Comment: Corrected an error in Section 4.2, and more examples + clarifications + minor-fixes throughout. To appear in Advances in Mathematics

Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. These properties, in combination, make it challenging to reliably map from sensor data to physical actuation. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) in combination with straightforward pre-processing. We apply a number of exponential smoothing filters on a re-sampled sensor signal, to produce features that preserve different levels of historical sensor data and, in combination, represent an adequate state of previous sensor actuation. By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force. Our findings also show that our technique translates to sensors of reasonably different composition in terms of material and structure, and it can furthermore be applied to related physical features such as strain.

This study presents a novel approach for touch sensing using semi-elastic textile surfaces that does not require the placement of additional sensors in the sensing area, instead relying on sensors located on the border of the textile. The proposed approach is demonstrated through experiments involving an elastic Jersey fabric and a variety of machine-learning models. The performance of one particular border-based sensor design is evaluated in depth. By using visual markers, the best-performing visual sensor arrangement predicts a single touch point with a mean squared error of 1.36 mm on an area of 125mm by 125mm. We built a textile only prototype that is able to classify touch at three indent levels (0, 15, and 20 mm) with an accuracy of 82.85%. Our results suggest that this approach has potential applications in wearable technology and smart textiles, making it a promising avenue for further exploration in these fields., Comment: 8 pages, 3 figures, submitted to IHSED 2023

For edge coloring, the online and the W-streaming models seem somewhat orthogonal: the former needs edges to be assigned colors immediately after insertion, typically without any space restrictions, while the latter limits memory to sublinear in the input size but allows an edge's color to be announced any time after its insertion. We aim for the best of both worlds by designing small-space online algorithms for edge coloring. We study the problem under both (adversarial) edge arrivals and vertex arrivals. Our results significantly improve upon the memory used by prior online algorithms while achieving an $O(1)$-competitive ratio. In particular, for $n$-node graphs with maximum vertex-degree $\Delta$ under edge arrivals, we obtain an online $O(\Delta)$-coloring in $\tilde{O}(n\sqrt{\Delta})$ space. This is also the first W-streaming edge-coloring algorithm using $O(\Delta)$ colors (in sublinear memory). All prior works either used linear memory or $\omega(\Delta)$ colors. We also achieve a smooth color-space tradeoff: for any $t=O(\Delta)$, we get an $O(\Delta t (\log^2 \Delta))$-coloring in $\tilde{O}(n\sqrt{\Delta/t})$ space, improving upon the state of the art that used $\tilde{O}(n\Delta/t)$ space for the same number of colors (the $\tilde{O}(.)$ notation hides polylog$(n)$ factors). The improvements stem from extensive use of random permutations that enable us to avoid previously used colors. Most of our algorithms can be derandomized and extended to multigraphs, where edge coloring is known to be considerably harder than for simple graphs., Comment: 36 pages, 1 figure; improvements to Thm 1.8 and minor edits since v1

In recent years, there has been a growing interest in solving various graph coloring problems in the streaming model. The initial algorithms in this line of work are all crucially randomized, raising natural questions about how important a role randomization plays in streaming graph coloring. A couple of very recent works have made progress on this question: they prove that deterministic or even adversarially robust coloring algorithms (that work on streams whose updates may depend on the algorithm's past outputs) are considerably weaker than standard randomized ones. However, there is still a significant gap between the upper and lower bounds for the number of colors needed (as a function of the maximum degree $\Delta$) for robust coloring and multipass deterministic coloring. We contribute to this line of work by proving the following results. In the deterministic semi-streaming (i.e., $O(n \cdot \text{polylog } n)$ space) regime, we present an algorithm that achieves a combinatorially optimal $(\Delta+1)$-coloring using $O(\log{\Delta} \log\log{\Delta})$ passes. This improves upon the prior $O(\Delta)$-coloring algorithm of Assadi, Chen, and Sun (STOC 2022) at the cost of only an $O(\log\log{\Delta})$ factor in the number of passes. In the adversarially robust semi-streaming regime, we design an $O(\Delta^{5/2})$-coloring algorithm that improves upon the previously best $O(\Delta^{3})$-coloring algorithm of Chakrabarti, Ghosh, and Stoeckl (ITCS 2022). Further, we obtain a smooth colors/space tradeoff that improves upon another algorithm of the said work: whereas their algorithm uses $O(\Delta^2)$ colors and $O(n\Delta^{1/2})$ space, ours, in particular, achieves (i)~$O(\Delta^2)$ colors in $O(n\Delta^{1/3})$ space, and (ii)~$O(\Delta^{7/4})$ colors in $O(n\Delta^{1/2})$ space., Comment: 29 pages

Many problems on data streams have been studied at two extremes of difficulty: either allowing randomized algorithms, in the static setting (where they should err with bounded probability on the worst case stream); or when only deterministic and infallible algorithms are required. Some recent works have considered the adversarial setting, in which a randomized streaming algorithm must succeed even on data streams provided by an adaptive adversary that can see the intermediate outputs of the algorithm. In order to better understand the differences between these models, we study a streaming task called "Missing Item Finding". In this problem, for $r < n$, one is given a data stream $a_1,\ldots,a_r$ of elements in $[n]$, (possibly with repetitions), and must output some $x \in [n]$ which does not equal any of the $a_i$. We prove that, for $r = n^{\Theta(1)}$ and $\delta = 1/\mathrm{poly}(n)$, the space required for randomized algorithms that solve this problem in the static setting with error $\delta$ is $\Theta(\mathrm{polylog}(n))$; for algorithms in the adversarial setting with error $\delta$, $\Theta((1 + r^2 / n) \mathrm{polylog}(n))$; and for deterministic algorithms, $\Theta(r / \mathrm{polylog}(n))$. Because our adversarially robust algorithm relies on free access to a string of $O(r \log n)$ random bits, we investigate a "random start" model of streaming algorithms where all random bits used are included in the space cost. Here we find a conditional lower bound on the space usage, which depends on the space that would be needed for a pseudo-deterministic algorithm to solve the problem. We also prove an $\Omega(r / \mathrm{polylog}(n))$ lower bound for the space needed by a streaming algorithm with $< 1/2^{\mathrm{polylog}(n)}$ error against "white-box" adversaries that can see the internal state of the algorithm, but not predict its future random decisions., Comment: 34 pages, 6 figures, Full version of a paper accepted to SODA 2023

We generate synthetic images with the "Stable Diffusion" image generation model using the Wordnet taxonomy and the definitions of concepts it contains. This synthetic image database can be used as training data for data augmentation in machine learning applications, and it is used to investigate the capabilities of the Stable Diffusion model. Analyses show that Stable Diffusion can produce correct images for a large number of concepts, but also a large variety of different representations. The results show differences depending on the test concepts considered and problems with very specific concepts. These evaluations were performed using a vision transformer model for image classification.

A streaming algorithm is considered to be adversarially robust if it provides correct outputs with high probability even when the stream updates are chosen by an adversary who may observe and react to the past outputs of the algorithm. We grow the burgeoning body of work on such algorithms in a new direction by studying robust algorithms for the problem of maintaining a valid vertex coloring of an $n$-vertex graph given as a stream of edges. Following standard practice, we focus on graphs with maximum degree at most $\Delta$ and aim for colorings using a small number $f(\Delta)$ of colors. A recent breakthrough (Assadi, Chen, and Khanna; SODA~2019) shows that in the standard, non-robust, streaming setting, $(\Delta+1)$-colorings can be obtained while using only $\widetilde{O}(n)$ space. Here, we prove that an adversarially robust algorithm running under a similar space bound must spend almost $\Omega(\Delta^2)$ colors and that robust $O(\Delta)$-coloring requires a linear amount of space, namely $\Omega(n\Delta)$. We in fact obtain a more general lower bound, trading off the space usage against the number of colors used. From a complexity-theoretic standpoint, these lower bounds provide (i)~the first significant separation between adversarially robust algorithms and ordinary randomized algorithms for a natural problem on insertion-only streams and (ii)~the first significant separation between randomized and deterministic coloring algorithms for graph streams, since deterministic streaming algorithms are automatically robust. We complement our lower bounds with a suite of positive results, giving adversarially robust coloring algorithms using sublinear space. In particular, we can maintain an $O(\Delta^2)$-coloring using $\widetilde{O}(n \sqrt{\Delta})$ space and an $O(\Delta^3)$-coloring using $\widetilde{O}(n)$ space.

Two widely-used computational paradigms for sublinear algorithms are using linear measurements to perform computations on a high dimensional input and using structured queries to access a massive input. Typically, algorithms in the former paradigm are non-adaptive whereas those in the latter are highly adaptive. This work studies the fundamental search problem of \textsc{element-extraction} in a query model that combines both: linear measurements with bounded adaptivity. In the \textsc{element-extraction} problem, one is given a nonzero vector $\mathbf{z} = (z_1,\ldots,z_n) \in \{0,1\}^n$ and must report an index $i$ where $z_i = 1$. The input can be accessed using arbitrary linear functions of it with coefficients in some ring. This problem admits an efficient nonadaptive randomized solution (through the well known technique of $\ell_0$-sampling) and an efficient fully adaptive deterministic solution (through binary search). We prove that when confined to only $k$ rounds of adaptivity, a deterministic \textsc{element-extraction} algorithm must spend $\Omega(k (n^{1/k} -1))$ queries, when working in the ring of integers modulo some fixed $q$. This matches the corresponding upper bound. For queries using integer arithmetic, we prove a $2$-round $\widetilde{\Omega}(\sqrt{n})$ lower bound, also tight up to polylogarithmic factors. Our proofs reduce to classic problems in combinatorics, and take advantage of established results on the {\em zero-sum problem} as well as recent improvements to the {\em sunflower lemma}.

In April 2020, the QUality Assessment and REProducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) initiative was formed. This initiative comprises imaging scientists from academia and industry who share a common interest in achieving a better understanding of the performance and limitations of microscopes and improved quality control (QC) in light microscopy. The ultimate goal of the QUAREP-LiMi initiative is to establish a set of common QC standards, guidelines, metadata models, and tools, including detailed protocols, with the ultimate aim of improving reproducible advances in scientific research. This White Paper 1) summarizes the major obstacles identified in the field that motivated the launch of the QUAREP-LiMi initiative; 2) identifies the urgent need to address these obstacles in a grassroots manner, through a community of stakeholders including, researchers, imaging scientists, bioimage analysts, bioimage informatics developers, corporate partners, funding agencies, standards organizations, scientific publishers, and observers of such; 3) outlines the current actions of the QUAREP-LiMi initiative, and 4) proposes future steps that can be taken to improve the dissemination and acceptance of the proposed guidelines to manage QC. To summarize, the principal goal of the QUAREP-LiMi initiative is to improve the overall quality and reproducibility of light microscope image data by introducing broadly accepted standard practices and accurately captured image data metrics., Comment: 17 pages, 3 figures, shortened abstract, Co-Lead Authors: Glyn Nelson and Ulrike Boehm, Corresponding author: Roland Nitschke

The high flexibility of organic molecules offers great potential for designing the optical properties of light-active materials for the next generation of optoelectronic and photonic applications. However, despite successful implementations of molecular materials in todays' display and photovoltaic technology, many fundamental aspects of the light-to-charge conversion have still to be uncovered. Here, we focus on the ultrafast dynamics of optically excited excitons in C$_{60}$ thin films depending on the molecular coverage and the light-polarization of the optical excitons. Using time- and momentum-resolved photoemission with fs-XUV radiation, we follow the depopulation dynamics in the excited states while simultaneously monitoring the signatures of the excitonic charge character in the molecular valence states. Optical excitation with visible light results in the instantaneous formation of charge-transfer (CT) excitons, which transform stepwise into energetically lower Frenkel-like excitons. While the number and energetic position of energy levels within this cascade process are independent of the molecular coverage and the light polarization of the optical excitation, we find quantitative differences in the depopulation times and the optical excitation efficiency. Our comprehensive study reveals the crucial role of CT excitons for the excited state dynamics of homo-molecular fullerene materials and thin films.

Spike-based neuromorphic hardware promises to reduce the energy consumption of image classification and other deep learning applications, particularly on mobile phones or other edge devices. However, direct training of deep spiking neural networks is difficult, and previous methods for converting trained artificial neural networks to spiking neurons were inefficient because the neurons had to emit too many spikes. We show that a substantially more efficient conversion arises when one optimizes the spiking neuron model for that purpose, so that it not only matters for information transmission how many spikes a neuron emits, but also when it emits those spikes. This advances the accuracy that can be achieved for image classification with spiking neurons, and the resulting networks need on average just two spikes per neuron for classifying an image. In addition, our new conversion method improves latency and throughput of the resulting spiking networks., Comment: 23 pages, 5 figures, 1 tables

In order to port the performance of trained artificial neural networks (ANNs) to spiking neural networks (SNNs), which can be implemented in neuromorphic hardware with a drastically reduced energy consumption, an efficient ANN to SNN conversion is needed. Previous conversion schemes focused on the representation of the analog output of a rectified linear (ReLU) gate in the ANN by the firing rate of a spiking neuron. But this is not possible for other commonly used ANN gates, and it reduces the throughput even for ReLU gates. We introduce a new conversion method where a gate in the ANN, which can basically be of any type, is emulated by a small circuit of spiking neurons, with At Most One Spike (AMOS) per neuron. We show that this AMOS conversion improves the accuracy of SNNs for ImageNet from 74.60% to 80.97%, thereby bringing it within reach of the best available ANN accuracy (85.0%). The Top5 accuracy of SNNs is raised to 95.82%, getting even closer to the best Top5 performance of 97.2% for ANNs. In addition, AMOS conversion improves latency and throughput of spike-based image classification by several orders of magnitude. Hence these results suggest that SNNs provide a viable direction for developing highly energy efficient hardware for AI that combines high performance with versatility of applications., Comment: 14 pages, 7 figures

As an alternative inertial confinement fusion scheme with predicted high energy gain and more robust designs, shock ignition requires a strong converging shock driven by a shaped pulse with a high-intensity spike at the end to ignite a pre-compressed fusion capsule. Understanding nonlinear laser-plasma instabilities in shock ignition conditions is crucial to assess and improve the laser-shock energy coupling. Recent experiments conducted on the OMEGA-EP laser facility have for the first time demonstrated that such instabilities can $\sim$100\% deplete the first 0.5 ns of the high-intensity laser pump. Analysis of the observed laser-generated blast wave suggests that this pump-depletion starts at 0.01--0.02 critical density and progresses to 0.1--0.2 critical density. This pump-depletion is also confirmed by the time-resolved stimulated Raman backscattering spectra. The dynamics of the pump-depletion can be explained by the breaking of ion-acoustic waves in stimulated Brillouin scattering. Such strong pump-depletion would inhibit the collisional laser energy absorption but may benefit the generation of hot electrons with moderate temperatures for electron shock ignition [Shang et al. Phys. Rev. Lett. 119 195001 (2017)].

We built models with Logistic Regression and linear Support Vector Machines on a large dataset consisting of regular news articles and news from satirical websites, and showed that such linear classifiers on a corpus with about 60,000 articles can perform with a precision of 98.7% and a recall of 95.2% on a random test set of the news. On the other hand, when testing the classifier on "publication sources" which are completely unknown during training, only an accuracy of 88.2% and an F1-score of 76.3% are achieved. As another result, we showed that the same algorithm can distinguish between news written by the news agency itself and paid articles from customers. Here the results had an accuracy of 99%.

For the webportal "Who is in the News!" with statistics about the appearence of persons in written news we developed an extension, which measures the relationship of public persons depending on a time parameter, as the relationship may vary over time. On a training corpus of English and German news articles we built a measure by extracting the persons occurrence in the text via pretrained named entity extraction and then construct time series of counts for each person. Pearson correlation over a sliding window is then used to measure the relation of two persons.

This paper describes a technique for identifying trends in performance degradation for inertial confinement fusion implosion experiments. It is based on reconstruction of the implosion core with a combination of low- and mid-mode asymmetries. This technique was applied to an ensemble of hydro-equivalent deuterium-tritium implosions on OMEGA that achieved inferred hot-spot pressures ~56+/-7 Gbar [S. Regan et al., Phys. Rev. Lett. 117, 025001 (2016)]. All the experimental observables pertaining to the core could be reconstructed simultaneously with the same combination of low and mid modes. This suggests that in addition to low modes, that can cause a degradation of the stagnation pressure, mid modes are present that reduce the size of the neuron and x-ray producing volume. The systematic analysis shows that asymmetries can cause an overestimation of the total areal density in these implosions. It is also found that an improvement in implosion symmetry resulting from correction of either the systematic mid or low modes would result in an increase of the hot-spot pressure from 56 Gbar to ~80 Gbar and could produce a burning plasma when the implosion core is extrapolated to an equivalent 1.9 MJ symmetric direct illumination [A. Bose et al., Phys. Rev. E 94, 011201(R) (2016)].

Magnetic reconnection is a fundamental plasma process involving an exchange of magnetic energy to plasma kinetic energy through changes in the magnetic field topology. In many astrophysical plasmas magnetic reconnection plays a key role in the release of large amounts of energy \cite{hoshino1}, although making direct measurements is challenging in the case of high-energy astrophysical systems such as pulsar wind emissions \cite{lyubarsky1}, gamma-ray bursts \cite{thompson1}, and jets from active galactic nuclei \cite{liu1}. Therefore, laboratory studies of magnetic reconnection provide an important platform for testing theories and characterising different regimes. Here we present experimental measurements as well as numerical modeling of relativistic magnetic reconnection driven by short-pulse, high-intensity lasers that produce relativistic plasma along with extremely strong magnetic fields. Evidence of magnetic reconnection was identified by the plasma's X-ray emission patterns, changes to the electron energy spectrum, and by measuring the time over which reconnection occurs. Accessing these relativistic conditions in the laboratory allows for further investigation that may provide insight into unresolved areas in space and astro-physics.

DNA autoionization is a fundamental process wherein UV-photoexcited nucleobases dissipate energy by charge transfer to the environment without undergoing chemical damage. Here, single-wall carbon nanotubes (SWNT) are explored as a photoluminescent reporter for studying the mechanism and rates of DNA autoionization. Two-color photoluminescence spectroscopy allows separate photoexcitation of the DNA and the SWNTs in the UV and visible range, respectively. A strong SWNT photoluminescence quenching is observed when the UV pump is resonant with the DNA absorption, consistent with charge transfer from the excited states of the DNA to the SWNT. Semiempirical calculations of the DNA-SWNT electronic structure, combined with a Green's function theory for charge transfer, show a 20 fs autoionization rate, dominated by the hole transfer. Rate-equation analysis of the spectroscopy data confirms that the quenching rate is limited by the thermalization of the free charge carriers transferred to the nanotube reservoir. The developed approach has a great potential for monitoring DNA excitation, autoionization, and chemical damage both {\it in vivo} and {\it in vitro}., Comment: Nano Research, ASAP

We present a simulation setup for studying the dynamical and chemical evolution of the intracluster medium (ICM) and analyze a sample of 12 galaxy clusters that are diverse both kinetically (pre-merger, merging, virialized) and in total mass (M vir = 1.17 x 10^14 - 1.06 x 10^15 M). We analyzed the metal mass fraction in the ICM as a function of redshift and discuss radial trends as well as projected 2D metallicity maps. The setup combines high mass resolution N-body simulations with the semi-analytical galaxy formation model Galacticus for consistent treatment of the subgrid physics (such as galactic winds and ram-pressure stripping) in the cosmological hydrodynamical simulations. The interface between Galacticus and the hydro simulation of the ICM with FLASH is discussed with respect to observations of star formation rate histories, radial star formation trends in galaxy clusters, and the metallicity at different redshifts. As a test for the robustness of the wind model, we compare three prescriptions from different approaches. For the wind model directly taken from Galacticus, we find mean ICM metallicities between 0.2 - 0.8Z within the inner 1Mpc at z = 0. The main contribution to the metal mass fraction comes from galactic winds. The outflows are efficiently mixed in the ICM, leading to a steady homogenization of metallicities until ram-pressure stripping becomes effective at low redshifts. We find a very peculiar and yet common drop in metal mass fractions within the inner ~200kpc of the cool cores, which is due to a combination of wind suppression by outer pressure within our model and a lack of mixing after the formation of these dense regions.

Non-classical interference of photons lies at the heart of optical quantum information processing. This effect is exploited in universal quantum gates as well as in purpose-built quantum computers that solve the BosonSampling problem. Although non-classical interference is often associated with perfectly indistinguishable photons this only represents the degenerate case, hard to achieve under realistic experimental conditions. Here we exploit tunable distinguishability to reveal the full spectrum of multi-photon non-classical interference. This we investigate in theory and experiment by controlling the delay times of three photons injected into an integrated interferometric network. We derive the entire coincidence landscape and identify transition matrix immanants as ideally suited functions to describe the generalized case of input photons with arbitrary distinguishability. We introduce a compact description by utilizing a natural basis which decouples the input state from the interferometric network, thereby providing a useful tool for even larger photon numbers.