Search

Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations., Comment: Accepted as a short paper at the 5th Molecular Machine Learning Conference (MoML 2024)

We introduce a new parallelized approach to photothermal microscopy that utilizes mid-infrared dual-comb lasers, enabling simultaneous measurements at hundreds of wavelengths. This technology allows for high-speed, label-free chemical identification with super-resolution infrared imaging, overcoming the limitations of slow, single-wavelength-laser methods.

The mechanical properties of metal-organic frameworks (MOFs) are of high fundamental and also practical relevance. A particularly intriguing technique for determining anisotropic elastic tensors is Brillouin scattering, which so far has rarely been used for highly complex materials like MOFs. In the present contribution, we apply this technique to study a newly synthesized MOF-type material, referred to as GUT2. We show that when combining the experiments with state-of-the-art simulations of elastic properties and phonon bands (based on machine-learned force fields and dispersion-corrected density-functional theory). This provides a comprehensive understanding of the experimental signals, which are correlated with the longitudinal and transverse sound velocities. Moreover, even when dealing with comparably small single crystals, which limit the range of accessible experimental data, combining the insights from simulations and experiments allows the determination of approximate values for the components of the elastic tensor of the studied material.

Mid-infrared microscopy is an important tool for biological analyses, allowing a direct probe of molecular bonds in their low energy landscape. In addition to the label-free extraction of spectroscopic information, the application of broadband sources can provide a third dimension of chemical specificity. However, to enable widespread deployment, mid-infrared microscopy platforms need to be compact and robust while offering high speed, broad bandwidth and high signal-to-noise ratio (SNR). In this study, we experimentally showcase the integration of a broadband, high-repetition-rate dual-comb spectrometer (DCS) in the mid-infrared range with a scanning microscope. We employ a set of 1-GHz mid-infrared frequency combs, demonstrating their capability for high-speed and broadband hyperspectral imaging of polymers and ovarian tissue. The system covers 1000 $\mathrm{cm^{-1}}$ at $\mathrm{\nu_c=2941 \; cm^{-1}}$ with 12.86 kHz spectra acquisition rate and 5 $\mathrm{\mu m}$ spatial resolution. Taken together, our experiments and analysis elucidate the trade-off between bandwidth and speed in DCS as it relates to microscopy. This provides a roadmap for the future advancement and application of high-repetition-rate DCS hyperspectral imaging.

This paper introduces the Bosch street dataset (BSD), a novel multi-modal large-scale dataset aimed at promoting highly automated driving (HAD) and advanced driver-assistance systems (ADAS) research. Unlike existing datasets, BSD offers a unique integration of high-resolution imaging radar, lidar, and camera sensors, providing unprecedented 360-degree coverage to bridge the current gap in high-resolution radar data availability. Spanning urban, rural, and highway environments, BSD enables detailed exploration into radar-based object detection and sensor fusion techniques. The dataset is aimed at facilitating academic and research collaborations between Bosch and current and future partners. This aims to foster joint efforts in developing cutting-edge HAD and ADAS technologies. The paper describes the dataset's key attributes, including its scalability, radar resolution, and labeling methodology. Key offerings also include initial benchmarks for sensor modalities and a development kit tailored for extensive data analysis and performance evaluation, underscoring our commitment to contributing valuable resources to the HAD and ADAS research community.

Ash dieback (ADB) is threatening populations of European ash (Fraxinus excelsior & F. angustifolia) for more than three decades. Although much knowledge has been gathered in the recent past, practical conservation measures have been mostly implemented at local scale. Since range contraction in both ash species will be exacerbated in the near future by westward expansion of the emerald ash borer and climate change, systematic conservation frameworks need to be developed to avoid long-term population-genetic consequences and depletion of genomic diversity. In this article, we address the advantages and obstacles of conservation approaches aiming to conserve genetic diversity in-situ or ex-situ during tree pandemics. We are reviewing 47 studies which were published on ash dieback to unravel three important dimensions of ongoing conservation approaches or perceived conservation problems: i) conservation philosophy (i.e. natural selection, resistance breeding or genetic conservation), ii) the spatial scale (ecosystem, country, continent), and iii) the integration of genetic safety margins in conservation planning. Although nearly equal proportions of the reviewed studies mention breeding or active conservation as possible long-term solutions, only 17% consider that additional threats exist which may further reduce genetic diversity in both ash species. We also identify and discuss several knowledge gaps and limitations which may have limited the initiation of conservation projects at national and international level so far. Finally, we demonstrate that there is not much time left for filling these gaps, because European-wide forest health monitoring data indicates a significant decline of ash populations in the last 5 years.

Accurate diagnostics of the combustor region of ramjet engines can improve engine design and create benchmarks for computational fluid dynamics models. Previous works demonstrate that dual frequency comb spectroscopy can provide low uncertainty diagnostics of multiple flow parameters in the non-combusting regions of ramjets. However, the high temperatures present in the combustor present a challenge for broadband spectroscopic absorption models that are used to interpret measurements in these regions. Here, we utilize a new water vapor absorption database created for high temperature water-air mixtures to fit spectra measured in a ground-test ramjet engine with a broadband near-infrared dual comb absorption spectrometer. We extract 2D profiles of pressure, temperature, water mole fraction, and velocity using this new database. We demonstrate that the new database provides the lowest fit residuals compared to other water vapor absorption databases. We compare computational fluid dynamics simulations of the combustor with the measured data to demonstrate that the simulations overpredict heat release and water vapor production., Comment: This work has been cleared by the Air Force under case number AFRL-2023-4205

Laser absorption Doppler velocimeters use a crossed-beam configuration to cancel error due to laser frequency drift and absorption model uncertainty. This configuration complicates the spatial interpretation of the measurement since the two beams sample different volumes of gas. Here, we achieve single-beam velocimetry with a portable dual comb spectrometer (DCS) with high frequency accuracy and stability enabled by GPS-referencing, and a new high-temperature water vapor absorption database. We measure the inlet flow in a supersonic ramjet engine and demonstrate single-beam measurements that are on average within 19 m/s of concurrent crossed-beam measurements. We estimate that the DCS and the new database contribute 1.6 and 13 m/s to this difference respectively., Comment: This work has been cleared by the Air Force under case number AFRL-2023-4805

We present broadband dual frequency comb laser absorption measurements of 2% H$_2$O (natural isotopic abundance of 99.7% H$_2^{16}$O) in air from 6600-7650 cm$^{-1}$ (1307-1515 nm) with a spectral point spacing of 0.0068 cm$^{-1}$. Twenty-nine datasets were collected at temperatures between 300 and 1300 K ($\pm$0.82% average uncertainty) and pressures ranging from 20 to 600 Torr ($\pm$0.25%) with an average residual absorbance noise of 8.0E-4 across the spectrum for all measurements. We fit measurements using a quadratic speed-dependent Voigt profile to determine 7088 absorption parameters for 3366 individual transitions found in HITRAN2020. These measurements build on the line strength, line center, self-broadening, and self-shift parameters determined in the Part I companion of this work. Here we measure air-broadened width (with temperature- and speed-dependence) and air pressure shift (with temperature dependence) parameters. Various trends are explored for extrapolation to weak transitions that were not covered in this work. Improvements made in this work are predominantly due to the inclusion of air pressure shift temperature dependence values. In aggregate, these updates improved RMS absorbance error by a factor of 4.2 on average, and the remaining residual is predominantly spectral noise. This updated database improves high temperature spectroscopic knowledge across the 6600 7650 cm$^{-1}$ region of H$_2$O absorption., Comment: Database files available upon request. Will be included with published manuscript following review process

We characterize the epimorphisms in homotopy type theory (HoTT) as the fiberwise acyclic maps and develop a type-theoretic treatment of acyclic maps and types in the context of synthetic homotopy theory as developed in univalent foundations. We present examples and applications in group theory, such as the acyclicity of the Higman group, through the identification of groups with 0-connected, pointed 1-types. Many of our results are formalized as part of the agda-unimath library., Comment: To appear in The Journal of Symbolic Logic

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods to aid the novice and experienced researcher. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.

Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid materials, which are interesting for a wide range of possible applications. For a reliable description of many of their properties accurate computationally highly efficient methods, like force-field potentials (FFPs), are required. With the advent of machine learning approaches, it is now possible to generate such potentials with relatively little human effort. Here, we present a recipe to parametrize two fundamentally different types of exceptionally accurate and computationally highly efficient machine learned potentials, which belong to the moment-tensor and kernel-based potential families. They are parametrized relying on reference configurations generated in the course of molecular dynamics based, active learning runs and their performance is benchmarked for a representative selection of commonly studied MOFs. For both potentials, comparison to a random set of validation structures reveals close to DFT precision in predicted forces and structural parameters of all MOFs. Essentially the same applies to elastic constants and phonon band structures. Additionally, for MOF-5 the thermal conductivity is obtained with full quantitative agreement to single-crystal experiments. All this is possible while maintaining a high degree of computational efficiency, with the obtained machine learned potentials being only moderately slower than the extremely simple UFF4MOF or Dreiding force fields. The exceptional accuracy of the presented FFPs combined with their computational efficiency has the potential of lifting the computational modelling of MOFs to the next level.

Molecular diagnostics are a primary tool of modern chemistry, enabling researchers to map chemical reaction pathways and rates to better design and control chemical systems. Many chemical reactions are complex and fast, and existing diagnostic approaches provide incomplete information. For example, mass spectrometry is optimized to gather snapshots of the presence of many chemical species, while conventional laser spectroscopy can quantify a single chemical species through time. Here we optimize for multiple objectives by introducing a high-speed and broadband, mid-infrared dual frequency comb absorption spectrometer. The optical bandwidth of >1000 cm-1 covers absorption fingerprints of many species with spectral resolution <0.03 cm-1 to accurately discern their absolute quantities. Key to this advance are 1 GHz pulse repetition rate frequency combs covering the 3-5 um region that enable microsecond tracking of fast chemical process dynamics. We demonstrate this system to quantify the abundances and temperatures of each species in the complete reactants-to-products breakdown of 1,3,5-trioxane, which exhibits a formaldehyde decomposition pathway that is critical to modern low temperature combustion systems. By maximizing the number of observed species and improving the accuracy of temperature and concentration measurements, this spectrometer advances understanding of chemical reaction pathways and rates and opens the door for novel developments such as combining high-speed chemistry with machine learning.

Managing divertor plasmas is crucial for operating reactor scale tokamak devices due to heat and particle flux constraints on the divertor target. Simulation is an important tool to understand and control these plasmas, however, for real-time applications or exhaustive parameter scans only simple approximations are currently fast enough. We address this lack of fast simulators using neural PDE surrogates, data-driven neural network-based surrogate models trained using solutions generated with a classical numerical method. The surrogate approximates a time-stepping operator that evolves the full spatial solution of a reference physics-based model over time. We use DIV1D, a 1D dynamic model of the divertor plasma, as reference model to generate data. DIV1D's domain covers a 1D heat flux tube from the X-point (upstream) to the target. We simulate a realistic TCV divertor plasma with dynamics induced by upstream density ramps and provide an exploratory outlook towards fast transients. State-of-the-art neural PDE surrogates are evaluated in a common framework and extended for properties of the DIV1D data. We evaluate (1) the speed-accuracy trade-off; (2) recreating non-linear behavior; (3) data efficiency; and (4) parameter inter- and extrapolation. Once trained, neural PDE surrogates can faithfully approximate DIV1D's divertor plasma dynamics at sub real-time computation speeds: In the proposed configuration, 2ms of plasma dynamics can be computed in $\approx$0.63ms of wall-clock time, several orders of magnitude faster than DIV1D., Comment: Published in Nuclear Fusion

In univalent mathematics there are at least two equivalent ways to present the category of groups. Groups presented in their usual algebraic form are called abstract groups, and groups presented as pointed connected $1$-types are called concrete groups. Since these two descriptions of the category of groups are equivalent, we find that every algebraic group corresponds uniquely to a concrete group -- its delooping -- and that each abstract group homomorphisms corresponds uniquely to a pointed map between concrete groups. The $n$-th abstract symmetric group $S_n$ of all bijections $[n]\simeq [n]$, for instance, corresponds to the concrete group of all $n$-element types. The sign homomorphism from $S_n$ to $S_2$ should therefore correspond to a pointed map from the type $BS_n$ of all $n$-element types to the type $BS_2$ of all $2$-element types. Making use of the univalence axiom, we characterize precisely when a pointed map $BS_n\to_\ast BS_2$ is a delooping of the sign homomorphism. Then we proceed to give several constructions of the delooping of the sign homomorphism. Notably, the construction following a method of Cartier can be given without reference to the sign homomorphism. Our results are formalized in the agda-unimath library.

We introduce and study central types, which are generalizations of Eilenberg-Mac Lane spaces. A type is central when it is equivalent to the component of the identity among its own self-equivalences. From centrality alone we construct an infinite delooping in terms of a tensor product of banded types, which are the appropriate notion of torsor for a central type. Our constructions are carried out in homotopy type theory, and therefore hold in any $\infty$-topos. Even when interpreted into the $\infty$-topos of spaces, our main results and constructions are new. Along the way, we further develop the theory of H-spaces in homotopy type theory, including their relation to evaluation fibrations and Whitehead products. These considerations let us, for example, rule out the existence of H-space structures on the $2n$-sphere for $n > 0$. We also give a novel description of the moduli space of H-space structures on an H-space. Using this description, we generalize a formula of Arkowitz-Curjel and Copeland for counting the number of path components of this moduli space. As an application, we deduce that the moduli space of H-space structures on the $3$-sphere is $\Omega^6 \mathbb{S}^3$., Comment: v1: 22 pages; v2: 25 pages, with many improvements and additions

This is an introductory textbook to univalent mathematics and homotopy type theory, a mathematical foundation that takes advantage of the structural nature of mathematical definitions and constructions. It is common in mathematical practice to consider equivalent objects to be the same, for example, to identify isomorphic groups. In set theory it is not possible to make this common practice formal. For example, there are as many distinct trivial groups in set theory as there are distinct singleton sets. Type theory, on the other hand, takes a more structural approach to the foundations of mathematics that accommodates the univalence axiom. This, however, requires us to rethink what it means for two objects to be equal. This textbook introduces the reader to Martin-L\"of's dependent type theory, to the central concepts of univalent mathematics, and shows the reader how to do mathematics from a univalent point of view. Over 200 exercises are included to train the reader in type theoretic reasoning. The book is entirely self-contained, and in particular no prior familiarity with type theory or homotopy theory is assumed.