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Nematic quantum fluids appear in strongly interacting systems and break the rotational symmetry of the crystallographic lattice. In metals, this is connected to a well-known instability of the Fermi liquid-the Pomeranchuk instability. Using scanning tunneling microscopy, we identified this instability in a highly unusual setting: on the surface of an elemental topological metal, arsenic. By directly visualizing the Fermi surface of the surface state via scanning tunneling spectroscopy and photoemission spectroscopy, we find that the Fermi surface gets deformed and becomes elliptical at the energies where the nematic state is present. Known instances of nematic instability typically need van-Hove singularities or multi-orbital physics as drivers. In contrast, the surface states of arsenic are essentially indistinguishable from well-confined isotropic Rashba bands near the Fermi level, rendering our finding the first realization of Pomeranchuk instability of the topological surface state.

Recent experiments and theoretical studies strongly indicate that water exhibits a liquid-liquid phase transition (LLPT) in the supercooled domain. An open question is how the LLPT of water can affect the properties of aqueous solutions. Here, we study the structural and thermodynamic properties of supercooled glycerol-water microdroplets at dilute conditions ($\chi_g=3.2~\%$ glycerol mole fraction). The combination of rapid evaporative cooling with ultrafast X-ray scattering allows us to outrun crystallization and gain access to the deeply supercooled regime down to $T=229.3$ K. We find that the density fluctuations of the glycerol-water solution or, equivalently, its isothermal compressibility, $\kappa_T$, increases upon cooling. This is confirmed by molecular dynamics simulations, which indicate that the presence of glycerol shifts the temperature of maximum $\kappa_T$ from $T=230$ K in pure water down to $T=223$ K in the solution. Our findings elucidate the interplay between the complex behavior of water, including its LLPT, and the properties of aqueous solutions at low temperatures, which can have practical consequences in cryogenic biological applications and cryopreservation techniques.

Understanding protein motion within the cell is crucial for predicting reaction rates and macromolecular transport in the cytoplasm. A key question is how crowded environments affect protein dynamics through hydrodynamic and direct interactions at molecular length scales. Using megahertz X-ray Photon Correlation Spectroscopy (MHz-XPCS) at the European X-ray Free Electron Laser (EuXFEL), we investigate ferritin diffusion at microsecond time scales. Our results reveal anomalous diffusion, indicated by the non-exponential decay of the intensity autocorrelation function $g_2(q,t)$ at high concentrations. This behavior is consistent with the presence of cage-trapping in between the short- and long-time protein diffusion regimes. Modeling with the $\delta\gamma$-theory of hydrodynamically interacting colloidal spheres successfully reproduces the experimental data by including a scaling factor linked to the protein direct interactions. These findings offer new insights into the complex molecular motion in crowded protein solutions, with potential applications for optimizing ferritin-based drug delivery, where protein diffusion is the rate-limiting step.

Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have shown promising results in forecasting on common time series benchmark datasets. However, application to supply chain demand forecasting, which can have challenging characteristics such as sparsity and cross-series effects, has been limited. In this work, we explore the application of Transformer-based models to supply chain demand forecasting. In particular, we develop a new Transformer-based forecasting approach using a shared, multi-task per-time series network with an initial component applying attention across time series, to capture interactions and help address sparsity. We provide a case study applying our approach to successfully improve demand prediction for a medical device manufacturing company. To further validate our approach, we also apply it to public demand forecasting datasets as well and demonstrate competitive to superior performance compared to a variety of baseline and state-of-the-art forecast methods across the private and public datasets.

We address a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns. Many existing methods either do not utilize the potential underlying structure in panel data or have limitations in the allowable treatment patterns. In this work, we propose and evaluate a new method that first partitions observations into disjoint clusters with similar treatment effects using a regression tree, and then leverages the (assumed) low-rank structure of the panel data to estimate the average treatment effect for each cluster. Our theoretical results establish the convergence of the resulting estimates to the true treatment effects. Computation experiments with semi-synthetic data show that our method achieves superior accuracy compared to alternative approaches, using a regression tree with no more than 40 leaves. Hence, our method provides more accurate and interpretable estimates than alternative methods.

Nonlinear interactions of spin-waves and their quanta, magnons, have emerged as prominent candidates for interference-based technology, ranging from quantum transduction to antiferromagnetic spintronics. Yet magnon multiplication in the terahertz (THz) spectral region represents a major challenge. Intense, resonant magnetic fields from THz pulse-pairs with controllable phases and amplitudes enable high order THz magnon multiplication, distinct from non-resonant nonlinearities such as the high harmonic generation by below-band gap electric fields. Here, we demonstrate exceptionally high-order THz nonlinear magnonics. It manifests as 7$^\text{th}$-order spin-wave-mixing and 6$^\text{th}$ harmonic magnon generation in an antiferromagnetic orthoferrite. We use THz multi-dimensional coherent spectroscopy to achieve high-sensitivity detection of nonlinear magnon interactions up to six-magnon quanta in strongly-driven many-magnon correlated states. The high-order magnon multiplication, supported by classical and quantum spin simulations, elucidates the significance of four-fold magnetic anisotropy and Dzyaloshinskii-Moriya symmetry breaking. Moreover, our results shed light on the potential quantum fluctuation properties inherent in nonlinear magnons.

Nonlinearities in quantum systems are fundamentally characterized by the interplay of phase coherences, their interference, and state transition amplitudes. Yet the question of how quantum coherence and interference manifest in transient, massive Higgs excitations, prevalent within both the quantum vacuum and superconductors, remains elusive. One hallmark example is photon echo, enabled by the generation, preservation, and retrieval of phase coherences amid multiple excitations. Here we reveal an unconventional quantum echo arising from the Higgs coherence in superconductors, and identify distinctive signatures attributed to Higgs anharmonicity. A terahertz pulse-pair modulation of the superconducting gap generates a "time grating" of coherent Higgs population, which scatters echo signals distinct from conventional spin- and photon-echoes in atoms and semiconductors. These manifestations appear as Higgs echo spectral peaks occurring at frequencies forbidden by equilibrium particle-hole symmetry, an asymmetric delay in the echo formation from the dynamics of the "reactive" superconducting state, and negative time signals arising from Higgs-quasiparticle anharmonic coupling. The Higgs interference and anharmonicity control the decoherence of driven superconductivity and may enable applications in quantum memory and entanglement.

We present a comprehensive theory of light-controlled multi-band superconductivity and apply it to predict distinctive signatures of light-driven superconducting (SC) states in terahertz multi-dimensional coherent spectroscopy (THz-MDCS) experiments. We first derive gauge-invariant Maxwell-Bloch equations for multi-band BCS superconductors. For this, we go beyond previously considered Anderson pseudo-spin precession models to include quantum transport effects. By calculating the THz-MDCS spectra measured experimentally, we then identify unique signatures of finite-momentum Cooper-pairing states that live longer than the laser pulse. These non-equilibrium SC states are characterized by long-lived canting of Anderson pseudo-spins. The pseudo-spin oscillators that describe the properties of these SC states are parametrically driven by both finite-momentum Cooper pairing and by time oscillations of the order parameter relative phase. We show that such strong parametric driving leads to drastic changes in the THz-MDCS spectral shape from the predictions of third-order nonlinear susceptibility calculations. These spectral changes strongly depend on the interband-to-intraband interaction ratio and on the collective modes of the light-driven state., Comment: 27 pages, 10 figures

The dynamics of Higgs mode in superconductors, manifested as coherent oscillations of the superconducting order parameter amplitude, provides vital insights into the nature of the superconducting gap structure and symmetry. Here we utilize two-dimensional terahertz coherent spectroscopy to investigate Higgs dynamics of a newly discovered infinite-layer nickelate superconductor. While we observe distinct nonlinear terahertz responses from the superconducting state, well-defined long-lived Higgs modes, as commonly observed in $s$-wave superconductors, are entirely absent in the nickelate film. Instead, we find the coherent nonlinear terahertz response is dominated by the quasiparticle excitations. These observations strongly indicate that the Higgs mode in infinite-layer nickelates is heavily damped by the quasiparticle excitations at arbitrarily low energies, which is a characteristic of $d$-wave pairing symmetry. Additionally, by examining the temperature dependence of the nonlinear terahertz response, we discover short-range superconducting fluctuations in the vicinity of $T_\mathrm{c}$. Our findings provide proof of a new $d$-wave system and establish a foundation for investigating the unconventional superconductivity in nickelates., Comment: 10 pages, 5 figures

The discovery of superconductivity in infinite-layer nickelates establishes a new category of unconventional superconductors that share structural and electronic similarities with cuprates. Despite exciting advances, such as the establishment of a cuprate-like phase diagram and the observation of charge order and short-range antiferromagnetic fluctuation, the key issues of superconducting pairing symmetry, gap amplitude, and superconducting fluctuation remain elusive. In this work, we utilize static and ultrafast terahertz spectroscopy to address these outstanding problems. We demonstrate that the equilibrium terahertz conductivity and nonequilibrium terahertz responses of an optimally Sr-doped nickelate film ($T_c$ = 17 K) are in line with the electrodynamics of $d$-wave superconductivity in the dirty limit. The gap-to-$T_c$ ratio 2$\Delta/k_\mathrm{B}T_\mathrm{c}$ is extracted to be 3.4, indicating the superconductivity falls in the weak-coupling regime. In addition, we observed significant superconducting fluctuation near $T_\mathrm{c}$, while it does not extend into the deep normal state as optimally hole-doped cuprates. Our result highlights a new $d$-wave system which closely resembles the electron-doped cuprates, expanding the family of unconventional superconductivity in oxides., Comment: 8 pages, 4 figures

A recently emerging concept for quantum phase discovery is the controlled gapping of linear band crossings in topological semimetals. For example, achieving topological superconducting and charge-density-wave (CDW) gapping could introduce Majorana zero modes and axion electrodynamics, respectively. Light engineering of correlation gaps in topological materials provides a new avenue of achieving exotic topological phases inaccessible by conventional tuning methods such as doping and straining. Here we demonstrate a light control of correlation gaps and ultrafast phase switchings in a model CDW and polaron insulator (TaSe$_4$)$_2$I recently predicted to be an axion insulator. Our ultrafast terahertz photocurrent spectroscopy reveals a two-step, non-thermal melting of polarons and electronic CDW gap via studying the fluence dependence of a {\em longitudinal} circular photogalvanic current. The helicity-dependent photocurrent observed along the propagation of light reveals continuous ultrafast switchings from the polaronic state, to the CDW (axion) phase, and finally to a hidden Weyl phase as the pump fluence increases. Other distinguishing features corroborating with the light-induced switchings include: mode-selective coupling of coherent phonons to polaron and CDW modulation, and the emergence of a {\em non-thermal} chiral photocurrent above pump threshold of CDW-related phonons. The ultrafast chirality control of correlated topological states revealed here is important to realize axion electrodynamics and quantum computing., Comment: 9 pages, 4 figures

Studying protein interactions at low temperatures has important implications for optimizing cryostorage processes of biological tissue, food, and protein-based drugs. One of the major challenges is related to the formation of ice nanocrystals which can occur even in the presence of cryoprotectants and can lead to protein denaturation. Here, using a combination of small- and wide-angle X-ray scattering (SAXS and WAXS), we investigate the structural evolution of concentrated Lysozyme solutions in a cryoprotected glycerol-water mixture upon cooling from room temperature (T=300 K) down to cryogenic temperatures (T=195 K). Upon cooling, we observe a transition near the melting temperature of the solution (T~245 K), which manifests both in the temperature dependence of the scattering intensity peak position reflecting protein-protein length scales (SAXS) and the interatomic distances within the solvent (WAXS). Upon thermal cycling, a hysteresis is observed in the scattering intensity, which is attributed to the formation of nanocrystallites in the order of 10 nm. The experimental data is well described by the two-Yukawa model, which indicates temperature-dependent changes in the short-range attraction of the protein-protein interaction potential. Our results demonstrate that the nanocrystal growth yields effectively stronger protein-protein attraction and influences the protein pair distribution function beyond the first coordination shell.

We focus on modeling the relationship between an input feature vector and the predicted outcome of a trained decision tree using mixed-integer optimization. This can be used in many practical applications where a decision tree or tree ensemble is incorporated into an optimization problem to model the predicted outcomes of a decision. We propose tighter mixed-integer optimization formulations than those previously introduced. Existing formulations can be shown to have linear relaxations that have fractional extreme points, even for the simple case of modeling a single decision tree. A formulation we propose, based on a projected union of polyhedra approach, is ideal for a single decision tree. While the formulation is generally not ideal for tree ensembles or if additional constraints are added, it generally has fewer extreme points, leading to a faster time to solve, particularly if the formulation has relatively few trees. However, previous work has shown that formulations based on a binary representation of the feature vector perform well computationally and hence are attractive for use in practical applications. We present multiple approaches to tighten existing formulations with binary vectors, and show that fractional extreme points are removed when there are multiple splits on the same feature. At an extreme, we prove that this results in ideal formulations for tree ensembles modeling a one-dimensional feature vector. Building on this result, we also show via numerical simulations that these additional constraints result in significantly tighter linear relaxations when the feature vector is low dimensional. We also present instances where the time to solve to optimality is significantly improved using these formulations.

The phase transition between water and ice is ubiquitous and one of the most important phenomena in nature. Here, we performed time-resolved x-ray scattering experiments capturing the melting and recrystallization dynamics of ice. The ultrafast heating of ice I is induced by an IR laser pulse and probed with an intense x-ray pulse, which provided us with direct structural information on different length scales. From the wide-angle x-ray scattering (WAXS) patterns, the molten fraction, as well as the corresponding temperature at each delay, were determined. The small-angle x-ray scattering (SAXS) patterns, together with the information extracted from the WAXS analysis, provided the time-dependent change of the size and the number of the liquid domains. The results show partial melting (~13 %) and superheating of ice occurring at around 20 ns. After 100 ns, the average size of the liquid domains grows from about 2.5 nm to 4.5 nm by the coalescence of approximately six adjacent domains. Subsequently, we capture the recrystallization of the liquid domains, which occurs on microsecond timescales due to the cooling by heat dissipation and results to a decrease of the average liquid domain size.

Hydrated proteins undergo a transition in the deeply supercooled regime, which is attributed to rapid changes in hydration water and protein structural dynamics. Here, we investigate the nanoscale stress relaxation in hydrated lysozyme proteins stimulated and probed by X-ray Photon Correlation Spectroscopy (XPCS). This approach allows us to access the nanoscale dynamic response in the deeply supercooled regime (T = 180 K) which is typically not accessible through equilibrium methods. The relaxation time constants exhibit Arrhenius temperature dependence upon cooling with a minimum in the Kohlrausch-Williams-Watts exponent at T = 227 K. The observed minimum is attributed to an increase in dynamical heterogeneity, which coincides with enhanced fluctuations observed in the two-time correlation functions and a maximum in the dynamic susceptibility quantified by the normalised variance $\chi_T$. Our study provides new insights into X-ray stimulated stress relaxation and the underlying mechanisms behind spatio-temporal fluctuations in biological granular materials.

The electronic excitation occurring on adsorbates at ultrafast time scales from optical lasers that initiate surface chemical reactions is still an open question. Here, we report the ultrafast temporal evolution of X-ray absorption spectroscopy (XAS) and X-ray emission spectroscopy (XES) of a simple well known adsorbate prototype system, namely carbon (C) atoms adsorbed on a nickel (Ni(100)) surface, following intense laser optical pumping at 400 nm. We observe ultrafast (~100 fs) changes in both XAS and XES showing clear signatures of the formation of a hot electron-hole pair distribution on the adsorbate. This is followed by slower changes on a few ps time scale, shown to be consistent with thermalization of the complete C/Ni system. Density functional theory spectrum simulations support this interpretation., Comment: 33 pages, 12 figures. Submitted to Physical Review Letters

Lightwave periodic driving of nearly dissipation-less currents has recently emerged as a universal control concept for both superconducting (SC) and topological electronics applications. While exciting progress has been made towards THz-driven superconductivity, our understanding of the interactions able to drive non-equilibrium pairing is still limited, partially due to the lack of direct measurements of high-order correlation functions. Such measurements would exceed conventional single-particle spectroscopies and perturbative responses to fully characterize quantum states far-from-equilibrium. Particularly, sensing of the exotic collective modes that would uniquely characterize lightwave-driven SC coherence, in a way analogous to the Meissner effect, is very challenging but much needed. Here we report the discovery of lightwave-controlled superconductivity via parametric time-periodic driving of the strongly-coupled bands in iron-based superconductors by a unique phase-amplitude collective mode assisted by broken-symmetry THz supercurrents. We are able to measure non-perturbative, high-order correlations in this strongly-driven superconductivity by separating the THz multi-dimensional coherent spectra into conventional pump-probe, Higgs collective mode, and pronounced bi--Higgs frequency sideband peaks with highly nonlinear field dependence. We attribute the drastic transition in the coherent spectra to parametric excitation of time-dependent pseudo--spin canting states modulated by a phase-amplitude collective mode that manifests as a strongly nonlinear shift from $\omega_\mathrm{Higgs}$ to 2$\omega_\mathrm{Higgs}$. Remarkably, the latter higher--order sidebands dominate over the lower-order pump-probe and Higgs mode peaks above critical field, which indicates the breakdown of the susceptibility perturbative expansion in the parametrically-driven SC state.

Triangular meshes are the most popular representations of 3D objects, but many mesh surfaces contain topological singularities that represent a challenge for displaying or further processing them properly. One such singularity is the self-intersections that may be present in mesh surfaces that have been created by a scanning procedure or by a deformation transformation, such as off-setting. Mesh foldings comprise a special case of mesh surface self-intersections, where the faces of the 3D model intersect and become reversed, with respect to the unfolded part of the mesh surface. A novel method for the recognition and repair of mesh surface foldings is presented, which exploits the structural characteristics of the foldings in order to efficiently detect the folded regions. Following detection, the foldings are removed and any gaps so created are filled based on the geometry of the 3D model. The proposed method is directly applicable to simple mesh surface representations while it does not perform any embedding of the 3D mesh (i.e. voxelization, projection). Target of the proposed method is to facilitate mesh degradation procedures in a fashion that retains the original structure, given the operator, in the most efficient manner.

This paper introduces scalable, sampling-based algorithms that optimize trained neural networks with ReLU activations. We first propose an iterative algorithm that takes advantage of the piecewise linear structure of ReLU neural networks and reduces the initial mixed-integer optimization problem (MIP) into multiple easy-to-solve linear optimization problems (LPs) through sampling. Subsequently, we extend this approach by searching around the neighborhood of the LP solution computed at each iteration. This scheme allows us to devise a second, enhanced algorithm that reduces the initial MIP problem into smaller, easier-to-solve MIPs. We analytically show the convergence of the methods and we provide a sample complexity guarantee. We also validate the performance of our algorithms by comparing them against state-of-the-art MIP-based methods. Finally, we show computationally how the sampling algorithms can be used effectively to warm-start MIP-based methods., Comment: Review 2: Fixed typo in Table 1 and page 7. Bold values in Tables 2 and 4

X-ray free-electron lasers (XFELs) with megahertz repetition rate can provide novel insights into structural dynamics of biological macromolecule solutions. However, very high dose rates can lead to beam-induced dynamics and structural changes due to radiation damage. Here, we probe the dynamics of dense antibody protein (Ig-PEG) solutions using megahertz X-ray photon correlation spectroscopy (MHz-XPCS) at the European XFEL. By varying the total dose and dose rate, we identify a regime for measuring the motion of proteins in their first coordination shell, quantify XFEL-induced effects such as driven motion, and map out the extent of agglomeration dynamics. The results indicate that for average dose rates below $1.06\,\mathrm{kGy}\mathrm{\mu s}^{-1}$ in a time window up to $10\,\mathrm{\mu s}$, it is possible to capture the protein dynamics before the onset of beam induced aggregation. We refer to this approach as correlation before aggregation and demonstrate that MHz-XPCS bridges an important spatio-temporal gap in measurement techniques for biological samples., Comment: 22 pages, 6 figures

The unprecedented volume, diversity and richness of aviation data that can be acquired, generated, stored, and managed provides unique capabilities for the aviation-related industries and pertains value that remains to be unlocked with the adoption of the innovative Big Data Analytics technologies. Despite the large efforts and investments on research and innovation, the Big Data technologies introduce a number of challenges to its adopters. Besides the effective storage and access to the underlying big data, efficient data integration and data interoperability should be considered, while at the same time multiple data sources should be effectively combined by performing data exchange and data sharing between the different stakeholders. However, this reveals additional challenges for the crucial preservation of the information security of the collected data, the trusted and secure data exchange and data sharing, as well as the robust data access control. The current paper aims to introduce the ICARUS big data-enabled platform that aims provide a multi-sided platform that offers a novel aviation data and intelligence marketplace accompanied by a trusted and secure analytics workspace. It holistically handles the complete big data lifecycle from the data collection, data curation and data exploration to the data integration and data analysis of data originating from heterogeneous data sources with different velocity, variety and volume in a trusted and secure manner.

The aviation industry as well as the industries that benefit and are linked to it are ripe for innovation in the form of Big Data analytics. The number of available big data technologies is constantly growing, while at the same time the existing ones are rapidly evolving and empowered with new features. However, the Big Data era imposes the crucial challenge of how to effectively handle information security while managing massive and rapidly evolving data from heterogeneous data sources. While multiple technologies have emerged, there is a need to find a balance between multiple security requirements, privacy obligations, system performance and rapid dynamic analysis on large datasets. The current paper aims to introduce the ICARUS Secure Experimentation Sandbox of the ICARUS platform. The ICARUS platform aims to provide a big data-enabled platform that aspires to become an 'one-stop shop' for aviation data and intelligence marketplace that provides a trusted and secure 'sandboxed' analytics workspace, allowing the exploration, integration and deep analysis of original and derivative data in a trusted and fair manner. Towards this end, a Secure Experimentation Sandbox has been designed and integrated in the ICARUS platform offering, that enables the provisioning of a sophisticated environment that can completely guarantee the safety and confidentiality of data, allowing to any interested party to utilise the platform to conduct analytical experiments in closed-lab conditions.

We observe pronounced oscillations in differential reflectivity of 9 nm and 60 nm BaFe\textsubscript{2}As\textsubscript{2} (Ba-122) thin films using ultrafast optical spectroscopy. Our studies show that the oscillations result from propagating longitudinal acoustic (LA) phonon wave packets with strong thickness and temperature dependence. Particularly, the experimentally measured oscillation frequency approaches to 50 GHz for the ultra-thin film. Our calculations show that Young's modulus of 9 nm thin film is nearly four times as large as that of 60 nm thin film, consistent with the experiment. The increase in Young's modulus as thickness decrease was attributed to the decrease in parent Ba-122 tetragonality $c/a$ near the film-substrate interface due to material-substrate mismatch effect. %Temperature dependence of LA phonon mode frequency for 9 nm Ba-122 thin film is reported. The temperature-dependent change in LA phonon frequency was attributed to the change in parent Ba-122 othorhombicity $(a-b)/(a+b)$.

We use a pump-probe scheme to measure the time evolution of the C K-edge X-ray absorption spectrum (XAS) from CO/Ru(0001) after excitation by an ultrashort high-intensity optical laser pulse. Due to the short duration of the X-ray probe pulse and precise control of the pulse delay, the excitation-induced dynamics during the first ps after the pump can be resolved with unprecedented time resolution. By comparing with theoretical (DFT) spectrum calculations we find high excitation of the internal stretch and frustrated rotation modes occurring within 200 fs of laser excitation, as well as thermalization of the system in the ps regime. The ~100 fs initial excitation of these CO vibrational modes is not readily rationalized by traditional theories of nonadiabatic coupling of adsorbates to metal surfaces, e. g. electronic frictions based on first order electron-phonon coupling or transient population of adsorbate resonances. We suggest that coupling of the adsorbate to non-thermalized electron-hole pairs is responsible for the ultrafast initial excitation of the modes., Comment: 16 pages, 16 figures. To be published in Physical Review Letters: https://journals.aps.org/prl/accepted/c1070Y74M8b18063d9cd0221b000631d50ef7a249

Learning robust representations to discriminate cell phenotypes based on microscopy images is important for drug discovery. Drug development efforts typically analyse thousands of cell images to screen for potential treatments. Early works focus on creating hand-engineered features from these images or learn such features with deep neural networks in a fully or weakly-supervised framework. Both require prior knowledge or labelled datasets. Therefore, subsequent works propose unsupervised approaches based on generative models to learn these representations. Recently, representations learned with self-supervised contrastive loss-based methods have yielded state-of-the-art results on various imaging tasks compared to earlier unsupervised approaches. In this work, we leverage a contrastive learning framework to learn appropriate representations from single-cell fluorescent microscopy images for the task of Mechanism-of-Action classification. The proposed work is evaluated on the annotated BBBC021 dataset, and we obtain state-of-the-art results in NSC, NCSB and drop metrics for an unsupervised approach. We observe an improvement of 10% in NCSB accuracy and 11% in NSC-NSCB drop over the previously best unsupervised method. Moreover, the performance of our unsupervised approach ties with the best supervised approach. Additionally, we observe that our framework performs well even without post-processing, unlike earlier methods. With this, we conclude that one can learn robust cell representations with contrastive learning., Comment: 12 pages, 2 figures, 7 tables. This article is a pre-print and is currently under review at a conference

1I/'Oumuamua (or 1I) and 2I/Borisov (or 2I), the first InterStellar Objects (ISOs) discovered passing through the solar system, have opened up entirely new areas of exobody research. Finding additional ISOs and planning missions to intercept or rendezvous with these bodies will greatly benefit from knowledge of their likely orbits and arrival rates. Here, we use the local velocity distribution of stars from the Gaia Early Data Release 3 Catalogue of Nearby Stars and a standard gravitational focusing model to predict the velocity dependent flux of ISOs entering the solar system. With an 1I-type ISO number density of $\sim$0.1 AU$^{-3}$, we predict that a total of $\sim$6.9 such objects per year should pass within 1 AU of the Sun. There will be a fairly large high-velocity tail to this flux, with half of the incoming ISOs predicted to have a velocity at infinity, v$_{\infty}$, $>$ 40 km s$^{-1}$. Our model predicts that $\sim$92\% of incoming ISOs will be residents of the galactic thin disk, $\sim$6\% ($\sim$4 per decade) will be from the thick disk, $\sim$1 per decade will be from the halo and at most $\sim$3 per century will be unbound objects, ejected from our galaxy or entering the Milky Way from another galaxy. The rate of ISOs with very low v$_{\infty}$ $\lesssim$ 1.5 km s$^{-1}$ is so low in our model that any incoming very low velocity ISOs are likely to be previously lost solar system objects. Finally, we estimate a cometary ISO number density of $\sim$7 $\times$ 10$^{-5}$ AU$^{-3}$ for 2I type ISOs, leading to discovery rates for these objects possibly approaching once per decade with future telescopic surveys., Comment: Submitted to the Astronomical Journal

A study of a reacting boundary layer flow with heat transfer at conditions typical for configurations at elevated pressures has been performed using a set of direct numerical simulations. Effects of wall temperatures are investigated, representative for cooled walls of gas turbines and sub-scale rocket engines operating with hydrocarbon as fuels. The results show that exothermic chemical reactions induced by the low-enthalpy in the boundary layer take place predominantly in the logarthimic sub-layer. The majority of the heat release is attributed to the exothermic recombination of OH and CO to produce CO2 and H2O. The recombination reactions result in an increase of the wall heat loads by up to 20% compared to the inert flow. The gas composition experiences strong deviations from the chemical equilibrium conditions. In fact, a quenching of the major species is observed within the viscous sub-layer and the transition region. Analysis of chemical time-scales shows that the location of quenched composition coincides with the region where the Damkoehler number decreases below unity. Within the viscous sub-layer, a secondary reaction zone is detected, involving the production of formyl and formaldehyde radicals that provide an additional source of energy release. The analysis of the reaction paths showed that reactions with zero activation energy are responsible for this change in gas composition, which also account for the initial branching of hydrocarbon fuels decomposition according to previous auto-ignition studies. The effect of the secondary recombination reactions is more prominent for the lower wall temperature case. Finally, the role of turbulent fluctuations on the species net chemical production rates is evaluated, showing a strong correlation between species and temperature fluctuations.