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The Karhunen-Lo\`eve transform (KLT) stands as a well-established discrete transform, demonstrating optimal characteristics in data decorrelation and dimensionality reduction. Its ability to condense energy compression into a select few main components has rendered it instrumental in various applications within image compression frameworks. However, computing the KLT depends on the covariance matrix of the input data, which makes it difficult to develop fast algorithms for its implementation. Approximations for the KLT, utilizing specific rounding functions, have been introduced to reduce its computational complexity. Therefore, our paper introduces a category of low-complexity, data-independent KLT approximations, employing a range of round-off functions. The design methodology of the approximate transform is defined for any block-length $N$, but emphasis is given to transforms of $N = 8$ due to its wide use in image and video compression. The proposed transforms perform well when compared to the exact KLT and approximations considering classical performance measures. For particular scenarios, our proposed transforms demonstrated superior performance when compared to KLT approximations documented in the literature. We also developed fast algorithms for the proposed transforms, further reducing the arithmetic cost associated with their implementation. Evaluation of field programmable gate array (FPGA) hardware implementation metrics was conducted. Practical applications in image encoding showed the relevance of the proposed transforms. In fact, we showed that one of the proposed transforms outperformed the exact KLT given certain compression ratios., Comment: 19 pages, 10 figures, 7 tables

The Land\'e $g$-factor of charge carriers is a key parameter in spin physics controlling spin polarization and spin dynamics. In turn, it delivers information of the electronic band structure in vicinity of the band gap and its modification in nanocrystals provided by strong carrier confinement. The coherent spin dynamics of electrons and holes are investigated in CsPbI$_3$ perovskite nanocrystals with sizes varied from 4 to 16 nm by means of time-resolved Faraday ellipticity at the temperature of 6 K. The Land\'e $g$-factors of the charge carriers are evaluated through the Larmor spin precession in magnetic fields up to 430 mT across the spectral range from 1.69 to 2.25 eV, provided by variation of the nanocrystal size. The spectral dependence of the electron $g$-factor follows the model predictions when accounting for the mixing of the electronic bands with increasing confinement resulting from a decrease of the nanocrystal size. The spectral dependence of the hole $g$-factor, changing from $-0.19$ to $+1.69$, is considerably stronger than expected from the model. We analyze several mechanisms and conclude that none of them can be responsible for this difference. The renormalizations of the electron and hole $g$-factors roughly compensate each other, providing spectral independence for the bright exciton $g$-factor with a value of about $+2.2$., Comment: arXiv admin note: text overlap with arXiv:2407.21610

The High Energy Modular Array (HEMA) is one of three instruments that compose the STROBE-X mission concept. The HEMA is a large-area, high-throughput non-imaging pointed instrument based on the Large Area Detector developed as part of the LOFT mission concept. It is designed for spectral timing measurements of a broad range of sources and provides a transformative increase in sensitivity to X-rays in the energy range of 2--30 keV compared to previous instruments, with an effective area of 3.4 m$^{2}$ at 8.5 keV and an energy resolution of better than 300 eV at 6 keV in its nominal field of regard., Comment: 16 pages, 10 figures

We study time-delays in atomic multiphoton ionization (MPI) using the phase-sensitive detection of photoelectron vortices. Photoelectron vortices are created by MPI with counter-rotating circularly polarized (CRCP) ultrashort double pulse sequences. The vortices are characterized by a spiral-shaped photoelectron momentum distribution (PMD) resulting from the Ramsey-type interference of the partial photoelectron wave packets created by each pulse. The slope of the spiral arms encodes the time-delay between the partial wave packets with interferometric precision. We study the ionization time-delay in the (1+2) resonance-enhanced multiphoton ionization (REMPI) of potassium atoms using femtosecond CRCP white-light supercontinuum pulses. Pairwise interference of the partial wave packets with a reference wave packet gives rise to vortices of different rotational symmetry, which are superimposed to form the total PMD. The PMD is reconstructed tomographically and decomposed into the vortices using 3D Fourier analysis. From the energy-dependent spectral phases of the vortices, we retrieve a resonant ionization delay in the order of 1 fs, much shorter than the pulse duration. Our results demonstrate the power of photoelectron vortices for the accurate determination of photoionization time-delays and open up new perspectives for photoelectron chronoscopy including the measurement of ionization delays on the attosecond timescale., Comment: 11 pages, 5 figures

Modern cosmological surveys probe the Universe deep into the nonlinear regime, where massive neutrinos suppress cosmic structure. Traditional cosmological analyses, which use the 2-point correlation function to extract information, are no longer optimal in the nonlinear regime, and there is thus much interest in extracting beyond-2-point information to improve constraints on neutrino mass. Quantifying and interpreting the beyond-2-point information is thus a pressing task. We study the field-level information in weak lensing convergence maps using convolution neural networks. We find that the network performance increases as higher source redshifts and smaller scales are considered -- investigating up to a source redshift of 2.5 and $\ell_{\rm max}\simeq10^4$ -- verifying that massive neutrinos leave a distinct effect on weak lensing. However, the performance of the network significantly drops after scaling out the 2-point information from the maps, implying that most of the field-level information can be found in the 2-point correlation function alone. We quantify these findings in terms of the likelihood ratio and also use Integrated Gradient saliency maps to interpret which parts of the map the network is learning the most from, finding that the network extracts information from the most overdense and underdense regions., Comment: 10+5 pages, 5 figures, minor change to fig in v2

Since their first theoretical proposal [Ngoko Djiokap et al., Phys. Rev. Lett. 115, 113004 (2015)] and experimental demonstration [Pengel et al., Phys. Rev. Lett. 118, 053003 (2017)], free electron vortices (FEVs) have attracted considerable attention. Recently, a new class of unusually shaped FEVs, termed `reversible electron spirals', has been proposed theoretically [Strandquist et al., Phys. Rev. A 106, 043110 (2022)] for atomic single-photon ionization by two oppositely chirped and counterrotating circularly polarized (CRCP) attosecond pulses. Building on this concept, we present the first experimental demonstration of shaped FEVs created by atomic multiphoton ionization (MPI) using oppositely chirped CRCP femtosecond pulse pairs. In MPI by polarization-shaped pulses, several shaped FEVs of different rotational symmetry are superimposed resulting in the total photoelectron wave packet observed in the experiment. We employ velocity map imaging techniques to measure the photoelectron momentum distribution (PMD) and reconstruct the three-dimensional PMD by photoelectron tomography. Fourier analysis is used to decompose the measured PMD into the contributing FEVs with different rotational symmetries. The experimental results are supported by an analytical model and reproduced by numerical simulations, which provide insight into the role of resonant intermediate states. Our approach allows us to retrieve spectral information from the shaped laser field and the signatures of the MPI dynamics inscribed within the shaped FEVs., Comment: 12 pages, 6 figures

Standard cosmological analysis, which relies on two-point statistics, fails to extract the full information of the data. This limits our ability to constrain with precision cosmological parameters. Thus, recent years have seen a paradigm shift from analytical likelihood-based to simulation-based inference. However, such methods require a large number of costly simulations. We focus on full-field inference, considered the optimal form of inference. Our objective is to benchmark several ways of conducting full-field inference to gain insight into the number of simulations required for each method. We make a distinction between explicit and implicit full-field inference. Moreover, as it is crucial for explicit full-field inference to use a differentiable forward model, we aim to discuss the advantages of having this property for the implicit approach. We use the sbi_lens package which provides a fast and differentiable log-normal forward model. This forward model enables us to compare explicit and implicit full-field inference with and without gradient. The former is achieved by sampling the forward model through the No U-Turns sampler. The latter starts by compressing the data into sufficient statistics and uses the Neural Likelihood Estimation algorithm and the one augmented with gradient. We perform a full-field analysis on LSST Y10 like weak lensing simulated mass maps. We show that explicit and implicit full-field inference yield consistent constraints. Explicit inference requires 630 000 simulations with our particular sampler corresponding to 400 independent samples. Implicit inference requires a maximum of 101 000 simulations split into 100 000 simulations to build sufficient statistics (this number is not fine tuned) and 1 000 simulations to perform inference. Additionally, we show that our way of exploiting the gradients does not significantly help implicit inference., Comment: 20 pages, 16 figures, submitted to A&A

The generation of humanoid animation from text prompts can profoundly impact animation production and AR/VR experiences. However, existing methods only generate body motion data, excluding facial expressions and hand movements. This limitation, primarily due to a lack of a comprehensive whole-body motion dataset, inhibits their readiness for production use. Recent attempts to create such a dataset have resulted in either motion inconsistency among different body parts in the artificially augmented data or lower quality in the data extracted from RGB videos. In this work, we propose T2M-X, a two-stage method that learns expressive text-to-motion generation from partially annotated data. T2M-X trains three separate Vector Quantized Variational AutoEncoders (VQ-VAEs) for body, hand, and face on respective high-quality data sources to ensure high-quality motion outputs, and a Multi-indexing Generative Pretrained Transformer (GPT) model with motion consistency loss for motion generation and coordination among different body parts. Our results show significant improvements over the baselines both quantitatively and qualitatively, demonstrating its robustness against the dataset limitations., Comment: 10 pages, 4 figures, conference paper

To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations that evolve particles under gravity by estimating particle-particle interactions (N-body simulations) are computationally expensive and prohibitive to scale to the large volumes and resolutions necessary for the upcoming datasets. Moreover, modeling the distribution of galaxies typically involves identifying virialized dark matter halos, which is also a time- and memory-consuming process for large N-body simulations, further exacerbating the computational cost. In this study, we introduce CHARM, a novel method for creating mock halo catalogs by matching the spatial, mass, and velocity statistics of halos directly from the large-scale distribution of the dark matter density field. We develop multi-stage neural spline flow-based networks to learn this mapping at redshift z=0.5 directly with computationally cheaper low-resolution particle mesh simulations instead of relying on the high-resolution N-body simulations. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space. Finally, we use these mock catalogs for cosmological inference using redshift-space galaxy power spectrum, bispectrum, and wavelet-based statistics using simulation-based inference, performing the first inference with accelerated forward model simulations and finding unbiased cosmological constraints with well-calibrated posteriors. The code was developed as part of the Simons Collaboration on Learning the Universe and is publicly available at \url{https://github.com/shivampcosmo/CHARM}., Comment: 12 pages and 8 figures. This is a Learning the Universe Publication

The coherent spin dynamics of holes are investigated for CsPbBr$_3$ and CsPb(Cl,Br)$_3$ perovskite nanocrystals in a glass matrix using the time-resolved Faraday rotation/ellipticity techniques. In an external magnetic field, pronounced Larmor spin precession of the hole spins is detected across a wide temperature range from 5 to 300 K. The hole Land\'e $g$-factor varies in the range of $0.8-1.5$, in which it increases with increasing high energy shift of the exciton due to enhanced confinement in small nanocrystals. The hole spin dephasing time decreases from 1 ns to 50 ps in this temperature range. Nuclear spin fluctuations have a pronounced impact on the hole spin dynamics. The hyperfine interaction of the holes with nuclear spins modifies their spin polarization decay and induces their spin precession in zero external magnetic field. The results can be well described by the model developed in Ref. 41, from which the hyperfine interaction energy of a hole spin with the nuclear spin fluctuation in range of $2-5$ $\mu$eV is evaluated.

Adversarial patches in computer vision can be used, to fool deep neural networks and manipulate their decision-making process. One of the most prominent examples of adversarial patches are evasion attacks for object detectors. By covering parts of objects of interest, these patches suppress the detections and thus make the target object 'invisible' to the object detector. Since these patches are usually optimized on a specific network with a specific train dataset, the transferability across multiple networks and datasets is not given. This paper addresses these issues and investigates the transferability across numerous object detector architectures. Our extensive evaluation across various models on two distinct datasets indicates that patches optimized with larger models provide better network transferability than patches that are optimized with smaller models., Comment: 7 pages, 6 figures, 1 table

Following Valloni, we study complex projective K3 surfaces having complex multiplication by rings of integers.

The electrification of the transportation and heating sector, the so-called sector coupling, is one of the core elements to achieve independence from fossil fuels. As it highly affects the electricity demand, especially on the local level, the integrated modeling and simulation of all sectors is a promising approach for analyzing design decisions or complex control strategies. This paper analyzes the increase in electricity demand resulting from sector coupling, mainly due to integrating electric vehicles into urban energy systems. Therefore, we utilize a digital twin of an existing local energy system and extend it with a mobility simulation model to evaluate the impact of electric vehicles on the distribution grid level. Our findings indicate a significant rise in annual electricity consumption attributed to electric vehicles, with home charging alone resulting in a 78% increase. However, we demonstrate that integrating photovoltaic and battery energy storage systems can effectively mitigate this rise., Comment: Paper is to be published in Proceedings of the 2024 Winter Simulation Conference

The coherent spin dynamics of electrons and holes in CsPbI3 perovskite nanocrystals in a glass matrix are studied by the time-resolved Faraday ellipticity technique in magnetic fields up to 430 mT across a temperature range from 6 K up to 120 K. The Land\'e g-factors and spin dephasing times are evaluated from the observed Larmor precession of electron and hole spins. The nanocrystal size in the three studied samples varies from about 8 to 16 nm, resulting in exciton transition varying from 1.69 to 1.78 eV at the temperature of 6 K, allowing us to study the corresponding energy dependence of the g-factors. The electron g-factor decreases with increasing confinement energy in the NCs as result of NC size reduction, and also with increasing temperature. The hole g-factor shows the opposite trend. A model analysis shows that the variation of g-factors with NC size arises from the transition energy dependence of the g-factors, which becomes strongly renormalized by temperature.

This work addresses stochastic optimal control problems where the unknown state evolves in continuous time while partial, noisy, and possibly controllable measurements are only available in discrete time. We develop a framework for controlling such systems, focusing on the measure-valued process of the system's state and the control actions that depend on noisy and incomplete data. Our approach uses a stochastic optimal control framework with a probability measure-valued state, which accommodates noisy measurements and integrates them into control decisions through a Bayesian update mechanism. We characterize the control optimality in terms of a sequence of interlaced Hamilton Jacobi Bellman (HJB) equations coupled with controlled impulse steps at the measurement times. For the case of Gaussian-controlled processes, we derive an equivalent HJB equation whose state variable is finite-dimensional, namely the state's mean and covariance. We demonstrate the effectiveness of our methods through numerical examples. These include control under perfect observations, control under no observations, and control under noisy observations. Our numerical results highlight significant differences in the control strategies and their performance, emphasizing the challenges and computational demands of dealing with uncertainty in state observation.

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template regardless of the noise probability distribution, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal to (white) noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic, in contrast to the standard signal to white noise. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, using the Bayes Factor approach also results in no statistically significant detections in the PTF data., Comment: 13 pages, 12 figures

Upcoming cosmological surveys have the potential to reach groundbreaking discoveries on multiple fronts, including the neutrino mass, dark energy, and inflation. Most of the key science goals require the joint analysis of datasets from multiple surveys to break parameter degeneracies and calibrate systematics. To realize such analyses, a large set of mock simulations that realistically model correlated observables is required. In this paper we present the N-body component of the HalfDome cosmological simulations, designed for the joint analysis of Stage-IV cosmological surveys, such as Rubin LSST, Euclid, SPHEREx, Roman, DESI, PFS, Simons Observatory, CMB-S4, and LiteBIRD. Our 300TB initial data release includes full-sky lightcones and halo catalogs between $z$=0--4 for 11 fixed cosmology realizations, as well as an additional run with local primordial non-Gaussianity ($f_{\rm NL}$=20). The simulations evolve $6144^3$ particles in a 3.75$\,h^{-1} {\rm Gpc}$ box, reaching a minimum halo mass of $\sim 6 \times 10^{12}\,h^{-1} M_\odot$ and maximum scale of $k \sim 1\,h{\rm Mpc}^{-1}$. Instructions to access the data, and plans for future data releases, can be found at https://halfdomesims.github.io., Comment: 10 pages, 7 figures, 2 tables. Data released at https://halfdomesims.github.io

The software project kdotpy provides a Python application for simulating electronic band structures of semiconductor devices with $\mathbf{k}\cdot\mathbf{p}$ theory on a lattice. The application implements the widely used Kane model, capable of reliable predictions of transport and optical properties for a large variety of topological and non-topological materials with a zincblende crystal structure. The application automates the tedious steps of simulating band structures. The user inputs the relevant physical parameters on the command line, for example materials and dimensions of the device, magnetic field, and temperature. The program constructs the appropriate matrix Hamiltonian on a discretized lattice of spatial coordinates and diagonalizes it. The physical observables are extracted from the eigenvalues and eigenvectors and saved as output. The program is highly customizable with a large set of configuration options and material parameters., Comment: 143 pages, submission to SciPost Physics Codebases

We continue the study of the $k$-cut complex $\Delta_k(G)$ of a graph $G$ initiated in the paper of Bayer, Denker, Jeli\'c Milutinovi\'c, Rowlands, Sundaram and Xue [Topology of cut complexes of graphs, SIAM J. on Discrete Math. 38(2): 1630--1675 (2024)]. We give explicit formulas for the $f$- and $h$-polynomials of the cut complex $\Delta_k(G_1+G_2) $ of the disjoint union of two graphs $G_1$ and $G_2$, and for the homology representation of $\Delta_k(K_m+K_n)$. We also study the cut complex of the squared path and the grid graph. Our techniques include tools from combinatorial topology, discrete Morse theory and equivariant poset topology., Comment: 29 pages, 7 figures, 3 tables

We study the Hanle and spin polarization recovery effects on resident electrons in a monolayer MoSe$_2$ on EuS. We demonstrate that localized electrons provide the main contribution to the spin dynamics signal at low temperatures below 15~K for small magnetic fields of only a few mT. The spin relaxation of these electrons is determined by random effective magnetic fields due to a contact spin interaction, namely the hyperfine interaction with the nuclei in MoSe$_2$ or the exchange interaction with the magnetic ions of the EuS film. From the magnetic field angular dependence of the spin polarization we evaluate the anisotropy of the intervalley electron $g$-factor and the spin relaxation time. The non-zero in-plane $g$-factor $|g_x|\approx 0.1$, the value of which is comparable to its dispersion, is attributed to randomly localized electrons in the MoSe$_2$ layer., Comment: 6 pages, 4 figures

The coupled electron-nuclear spin system in an InGaAs semiconductor as testbed of nonlinear dynamics can develop auto-oscillations, resembling time-crystalline behavior, when continuously excited by a circularly polarized laser. We expose this system to deviations from continuous driving by periodic modulation of the excitation polarization, revealing a plethora of nonlinear phenomena that depend on modulation frequency and depth. We find ranges in which the system's oscillations are entrained with the modulation frequency. The width of these ranges depends on the polarization modulation depth, resulting in an Arnold tongue. Outside the tongue, the system shows a variety of fractional subharmonic responses connected through bifurcation jets when varying the modulation frequency. Here, each branch in the frequency spectrum forms a devil's staircase. When an entrainment range is approached by going through an increasing order of bifurcations, chaotic behavior emerges. These findings can be described by an advanced model of the periodically pumped electron-nuclear spin system. We discuss the connection of the obtained results to different phases of time matter.

This paper proposes to parameterize open loop controls in stochastic optimal control problems via suitable classes of functionals depending on the driver's path signature, a concept adopted from rough path integration theory. We rigorously prove that these controls are dense in the class of progressively measurable controls and use rough path methods to establish suitable conditions for stability of the controlled dynamics and target functional. These results pave the way for Monte Carlo methods to stochastic optimal control for generic target functionals and dynamics. We discuss the rather versatile numerical algorithms for computing approximately optimal controls and verify their accurateness in benchmark problems from Mathematical Finance.

Group IV color centers in diamond are among the most promising optically active spin systems with strong optical transitions and long spin coherences. The ground-state splitting of the center is particularly important to suppress the interaction with coherence-limiting phonons, which improves the coherence properties and sets the upper limit for the operating temperature. Negatively charged silicon-vacancy centers have an ordinary ground-state splitting of only 48GHz, resulting in required temperatures below one Kelvin, which can only be achieved by dilution refrigerators. Here, we increase the ground-state splitting by up to an order of magnitude by induced strain in a single-crystal diamond membrane. Furthermore, we demonstrate cavity-assisted spectroscopy enabled by coupling the emitter ensemble with a selectable strain to the mode of a Fabry-Perot microcavity. Calculation of the absorption cross-section yields $\sigma_{ens} = $4.9*10^-11 cm^2. Together with the Purcell-enhanced twofold reduction in emitter lifetime below 1ns, this makes the system a promising spin-photon interface at moderate temperatures of 4K., Comment: 12 pages, 9 figures

The tunneling of interacting charges in nanostructures is a random and noisy process, which gives rise to a variety of intriguing physical phenomena, including Coulomb blockade, the Kondo effect, and Fermi-edge singularities. Such correlation effects are visible in low-frequency measurements of the electric current or the shot noise, however, they are also expected to leave clear fingerprints in the tunneling times of individual charges. Here, we experimentally demonstrate the real-time detection and control of correlated charge tunneling in a dynamically driven quantum dot. To this end, we measure the joint distribution of waiting times between tunneling charges, and we show that subsequent waiting times are strongly correlated due to the Coulomb interactions between the tunneling charges and the external periodic drive. Our measurements are in excellent agreement with a theoretical model that allows us to develop a detailed understanding of the correlated tunneling events. We also demonstrate that the degree of correlations can be controlled by the external drive. Our experiment paves the way for systematic real-time investigations of correlated electron transport in low-dimensional nanostructures with potential applications in metrology and sensing., Comment: 7 pages, 5 figures

The Pristine-\textit{Gaia} synthetic catalogue provides reliable photometric metallicities for $\sim$30 million FGK stars using the Pristine survey model and Gaia XP spectra. We perform the first low-to-medium-resolution spectroscopic follow-up of bright (G<15) and distant (up to 35 kpc) very and extremely metal-poor (V/EMP, [Fe/H]<-2.5) red giant branch stars from this. We use Isaac Newton Telescope/Intermediate Dispersion Spectrograph (INT/IDS) observations centred around the calcium triplet region ideal for V/EMP stars. We find that 76\% of our stars indeed have [Fe/H]<-2.5 with these inferred spectroscopic metallicities and only 3\% are outliers with [Fe/H] > -2.0. We report a success rate of 77\% and 38\% in finding stars with [Fe/H]<-2.5 and -3.0 respectively. This will allow for 10,000-20,000 homogeneously analysed EMP stars using the WEAVE survey follow-up of Pristine EMP candidates. We associate 20\%, 46\%, and 34\% of the stars to be confined to the disc plane, or to have inner and outer halo orbits, respectively. We also associate these V/EMP stars to known accretion events such as Gaia-Enceladus-Sausage (GES), LMS-1/Wukong, Thamnos, Helmi streams, Sagittarius, Sequoia, etc. For the stars that orbit close to the disc plane, we find that the prograde region with low vertical action is overdense with a significance of 4$\sigma$ as compared to its retrograde counterpart. We also find three new (brightest) members of the most metal-poor stellar stream, C-19, one of which is 50$^\circ$ away from the main body of the stream. Our measured mean metallicity, velocity dispersion, and stream width are consistent with the literature, but our results favour a higher distance ($\sim$21.5 kpc) for the stream. We publish a catalogue (and 1D spectra) of 215 V/EMP stars from this spectroscopic follow-up and showcase the power of chemokinematic analysis of V/EMP end., Comment: Submitted to A&A. 17 pages (9 figures) + 3 pages (3 figures) in Appendix. Comments are very welcome! The catalogue and 1D spectra will be made available public after acceptance and before upon reasonable request to the first author

Massive neutrinos suppress the growth of cosmic structure on nonlinear scales, motivating the use of information beyond the power spectrum to tighten constraints on the neutrino mass, for example by considering cosmic voids. It was recently proposed that constraints on neutrino mass from the halo mass function (HMF) can be improved by considering only the halos that reside within voids -- the void-halo mass function (VHMF). We extend this analysis, which made spherical assumptions about the shape of voids, to take into account the non-spherical nature of voids as defined by the Voronoi-tessellation-based void finder, VIDE. In turn, after accounting for one spurious non-spherical void, we find no evidence that the VHMF contains information beyond the HMF. Given this finding, we then introduce a novel summary statistic by splitting halos according to the emptiness of their individual environments, defined by the Voronoi cell volume each halo resides in, and combining the mass functions from each split. We name the corresponding statistic the VorHMF and find that it could provide information regarding neutrino mass beyond the HMF. Our work thus motivates the importance of accounting for the full shape of voids in future analyses, both in terms of removing outliers to achieve robust results and as an additional source of cosmological information., Comment: 8 pages, 5 figures, published as letter in PRD

Active learning is designed to minimize annotation efforts by prioritizing instances that most enhance learning. However, many active learning strategies struggle with a 'cold start' problem, needing substantial initial data to be effective. This limitation often reduces their utility for pre-trained models, which already perform well in few-shot scenarios. To address this, we introduce ActiveLLM, a novel active learning approach that leverages large language models such as GPT-4, Llama 3, and Mistral Large for selecting instances. We demonstrate that ActiveLLM significantly enhances the classification performance of BERT classifiers in few-shot scenarios, outperforming both traditional active learning methods and the few-shot learning method SetFit. Additionally, ActiveLLM can be extended to non-few-shot scenarios, allowing for iterative selections. In this way, ActiveLLM can even help other active learning strategies to overcome their cold start problem. Our results suggest that ActiveLLM offers a promising solution for improving model performance across various learning setups., Comment: 18 pages, 7 figures, 4 tables

The present paper introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. While much of the literature on media and technology habits has relied on self-report questionnaires and simple behavioral frequency measures, we examine an important yet understudied aspect of media and technology habits: their embeddedness in repetitive behavioral sequences. Leveraging Long Short-Term Memory (LSTM) and transformer neural networks, we show that (i) social media use is predictable at the within and between-person level and that (ii) there are robust individual differences in the predictability of social media use. We examine the performance of several modeling approaches, including (i) global models trained on the pooled data from all participants, (ii) idiographic person-specific models, and (iii) global models fine-tuned on person-specific data. Neither person-specific modeling nor fine-tuning on person-specific data substantially outperformed the global models, indicating that the global models were able to represent a variety of idiosyncratic behavioral patterns. Additionally, our analyses reveal that the person-level predictability of social media use is not substantially related to the frequency of smartphone use in general or the frequency of social media use, indicating that our approach captures an aspect of habits that is distinct from behavioral frequency. Implications for habit modeling and theoretical development are discussed.

We investigate the two-pulse photon echo response of excitons in the mixed lead halide perovskite crystal \sample in dependence on the excitation intensity and polarization of the incident laser pulses. Using spectrally narrow picosecond laser pulses, we address localized excitons with long coherence times $T_2 \approx 100\,$ps. This approach offers high sensitivity for the observation of excitation-induced changes in the homogeneous linewidth $\Gamma_2=2\hbar/T_2$ on the $\mu$eV scale. Through intensity-dependent measurements, we evaluate the increase of $\Gamma_2$ by 10~$\mu$eV at an exciton density of 10$^{17}$~cm$^{-3}$ being comparable with the intrinsic linewidth of 14$\,\mu$eV. We observe that the decay of the photon echo and its power dependence are sensitive to the polarization configuration of the excitation pulses, which indicates that spin-dependent exciton-exciton interactions contribute to excitation-induced dephasing. In cross-linear polarization, the decay is faster and its dependence on exciton density is stronger as compared to the co-polarized configuration. Using a two-exciton model accounting for different spin configurations we are able to reproduce the experimental results.

Diluted magnetic semiconductor (DMS) colloidal nanocrystals demonstrate remarkable magneto-optical properties. However, the behavior of circular polarization of their emission in high magnetic fields remains unclear. We measure magneto-optical properties of colloidal CdSe/CdMnS nanoplatelets in high magnetic fields up to 30 T and at cryogenic temperatures. The degree of circular polarization of photoluminescence demonstrates non-monotonous behavior in a magnetic field. In low magnetic fields, the polarization degree is positive, due to an exchange interaction of excitons with localized spins of magnetic Mn ions. The exchange interaction is strong enough to overcome the intrinsic Zeeman splitting, which provides negatively polarized emission in nonmagnetic CdSe/CdS nanoplatelets. After reaching a maximum the polarization degree starts to decrease and reverses the sign to negative in high magnetic fields. The critical magnetic field, in which the sign is reversed, increases when temperature is elevated. We develop a model, which explains this behavior by an interplay of bright and dark exciton recombination.

The navigation of complex labyrinths with tens of rooms under visual partially observable state is typically addressed using recurrent deep reinforcement learning architectures. In this work, we show that navigation can be achieved through the emergent evolution of a simple Braitentberg-style heuristic that structures the interaction between agent and labyrinth, i.e. complex behaviour from simple heuristics. To do so, the approach of tangled program graphs is assumed in which programs cooperatively coevolve to develop a modular indexing scheme that only employs 0.8\% of the state space. We attribute this simplicity to several biases implicit in the representation, such as the use of pixel indexing as opposed to deploying a convolutional kernel or image processing operators.

Optical orientation of carrier spins by circularly polarized light is the basic concept and tool of spin physics in semiconductors. We study the optical orientation of electrons and holes in a crystal of the FA$_{0.9}$Cs$_{0.1}$PbI$_{2.8}$Br$_{0.2}$ lead halide perovskite by means of polarized photoluminescence, time-resolved differential reflectivity, and time-resolved Kerr rotation. At the cryogenic temperature of 1.6 K the optical orientation degree measured for continuous-wave excitaton reaches 6% for localized electrons and 2\% for localized holes. Their contributions are distinguished from each other and from exciton optical orientation through the pronounced Hanle effect in transverse magnetic fields and the polarization recovery effect in longitudinal magnetic fields. The optical orientation degree is highly stable against detuning of the laser photon energy from the band gap by up to 0.25 eV, showing then a gradual decrease for detunings up to 0.9 eV. This evidences the inefficiency of spin relaxation mechanisms for free carriers during their energy relaxation. Spin relaxation for localized electrons and holes is provided by the hyperfine interaction with the nuclear spins. Dynamic polarization of nuclear spins is demonstrated by the Overhauser field reaching 4 mT acting on the electrons and $-76$ mT acting on the holes. This confirms the specifics of lead halide perovskite semiconductors, where the hole hyperfine interaction with the nuclei considerably exceeds that of the electron.

Excitons, as bound states of electrons and holes, embody the solid state analogue of the hydrogen atom, whose quantum spectrum is explained within a classical framework by the Bohr-Sommerfeld atomic model. In a first hydrogenlike approximation the spectra of excitons are also well described by a Rydberg series, however, due to the surrounding crystal environment deviations from this series can be observed. A theoretical treatment of excitons in cuprous oxide needs to include the band structure of the crystal, leading to a prominent fine-structure splitting in the quantum spectra. This is achieved by introducing additional spin degrees of freedom into the system, making the existence and meaningfulness of classical exciton orbits in the physical system a non-trivial question. Recently, we have uncovered the contributions of periodic exciton orbits directly in the quantum mechanical recurrence spectra of cuprous oxide [J. Ertl et al., Phys. Rev. Lett. 129, 067401 (2022)] by application of a scaling technique and fixing the energy of the classical dynamics to a value corresponding to a principle quantum number $n=5$ in the hydrogenlike case. Here, we present a comprehensive derivation of the classical and semiclassical theory of excitons in cuprous oxide. In particular, we investigate the energy dependence of the exciton dynamics. Both the semiclassical and quantum mechanical recurrence spectra exhibit stronger deviations from the hydrogenlike behavior with decreasing energy, which is related to a growing influence of the spin-orbit coupling and thus a higher velocity of the secular motion of the exciton orbits. The excellent agreement between semiclassical and quantum mechanical exciton recurrence spectra demonstrates the validity of the classical and semiclassical approach to excitons in cuprous oxide., Comment: 16 pages, 10 figures, accepted for publication in Phys. Rev. B

Control of water ice formation on surfaces is of key technological and economic importance, but the fundamental understanding of ice nucleation and growth mechanisms and the design of surfaces for controlling water freezing behaviour remain incomplete. Graphene is a two-dimensional (2D) material that has been extensively studied for its peculiar wetting properties with liquid water incl. a heavily debated wetting transparency. Furthermore, graphene is the parent structure of soot particles that are heavily implicated as nuclei in atmospheric ice formation and consequently graphene is often used as a model surface for computational ice nucleation studies. Despite this, to date experimental reports on ice formation on scalable graphene films remain missing. Towards filling this gap, we here report on the water freezing behaviour on scalably grown chemical vapour deposited (CVD) graphene films on application-relevant polycrystalline copper (Cu). We find that as-grown CVD graphene on Cu can be (as we term it) freezing transparent i.e. the graphene presence does not change the freezing temperature curves of liquid water to solid ice on Cu in our measurements. Such freezing transparency has to date not been considered. We also show that chemical functionalization of the graphene films can result in controllable changes to the freezing behaviour to lower/higher temperatures and that also the observed freezing transparency can be lifted via functionalization. Our work thereby introduces the concept of freezing transparency of graphene on a metal support and also introduces scalable CVD graphene/Cu as an ultimately thin platform towards control of ice nucleation behaviour on a technologically highly relevant metal.

Computed Tomography (CT) image reconstruction is crucial for accurate diagnosis and deep learning approaches have demonstrated significant potential in improving reconstruction quality. However, the choice of loss function profoundly affects the reconstructed images. Traditional mean squared error loss often produces blurry images lacking fine details, while alternatives designed to improve may introduce structural artifacts or other undesirable effects. To address these limitations, we propose Eagle-Loss, a novel loss function designed to enhance the visual quality of CT image reconstructions. Eagle-Loss applies spectral analysis of localized features within gradient changes to enhance sharpness and well-defined edges. We evaluated Eagle-Loss on two public datasets across low-dose CT reconstruction and CT field-of-view extension tasks. Our results show that Eagle-Loss consistently improves the visual quality of reconstructed images, surpassing state-of-the-art methods across various network architectures. Code and data are available at \url{https://github.com/sypsyp97/Eagle_Loss}., Comment: Preprint

For $\mathbf{b}=(b_1,\dots,b_n)\in \mathbb{Z}_{>0}^n$, a $\mathbf{b}$-parking function is defined to be a sequence $(\beta_1,\dots,\beta_n)$ of positive integers whose nondecreasing rearrangement $\beta'_1\leq \beta'_2\leq \cdots \leq \beta'_n$ satisfies $\beta'_i\leq b_1+\cdots + b_i$. The $\mathbf{b}$-parking-function polytope $\mathfrak{X}_n(\mathbf{b})$ is the convex hull of all $\mathbf{b}$-parking functions of length $n$ in $\mathbb{R}^n$. Geometric properties of $\mathfrak{X}_n(\mathbf{b})$ were previously explored in the specific case where $\mathbf{b}=(a,b,b,\dots,b)$ and were shown to generalize those of the classical parking-function polytope. In this work, we study $\mathfrak{X}_n(\mathbf{b})$ in full generality. We present a minimal inequality and vertex description for $\mathfrak{X}_n(\mathbf{b})$, prove it is a generalized permutahedron, and study its $h$-polynomial. Furthermore, we investigate $\mathfrak{X}_n(\mathbf{b})$ through the perspectives of building sets and polymatroids, allowing us to identify its combinatorial types and obtain bounds on its combinatorial and circuit diameters., Comment: 27 pages, 4 figures, Comments welcomed!

A magic labeling of a graph is a labeling of the edges by nonnegative integers such that the label sum over the edges incident to every vertex is the same. This common label sum is known as the index. We count magic labelings by maximum edge label, rather than index, using an Ehrhart-theoretic approach. In contrast to Stanley's 1973 work showing that the function counting magic labelings with bounded index is a quasipolynomial with quasiperiod $2$, we show by construction that the minimum quasiperiod of the quasipolynomial counting magic labelings with bounded maximum label can be arbitrarily large, even for planar bipartite graphs. Unfortunately, this rules out a certain Ehrhart-theoretic approach to proving Hartsfield and Ringel's Antimagic Graph Conjecture. However, we show that this quasipolynomial is in fact a polynomial for any bipartite graph with matching preclusion number at most $1$, which includes any bipartite graph with a leaf., Comment: 16 pages, 3 figures

Efficiently pricing multi-asset options poses a significant challenge in quantitative finance. The Monte Carlo (MC) method remains the prevalent choice for pricing engines; however, its slow convergence rate impedes its practical application. Fourier methods leverage the knowledge of the characteristic function to accurately and rapidly value options with up to two assets. Nevertheless, they face hurdles in the high-dimensional settings due to the tensor product (TP) structure of commonly employed quadrature techniques. This work advocates using the randomized quasi-MC (RQMC) quadrature to improve the scalability of Fourier methods with high dimensions. The RQMC technique benefits from the smoothness of the integrand and alleviates the curse of dimensionality while providing practical error estimates. Nonetheless, the applicability of RQMC on the unbounded domain, $\mathbb{R}^d$, requires a domain transformation to $[0,1]^d$, which may result in singularities of the transformed integrand at the corners of the hypercube, and deteriorate the rate of convergence of RQMC. To circumvent this difficulty, we design an efficient domain transformation procedure based on the derived boundary growth conditions of the integrand. This transformation preserves the sufficient regularity of the integrand and hence improves the rate of convergence of RQMC. To validate this analysis, we demonstrate the efficiency of employing RQMC with an appropriate transformation to evaluate options in the Fourier space for various pricing models, payoffs, and dimensions. Finally, we highlight the computational advantage of applying RQMC over MC or TP in the Fourier domain, and over MC in the physical domain for options with up to 15 assets.

As digitization in engineering progressed, circuit diagrams (also referred to as schematics) are typically developed and maintained in computer-aided engineering (CAE) systems, thus allowing for automated verification, simulation and further processing in downstream engineering steps. However, apart from printed legacy schematics, hand-drawn circuit diagrams are still used today in the educational domain, where they serve as an easily accessible mean for trainees and students to learn drawing this type of diagrams. Furthermore, hand-drawn schematics are typically used in examinations due to legal constraints. In order to harness the capabilities of digital circuit representations, automated means for extracting the electrical graph from raster graphics are required. While respective approaches have been proposed in literature, they are typically conducted on small or non-disclosed datasets. This paper describes a modular end-to-end solution on a larger, public dataset, in which approaches for the individual sub-tasks are evaluated to form a new baseline. These sub-tasks include object detection (for electrical symbols and texts), binary segmentation (drafter's stroke vs. background), handwritten character recognition and orientation regression for electrical symbols and texts. Furthermore, computer-vision graph assembly and rectification algorithms are presented. All methods are integrated in a publicly available prototype., Comment: As submitted to ICDAR24; 11 pages, 9 figures, 1 table

We give a proof of Mukai's Theorem on the existence of certain exceptional vector bundles on prime Fano threefolds. To our knowledge this is the first complete proof in the literature. The result is essential for Mukai's biregular classification of prime Fano threefolds, and for the existence of semiorthogonal decompositions in their derived categories. Our approach is based on Lazarsfeld's construction that produces vector bundles on a variety from globally generated line bundles on a divisor, on Mukai's theory of stable vector bundles on K3 surfaces, and on Brill--Noether properties of curves and (in the sense of Mukai) of K3 surfaces., Comment: 38 pages