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The transition to a low-carbon economy necessitates effective carbon capture and storage (CCS) solutions, particularly for hard-to-abate sectors. Herein, pipeline networks are indispensable for cost-efficient $CO_2$ transportation over long distances. However, there is deep uncertainty regarding which industrial sectors will participate in such systems. This poses a significant challenge due to substantial investments as well as the lengthy planning and development timelines required for $CO_2$ pipeline projects, which are further constrained by limited upgrade options for already built infrastructure. The economies of scale inherent in pipeline construction exacerbate these challenges, leading to potential regret over earlier decisions. While numerous models were developed to optimize the initial layout of pipeline infrastructure based on known demand, a gap exists in addressing the incremental development of infrastructure in conjunction with deep uncertainty. Hence, this paper introduces a novel optimization model for $CO_2$ pipeline infrastructure development, minimizing regret as its objective function and incorporating various upgrade options, such as looping and pressure increases. The model's effectiveness is also demonstrated by presenting a comprehensive case study of Germany's cement and lime industries. The developed approach quantitatively illustrates the trade-off between different options, which can help in deriving effective strategies for $CO_2$ infrastructure development.

These days, Key-Value Stores are widely used for scalable data storage. In this environment, Bloom filters serve as an efficient probabilistic data structure for the representation of sets of keys as they allow for set membership queries with controllable false positive rates and no false negatives. For optimal error rates, the right choice of the main parameters, namely the length of the Bloom filter array, the number of hash functions used to map an element to the array's indices, and the number of elements to be inserted in one filter, is crucial. However, these parameters are constrained: The number of hash functions is bounded to integer values, and the length of a Bloom filter is usually chosen to be a power-of-two to allow for efficient modulo operations using binary arithmetics. These modulo calculations are necessary to map from the output universe of the applied universal hash functions, like Murmur, to the set of indices of the Bloom filter. In this paper, we relax these constraints by proposing the Rational Bloom filter, which allows for non-integer numbers of hash functions. This results in optimized fraction-of-zero values for a known number of elements to be inserted. Based on this, we construct the Variably-Sized Block Bloom filters to allow for a flexible filter length, especially for large filters, while keeping computation efficient., Comment: 21 pages, 7 figures

We review concepts and recent work related to creating light-matter interfaces for future quantum networks based on rare-earth ion-doped crystals. More precisely, we explore their unique suitability for creating photon sources, optical quantum memories for light, and qubits that allow quantum information processing. In addition, we review the state-of-the-art of elementary quantum repeater links, and provide suggestions for future research., Comment: Review article, 21 pages including 227 references

Motivated by the applications of secure multiparty computation as a privacy-protecting data analysis tool, and identifying oblivious transfer as one of its main practical enablers, we propose a practical realization of randomized quantum oblivious transfer. By using only symmetric cryptography primitives to implement commitments, we construct computationally-secure randomized oblivious transfer without the need for public-key cryptography or assumptions imposing limitations on the adversarial devices. We show that the protocol is secure under an indistinguishability-based notion of security and demonstrate an experimental implementation to test its real-world performance. Its security and performance are then compared to both quantum and classical alternatives, showing potential advantages over existing solutions based on the noisy storage model and public-key cryptography., Comment: 40 pages, 5 images

Shilnikov's scenario in 3D consists of a vectorfield $V$ so that the equation $$ x'(t)=V(x(t))\in\mathbb{R}^3 $$ with $V(0)=0$ has a solution homoclinic to the origin and the eigenvalues of $DV(0)$ are $u>0$ and $\sigma\pm i\mu$, $\sigma<0<\mu$, with $0<\sigma+u$. We give a detailed proof that close to the homoclinic loop complicated motion exists provided $V$ is just once continuously differentiable. The result requires working with flows instead of an ODE, which necessitates major modifications compared to the earlier approach for twice continuously differentiable vectorfields in arXiv:2406.18289 ., Comment: 37 pages, 6 figures

We prove that hypersurfaces defined by irreducible square-free polynomials have rational singularities., Comment: 8 pages

Abs-smooth functions are given by compositions of smooth functions and the evaluation of the absolute value. The linear independence kink qualification (LIKQ) is a fundamental assumption in optimization problems governed by these abs-smooth functions, generalizing the well-known LICQ from smooth optimization. In particular, provided that LIKQ holds it is possible to derive optimality conditions for abs-smooth optimization problems that can be checked in polynomial time. Utilizing tools from differential topology, namely a version of the jet-transversality theorem, it is shown that assuming LIKQ for all feasible points of an abs-smooth optimization problem is a generic assumption.

We present the Lyssa suite of high-resolution cosmological simulations of the Lyman-$\alpha$ forest designed for cosmological analyses. These 18 simulations have been run using the Nyx code with $4096^3$ hydrodynamical cells in a 120 Mpc comoving box and individually provide sub-percent level convergence of the Lyman-$\alpha$ forest 1d flux power spectrum. We build a Gaussian process emulator for the Lyssa simulations in the lym1d likelihood framework to interpolate the power spectrum at arbitrary parameter values. We validate this emulator based on leave-one-out tests and based on the parameter constraints for simulations outside of the training set. We also perform comparisons with a previous emulator, showing a percent level accuracy and a good recovery of the expected cosmological parameters. Using this emulator we derive constraints on the linear matter power spectrum amplitude and slope parameters $A_{\mathrm{Ly}\alpha}$ and $n_{\mathrm{Ly}\alpha}$. While the best-fit Planck $\Lambda$CDM model has $A_{\mathrm{Ly}\alpha}=8.79$ and $n_{\mathrm{Ly}\alpha}=-2.363$, from DR14 eBOSS data we find that $A_{\mathrm{Ly}\alpha}<7.6$ (95\% CI) and $n_{\mathrm{Ly}\alpha}=-2.369 \pm 0.008$. The low value of $A_{\mathrm{Ly}\alpha}$, in tension with Planck, is driven by the correlation of this parameter with the mean transmission of the Lyman-$\alpha$ forest. This tension disappears when imposing a well-motivated external prior on this mean transmission, in which case we find $A_{\mathrm{Ly}\alpha}=9.8\pm1.1$ in accordance with Planck., Comment: 34 pages, 17 figures, 4 tables. Comments are welcome!

We present time- and frequency-resolved simulations of quantum key distribution~(QKD) systems employing highly entangled biphoton quantum states. Our simulations are based on expansions of the covariance matrix and photon detection probabilities of biphoton states in terms of increasing orders of the joint spectral amplitude that were introduced in the first part of this series. Employing these expansions allows us to efficiently evaluate the impact of multi-pair events on the performance of the QKD systems while systematically taking into account effects from the photon spectra and many relevant imperfections of the setup. The results are shown to be in agreement with corresponding measurements of the key rates and quantum bit error rates., Comment: 36 pages, 10 figures

We discuss an expansion of the detection probabilities of biphoton states in terms of increasing orders of the joint spectral amplitude. The expansion enables efficient time- or frequency-resolved numerical simulations involving quantum states exhibiting a high degree of spectral entanglement. Contrary to usual approaches based on one- or two-pair approximations, we expand the expressions in terms corresponding to the amount of correlations between different pairs. The lowest expansion order corresponds to the limit of infinitely entangled states, where different pairs are completely uncorrelated and the full multi-pair statistics are inferred from a single pair. We show that even this limiting case always yields more accurate results than the single-pair approximation. Higher expansion orders describe deviations from the infinitely entangled case and introduce correlations between the photons of different pairs., Comment: 38 pages, 4 figures

We report on experimental investigation of potential high-performance cavity length stabilization using odd-indexed higher-order spatial modes. Schemes based on higher-order modes are particularly useful for micro-cavities that are used for enhanced fluorescence detection of a few emitters, which need to minimize photons leaking from a stabilization beam. We describe the design and construction of an assembly for a microcavity setup with tunable high passive stability. In addition, different types of active stabilization techniques based on higher-order modes, are then implemented and characterized based on their performance. We achieved a stability of about 0.5 pm RMS, while the error photons leaking from the continuous locking beam to a fluorescence detector are suppressed by more than 100-fold. We expect these results to be important for quantum technology implementations of various emitter-cavity setups, where these techniques provide a useful tool to meet the highly challenging demands.

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

We show simplified DPS-QKD using a SiN micro-ring resonator operated at 852 nm. A raw-key rate of up to 25.3 kb/s is reached at a QBER suitable for secure-key extraction. Short-reach QKD operation is maintained for zero-touch link layouts with C-band telecom fiber.

Due to its fundamental principles, quantum theory holds the promise to enhance the security of modern cryptography, from message encryption to anonymous communication, digital signatures, online banking, leader election, one-time passwords and delegated computation. While quantum key distribution (QKD) has already enabled secure key exchange over hundreds of kilometers, a myriad of other quantum-cryptographic primitives are being developed to secure future applications against quantum adversaries. This article surveys the theoretical and experimental developments in quantum cryptography beyond QKD over the past decades, along with advances in secure quantum computation. It provides an intuitive classification of the main quantum primitives and their security levels, summarizes their possibilities and limits, and discusses their implementation with current photonic technology., Comment: Submitted to Reviews of Modern Physics. Comments are welcome

The present paper reports on wind tunnel tests and analyses carried out to investigate the effect of the static angle of attack on the aerodynamic stability of a twin-box bridge deck section. It is found that the critical wind speed for onset of flutter increases with increasing positive static angles (nose-up) and that this effect relates mainly to a decrease in the loss of aerodynamic stiffness. A simplified flutter analysis, linking the slope of the static moment coefficient to the increase of flutter stability for increasing positive angles of attack. It is concluded that it is desirable to design twin-box bridge deck sections to have a positive moment coefficient at zero angle of attack and a positive, decreasing moment slope for increasing nose-up angles. With these requirements fulfilled, the present study show that the critical wind speeds for onset of flutter increase with increasing angles, and ensures that the elastically supported deck will always meet the wind at ever increasing angles for increasing wind speeds., Comment: 12 pages, 6 figures, submitted to proceedings of the 16th International Conference on Wind Engineering (ICWE16) 2023, Florence, Italy

The paper considers the problem of detecting and localizing changepoints in a sequence of independent observations. We propose to evaluate a local test statistic on a triplet of time points, for each such triplet in a particular collection. This collection is sparse enough so that the results of the local tests can simply be combined with a weighted Bonferroni correction. This results in a simple and fast method, {\sl Lean Bonferroni Changepoint detection} (LBD), that provides finite sample guarantees for the existance of changepoints as well as simultaneous confidence intervals for their locations. LBD is free of tuning parameters, and we show that LBD allows optimal inference for the detection of changepoints. To this end, we provide a lower bound for the critical constant that measures the difficulty of the changepoint detection problem, and we show that LBD attains this critical constant. We illustrate LBD for a number of distributional settings, namely when the observations are homoscedastic normal with known or unknown variance, for observations from a natural exponential family, and in a nonparametric setting where we assume only exchangeability for segments without a changepoint.

We present a detailed study of a scalar differential equation with threshold state-dependent delayed feedback. This equation arises as a simplification of a gene regulatory model. There are two monotone nonlinearities in the model: one describes the dependence of delay on state, and the other is the feedback nonlinearity. Both increasing and decreasing nonlinearities are considered. Our analysis is exhaustive both analytically and numerically as we examine the bifurcations of the system for various combinations of increasing and decreasing nonlinearities. We identify rich bifurcation patterns including Bautin, Bogdanov-Takens, cusp, fold, homoclinic, and Hopf bifurcations whose existence depend on the derivative signs of nonlinearities. Our analysis confirms many of these patterns in the limit where the nonlinearities are switch-like and change their value abruptly at a threshold. Perhaps one of the most surprising findings is the existence of a Hopf bifurcation to a periodic solution when the nonlinearity is monotone increasing and the time delay is a decreasing function of the state variable--something that is impossible in the case of a constant delay.

Emergent strongly-correlated electronic phenomena in atomically-thin transition metal dichalcogenides are an exciting frontier in condensed matter physics, with examples ranging from bilayer superconductivity~\cite{zhao2023evidence} and electronic Wigner crystals~\cite{smolenski2021signatures,zhou2021bilayer} to the ongoing quest for exciton condensation~\cite{wang2019evidence,ma2021strongly,shi2022bilayer}. Here, we experimentally investigate the properties of indirect excitons in naturally-grown MoS$_2$-homobilayer, integrated in a dual-gate device structure allowing independent control of the electron density and out-of-plane electric field. Under conditions when electron tunneling between the layers is negligible~\cite{pisoni2019absence}, upon electron doping the sample, we observe that the two excitons with opposing dipoles hybridize, displaying unusual behavior distinct from both conventional level crossing and anti-crossing. We show that these observations can be explained by static random coupling between the excitons, which increases with electron density and decreases with temperature. We argue that this phenomenon is indicative of a spatially fluctuating order parameter in the form of interlayer electron coherence, a theoretically predicted many-body state~\cite{zheng1997exchange} that has yet to be unambiguously established experimentally outside of the quantum Hall regime~\cite{sarma2008perspectives,spielman2000resonantly,kellogg2004vanishing,kellogg2002observation,spielman2001observation,fertig1989energy,shi2022bilayer}. Implications of our findings for future experiments and quantum optics applications are discussed.

In the manufacturing industry, the digital twin (DT) is becoming a central topic. It has the potential to enhance the efficiency of manufacturing machines and reduce the frequency of errors. In order to fulfill its purpose, a DT must be an exact enough replica of its corresponding physical object. Nevertheless, the physical object endures a lifelong process of degradation. As a result, the digital twin must be modified accordingly in order to satisfy the accuracy requirement. This article introduces the novel concept of "learning digital twin (LDT)," which concentrates on the temporal behavior of the physical object and highlights the digital twin's capacity for lifelong learning. The structure of a LDT is first described. Then, in-depth descriptions of various algorithms for implementing each component of a LDT are provided. The proposed LDT is validated on the simulated degradation process of an anisotropic non-ideal rotor system.

State-of-the-art laser frequency stabilization is limited by miniscule length changes caused by thermal noise. In this work, a cavity-length-insensitive frequency stabilization scheme is implemented using strong dispersion in a $21\,\mathrm{mm}$ long cavity with a europium-ion-doped spacer of yttrium orthosilicate. A number of limiting factors for slow light laser stabilization are evaluated, including the inhomogeneous and homogeneous linewidth of the ions, the deterioration of spectral windows, and the linewidth of the cavity modes. Using strong dispersion, the cavity modes were narrowed by a factor $1.6\cdot 10^5$, leading to a cavity linewidth of $3.0\,\mathrm{kHz}$ and a $Q$ factor of $1.7\cdot 10^{11}$. Frequency stabilization was demonstrated using a cavity mode in a spectral transparency region near the center of the inhomogeneous profile, showing an overlapping Allan deviation below $6\cdot 10^{-14}$ and a linear drift rate of $3.66\,\mathrm{Hz}/\mathrm{s}$. Considering improvements that could be implemented, this makes the europium-based slow light laser frequency reference a promising candidate for ultra-precise tabletop frequency stabilization., Comment: 15 pages, 7 figures

Quantum computing has brought a paradigm change in computer science, where non-classical technologies have promised to outperform their classical counterpart. Such an advantage was only demonstrated for tasks without practical applications, still out of reach for the state-of-art quantum technologies. In this context, a promising strategy to find practical use of quantum computers is to exploit hybrid quantum-classical models, where a quantum device estimates a hard-to-compute quantity, while a classical optimizer trains the parameters of the model. In this work, we demonstrate that single photons and linear optical networks are sufficient for implementing Variational Quantum Algorithms, when the problem specification, or ansatz, is tailored to this specific platform. We show this by a proof-of-principle demonstration of a variational approach to tackle an instance of a factorization task, whose solution is encoded in the ground state of a suitable Hamiltonian. This work which combines Variational Quantum Algorithms with hardware efficient ansatzes for linear-optics networks showcases a promising pathway towards practical applications for photonic quantum platforms.

Nature disasters play a key role in shaping human-urban infrastructure interactions. Effective and efficient response to natural disasters is essential for building resilience and a sustainable urban environment. Two types of information are usually the most necessary and difficult to gather in disaster response. The first information is about disaster damage perception, which shows how badly people think that urban infrastructure has been damaged. The second information is geolocation awareness, which means how people whereabouts are made available. In this paper, we proposed a novel disaster mapping framework, namely CVDisaster, aiming at simultaneously addressing geolocalization and damage perception estimation using cross-view Street-View Imagery (SVI) and Very High-Resolution satellite imagery. CVDisaster consists of two cross-view models, where CVDisaster-Geoloc refers to a cross-view geolocalization model based on a contrastive learning objective with a Siamese ConvNeXt image encoder, and CVDisaster-Est is a cross-view classification model based on a Couple Global Context Vision Transformer (CGCViT). Taking Hurricane IAN as a case study, we evaluate the CVDisaster framework by creating a novel cross-view dataset (CVIAN) and conducting extensive experiments. As a result, we show that CVDisaster can achieve highly competitive performance (over 80% for geolocalization and 75% for damage perception estimation) with even limited fine-tuning efforts, which largely motivates future cross-view models and applications within a broader GeoAI research community. The data and code are publicly available at: https://github.com/tum-bgd/CVDisaster.

The Three-Body Problem has fascinated scientists for centuries and it has been crucial in the design of modern space missions. Recent developments in Generative Artificial Intelligence hold transformative promise for addressing this longstanding problem. This work investigates the use of Variational Autoencoder (VAE) and its internal representation to generate periodic orbits. We utilize a comprehensive dataset of periodic orbits in the Circular Restricted Three-Body Problem (CR3BP) to train deep-learning architectures that capture key orbital characteristics, and we set up physical evaluation metrics for the generated trajectories. Through this investigation, we seek to enhance the understanding of how Generative AI can improve space mission planning and astrodynamics research, leading to novel, data-driven approaches in the field., Comment: SPAICE Conference 2024 (7 pages)

The interaction between a single emitter and a single photon is a fundamental aspect of quantum optics. This interaction allows for the study of various quantum processes, such as emitter-mediated single-photon scattering and effective photon-photon interactions. However, empirical observations of this scenario and its dynamics are rare, and in most cases, only partial approximations to the fully quantized case have been possible. Here, we demonstrate the resonant excitation of a solid-state quantum emitter using quantized input light. For this light-matter interaction, with both entities quantized, we observe single-photon interference introduced by the emitter in a coherent scattering process, photon-number-depended optical non-linearities, and stimulated emission processes involving only two photons. We theoretically reproduce our observations using a cascaded master equation model. Our findings demonstrate that a single photon is sufficient to change the state of a solid-state quantum emitter, and efficient emitter-mediated photon-photon interactions are feasible. These results suggest future possibilities ranging from enabling quantum information transfer in a quantum network to building deterministic entangling gates for photonic quantum computing., Comment: 13 pages, 8 figures

Recently, machine learning had a remarkable impact, from scientific to everyday-life applications. However, complex tasks often imply unfeasible energy and computational power consumption. Quantum computation might lower such requirements, although it is unclear whether enhancements are reachable by current technologies. Here, we demonstrate a kernel method on a photonic integrated processor to perform a binary classification. We show that our protocol outperforms state-of-the-art kernel methods including gaussian and neural tangent kernels, exploiting quantum interference, and brings a smaller improvement also by single photon coherence. Our scheme does not require entangling gates and can modify the system dimension through additional modes and injected photons. This result opens to more efficient algorithms and to formulating tasks where quantum effects improve standard methods.

We present a method for blind acoustic parameter estimation from single-channel reverberant speech. The method is structured into three stages. In the first stage, a variational auto-encoder is trained to extract latent representations of acoustic impulse responses represented as mel-spectrograms. In the second stage, a separate speech encoder is trained to estimate low-dimensional representations from short segments of reverberant speech. Finally, the pre-trained speech encoder is combined with a small regression model and evaluated on two parameter regression tasks. Experimentally, the proposed method is shown to outperform a fully end-to-end trained baseline model., Comment: Accepted for publication at IWAENC 2024

Entangled quantum states are essential ingredients for many quantum technologies, but they must be validated before they are used. As a full characterization is prohibitively resource-intensive, recent work has focused on developing methods to efficiently extract a few parameters of interest, in a so-called verification framework. Most existing approaches are based on preparing an ensemble of nominally identical and independent (IID) quantum states, and then measuring each copy of the ensemble. However, this leaves no states left for the intended quantum tasks and the IID assumptions do not always hold experimentally. To overcome these challenges, we experimentally implement quantum state certification (QSC), which measures only a subset of the ensemble, certifying the fidelity of the remaining states. We use active optical switches to randomly sample from sources of two-photon Bell states and three-photon GHZ states, reporting statistically-sound fidelities in real time without destroying the entire ensemble. Additionally, our QSC protocol removes the assumption that the states are identical, is device-independent, and can achieve close $N^{-1}$ scaling, in the number of states measured $N$. Altogether, these benefits make our QSC protocol suitable for benchmarking large-scale quantum computing devices and deployed quantum communication setups relying on entanglement in both standard and adversarial situations., Comment: 8 pages, 4 figures

Excitons in transition metal dichalcogenides (TMDs) have emerged as a promising platform for novel applications ranging from optoelectronic devices to quantum optics and solid state quantum simulators. While much progress has been made towards characterizing and controlling excitons in TMDs, manipulating their properties during the course of their lifetime - a key requirement for many optoelectronic device and information processing modalities - remains an outstanding challenge. Here we combine long-lived interlayer excitons in angle-aligned MoSe$_2$/WSe$_2$ heterostructures with fast electrical control to realize dynamical control schemes, in which exciton properties are not predetermined at the time of excitation but can be dynamically manipulated during their lifetime. Leveraging the out-of-plane exciton dipole moment, we use electric fields to demonstrate dynamical control over the exciton emission wavelength. Moreover, employing a patterned gate geometry, we demonstrate rapid local sample doping and toggling of the radiative decay rate through exciton-charge interactions during the exciton lifetime. Spatially mapping the exciton response reveals charge redistribution, offering a novel probe of electronic transport in twisted TMD heterostructures. Our results establish the feasibility of dynamical exciton control schemes, unlocking new directions for exciton-based information processing and optoelectronic devices, and the realization of excitonic phenomena in TMDs., Comment: 37 pages, 4 figures in main text, 6 figures in supplemental materials; (v2) corrected funding acknowledgements

The prediction of time series is a challenging task relevant in such diverse applications as analyzing financial data, forecasting flow dynamics or understanding biological processes. Especially chaotic time series that depend on a long history pose an exceptionally difficult problem. While machine learning has shown to be a promising approach for predicting such time series, it either demands long training time and much training data when using deep Recurrent Neural Networks. Alternative, when using a Reservoir Computing approach it comes with high uncertainty and typically a high number of random initializations and extensive hyper-parameter tuning. In this paper, we focus on the Reservoir Computing approach and propose a new mapping of input data into the reservoir's state space. Furthermore, we incorporate this method in two novel network architectures increasing parallelizability, depth and predictive capabilities of the neural network while reducing the dependence on randomness. For the evaluation, we approximate a set of time series from the Mackey-Glass equation, inhabiting non-chaotic as well as chaotic behavior as well as the SantaFe Laser dataset and compare our approaches in regard to their predictive capabilities to Echo State Networks, Autoencoder connected Echo State Networks and Gated Recurrent Units. For the chaotic time series, we observe an error reduction of up to $85.45\%$ compared to Echo State Networks and $90.72\%$ compared to Gated Recurrent Units. Furthermore, we also observe tremendous improvements for non-chaotic time series of up to $99.99\%$ in contrast to the existing approaches.

With the growth of digital monetary transactions and cashless payments, encouraged by the COVID-19 pandemic, use of e-payment applications is on the rise. It is thus imperative to understand and evaluate the current posture of e-payment applications from three major user-facing angles: security, privacy, and usability. To this, we created a high-fidelity prototype of an e-payment application that encompassed features that we wanted to test with users. We then conducted a pilot study where we recruited 12 participants who tested our prototype. We find that both security and privacy are important for users of e-payment applications. Additionally, some participants perceive the strength of security and privacy based on the usability of the application. We provide recommendations such as universal design of e-payment applications.

The paper provides a detailed proof that complicated motion exists in Shilnikov's scenario of a smooth vectorfield $V$ on $mathbb{R}^3$ with $V(0)=0$ so that the equation $x'=V(x)$ has a homoclinic solution $h$ with $\lim_{|t|\to\infty}h(t)=0$, and $DV(0)$ has eigenvalues $u>0$ and $\sigma\pm\mu$, $\sigma<0<\mu$, with $0<\sigma+u$., Comment: 34 pages, 8 figures. Corrections of errors in pages/lines 3/6, 14/9, 14/bottom,17/3, 19/10, 21/12+13+14+16+18, 29/26+29+32, 30/2+5+6,31/6+11, 33/26+28+29 of the first version

We experimentally and numerically study the collapse dynamics of a quantum vortex in a two-dimensional atomic superfluid following a fast interaction ramp from repulsion to attraction. We find the conditions and time scales for a superfluid vortex to radially converge into a quasi-stationary density profile, demonstrating the first spontaneous formation of a vortex soliton in an atomic Bose gas. We record an emergent universal dynamics of an azimuthal modulational instability, which amplifies initial density perturbations and leads to the eventual splitting of a vortex soliton or direct fragmentation of a superfluid into disordered, but roughly circular arrays of Townes soliton-like wavepackets. Our study sets the stage for exploring universal out-of-equilibrium dynamics of vortex quantum matter quenched to attractive interactions.