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This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

Cognition and emotion must be partnered in any complete model of a humanlike mind. This article proposes an extension to the Common Model of Cognition -- a developing consensus concerning what is required in such a mind -- for emotion that includes a linked pair of modules for emotion and metacognitive assessment, plus pervasive connections between these two new modules and the Common Model's existing modules and links., Comment: A version of this article was published in Proceedings of the 22nd International Conference on Cognitive Modeling (2024)

The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamic foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook., Comment: 15 pages, 6 figures, submitted to CAiSE 2025

Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose a self-supervised masked mesh learning for unsupervised anomaly detection in 3D cortical surfaces. Our framework leverages the intrinsic geometry of the cortical surface to learn a self-supervised representation that captures the underlying structure of the brain. We introduce a masked mesh convolutional neural network (MMN) that learns to predict masked regions of the cortical surface. By training the MMN on a large dataset of healthy subjects, we learn a representation that captures the normal variation in the cortical surface. We then use this representation to detect anomalies in unseen individuals by calculating anomaly scores based on the reconstruction error of the MMN. We evaluate our framework by training on population-scale dataset UKB and HCP-Aging and testing on two datasets of Alzheimer's disease patients ADNI and OASIS3. Our results show that our framework can detect anomalies in cortical thickness, cortical volume, and cortical sulcus features, which are known to be sensitive biomarkers for Alzheimer's disease. Our proposed framework provides a promising approach for unsupervised anomaly detection based on normative variation of cortical features.

Time-resolved X-ray absorption spectroscopy (tr-XAS) has been shown to be a versatile measurement technique for investigating non-equilibrium dynamics. Novel X-ray free electron laser (XFEL) facilities like the European XFEL offer increased repetition rates for stroboscopic XAS experiments through a burst operation mode, which enables measurements with up to 4.5 MHz. These higher repetition rates lead to higher data acquisition rates but can also introduce long-lived excitations that persist and thus build up during each burst. Here, we report on such long-lived effects in Ni and NiO thin film samples that were measured at the European XFEL. We disentangle the long-lived excitations from the initial pump-induced change and perform a detailed modelling-based analysis of how they modify transient X-ray spectra. As a result, we link the long-lived effects in Ni to a local temperature increase, as well as the effects in NiO to excited charge carrier trapping through polaron formation. In addition, we present possible correction methods, as well as discuss ways in which the effects of these long-lived excitations could be minimized for future time-resolved X-ray absorption spectroscopy measurements.

In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion. For the two most common benchmarks, we find that a substantial fraction of the performance difference can be explained by one unique negative pattern on each dataset that is hidden from the rule-based approach. Our findings add a unique perspective on the performance difference of different model classes for knowledge graph completion: Models can achieve a predictive performance advantage by penalizing scores of incorrect facts opposed to providing high scores for correct facts.

The EAGLE-1 mission aims to develop Europe's first sovereign, end-to-end space-based quantum key distribution (QKD) system. The mission is led by the European Space Agency (ESA) and SES in collaboration with several European National Space Agencies and private partners. The state-of-the-art QKD system will consist of a payload on board the EAGLE-1 low Earth orbit (LEO) satellite, optical ground stations, quantum operational networks, and key management system. The EAGLE-1 mission represents a major step for next-generation quantum communication infrastructures, delivering valuable technical results and mission data. The Institute of Communications and Navigation (IKN) of the German Aerospace Center (DLR) is a key partner in the EAGLE-1 mission and is involved in the research and development of elements in both space and ground segments. Here we report on the development of the QKD transmitter, a vital part of the QKD payload, and the customization of the Optical Ground Station Oberpfaffenhofen (OGS-OP) to conduct the IOT phase of EAGLE-1. For the space segment, DLR-IKN is in charge of the design of the QKD transmitter, including the development of the software and firmware. This transmitter generates quantum states which are used to implement a QKD protocol based on an optical signal, that will be transmitted to ground. For the ground segment, The OGS-OP will serve as the in-orbit testing ground station for EAGLE-1. Building upon the expertise with a range of satellites for quantum communication, as well as new implementations, OGS-OP will validate the performance of the payload, optical link and QKD system for the first time. We present the main developments of OGS-OP for the mission, which includes the implementation of an upgraded adaptive optics system to correct for atmospheric distortions and optimize the coupling of the incoming light into a single mode optical fiber., Comment: 9 pages, 5 figures. Manuscript presented at the 75th International Astronautical Congress (IAC), Milan, Italy, 14-18 October 2024

To take full advantage of twisted bilayers of graphene or other two-dimensional materials, it is essential to precisely control the twist angle between the stacked layers, as this parameter determines the properties of the heterostructure. In this context, a growth routine using borazine as a surfactant molecule on SiC(0001) surfaces has been reported, leading to the formation of high-quality epitaxial graphene layers that are unconventionally oriented, i.e., aligned with the substrate lattice (G-$R0^\circ$) [Bocquet et al. Phys. Rev. Lett. 125, 106102 (2020)]. Since the G-$R0^\circ$ layer sits on a buffer layer, also known as zeroth-layer graphene (ZLG), which is rotated $30^\circ$ with respect to the SiC substrate and still covalently bonded to it, decoupling the ZLG-$R30^\circ$ from the substrate can lead to high-quality twisted bilayer graphene (tBLG). Here we report the decoupling of ZLG-$R30^\circ$ by increasing the temperature during annealing in a borazine atmosphere. While this converts ZLG-$R30^\circ$ to G-$R30^\circ$ and thus produces tBLG, the growth process at elevated temperature is no longer self-limiting, so that the surface is covered by a patchwork of graphene multilayers of different thicknesses. We find a 20% coverage of tBLG on ZLG, while on the rest of the surface tBLG sits on one or more additional graphene layers. In order to achieve complete coverage with tBLG only, alternative ways of decoupling the ZLG, e.g., by intercalation with suitable atoms, may be advantageous., Comment: 14 + 1 pages, 9 + 1 figures

Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as well as brain disorders like multiple sclerosis and Alzheimer's disease. However, MLVs have only recently been described for the first time in magnetic resonance imaging (MRI), and their ramified structure renders manual segmentation particularly difficult. Further, as there is no consistent notion of their appearance, human-annotated MLV structures contain a high inter-rater variability that most automatic segmentation methods cannot take into account. In this work, we propose a new rater-aware training scheme for the popular nnU-Net model, and we explore rater-based ensembling strategies for accurate and consistent segmentation of MLVs. This enables us to boost nnU-Net's performance while obtaining explicit predictions in different annotation styles and a rater-based uncertainty estimation. Our final model, MLV$^2$-Net, achieves a Dice similarity coefficient of 0.806 with respect to the human reference standard. The model further matches the human inter-rater reliability and replicates age-related associations with MLV volume., Comment: ML4H 2024

We survey undergraduate students at a large public university to understand the pecuniary and non-pecuniary factors driving their college major and career decisions with a focus on K-12 teaching. While the average student reports there is a 6% chance they will pursue teaching, almost 27% report a nonzero chance of working as a teacher in the future. Students, relative to existing statistics, generally believe they would earn substantially more in a non-teaching job (relative to a teaching job). We run a randomized information experiment where we provide students with information on the pecuniary and non-pecuniary job characteristics of teachers and non-teachers. This low-cost informational intervention impacts students' beliefs about their job characteristics if they were to work as a teacher or non-teacher, and increases the reported likelihood they will major or minor in education by 35% and pursue a job as a teacher or in education by 14%. Linking the survey data with administrative transcript records, we find that the intervention had small (and weak) impacts on the decision to minor in education in the subsequent year. Overall, our results indicate that students hold biased beliefs about their career prospects, they update these beliefs when provided with information, and that this information has limited impacts on their choices regarding studying and having a career in teaching.

The infinitesimal counterpart of a Lie groupoid is its Lie algebroid. As a vector bundle, it is given by the source vertical tangent bundle restricted to the identity bisection. Its sections can be identified with the invariant vector fields on the groupoid, which are closed under the Lie bracket. We generalize this differentiation procedure to groupoid objects in any category with an abstract tangent structure in the sense of Rosick\'{y} and a scalar multiplication by a ring object that plays the role of the real numbers. We identify the categorical conditions that the groupoid object must satisfy to admit a natural notion of invariant vector fields. Then we show that invariant vector fields are closed under the Lie bracket defined by Rosick\'{y} and satisfy the Leibniz rule with respect to ring-valued morphisms on the base of the groupoid. The result is what we define axiomatically as an abstract Lie algebroid, by generalizing the underlying vector bundle to a module object in the slice category over its base. Examples include diffeomorphism groups, bisection groups of Lie groupoids, the diffeological symmetry groupoids of general relativity (Blohmann/Fernandes/Weinstein), symmetry groupoids in Lagrangian Field Theory, holonomy groupoids of singular foliations, elastic diffeological groupoids, and groupoid objects in differentiable stacks., Comment: 88 pages, comments welcome

We use a combination of ultra-high-resolution inelastic neutron scattering and Monte Carlo numerical simulations to study the thermodynamics and the structure of spin excitations in the spin-supersolid phase of the triangular lattice XXZ easy axis antiferromagnet K$_2$Co(SeO$_3$)$_2$ and its evolution in a magnetic field. BKT transitions heralding the onset of Ising and supersolid order are detected. Above the supersolid phase the value of Wannier entropy is experimentally recovered. At low temperatures, with an experimental resolution of about 23 $\mu$eV, no discrete coherent magnon modes are resolved within a broad continuum of scattering. In addition to gapless excitations, a pseudo-Goldstone mode with a small energy gap of 0.06 meV is found. A second excitation continuum is seen at higher energy, in place of single-spin-flip excitations of the Ising model. In applied fields the continuum gradually morphs into coherent spin waves, with the Goldstone and pseudo-Goldstone sectors showing distinct evolution. The agreement between experiment and numerical simulations is excellent on the quantitative level., Comment: 18 pages, 25 figures

Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algorithm for efficiently learning a large set of non-commuting observables, using shallow circuits to rotate into measurement bases. Exploiting tensor network techniques to ensure polynomial scaling of classical resources, our algorithm outputs a set of shallow measurement circuits that approximately minimizes the sample complexity of estimating a given set of Pauli strings. We numerically demonstrate systematic improvement, in comparison with state-of-the-art techniques, for energy estimation of quantum chemistry benchmarks and verification of quantum many-body systems, and we observe DSS's performance consistently improves as one allows deeper measurement circuits. These results indicate that in addition to being an efficient, low-depth, stand-alone algorithm, DSS can also benefit many larger quantum algorithms requiring estimation of multiple non-commuting observables., Comment: 10+29 pages, 9 figures

We present a proof of a conjecture of Goh and Wildberger on the factorization of the spread polynomials. We indicate how the factors can be effectively calculated and exhibit a connection to the factorization of Fibonacci numbers into primitive parts., Comment: 8 pages

We experimentally study cosmological particle production in a two-dimensional Bose-Einstein condensate, whose density excitations map to an analog cosmology. The expansion of spacetime is realized with tunable interactions. The particle spectrum can be understood through an analogy to quantum mechanical scattering, in which the dynamics of the spacetime metric determine the shape of the scattering potential. Hallmark scattering phenomena such as resonant forward scattering and Bragg reflection are connected to their cosmological counterparts, namely linearly expanding space and bouncing universes. We compare our findings to a theoretical description that extends beyond the acoustic approximation, which enables us to apply the model to high-momentum excitations.

Extended objects (defects) in Quantum Field Theory exhibit rich, nontrivial dynamics describing a variety of physical phenomena. These systems often involve strong coupling at long distances, where the bulk and defects interact, making analytical studies challenging. By carefully analyzing the behavior of bulk symmetries in the presence of defects, we uncover robust topological constraints on defect RG flows. Specifically, we introduce the notions of $\textit{defect anomalies}$ and $\textit{strongly symmetric defects}$, both of which are RG-invariant. Several known notions, such as higher-form symmetries, fractionalization, and projective lines, are revealed to be manifestations of defect anomalies, which also encompass novel phenomena and forbid trivial defect dynamics in the IR. Meanwhile, strongly symmetric defects are shown to remain coupled at low energies, imposing powerful dynamical constraints. We verify our findings through concrete examples: exactly solvable defect RG flows in (1+1)d Conformal Field Theories with strongly symmetric lines and a surface defect in (2+1)d scalar QED., Comment: 31 pages, 3 figures, comments are welcome

The growing ubiquity of Retrieval-Augmented Generation (RAG) systems in several real-world services triggers severe concerns about their security. A RAG system improves the generative capabilities of a Large Language Models (LLM) by a retrieval mechanism which operates on a private knowledge base, whose unintended exposure could lead to severe consequences, including breaches of private and sensitive information. This paper presents a black-box attack to force a RAG system to leak its private knowledge base which, differently from existing approaches, is adaptive and automatic. A relevance-based mechanism and an attacker-side open-source LLM favor the generation of effective queries to leak most of the (hidden) knowledge base. Extensive experimentation proves the quality of the proposed algorithm in different RAG pipelines and domains, comparing to very recent related approaches, which turn out to be either not fully black-box, not adaptive, or not based on open-source models. The findings from our study remark the urgent need for more robust privacy safeguards in the design and deployment of RAG systems.

In distributed learning agents aim at collaboratively solving a global learning problem. It becomes more and more likely that individual agents are malicious or faulty with an increasing size of the network. This leads to a degeneration or complete breakdown of the learning process. Classical aggregation schemes are prone to breakdown at small contamination rates, therefore robust aggregation schemes are sought for. While robust aggregation schemes can generally tolerate larger contamination rates, many have been shown to be susceptible to carefully crafted malicious attacks. In this work, we show how the sensitivity curve (SC), a classical tool from robust statistics, can be used to systematically derive optimal attack patterns against arbitrary robust aggregators, in most cases rendering them ineffective. We show the effectiveness of the proposed attack in multiple simulations.

We consider a model of random curves in the plane related to the large-scale behavior of the Random Field Ising Model (RFIM) at temperature zero in two space dimensions. Our work is motivated by attempts to quantify the Imry--Ma phenomenon concerning the rounding of the phase transition by quenched disorder, and connects to recent advances regarding the decay of correlations in the RFIM. We study a continuum model of minimal surfaces in two space dimensions subject to an external, quenched random field, and restrict ourselves to isotropic surface integrands. The random fields we consider behave like white noise on large scales with an ultra-violet regularization reminiscent of the lattice structure of the RFIM. We give a finer description of the minimizer below the length scale starting from which the influence of boundary conditions is suppressed with a given probability, which has recently been shown to be of order $\exp(\varepsilon^{-\frac{4}{3}})$ in the amplitude $\varepsilon>0$ of the noise. More precisely, we prove flatness of the phase boundaries on scales up to order $\exp(\varepsilon^{-\frac{4}{13}})$., Comment: 36 pages

Time series forecasting has important applications across diverse domains. EasyTime, the system we demonstrate, facilitates easy use of time-series forecasting methods by researchers and practitioners alike. First, EasyTime enables one-click evaluation, enabling researchers to evaluate new forecasting methods using the suite of diverse time series datasets collected in the preexisting time series forecasting benchmark (TFB). This is achieved by leveraging TFB's flexible and consistent evaluation pipeline. Second, when practitioners must perform forecasting on a new dataset, a nontrivial first step is often to find an appropriate forecasting method. EasyTime provides an Automated Ensemble module that combines the promising forecasting methods to yield superior forecasting accuracy compared to individual methods. Third, EasyTime offers a natural language Q&A module leveraging large language models. Given a question like "Which method is best for long term forecasting on time series with strong seasonality?", EasyTime converts the question into SQL queries on the database of results obtained by TFB and then returns an answer in natural language and charts. By demonstrating EasyTime, we intend to show how it is possible to simplify the use of time series forecasting and to offer better support for the development of new generations of time series forecasting methods., Comment: Accepted by ICDE2025

Multi-channel photoplethysmography (PPG) sensors have found widespread adoption in wearable devices for monitoring cardiac health. Channels thereby serve different functions -- whereas green is commonly used for metrics such as heart rate and heart rate variability, red and infrared are commonly used for pulse oximetry. In this paper, we introduce a novel method that simultaneously fuses multi-channel PPG signals into a single recovered PPG signal that can be input to further processing. Via signal fusion, our learning-based method compensates for the artifacts that affect wavelengths to different extents, such as motion and ambient light changes. We evaluate our method on a novel dataset of multi-channel PPG recordings and electrocardiogram recordings for reference from 10 participants over the course of 13 hours during real-world activities outside the laboratory. Using the fusion PPG signal our method recovered, participants' heart rates can be calculated with a mean error of 4.5\,bpm (23\% lower than from green PPG signals at 5.9\,bpm)., Comment: Accepted at BSN2024

Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR estimates can be substantially impacted by factors such as the wearer's activities, sensor placement and resulting motion artifacts, as well as environmental characteristics such as temperature and ambient light. These and other factors can significantly impact and decrease HR prediction reliability. In this paper, we show that state-of-the-art HR estimation methods struggle when processing \emph{representative} data from everyday activities in outdoor environments, likely because they rely on existing datasets that captured controlled conditions. We introduce a novel multimodal dataset and benchmark results for continuous PPG recordings during outdoor activities from 16 participants over 13.5 hours, captured from four wearable sensors, each worn at a different location on the body, totaling 216\,hours. Our recordings include accelerometer, temperature, and altitude data, as well as a synchronized Lead I-based electrocardiogram for ground-truth HR references. Participants completed a round trip from Zurich to Jungfraujoch, a tall mountain in Switzerland over the course of one day. The trip included outdoor and indoor activities such as walking, hiking, stair climbing, eating, drinking, and resting at various temperatures and altitudes (up to 3,571\,m above sea level) as well as using cars, trains, cable cars, and lifts for transport -- all of which impacted participants' physiological dynamics. We also present a novel method that estimates HR values more robustly in such real-world scenarios than existing baselines., Comment: Accepted at NeurIPS2024

Smartwatches have become popular for monitoring physiological parameters outside clinical settings. Using reflective photoplethysmography (PPG) sensors, such watches can non-invasively estimate heart rate (HR) in everyday environments and throughout a patient's day. However, achieving consistently high accuracy remains challenging, particularly during moments of increased motion or due to varying device placement. In this paper, we introduce a novel sensor fusion method for estimating HR that flexibly combines samples from multiple PPG sensors placed across the patient's body, including wrist, ankle, head, and sternum (chest). Our method first estimates signal quality across all inputs to dynamically integrate them into a joint and robust PPG signal for HR estimation. We evaluate our method on a novel dataset of PPG and ECG recordings from 14 participants who engaged in real-world activities outside the laboratory over the course of a whole day. Our method achieves a mean HR error of 2.4\,bpm, which is 46\% lower than the mean error of the best-performing single device (4.4\,bpm, head)., Comment: Accepted at EMBC2024

We show that the state spaces of multifactor Markovian processes, coming from approximations of nonnegative Volterra processes, are given by explicit linear transformation of the nonnegative orthant. We demonstrate the usefulness of this result for applications, including simulation schemes and PDE methods for nonnegative Volterra processes.

In topological data analysis, the notions of persistent homology, birthtime, lifetime, and deathtime are used to assign and capture relevant cycles (i.e., topological features) of a point cloud, such as loops and cavities. In particular, cycles with a large lifetime are of special interest. In this paper, we study such large-lifetime cycles when the point cloud is modeled as a Poisson point process. First, we consider the case with no bound on the deathtime, where we establish Poisson convergence of the centers of large-lifetime features on the 2-dimensional flat torus. Afterwards, by imposing a bound on the deathtime, we enter a sparse connectivity regime, and we prove joint Poisson convergence of the centers, lifetimes, and deathtimes in dimensions $d \geq 2$ under suitable model conditions., Comment: 52 pages, 13 figures

Achieving fast and high-fidelity qubit operations is crucial for unlocking the potential of quantum computers. In particular, reaching low gate errors in two-qubit gates has been a long-standing challenge in the field of superconducting qubits due to their typically long duration relative to coherence times. To realize fast gates, we utilize the hybridization between fixed-frequency superconducting qubits with a strongly interacting coupler mode that is tunable in frequency. To reduce population leakage during required adiabatic passages through avoided level crossings, we employ a sensitivity-adaptive closed-loop optimization method to design complex pulse shapes. We compare the performance of Gaussian-square, Fourier-series, and piecewise-constant-slope (PiCoS) pulse parametrizations and are able to reach 99.9 % controlled-Z gate fidelity using a 64 ns long Fourier-series pulse defined by only seven parameters. These high-fidelity values are achieved by analyzing the optimized pulse shapes to identify and systematically mitigate signal-line distortions in the experiment. To improve the convergence speed of the optimization we implement an adaptive cost function, which continuously maximizes the sensitivity. The demonstrated method can be used for tune-up and recalibration of superconducting quantum processors.

The Integrated Science Investigation of the Sun (IS$\bigodot$IS) energetic particle instrument suite on Parker Solar Probe is dedicated to measuring energetic ions and electrons in the near-Sun environment. It includes a half-sky-viewing time-of-flight mass spectrometer (EPI-Lo) and five high-energy silicon solid-state detector-telescopes (EPI-Hi). To August 2024, eight of EPI-Lo's eighty separate telescope foils have experienced direct dust puncture events, most of which occurred inside 40 solar radii (0.19 au). These impacts represent the closest ever direct dust detections to the Sun. While there is limited information about the size/mass of each impact due to the lack of a dedicated dust instrument, we can determine the impact direction for six punctures, allowing us to partially constrain the inner zodiacal abundance. Remarkably, one of six unambiguous dust impacters was likely on a retrograde orbit, suggesting long-period cometary material may survive within 20 solar radii (0.09 au). We discuss observations in the context of improving our understanding of the inner zodiacal dust environment, highlighting multiple dust populations responsible for these events, and refining hazard assessment for near-Sun spacecraft.

The use of the THz frequency domain in future network generations offers an unparalleled level of capacity, which can enhance innovative applications in wireless communication, analytics, and imaging. Communication technologies rely on frequency mixing, enabling signals to be converted from one frequency to another and transmitted from a sender to a receiver. Technically, this process is implemented using nonlinear components such as diodes or transistors. However, the highest operation frequency of this approach is limited to sub-THz bands. Here, we demonstrate the upconversion of a weak sub-THz signal from a photoconductive antenna to multiple THz bands. The key element is a high-mobility HgTe-based heterostructure with electronic band inversion, leading to one of the strongest third-order nonlinearities among all materials in the THz range. Due to the Dirac character of electron dispersion, the highly intense sub-THz radiation is efficiently mixed with the antenna signal, resulting in a THz response at linear combinations of their frequencies. The field conversion efficiency above 2$\%$ is provided by a bare tensile-strained HgTe layer with a thickness below 100 nm at room temperature under ambient conditions. Devices based on Dirac materials allow for high degree of integration, with field-enhancing metamaterial structures, making them very promising for THz communication with unprecedented data transfer rate., Comment: 7 pages, 4 figures

The non-uniform evaporation rate at the liquid-gas interface of binary droplets induces solutal Marangoni flows. In glycerol-water mixtures (positive Marangoni number, where the more volatile fluid has higher surface tension), these flows stabilise into steady patterns. Conversely, in water-ethanol mixtures (negative Marangoni number, where the less volatile fluid has higher surface tension), Marangoni instabilities emerge, producing seemingly chaotic flows. This behaviour arises from the opposing signs of the Marangoni number. Perturbations locally reducing surface tension at the interface drive Marangoni flows away from the perturbed region. Incompressibility enforces a return flow, drawing fluid from the bulk towards the interface. In mixtures with a negative Marangoni number, preferential evaporation of the lower-surface-tension component leads to a higher concentration of the higher-surface-tension component at the interface as compared to the bulk. The return flow therefore creates a positive feedback loop, further reducing surface tension and enhancing the instability. We investigate bistable quasi-stationary solutions in evaporating binary droplets with negative Marangoni numbers and we examine symmetry breaking across a range of Marangoni number and contact angles. Remarkably, droplets with low contact angle show instabilities at lower critical Marangoni numbers than droplets with larger contact angles. Our numerical simulations reveal that interactions between droplet height profiles and non-uniform evaporation rates trigger azimuthal Marangoni instabilities in flat droplets. This geometrically confined instability can even destabilise mixtures with positive Marangoni numbers, particularly for concave liquid-gas interfaces. Finally, through Lyapunov exponent analysis, we confirm the chaotic nature of flows in droplets with a negative Marangoni number.

Gaussian processes are now commonly used in dimensionality reduction approaches tailored to neuroscience, especially to describe changes in high-dimensional neural activity over time. As recording capabilities expand to include neuronal populations across multiple brain areas, cortical layers, and cell types, interest in extending Gaussian process factor models to characterize multi-population interactions has grown. However, the cubic runtime scaling of current methods with the length of experimental trials and the number of recorded populations (groups) precludes their application to large-scale multi-population recordings. Here, we improve this scaling from cubic to linear in both trial length and group number. We present two approximate approaches to fitting multi-group Gaussian process factor models based on (1) inducing variables and (2) the frequency domain. Empirically, both methods achieved orders of magnitude speed-up with minimal impact on statistical performance, in simulation and on neural recordings of hundreds of neurons across three brain areas. The frequency domain approach, in particular, consistently provided the greatest runtime benefits with the fewest trade-offs in statistical performance. We further characterize the estimation biases introduced by the frequency domain approach and demonstrate effective strategies to mitigate them. This work enables a powerful class of analysis techniques to keep pace with the growing scale of multi-population recordings, opening new avenues for exploring brain function.

We study a deterministic gas of breathers for the Focusing Nonlinear Schr\"odinger equation. The gas of breathers is obtained from a $N$-breather solution in the limit $N\to \infty$.\\ The limit is performed at the level of scattering data by letting the $N$-breather spectrum to fill uniformly a suitable compact domain of the complex plane in the limit $N\to\infty$. The corresponding norming constants are interpolated by a smooth function and scaled as $1/N$. For particular choices of the domain and the interpolating function, the gas of breathers behaves as finite breathers solution. This extends the shielding effect discovered in "M. Bertola, T. Grava, and G. Orsatti - Physical Review Letters, 130.12 (2023): 1" for a soliton gas also to a breather gas.

Coherent optical phonons in the degenerate inter-layer shear mode of graphite launched by femtosecond laser pulses were investigated using ultrafast electron diffraction. The collective atomic motion is shown to be polarized in a direction related to the linear polarization of the incoming laser pulse. Using a driven-oscillator model, the lifetime of the oscillator's driving force is determined to 37(30) fs. This is much shorter than the lifetime of excited carriers in graphite but similar to the time scale of the loss of the hot carrier's k-space anisotropy.

We present the first results from the Quantum Resolution-Optimized Cryogenic Observatory for Dark matter Incident at Low Energy (QROCODILE). The QROCODILE experiment uses a microwire-based superconducting nanowire single-photon detector (SNSPD) as a target and sensor for dark matter scattering and absorption, and is sensitive to energy deposits as low as 0.11 eV. We introduce the experimental configuration and report new world-leading constraints on the interactions of sub-MeV dark matter particles with masses as low as 30 keV. The thin-layer geometry of the system provides anisotropy in the interaction rate, enabling directional sensitivity. In addition, we leverage the coupling between phonons and quasiparticles in the detector to simultaneously constrain interactions with both electrons and nucleons. We discuss the potential for improvements to both the energy threshold and effective volume of the experiment in the coming years., Comment: 5+3 pages, 2+2 figures

Devices in a device-to-device (D2D) network operating in sub-THz frequencies require knowledge of the spatial channel that connects them to their peers. Acquiring such high dimensional channel state information entails large overhead, which drastically increases with the number of network devices. In this paper, we propose an accelerated method to achieve network-wide beam alignment in an efficient way. To this aim, we consider compressed sensing estimation enabled by a novel design of pilot sequences. Our designed pilots have constant envelope to alleviate hardware requirements at the transmitters, while they exhibit a "comb-like"' spectrum that flexibly allocates energy only on certain frequencies. This design enables multiple devices to transmit thier pilots concurrently while remaining orthogonal in frequency, achieving simultaneous alignment of multiple devices. Furthermore, we present a sequential partitioning strategy into transmitters and receivers that results in logarithmic scaling of the overhead with the number of devices, as opposed to the conventional linear scaling. Finally, we show via accurate modeling of the indoor propagation environment and ray tracing simulations that the resulting sub-THz channels after successful beamforming are approximately frequency flat, therefore suitable for efficient single carrier transmission without equalization. We compare our results against an "802.11ad inspired" baseline and show that our method is capable to greatly reduce the number of pilots required to achieve network-wide alignment., Comment: 14 pages, 13 figures. Submitted to IEEE Transactions on Wireless Comunications

Vulnerability detection is crucial for maintaining software security, and recent research has explored the use of Language Models (LMs) for this task. While LMs have shown promising results, their performance has been inconsistent across datasets, particularly when generalizing to unseen code. Moreover, most studies have focused on the C/C++ programming language, with limited attention given to other popular languages. This paper addresses this gap by investigating the effectiveness of LMs for vulnerability detection in JavaScript, Java, Python, PHP, and Go, in addition to C/C++ for comparison. We utilize the CVEFixes dataset to create a diverse collection of language-specific vulnerabilities and preprocess the data to ensure quality and integrity. We fine-tune and evaluate state-of-the-art LMs across the selected languages and find that the performance of vulnerability detection varies significantly. JavaScript exhibits the best performance, with considerably better and more practical detection capabilities compared to C/C++. We also examine the relationship between code complexity and detection performance across the six languages and find only a weak correlation between code complexity metrics and the models' F1 scores.

We show that current open-source foundational LLMs possess instruction capability and German legal background knowledge that is sufficient for some legal analysis in an educational context. However, model capability breaks down in very specific tasks, such as the classification of "Gutachtenstil" appraisal style components, or with complex contexts, such as complete legal opinions. Even with extended context and effective prompting strategies, they cannot match the Bag-of-Words baseline. To combat this, we introduce a Retrieval Augmented Generation based prompt example selection method that substantially improves predictions in high data availability scenarios. We further evaluate the performance of pre-trained LLMs on two standard tasks for argument mining and automated essay scoring and find it to be more adequate. Throughout, pre-trained LLMs improve upon the baseline in scenarios with little or no labeled data with Chain-of-Thought prompting further helping in the zero-shot case., Comment: 11 pages

Bayesian inference in function space has gained attention due to its robustness against overparameterization in neural networks. However, approximating the infinite-dimensional function space introduces several challenges. In this work, we discuss function space inference via particle optimization and present practical modifications that improve uncertainty estimation and, most importantly, make it applicable for large and pretrained networks. First, we demonstrate that the input samples, where particle predictions are enforced to be diverse, are detrimental to the model performance. While diversity on training data itself can lead to underfitting, the use of label-destroying data augmentation, or unlabeled out-of-distribution data can improve prediction diversity and uncertainty estimates. Furthermore, we take advantage of the function space formulation, which imposes no restrictions on network parameterization other than sufficient flexibility. Instead of using full deep ensembles to represent particles, we propose a single multi-headed network that introduces a minimal increase in parameters and computation. This allows seamless integration to pretrained networks, where this repulsive last-layer ensemble can be used for uncertainty aware fine-tuning at minimal additional cost. We achieve competitive results in disentangling aleatoric and epistemic uncertainty for active learning, detecting out-of-domain data, and providing calibrated uncertainty estimates under distribution shifts with minimal compute and memory.

This paper rigorously and concisely defines, in the context of our (Elementary) Mathematical Data Model ((E)MDM), the mathematical concepts of self-map, compound mapping, totality, one-to-oneness, non-primeness, ontoness, bijectivity, default value, (null-)reflexivity, irreflexivity, (null-)symmetry, asymmetry, (null-)idempotency, anti-idempotency, (null-)equivalence, acyclicity, (null-)representative system mapping, the properties that relate them, and the corresponding corollaries on the coherence and minimality of sets made of such mapping properties viewed as database constraints. Its main contribution is the pseudocode algorithm used by MatBase, our intelligent database management system prototype based on both (E)MDM, the relational, and the entity-relationship data models, for enforcing self-map, atomic, and compound mapping constraint sets. We prove that this algorithm guarantees the satisfiability, coherence, and minimality of such sets, while being very fast, solid, complete, and minimal. In the sequel, we also presented the relevant MatBase user interface as well as the tables of its metacatalog used by this algorithm., Comment: Submitted to the PriMera Scientific Engineering Journal on 18 Dec. 2024. arXiv admin note: substantial text overlap with arXiv:2410.23485

Graph stream summarization refers to the process of processing a continuous stream of edges that form a rapidly evolving graph. The primary challenges in handling graph streams include the impracticality of fully storing the ever-growing datasets and the complexity of supporting graph queries that involve both topological and temporal information. Recent advancements, such as PGSS and Horae, address these limitations by using domain-based, top-down multi-layer structures in the form of compressed matrices. However, they either suffer from poor query accuracy, incur substantial space overheads, or have low query efficiency. This study proposes a novel item-based, bottom-up hierarchical structure, called HIGGS. Unlike existing approaches, HIGGS leverages its hierarchical structure to localize storage and query processing, thereby confining changes and hash conflicts to small and manageable subtrees, yielding notable performance improvements. HIGGS offers tighter theoretical bounds on query accuracy and space cost. Extensive empirical studies on real graph streams demonstrate that, compared to state-of-the-art methods, HIGGS is capable of notable performance enhancements: it can improve accuracy by over 3 orders of magnitude, reduce space overhead by an average of 30%, increase throughput by more than 5 times, and decrease query latency by nearly 2 orders of magnitude.

We propose the joint speech translation and recognition (JSTAR) model that leverages the fast-slow cascaded encoder architecture for simultaneous end-to-end automatic speech recognition (ASR) and speech translation (ST). The model is transducer-based and uses a multi-objective training strategy that optimizes both ASR and ST objectives simultaneously. This allows JSTAR to produce high-quality streaming ASR and ST results. We apply JSTAR in a bilingual conversational speech setting with smart-glasses, where the model is also trained to distinguish speech from different directions corresponding to the wearer and a conversational partner. Different model pre-training strategies are studied to further improve results, including training of a transducer-based streaming machine translation (MT) model for the first time and applying it for parameter initialization of JSTAR. We demonstrate superior performances of JSTAR compared to a strong cascaded ST model in both BLEU scores and latency., Comment: Submitted to ICASSP 2025

Generative AI, with its tendency to "hallucinate" incorrect results, may pose a risk to knowledge work by introducing errors. On the other hand, it may also provide unprecedented opportunities for users, particularly non-experts, to learn and apply advanced software features and greatly increase the scope and complexity of tasks they can successfully achieve. As an example of a complex knowledge workflow that is subject to risks and opportunities from generative AI, we consider the spreadsheet. AI hallucinations are an important challenge, but they are not the greatest risk posed by generative AI to spreadsheet workflows. Rather, as more work can be safely delegated to AI, the risk is that human critical thinking -- the ability to holistically and rigorously evaluate a problem and its solutions -- is degraded in the process. The solution is to design the interfaces of generative AI systems deliberately to foster and encourage critical thinking in knowledge work, building primarily on a long history of research on critical thinking tools for education. We discuss a prototype system for the activity of critical shortlisting in spreadsheets. The system uses generative AI to suggest shortlisting criteria and applies these criteria to sort rows in a spreadsheet. It also generates "provocations": short text snippets that critique the AI-generated criteria, highlighting risks, shortcomings, and alternatives. Our prototype opens up a rich and completely unexplored design space of critical thinking tools for modern AI-assisted knowledge work. We outline a research agenda for AI as a critic or provocateur, including questions about where and when provocations should appear, their form and content, and potential design trade-offs.

Sound designers and Foley artists usually sonorize a scene, such as from a movie or video game, by manually annotating and sonorizing each action of interest in the video. In our case, the intent is to leave full creative control to sound designers with a tool that allows them to bypass the more repetitive parts of their work, thus being able to focus on the creative aspects of sound production. We achieve this presenting Stable-V2A, a two-stage model consisting of: an RMS-Mapper that estimates an envelope representative of the audio characteristics associated with the input video; and Stable-Foley, a diffusion model based on Stable Audio Open that generates audio semantically and temporally aligned with the target video. Temporal alignment is guaranteed by the use of the envelope as a ControlNet input, while semantic alignment is achieved through the use of sound representations chosen by the designer as cross-attention conditioning of the diffusion process. We train and test our model on Greatest Hits, a dataset commonly used to evaluate V2A models. In addition, to test our model on a case study of interest, we introduce Walking The Maps, a dataset of videos extracted from video games depicting animated characters walking in different locations. Samples and code available on our demo page at https://ispamm.github.io/Stable-V2A.

It is advantageous for any quantum processor to support different classes of two-qubit quantum logic gates when compiling quantum circuits, a property that is typically not seen with existing platforms. In particular, access to a gate set that includes support for the CZ-type, the iSWAP-type, and the SWAP-type families of gates, renders conversions between these gate families unnecessary during compilation as any two-qubit Clifford gate can be executed using at most one two-qubit gate from this set, plus additional single-qubit gates. We experimentally demonstrate that a SWAP gate can be decomposed into one iSWAP gate followed by one CZ gate, affirming a more efficient compilation strategy over the conventional approach that relies on three iSWAP or three CZ gates to replace a SWAP gate. Our implementation makes use of a superconducting quantum processor design based on fixed-frequency transmon qubits coupled together by a parametrically modulated tunable transmon coupler, extending this platform's native gate set so that any two-qubit Clifford unitary matrix can be realized using no more than two two-qubit gates and single-qubit gates., Comment: 29 pages, 13 figures

Neuromorphic computing aims to replicate the brain's capabilities for energy efficient and parallel information processing, promising a solution to the increasing demand for faster and more efficient computational systems. Efficient training of neural networks on neuromorphic hardware requires the development of training algorithms that retain the sparsity of spike-based communication during training. Here, we report on the first implementation of event-based backpropagation on the SpiNNaker2 neuromorphic hardware platform. We use EventProp, an algorithm for event-based backpropagation in spiking neural networks (SNNs), to compute exact gradients using sparse communication of error signals between neurons. Our implementation computes multi-layer networks of leaky integrate-and-fire neurons using discretized versions of the differential equations and their adjoints, and uses event packets to transmit spikes and error signals between network layers. We demonstrate a proof-of-concept of batch-parallelized, on-chip training of SNNs using the Yin Yang dataset, and provide an off-chip implementation for efficient prototyping, hyper-parameter search, and hybrid training methods., Comment: 38th Second Workshop on Machine Learning with New Compute Paradigms at NeurIPS 2024(MLNCP 2024) : Poster Presentation

Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with these divergences and translating pronouns is a challenge in machine translation. Mentions are referring candidates of pronouns and have closer relations with pronouns compared to general tokens. We assume that extracting additional mention features can help pronoun translation. Therefore, we introduce an additional mention attention module in the decoder to pay extra attention to source mentions but not non-mention tokens. Our mention attention module not only extracts features from source mentions, but also considers target-side context which benefits pronoun translation. In addition, we also introduce two mention classifiers to train models to recognize mentions, whose outputs guide the mention attention. We conduct experiments on the WMT17 English-German translation task, and evaluate our models on general translation and pronoun translation, using BLEU, APT, and contrastive evaluation metrics. Our proposed model outperforms the baseline Transformer model in terms of APT and BLEU scores, this confirms our hypothesis that we can improve pronoun translation by paying additional attention to source mentions, and shows that our introduced additional modules do not have negative effect on the general translation quality., Comment: camera-ready version of the paper accepted by JCRAI-23 conference, in ACL format

We study the problem of realizing strategies for an LTLf goal specification while ensuring that at least an LTLf backup specification is satisfied in case of unreliability of certain input variables. We formally define the problem and characterize its worst-case complexity as 2EXPTIME-complete, like standard LTLf synthesis. Then we devise three different solution techniques: one based on direct automata manipulation, which is 2EXPTIME, one disregarding unreliable input variables by adopting a belief construction, which is 3EXPTIME, and one leveraging second-order quantified LTLf (QLTLf), which is 2EXPTIME and allows for a direct encoding into monadic second-order logic, which in turn is worst-case nonelementary. We prove their correctness and evaluate them against each other empirically. Interestingly, theoretical worst-case bounds do not translate into observed performance; the MSO technique performs best, followed by belief construction and direct automata manipulation. As a byproduct of our study, we provide a general synthesis procedure for arbitrary QLTLf specifications., Comment: 8 pages, to appear at AAAI2025

This paper focuses on the mathematical framework for reducing the complexity of models using path signatures. The structure of these signatures, which can be interpreted as collections of iterated integrals along paths, is discussed and their applications in areas such as stochastic differential equations (SDEs) and financial modeling are pointed out. In particular, exploiting the rough paths view, solutions of SDEs continuously depend on the lift of the driver. Such continuous mappings can be approximated using (truncated) signatures, which are solutions of high-dimensional linear systems. In order to lower the complexity of these models, this paper presents methods for reducing the order of high-dimensional truncated signature models while retaining essential characteristics. The derivation of reduced models and the universal approximation property of (truncated) signatures are treated in detail. Numerical examples, including applications to the (rough) Bergomi model in financial markets, illustrate the proposed reduction techniques and highlight their effectiveness.