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Research on school improvement has accumulated an extensive list of factors that facilitate turnarounds at underperforming schools. Given that contextual or resource constraints may limit the possibilities of putting all of these factors in place, an important question is what is necessary and sufficient to turn a school around. We use a qualitative comparative analysis (QCA) of 77 Swedish schools studied over 12 years to answer this question. Our core finding is that there is no "silver bullet" solution. There are, instead, several distinct combinations of factors that can enable a turnaround. The local school context is essential for which combinations of factors are necessary and sufficient for school turnaround. We discuss implications for research on school improvement and education policy.

Continuous-variable (CV) quantum key distribution (QKD) allows for quantum secure communication with the benefit of being close to existing classical coherent communication. In recent years, CV QKD protocols using a discrete number of displaced coherent states have been studied intensively, as the modulation can be directly implemented with real devices with a finite digital resolution. However, the experimental demonstrations until now only calculated key rates in the asymptotic regime. To be used in cryptographic applications, a QKD system has to generate keys with composable security in the finite-size regime. In this paper, we present a CV QKD system using discrete modulation that is especially designed for urban atmospheric channels. For this, we use polarization encoding to cope with the turbulent but non-birefringent atmosphere. This will allow to expand CV QKD networks beyond the existing fiber backbone. In a first laboratory demonstration, we implemented a novel type of security proof allowing to calculate composable finite-size key rates against i.i.d. collective attacks without any Gaussian assumptions. We applied the full QKD protocol including a QRNG, error correction and privacy amplification to extract secret keys. In particular, we studied the impact of frame errors on the actual key generation.

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic., Comment: Accepted version; not Version of Record

This paper considers an ergodic version of the bounded velocity follower problem, assuming that the decision maker lacks knowledge of the underlying system parameters and must learn them while simultaneously controlling. We propose algorithms based on moving empirical averages and develop a framework for integrating statistical methods with stochastic control theory. Our primary result is a logarithmic expected regret rate. To achieve this, we conduct a rigorous analysis of the ergodic convergence rates of the underlying processes and the risks of the considered estimators.

Between 2021 and 2023, crypto assets valued at over \$US2.6 billion were stolen via attacks on "bridges" -- decentralized services designed to allow inter-blockchain exchange. While the individual exploits in each attack vary, a single design flaw underlies them all: the lack of end-to-end value accounting in cross-chain transactions. In this paper, we empirically analyze twenty million transactions used by key bridges during this period. We show that a simple invariant that balances cross-chain inflows and outflows is compatible with legitimate use, yet precisely identifies every known attack (and several likely attacks) in this data. Further, we show that this approach is not only sufficient for post-hoc audits, but can be implemented in-line in existing bridge designs to provide generic protection against a broad array of bridge vulnerabilities., Comment: Currently under submission

Studying the orbital motion of stars around Sagittarius A* in the Galactic Center provides a unique opportunity to probe the gravitational potential near the supermassive black hole at the heart of our Galaxy. Interferometric data obtained with the GRAVITY instrument at the Very Large Telescope Interferometer (VLTI) since 2016 has allowed us to achieve unprecedented precision in tracking the orbits of these stars. GRAVITY data have been key to detecting the in-plane, prograde Schwarzschild precession of the orbit of the star S2, as predicted by General Relativity. By combining astrometric and spectroscopic data from multiple stars, including S2, S29, S38, and S55 - for which we have data around their time of pericenter passage with GRAVITY - we can now strengthen the significance of this detection to an approximately $10 \sigma$ confidence level. The prograde precession of S2's orbit provides valuable insights into the potential presence of an extended mass distribution surrounding Sagittarius A*, which could consist of a dynamically relaxed stellar cusp comprised of old stars and stellar remnants, along with a possible dark matter spike. Our analysis, based on two plausible density profiles - a power-law and a Plummer profile - constrains the enclosed mass within the orbit of S2 to be consistent with zero, establishing an upper limit of approximately $1200 \, M_\odot$ with a $1 \sigma$ confidence level. This significantly improves our constraints on the mass distribution in the Galactic Center. Our upper limit is very close to the expected value from numerical simulations for a stellar cusp in the Galactic Center, leaving little room for a significant enhancement of dark matter density near Sagittarius A*., Comment: Submitted to A&A on September 17, 2024

Online spaces allow people to discuss important issues and make joint decisions, regardless of their location or time zone. However, without proper support and thoughtful design, these discussions often lack structure and politeness during the exchanges of opinions. Artificial intelligence (AI) represents an opportunity to support both participants and organizers of large-scale online participation processes. In this paper, we present an extension of adhocracy+, a large-scale open source participation platform, that provides two additional debate modules that are supported by AI to enhance the discussion quality and participant interaction.

Many powerful tests of the Standard Model of particle physics and searches for new physics with precision atomic spectroscopy are plagued by our lack of knowledge of nuclear properties. Ideally, such properties may be derived from precise measurements of the most sensitive and theoretically best-understood observables, often found in hydrogen-like systems. While these measurements are abundant for the electric properties of nuclei, they are scarce for the magnetic properties, and precise experimental results are limited to the lightest of nuclei. Here, we focus on $^9$Be which offers the unique possibility to utilize comparisons between different charge states available for high-precision spectroscopy in Penning traps to test theoretical calculations typically obscured by nuclear structure. In particular, we perform the first high-precision spectroscopy of the $1s$ hyperfine and Zeeman structure in hydrogen-like $^9$Be$^{3+}$. We determine its effective Zemach radius with an uncertainty of $500$ ppm, and its bare nuclear magnetic moment with an uncertainty of $0.6$ parts-per-billion (ppb) - uncertainties unmatched beyond hydrogen. Moreover, we compare to measurements conducted on the three-electron charge state $^9$Be$^{+}$, which, for the first time, enables testing the calculation of multi-electron diamagnetic shielding effects of the nuclear magnetic moment at the ppb level. In addition, we test quantum electrodynamics (QED) methods used for the calculation of the hyperfine splitting. Our results serve as a crucial benchmark essential for transferring high-precision results of nuclear magnetic properties across different electronic configurations.

A crucial question in analyzing a concurrent system is to determine its long-run behaviour, and in particular, whether there are irreversible choices in its evolution, leading into parts of the reachability space from which there is no return to other parts. Casting this problem in the unifying framework of safe Petri nets, our previous work has provided techniques for identifying attractors, i.e. terminal strongly connected components of the reachability space. What we aim at is to determine the attraction basins associated to those attractors; that is, those states from where all infinite runs are doomed to end in the given attractor, as opposed to those that are free to evolve differently. Here, we provide a solution for the case of safe Petri nets. Our algorithm uses net unfoldings and provides a map of all of those configurations (concurrent executions of the system) that lead onto cliff-edges, i.e. any maximal extension for those configurations lies in some basin that is considered fatal., Comment: Will be submitted to a journal. arXiv admin note: substantial text overlap with arXiv:2209.10323

An isolated attosecond vortex $\gamma$-ray pulse is generated by using a relativistic spatiotemporal optical vortex (STOV) laser in particle-in-cell simulations. A $\sim$ 300-attosecond electron slice with transverse orbital angular momentum (TOAM) is initially selected and accelerated by the central spatiotemporal singularity of the STOV laser. This slice then collides with the laser's reflected Gaussian-like front from a planar target, initiating nonlinear Compton scattering and resulting in an isolated, attosecond ($\sim$ 300 as), highly collimated ($\sim$ 4$\degree$), ultra-brilliant ($\sim 5\times 10^{24}$ photons/s/mm$^2$/mrad$^2$/0.1\%BW at 1 MeV) $\gamma$-ray pulse. This STOV-driven approach overcomes the significant beam divergence and complex two-laser requirements of prior Gaussian-based methods while introducting TOAM to the attosecond $\gamma$-ray pulse, which opens avenues for ultrafast imaging, nuclear excitation, and detection applications.

The ongoing discrepancy in the Hubble constant ($H_0$) estimates obtained through local distance ladder methods and early universe observations poses a significant challenge to the $\Lambda$CDM model, suggesting potential new physics. Type II supernovae (SNe II) offer a promising technique for determining $H_0$ in the local universe independently of the traditional distance ladder approach, opening up a complimentary path for testing this discrepancy. We aim to provide the first $H_0$ estimate using the tailored expanding photosphere method (EPM) applied to SNe II, made possible by recent advancements in spectral modelling that enhance its precision and efficiency. Our tailored EPM measurement utilizes a spectral emulator to interpolate between radiative transfer models calculated with TARDIS, allowing us to fit supernova spectra efficiently and derive self-consistent values for luminosity-related parameters. We apply the method on public data for ten SNe II at redshifts between 0.01 and 0.04. Our analysis demonstrates that the tailored EPM allows for $H_0$ measurements with precision comparable to the most competitive established techniques, even when applied to literature data not designed for cosmological applications. We find an independent $H_0$ value of $74.9\pm1.9$ (stat) km/s/Mpc, which is consistent with most current local measurements. Considering dominant sources of systematic effects, we conclude that our systematic uncertainty is comparable to or less than the current statistical uncertainty. This proof-of-principle study highlights the potential of the tailored EPM as a robust and precise tool for investigating the Hubble tension independently of the local distance ladder. Observations of SNe II tailored to $H_0$ estimation can make this an even more powerful tool by improving the precision and by allowing us to better understand and control systematic uncertainties., Comment: 40 pages, 57 figures, 4 tables; submitted to A&A

Recent AI advances have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal AI for proteins that integrates structural, sequence, alignment, and binding site data. Using the ImageBind framework, OneProt aligns the latent spaces of modality encoders along protein sequences. It demonstrates strong performance in retrieval tasks and surpasses state-of-the-art methods in various downstream tasks, including metal ion binding classification, gene-ontology annotation, and enzyme function prediction. This work expands multi-modal capabilities in protein models, paving the way for applications in drug discovery, biocatalytic reaction planning, and protein engineering., Comment: 28 pages, 15 figures, 7 tables

We propose soft Hoeffding trees (SoHoT) as a new differentiable and transparent model for possibly infinite and changing data streams. Stream mining algorithms such as Hoeffding trees grow based on the incoming data stream, but they currently lack the adaptability of end-to-end deep learning systems. End-to-end learning can be desirable if a feature representation is learned by a neural network and used in a tree, or if the outputs of trees are further processed in a deep learning model or workflow. Different from Hoeffding trees, soft trees can be integrated into such systems due to their differentiability, but are neither transparent nor explainable. Our novel model combines the extensibility and transparency of Hoeffding trees with the differentiability of soft trees. We introduce a new gating function to regulate the balance between univariate and multivariate splits in the tree. Experiments are performed on 20 data streams, comparing SoHoT to standard Hoeffding trees, Hoeffding trees with limited complexity, and soft trees applying a sparse activation function for sample routing. The results show that soft Hoeffding trees outperform Hoeffding trees in estimating class probabilities and, at the same time, maintain transparency compared to soft trees, with relatively small losses in terms of AUROC and cross-entropy. We also demonstrate how to trade off transparency against performance using a hyperparameter, obtaining univariate splits at one end of the spectrum and multivariate splits at the other.

The Koopman operator plays a crucial role in analyzing the global behavior of dynamical systems. Existing data-driven methods for approximating the Koopman operator or discovering the governing equations of the underlying system typically require a fixed set of basis functions, also called dictionary. The optimal choice of basis functions is highly problem-dependent and often requires domain knowledge. We present a novel gradient descent-based optimization framework for learning suitable and interpretable basis functions from data and show how it can be used in combination with EDMD, SINDy, and PDE-FIND. We illustrate the efficacy of the proposed approach with the aid of various benchmark problems such as the Ornstein-Uhlenbeck process, Chua's circuit, a nonlinear heat equation, as well as protein-folding data.

Soft materials are ubiquitous in technological applications that require deformability, for instance, in flexible, water-repellent coatings. However, the wetting properties of pre-strained soft materials are only beginning to be explored. Here we study the sliding dynamics of droplets on pre-strained soft silicone gels, both in tension and in compression. Intriguingly, in compression we find a non-monotonic strain dependence of the sliding speed: mild compressions decelerate the droplets, but stronger compressions lead again to faster droplet motion. Upon further compression, creases nucleate under the droplets until finally, the entire surface undergoes the creasing instability, causing a ``run-and-stop" droplet motion. We quantitatively elucidate the speed modification for moderate pre-strains by incremental viscoelasticity, while the acceleration for larger strains turns out to be linked to the solid pressure, presumably through a lubrication effect of expelled oligomers., Comment: 6 pages, 4 figures, 3 ancillary files

Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative to existing techniques for identifying ground states of quantum problems. A significant advancement toward improving the practical scalability of NQS has been the incorporation of autoregressive models, most recently transformers, as variational ansatze. Transformers learn sequence information with greater expressiveness than recurrent models, but at the cost of increased time complexity with respect to sequence length. We explore the use of the retentive network (RetNet), a recurrent alternative to transformers, as an ansatz for solving electronic ground state problems in $\textit{ab initio}$ quantum chemistry. Unlike transformers, RetNets overcome this time complexity bottleneck by processing data in parallel during training, and recurrently during inference. We give a simple computational cost estimate of the RetNet and directly compare it with similar estimates for transformers, establishing a clear threshold ratio of problem-to-model size past which the RetNet's time complexity outperforms that of the transformer. Though this efficiency can comes at the expense of decreased expressiveness relative to the transformer, we overcome this gap through training strategies that leverage the autoregressive structure of the model -- namely, variational neural annealing. Our findings support the RetNet as a means of improving the time complexity of NQS without sacrificing accuracy. We provide further evidence that the ablative improvements of neural annealing extend beyond the RetNet architecture, suggesting it would serve as an effective general training strategy for autoregressive NQS., Comment: 16 pages, 1 figure, to be submitted for peer-reviewed publication

Global food production and trade networks are highly dynamic, especially in response to shortages when countries adjust their supply strategies. In this study, we examine adjustments across 123 agri-food products from 192 countries resulting in 23616 individual scenarios of food shortage, and calibrate a multi-layer network model to understand the propagation of the shocks. We analyze shock mitigation actions, such as increasing imports, boosting production, or substituting food items. Our findings indicate that these lead to spillover effects potentially exacerbating food inequality: an Indian rice shock resulted in a 5.8 % increase in rice losses in countries with a low Human Development Index (HDI) and a 14.2 % decrease in those with a high HDI. Considering multiple interacting shocks leads to super-additive losses of up to 12 % of the total available food volume across the global food production network. This framework allows us to identify combinations of shocks that pose substantial systemic risks and reduce the resilience of the global food supply.

Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical practitioners require not only estimating averaged causal quantities, such as the conditional average treatment effect, but also understanding the randomness of the treatment effect as a random variable. This randomness is referred to as aleatoric uncertainty and is necessary for understanding the probability of benefit from treatment or quantiles of the treatment effect. Yet, the aleatoric uncertainty of the treatment effect has received surprisingly little attention in the causal machine learning community. To fill this gap, we aim to quantify the aleatoric uncertainty of the treatment effect at the covariate-conditional level, namely, the conditional distribution of the treatment effect (CDTE). Unlike average causal quantities, the CDTE is not point identifiable without strong additional assumptions. As a remedy, we employ partial identification to obtain sharp bounds on the CDTE and thereby quantify the aleatoric uncertainty of the treatment effect. We then develop a novel, orthogonal learner for the bounds on the CDTE, which we call AU-learner. We further show that our AU-learner has several strengths in that it satisfies Neyman-orthogonality and is doubly robust. Finally, we propose a fully-parametric deep learning instantiation of our AU-learner.

Energy is a critical driver of modern economic systems. Accurate energy price forecasting plays an important role in supporting decision-making at various levels, from operational purchasing decisions at individual business organizations to policy-making. A significant body of literature has looked into energy price forecasting, investigating a wide range of methods to improve accuracy and inform these critical decisions. Given the evolving landscape of forecasting techniques, the literature lacks a thorough empirical comparison that systematically contrasts these methods. This paper provides an in-depth review of the evolution of forecasting modeling frameworks, from well-established econometric models to machine learning methods, early sequence learners such LSTMs, and more recent advancements in deep learning with transformer networks, which represent the cutting edge in forecasting. We offer a detailed review of the related literature and categorize forecasting methodologies into four model families. We also explore emerging concepts like pre-training and transfer learning, which have transformed the analysis of unstructured data and hold significant promise for time series forecasting. We address a gap in the literature by performing a comprehensive empirical analysis on these four family models, using data from the EU energy markets, we conduct a large-scale empirical study, which contrasts the forecasting accuracy of different approaches, focusing especially on alternative propositions for time series transformers.

Large language models (LLMs) have demonstrated remarkable capabilities in various scientific domains, from natural language processing to complex problem-solving tasks. Their ability to understand and generate human-like text has opened up new possibilities for advancing scientific research, enabling tasks such as data analysis, literature review, and even experimental design. One of the most promising applications of LLMs in this context is hypothesis generation, where they can identify novel research directions by analyzing existing knowledge. However, despite their potential, LLMs are prone to generating ``hallucinations'', outputs that are plausible-sounding but factually incorrect. Such a problem presents significant challenges in scientific fields that demand rigorous accuracy and verifiability, potentially leading to erroneous or misleading conclusions. To overcome these challenges, we propose KG-CoI (Knowledge Grounded Chain of Ideas), a novel system that enhances LLM hypothesis generation by integrating external, structured knowledge from knowledge graphs (KGs). KG-CoI guides LLMs through a structured reasoning process, organizing their output as a chain of ideas (CoI), and includes a KG-supported module for the detection of hallucinations. With experiments on our newly constructed hypothesis generation dataset, we demonstrate that KG-CoI not only improves the accuracy of LLM-generated hypotheses but also reduces the hallucination in their reasoning chains, highlighting its effectiveness in advancing real-world scientific research.

In recent years, the telecommunications field has experienced an unparalleled proliferation of wireless data traffic. Innovative solutions are imperative to circumvent the inherent limitations of the current technology, in particular in terms of capacity. Carrier frequencies in the sub-terahertz (sub-THz) range (~0.2-0.3 THz) can deliver increased capacity and low attenuation for short-range wireless applications. Here, we demonstrate a direct, passive and compact sub-THz receiver based on graphene, which outperforms state-of-the-art sub-THz receivers. These graphene-based receivers offer a cost-effective, CMOS-compatible, small-footprint solution that can fulfill the size, weight, and power consumption (SWaP) requirements of 6G technologies. We exploit a sub-THz cavity, comprising an antenna and a back mirror, placed in the vicinity of the graphene channel to overcome the low inherent absorption in graphene and the mismatch between the areas of the photoactive region and the incident radiation, which becomes extreme in the sub-THz range. The graphene receivers achieve a multigigabit per second data rate with a maximum distance of ~3 m from the transmitter, a setup-limited 3 dB bandwidth of 40 GHz, and a high responsivity of 0.16 A/W, enabling applications such as chip-to-chip communication and close proximity device-to-device communication., Comment: 13 pages, 4 figures

Lattice quantum field theory calculations may potentially combine the advantages of Hamiltonian formulations with the scalability and control of conventional Lagrangian frameworks. However, such hybrid approaches need to consider (1) the differences in renormalized coupling values between the two formulations, and (2) finite-volume and discretization effects when the Hamiltonian component of the calculation is characterized by a smaller volume or coarser lattice spacing than the Lagrangian component. This work investigates the role of both factors in the application of Hamiltonian-optimized interpolating operator constructions for the conventional Lagrangian framework. The numerical investigation is realized for the pseudoscalar meson in the Schwinger model, using tensor-network and Monte-Carlo calculations. It is demonstrated that tensor-network-optimized constructions are robust to both (1) and (2). In particular, accurate optimized constructions for the pseudoscalar meson can be obtained from calculations with a smaller number of Hamiltonian lattice sites, even when the meson mass itself receives significant finite-volume corrections. To the extent that these results generalize to theories with more complicated spectra, the method holds promise for near-term applications in large-scale calculations of lattice quantum field theory., Comment: 14 pages, 6 figures

We consider facet-Hamiltonian cycles of polytopes, defined as cycles in their skeleton such that every facet is visited exactly once. These cycles can be understood as optimal watchman routes that guard the facets of a polytope. We consider the existence of such cycles for a variety of polytopes, the facets of which have a natural combinatorial interpretation. In particular, we prove the following results: - Every permutahedron has a facet-Hamiltonian cycle. These cycles consist of circular sequences of permutations of $n$ elements, where two successive permutations differ by a single adjacent transposition, and such that every subset of $[n]$ appears as a prefix in a contiguous subsequence. With these cycles we associate what we call rhombic strips which encode interleaved Gray codes of the Boolean lattice, one Gray code for each rank. These rhombic strips correspond to simple Venn diagrams. - Every generalized associahedron has a facet-Hamiltonian cycle. This generalizes the so-called rainbow cycles of Felsner, Kleist, M\"utze, and Sering (SIDMA 2020) to associahedra of any finite type. We relate the constructions to the Conway-Coxeter friezes and the bipartite belts of finite type cluster algebras. - Graph associahedra of wheels, fans, and complete split graphs have facet-Hamiltonian cycles. For associahedra of complete bipartite graphs and caterpillars, we construct facet-Hamiltonian paths. The construction involves new insights on the combinatorics of graph tubings. We also consider the computational complexity of deciding whether a given polytope has a facet-Hamiltonian cycle and show that the problem is NP-complete, even when restricted to simple 3-dimensional polytopes., Comment: ACM-SIAM Symposium on Discrete Algorithms (SODA'25)

The need of atmospheric information with a higher spatial and temporal resolution drives the development of small satellites and satellite constellations to complement satellite flagship missions. Since optical systems are a main contributor to the satellite size, these are the prime candidate for their miniaturization. We present here a novel optical system where the complete spectrometer part of the optical system is compressed in one flat optical element. The element consists of an array of photonic crystals which is directly placed on a detector. The photonic crystals act as optical filters with a tunable spectral transmission response. From the integrated optical signals per filter and the atmosphere model, greenhouse gas concentrations are obtained using computational inversion. We present in this article the instrument concept, the manufacturing and measurement of the photonic crystals, methods for the filter array optimization, and discuss the predicted retrieval performance for the detection of methane and carbon dioxide., Comment: ICSO 2024

We develop a unifying framework for modeling multiple term structures arising in financial, insurance, and energy markets. We adopt the Heath-Jarrow-Morton approach under the real-world probability and provide a full description of the set of local martingale deflators, which ensure market viability. We perform a thorough analysis of the stochastic partial differential equation arising in the model, addressing existence and uniqueness of a solution, invariance properties and existence of affine realizations., Comment: 45 pages

Dispersion measures (DM) of fast radio bursts (FRBs) probe the density of electrons in the intergalactic medium (IGM) along their lines-of-sight, including the average density versus distance to the source and its variations in direction. While previous study focused on low-redshift, FRBs are potentially detectable out to high redshift, where their DMs can, in principle, probe the epoch of reionization (EOR) and its patchiness. We present the first predictions from large-scale, radiation-hydrodynamical simulation of fully-coupled galaxy formation and reionization, using Cosmic Dawn (``CoDa")~II to model the density and ionization fields of the universe down to redshifts through the end of the EOR at $z_{re}\approx6.1$. Combining this with an N-body simulation CoDa~II--Dark Matter of the fully-ionized epoch from the EOR to the present, we calculate the mean and standard deviation of FRB DMs as functions of their source redshift. The mean and standard deviation of DM increase with redshift, reaching a plateau by $z(x_{HII}\lesssim0.25)\gtrsim8$, i.e. well above $z_{re}$. The mean-DM asymptote $\mathcal{DM}_{max} \approx 5900~\mathrm{pc\, cm^{-3}}$ reflects the end of the EOR and its duration. The standard deviation there is $\sigma_{DM, max}\approx497 ~\mathrm{pc\, cm^{-3}}$, reflecting inhomogeneities of both patchy reionization and density. Inhomogeneities in ionization during the EOR contribute $\mathcal{O}(1$ per cent) of this value of $\sigma_{DM,max}$ from FRBs at redshifts $z\gtrsim 8$. Current estimates of FRB rates suggest this may be detectable within a few years of observation., Comment: 14 pages, 10 figures, 2 tables, 2 appendices

Developing foundational world models is a key research direction for embodied intelligence, with the ability to adapt to non-stationary environments being a crucial criterion. In this work, we introduce a new formalism, Hidden Parameter-POMDP, designed for control with adaptive world models. We demonstrate that this approach enables learning robust behaviors across a variety of non-stationary RL benchmarks. Additionally, this formalism effectively learns task abstractions in an unsupervised manner, resulting in structured, task-aware latent spaces., Comment: Accepted at NeurIPS 2024 Workshop Adaptive Foundation Models

We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation model (MFM) designed to extract hierarchical features from target music samples. By leveraging hierarchical intermediate features, SoniDo constrains the information granularity, leading to improved performance across various downstream tasks including both understanding and generative tasks. We specifically evaluated this approach on representative tasks such as music tagging, music transcription, music source separation, and music mixing. Our results reveal that the features extracted from foundation models provide valuable enhancements in training downstream task models. This highlights the capability of using features extracted from music foundation models as a booster for downstream tasks. Our approach not only benefits existing task-specific models but also supports music downstream tasks constrained by data scarcity. This paves the way for more effective and accessible music processing solutions., Comment: 41 pages with 14 figures

Classical Wolf-Rayet stars are descendants of massive OB-type stars that have lost their hydrogen-rich envelopes, and are in the final stages of stellar evolution, possibly exploding as type Ib/c supernovae. It is understood that the mechanisms driving this mass-loss are either strong stellar winds and or binary interactions, so intense studies of these binaries including their evolution can tell us about the importance of the two pathways in WR formation. WR 138 (HD 193077) has a period of just over 4 years and was previously reported to be resolved through interferometry. We report on new interferometric data combined with spectroscopic radial velocities in order to provide a three-dimensional orbit of the system. The precision on our parameters tend to be about an order of magnitude better than previous spectroscopic techniques. These measurements provide masses of the stars, namely $M_{\rm WR} = 13.93\pm1.49M_{\odot}$ and $M_{\rm O} = 26.28\pm1.71M_{\odot}$. The derived orbital parallax agrees with the parallax from \textit{Gaia}, namely with a distance of 2.13 kpc. We compare the system's orbit to models from BPASS, showing that the system likely may have been formed with little interaction but could have formed through some binary interactions either following or at the start of a red supergiant phase, but with the most likely scenario occurring as the red supergiant phase starts for a $\sim 40M_\odot$ star., Comment: accepted to ApJ

Self-similarity indicates that large and small earthquakes share the same physics, where all variables scale with rupture length $L$. Here I show that rupture tip acceleration during the start of dynamic rupture (break-out phase) is also self-similar, scaling with $L_c$ in space and $L_c/C_{lim}$ in time (where $L_c$ is the breakout patch length and $C_{lim}$ the limiting rupture velocity in the subsonic regime). Rupture acceleration in the breakout phase is slower for larger initial breakout patches $L_c$. Because small faults cannot host large breakout patches, a large and slower initial breakout may be indicative of a potentially large final earthquake magnitude. Initial moment rate $\dot{M}_o$ also grows slower for larger $L_c$, therefore it may reflect fault dimensions and carry a probabilistic forecast of magnitude as suggested in some Early Warning studies. This result does not violate causality and is fully compatible with the shared fundamental, self-similar physics across all the magnitude spectrum., Comment: 22 pages including supplementary information at bottom of article, 12 figures

Large Language Models (LLMs) have transformed how people interact with artificial intelligence (AI) systems, achieving state-of-the-art results in various tasks, including scientific discovery and hypothesis generation. However, the lack of a comprehensive and systematic evaluation framework for generating research ideas using LLMs poses a significant obstacle to understanding and assessing their generative capabilities in scientific discovery. To address this gap, we propose IdeaBench, a benchmark system that includes a comprehensive dataset and an evaluation framework for standardizing the assessment of research idea generation using LLMs. Our dataset comprises titles and abstracts from a diverse range of influential papers, along with their referenced works. To emulate the human process of generating research ideas, we profile LLMs as domain-specific researchers and ground them in the same context considered by human researchers. This maximizes the utilization of the LLMs' parametric knowledge to dynamically generate new research ideas. We also introduce an evaluation framework for assessing the quality of generated research ideas. Our evaluation framework is a two-stage process: first, using GPT-4o to rank ideas based on user-specified quality indicators such as novelty and feasibility, enabling scalable personalization; and second, calculating relative ranking based "Insight Score" to quantify the chosen quality indicator. The proposed benchmark system will be a valuable asset for the community to measure and compare different LLMs, ultimately advancing the automation of the scientific discovery process.

Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spatial location of brain sources from these signals remains difficult due to the ill-posed structure of the problem. Traditional methods predominantly rely on manually crafted priors, missing the flexibility of data-driven learning, while recent deep learning approaches focus on end-to-end learning, typically using the physical information of the forward model only for generating training data. We propose the novel hybrid method 3D-PIUNet for EEG source localization that effectively integrates the strengths of traditional and deep learning techniques. 3D-PIUNet starts from an initial physics-informed estimate by using the pseudo inverse to map from measurements to source space. Secondly, by viewing the brain as a 3D volume, we use a 3D convolutional U-Net to capture spatial dependencies and refine the solution according to the learned data prior. Training the model relies on simulated pseudo-realistic brain source data, covering different source distributions. Trained on this data, our model significantly improves spatial accuracy, demonstrating superior performance over both traditional and end-to-end data-driven methods. Additionally, we validate our findings with real EEG data from a visual task, where 3D-PIUNet successfully identifies the visual cortex and reconstructs the expected temporal behavior, thereby showcasing its practical applicability., Comment: Under Review in IEEE Transactions on Medical Imaging

The representations that can be defined starting from positive sesquilinear maps {\Phi} with values in an ordered Banach module are essentially of two types: if {\Phi} is positive, a natural generalization follows the path of the GNS construction; if {\Phi} is completely positive, one can move toward a possible generalization of the Stinespring dilation theorem. In this paper, both possibilities are studied.

Maximal sets of mutually unbiased bases are useful throughout quantum physics, both in a foundational context and for applications. To date, it remains unknown if complete sets of mutually unbiased bases exist in Hilbert spaces of dimensions different from a prime power, i.e. in composite dimensions such as six or ten. Fourteen mathematically equivalent formulations of the existence problem are presented. We comprehensively summarise analytic, computer-aided and numerical results relevant to the case of composite dimensions. Known modifications of the existence problem are reviewed and potential solution strategies are outlined., Comment: 83 pages, 1 figure, 3 tables. Please contact the authors if you notice any typos, mistakes, misrepresentations, or omissions

Given an integer or a non-negative integer solution $x$ to a system $Ax = b$, where the number of non-zero components of $x$ is at most $n$. This paper addresses the following question: How closely can we approximate $b$ with $Ay$, where $y$ is an integer or non-negative integer solution constrained to have at most $k$ non-zero components with $k

Battery degradation remains a critical challenge in the pursuit of green technologies and sustainable energy solutions. Despite significant research efforts, predicting battery capacity loss accurately remains a formidable task due to its complex nature, influenced by both aging and cycling behaviors. To address this challenge, we introduce a novel general-purpose model for battery degradation prediction and synthesis, DiffBatt. Leveraging an innovative combination of conditional and unconditional diffusion models with classifier-free guidance and transformer architecture, DiffBatt achieves high expressivity and scalability. DiffBatt operates as a probabilistic model to capture uncertainty in aging behaviors and a generative model to simulate battery degradation. The performance of the model excels in prediction tasks while also enabling the generation of synthetic degradation curves, facilitating enhanced model training by data augmentation. In the remaining useful life prediction task, DiffBatt provides accurate results with a mean RMSE of 196 cycles across all datasets, outperforming all other models and demonstrating superior generalizability. This work represents an important step towards developing foundational models for battery degradation., Comment: 15 pages, 6 figures

Even though much progress has been made in identifying and mitigating smart contract vulnerabilities, we often hear about coding or design issues leading to great financial losses. This paper presents our progress toward finding defects that are sometimes not detected or completely detected by state-of-the-art analysis tools. Although it is still in its incipient phase, we developed a working solution built on top of Slither that uses interval analysis to evaluate the contract state during the execution of each instruction. To improve the accuracy of our results, we extend interval analysis by also considering the constraints imposed by specific instructions. We present the current solution architecture in detail and show how it could be extended to other static analysis techniques, including how it can be integrated with other third-party tools. Our current benchmarks contain examples of smart contracts that highlight the potential of this approach to detect certain code defects., Comment: In Proceedings FROM 2024, arXiv:2410.23020

Quantum cryptography is a central topic in the quantum technology field that is particularly important for secure communication. The training of qualified experts in this field is necessary for continuous development. However, the abstract and complex nature of quantum physics makes the topic difficult to understand. Augmented reality (AR) allows otherwise invisible abstract concepts to be visualized and enables interactive learning, offering significant potential for improving quantum physics education in university lab courses. In addition, personalized feedback on challenging concepts can facilitate learning, and large language models (LLMs) like ChatGPT can effectively deliver such feedback. This study combines these two aspects and explores the impact of an AR-based quantum cryptography experiment with integrated ChatGPT-based feedback on university students' learning outcomes and cognitive processes. The study involved 38 students in a physics laboratory course at a German university and used four open-ended questions to measure learning outcomes and gaze data as a learning process assessment. Statistical analysis was used to compare scores between feedback and non-feedback questions, and the effect of ChatGPT feedback on eye-tracking data was examined. The results show that ChatGPT feedback significantly improved learning outcomes and affected gaze data. While the feedback on conceptual questions tended to direct attention to the visualizations of the underlying model, the feedback on questions about experimental procedures increased visual attention to the real experimental materials. Overall, the results show that AI-based feedback draws visual attention towards task-relevant factors and increases learning performance in general.

We consider the problem of learning an $\varepsilon$-optimal policy in controlled dynamical systems with low-rank latent structure. For this problem, we present LoRa-PI (Low-Rank Policy Iteration), a model-free learning algorithm alternating between policy improvement and policy evaluation steps. In the latter, the algorithm estimates the low-rank matrix corresponding to the (state, action) value function of the current policy using the following two-phase procedure. The entries of the matrix are first sampled uniformly at random to estimate, via a spectral method, the leverage scores of its rows and columns. These scores are then used to extract a few important rows and columns whose entries are further sampled. The algorithm exploits these new samples to complete the matrix estimation using a CUR-like method. For this leveraged matrix estimation procedure, we establish entry-wise guarantees that remarkably, do not depend on the coherence of the matrix but only on its spikiness. These guarantees imply that LoRa-PI learns an $\varepsilon$-optimal policy using $\widetilde{O}({S+A\over \mathrm{poly}(1-\gamma)\varepsilon^2})$ samples where $S$ (resp. $A$) denotes the number of states (resp. actions) and $\gamma$ the discount factor. Our algorithm achieves this order-optimal (in $S$, $A$ and $\varepsilon$) sample complexity under milder conditions than those assumed in previously proposed approaches.

Despite best efforts, various challenges remain in the creation and maintenance processes of digital twins (DTs). One of those primary challenges is the constant, continuous and omnipresent evolution of systems, their user's needs and their environment, demanding the adaptation of the developed DT systems. DTs are developed for a specific purpose, which generally entails the monitoring, analysis, simulation or optimization of a specific aspect of an actual system, referred to as the actual twin (AT). As such, when the twin system changes, that is either the AT itself changes, or the scope/purpose of a DT is modified, the DTs usually evolve in close synchronicity with the AT. As DTs are software systems, the best practices or methodologies for software evolution can be leveraged. This paper tackles the challenge of maintaining a (set of) DT(s) throughout the evolution of the user's requirements and priorities and tries to understand how this evolution takes place. In doing so, we provide two contributions: (i) we develop DarTwin, a visual notation form that enables reasoning on a twin system, its purposes, properties and implementation, and (ii) we introduce a set of architectural transformations that describe the evolution of DT systems. The development of these transformations is driven and illustrated by the evolution and transformations of a family home's DT, whose purpose is expanded, changed and re-prioritized throughout its ongoing lifecycle. Additionally, we evaluate the transformations on a lab-scale gantry crane's DT., Comment: Submitted to SoSyM - accepted in September 2024

Programmable optical devices provide performance enhancement and flexibility to spatial multiplexing systems enabling transmission of tributaries in high-order eigenmodes of spatially-diverse transmission media, like multimode fiber (MMF). Wavefront shaping with spatial light modulators (SLMs) facilitates scalability of the transmission media by allowing for channel diagonalization and quasi-single-input single-output operation. Programmable mode multiplexing configurations like multi-plane light conversion (MPLC) utilise the SLM and offer the potential to simultaneously launch an arbitrary subset of spatial tributaries in any N-mode MMF. Such programmable optical processor would enable the throughput of space-division multiplexing (SDM) systems to be progressively increased by addressing a growing number of tributaries over one MMF and in this way meet a growing traffic demand - similarly to the wavelength-division multiplexing evolution path. Conventionally, MPLC phasemasks are calculated using the wavefront matching algorithm (WMA). However, this method does not exploit the full potential of programmable mode multiplexers. We show, that computer-generated hologram algorithms like direct search enable significant improvement compared to the traditional WMA-approach. Such gains are enabled by tailored cost functions with dynamic constraints concerning insertion loss as well as mode extinction ratio. We show that average mode extinction ratio can be greatly improved by as much as 15 dB at the expense of insertion loss deterioration of < 3 dB. One particular feature of programmable mode multiplexers is the adaptability to optimised transmission functions. Besides conventional LP modes transmission, we employ our approach on Schmidt modes, which are spatial eigenchannels with minimum crosstalk derived from a measured transmission matrix.

Multimode fibers provide a promising platform for realizing high-power laser amplifiers with suppressed nonlinearities and instabilities. The potential degradation of optical beam quality has been a major concern for highly multimode fiber amplifiers. We show numerically that the beam propagation factor M2 of a single-frequency multimode fiber amplifier can be reduced to nearly unity by shaping the input or output beam profile with spatial phase-masks. Our method works for narrowband multimode fiber amplifiers with strong gain saturation, pump depletion, random mode coupling and polarization mixing. The numerical results validate our approach of utilizing highly multimode excitation to mitigate nonlinear effects in high-power fiber amplifiers and performing input wavefront shaping to control output beam profile and polarization state.

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity of activation responses to various inputs. Representational Similarity Matrices (RSMs) distill this similarity into scalar values for each input pair. These matrices encapsulate the entire similarity structure of a system, indicating which input leads to similar responses. While the similarity between images is ambiguous, we argue that the spatial location of semantic objects does neither influence human perception nor deep learning classifiers. Thus this should be reflected in the definition of similarity between image responses for computer vision systems. Revisiting the established similarity calculations for RSMs we expose their sensitivity to spatial alignment. In this paper, we propose to solve this through semantic RSMs, which are invariant to spatial permutation. We measure semantic similarity between input responses by formulating it as a set-matching problem. Further, we quantify the superiority of semantic RSMs over spatio-semantic RSMs through image retrieval and by comparing the similarity between representations to the similarity between predicted class probabilities., Comment: Accepted at NeurIPS2024

Building upon Diff-A-Riff, a latent diffusion model for musical instrument accompaniment generation, we present a series of improvements targeting quality, diversity, inference speed, and text-driven control. First, we upgrade the underlying autoencoder to a stereo-capable model with superior fidelity and replace the latent U-Net with a Diffusion Transformer. Additionally, we refine text prompting by training a cross-modality predictive network to translate text-derived CLAP embeddings to audio-derived CLAP embeddings. Finally, we improve inference speed by training the latent model using a consistency framework, achieving competitive quality with fewer denoising steps. Our model is evaluated against the original Diff-A-Riff variant using objective metrics in ablation experiments, demonstrating promising advancements in all targeted areas. Sound examples are available at: https://sonycslparis.github.io/improved_dar/., Comment: 5 pages; 1 table

We present the first application of quantics tensor trains (QTTs) and tensor cross interpolation (TCI) to the solution of a full set of self-consistent equations for multivariate functions, the so-called parquet equations. We show that the steps needed to evaluate the equations (Bethe-Salpeter equations, parquet equation and Schwinger-Dyson equation) can be decomposed into basic operations on the QTT-TCI (QTCI) compressed objects. The repeated application of these operations does not lead to the loss of accuracy beyond a specified tolerance and the iterative scheme converges even for numerically demanding parameters. As examples we take the Hubbard model in the atomic limit and the single impurity Anderson model, where the basic objects in parquet equations, the two-particle vertices, depend on three frequencies, but not on momenta. The results show that this approach is able to overcome major computational bottlenecks of standard numerical methods. The applied methods allow for an exponential increase of the number of grid points included in the calculations leading to an exponentially improving computational error for a linear increase in computational cost., Comment: 20 pages, 16 figures

A driven linear oscillator and a feedback mechanism are two necessary elements of any classical periodic clock. Here, we introduce a novel, fully quantum clock using a driven oscillator in the quantum regime and coherent quantum feedback. We show that if we treat the model semiclassically, this system supports limit cycles, or self-sustained oscillations, as needed for a periodic clock. We then analyse the noise of the system quantum mechanically and prove that the accuracy of this clock is higher compared to the clock implemented with the classical measurement feedback. We experimentally implement the model using two superconducting cavities with incorporated Josephson junctions and microwave circulators for the realisation of the quantum feedback. We confirm the appearance of the limit cycle and study the clock accuracy both in frequency and time domains. Under specific conditions of noisy driving, we observe that the clock oscillations are more coherent than the drive, pointing towards the implementation of a quantum autonomous clock.

We present the results of Phase I of an ongoing review of Monte Carlo tools relevant for low-energy hadronic cross sections. This includes a detailed comparison of Monte Carlo codes for electron-positron scattering into a muon pair, pion pair, and electron pair, for scan and radiative-return experiments. After discussing the various approaches that are used and effects that are included, we show differential cross sections obtained with AfkQed, BabaYaga@NLO, KKMC, MCGPJ, McMule, Phokhara, and Sherpa, for scenarios that are inspired by experiments providing input for the dispersive evaluation of the hadronic vacuum polarisation., Comment: RadioMonteCarLow 2 Working Group report Phase I, 67 pages, 34 figures

With the advent and development of real-world quantum technology applications, a practically-focused quantum education including student quantum experiments are gaining increasing importance in physics curricula. In this paper, using the DeFT framework, we present an analysis of the representations in our AR-enhanced quantum cryptography student experiment, in order to assess their potential for promoting conceptual learning. We also discuss learner visual attention with respect to the provided multiple representations based on the eye gaze data we have obtained from a pilot study where N=38 students carried out the tasks in our AR-enhanced quantum cryptography student experiment.