Search

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

Epidemics exhibit interconnected processes that operate at multiple time and organizational scales, a hallmark of complex adaptive systems. Modern epidemiological modeling frameworks incorporate feedback between individual-level behavioral choices and centralized interventions. Nonetheless, the realistic operational course for disease detection, planning, and response is often overlooked. Disease detection is a dynamic challenge, shaped by the interplay between surveillance efforts and transmission characteristics. It serves as a tipping point that triggers emergency declarations, information dissemination, adaptive behavioral responses, and the deployment of public health interventions. Evaluating the impact of disease surveillance systems as triggers for adaptive behavior and public health interventions is key to designing effective control policies. We examine the multiple behavioral and epidemiological dynamics generated by the feedback between disease surveillance and the intertwined dynamics of information and disease propagation. Specifically, we study the intertwined dynamics between: $(i)$ disease surveillance triggering health emergency declarations, $(ii)$ risk information dissemination producing decentralized behavioral responses, and $(iii)$ centralized interventions. Our results show that robust surveillance systems that quickly detect a disease outbreak can trigger an early response from the population, leading to large epidemic sizes. The key result is that the response scenarios that minimize the final epidemic size are determined by the trade-off between the risk information dissemination and disease transmission, with the triggering effect of surveillance mediating this trade-off. Finally, our results confirm that behavioral adaptation can create a hysteresis-like effect on the final epidemic size., Comment: 17 pages, 12 figures

Many studies combine text and audio to capture multi-modal information but they overlook the model's generalization ability on new datasets. Introducing new datasets may affect the feature space of the original dataset, leading to catastrophic forgetting. Meanwhile, large model parameters can significantly impact training performance. To address these limitations, we introduce a novel task called Text-Audio Incremental Learning (TAIL) task for text-audio retrieval, and propose a new method, PTAT, Prompt Tuning for Audio-Text incremental learning. This method utilizes prompt tuning to optimize the model parameters while incorporating an audio-text similarity and feature distillation module to effectively mitigate catastrophic forgetting. We benchmark our method and previous incremental learning methods on AudioCaps, Clotho, BBC Sound Effects and Audioset datasets, and our method outperforms previous methods significantly, particularly demonstrating stronger resistance to forgetting on older datasets. Compared to the full-parameters Finetune (Sequential) method, our model only requires 2.42\% of its parameters, achieving 4.46\% higher performance., Comment: 4 figures, 5 tables

A generative modeling framework is proposed that combines diffusion models and manifold learning to efficiently sample data densities on manifolds. The approach utilizes Diffusion Maps to uncover possible low-dimensional underlying (latent) spaces in the high-dimensional data (ambient) space. Two approaches for sampling from the latent data density are described. The first is a score-based diffusion model, which is trained to map a standard normal distribution to the latent data distribution using a neural network. The second one involves solving an It\^o stochastic differential equation in the latent space. Additional realizations of the data are generated by lifting the samples back to the ambient space using Double Diffusion Maps, a recently introduced technique typically employed in studying dynamical system reduction; here the focus lies in sampling densities rather than system dynamics. The proposed approaches enable sampling high dimensional data densities restricted to low-dimensional, a priori unknown manifolds. The efficacy of the proposed framework is demonstrated through a benchmark problem and a material with multiscale structure.

We examine a distributional fixed-point equation related to a multi-type branching process that is key in the cluster sizes analysis of multivariate heavy-tailed Hawkes processes. Specifically, we explore the tail behavior of its solution and demonstrate the emergence of a form of multivariate hidden regular variation. Large values of the cluster size vector result from one or several significant jumps; A discrete optimization problem involving any given rare event set of interest determines the exact configuration of these large jumps and the degree of hidden regular variation. Our proofs rely on a detailed probabilistic analysis of the spatiotemporal structure of multiple large jumps in multi-type branching processes.

A new arbitrary Lagrangian-Eulerian (ALE) formulation for Navier-Stokes flow on self-evolving surfaces is presented. It is based on a new curvilinear surface parameterization that describes the motion of the ALE frame. Its in-plane part becomes fully arbitrary, while its out-of-plane part follows the material motion of the surface. This allows for the description of flows on deforming surfaces using only surface meshes. The unknown fields are the fluid density or pressure, the fluid velocity and the surface motion, where the latter two share the same normal velocity. The corresponding field equations are the continuity equation or area-incompressibility constraint, the surface Navier-Stokes equations, and suitable surface mesh equations. The presentation focuses on their strong and weak forms, and presents several manufactured steady and transient solutions. These solutions are used to illustrate and discuss the properties of the proposed new ALE formulation. They also serve as basis for the development and verification of corresponding computational methods. The new formulation allows for a detailed study of fluidic membranes such as soap films, capillary menisci and lipid bilayers., Comment: 37 pages, 13 figures

We compute the Gross-Llewellyn Smith sum rule, i.e. the lowest odd moment of the parity-violating structure function, $F_3$, of the nucleon from a lattice QCD calculation of the Compton amplitude. Our calculations are performed on $48^3 \times 96$ lattices at the $SU(3)$ symmetric point for two lattice spacings. We extract the moments for several values of the current momenta in the range $0.5 \lesssim Q^2 \lesssim 10 \; {\rm GeV}^2$, covering both the nonperturbative and perturbative regimes. We compare our moments to the Gross-Llewellyn Smith sum rule and discuss the implications for higher-twist effects, a determination of $\alpha_s(Q^2)$ from a hadronic quantity complementing the phenomenological and other lattice approaches, and electroweak box contributions crucial for CKM unitarity studies., Comment: 16 pages, 12 figures

We present the luminosity function and volumetric rate of a sample of Type IIP supernovae (SNe) from the Zwicky Transient Facility Census of the Local Universe survey (CLU). This is the largest sample of Type IIP SNe from a systematic volume-limited survey to-date. The final sample includes 330 Type IIP SNe and 36 low-luminosity Type II (LLIIP) SNe with $M_{\textrm{r,peak}}>-16$ mag, which triples the literature sample of LLIIP SNe. The fraction of LLIIP SNe is $19^{+3}_{-4}\%$ of the total CLU Type IIP SNe population ($8^{+1}_{-2}\%$ of all core-collapse SNe). This implies that while LLIIP SNe likely represent the fate of core-collapse SNe of $8-12$ \Msun\ progenitors, they alone cannot account for the fate of all massive stars in this mass range. To derive an absolute rate, we estimate the ZTF pipeline efficiency as a function of the apparent magnitude and the local surface brightness. We derive a volumetric rate of $(3.9_{-0.4}^{+0.4}) \times 10^{4}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for Type IIP SNe and $(7.3_{-0.6}^{+0.6}) \times 10^{3}\ \textrm{Gpc}^{-3}\ \textrm{yr}^{-1}$ for LLIIP SNe. Now that the rate of LLIIP SNe is robustly derived, the unresolved discrepancy between core-collapse SN rates and star-formation rates cannot be explained by LLIIP SNe alone., Comment: Submitted to PASP

In this second paper on the variability survey of central stars of planetary nebulae (CSPNe) using ZTF, we focus on the 11 long-timescale variables with variability timescales ranging from months to years. We also present preliminary analyses based on spectroscopic and/or photometric follow-up observations for six of them. Among them is NGC 6833, which shows a 980 day periodic variability with strange characteristics: 'triangle-shaped' brightening in $r$, $i$, and WISE bands but almost coincidental shallow dips in the $g$-band. We speculate this to be a wide but eccentric binary with the same orbital period. Long-period near-sinusoidal variability was detected in two other systems, NGC 6905 and Kn 26, with periods of 700 days and 230 days, respectively, making them additional wide-binary candidates. The latter also shows a short period at 1.18 hours which can either be from a close inner binary or pulsational origin. We present CTSS 2 and PN K 3-5 which show brightening and significant reddening over the whole ZTF baseline. A stellar model fit to the optical spectrum of CTSS 2 reveals it to be one of the youngest post-AGB CSPN known. Both show high-density emission-line cores. These appear to be late thermal pulse candidates, currently evolving towards the AGB phase, though alternative explanations are possible. We then present recent HST/COS ultraviolet spectroscopy of the known wide-binary candidate LoTr 1 showing that the hot star is a spectroscopic twin of the extremely hot white dwarf in UCAC2 46706450. We think that the long photometric period of 11 years is the binary orbital period. Finally, we briefly discuss the ZTF light curves of the remaining variables, namely Tan 2, K 3-20, WHTZ 3, Kn J1857+3931, and IPHAS J1927+0814. With these examples, we present the effectiveness of the von Neumann statistics and Pearson Skew-based metric space in searching for long-timescale variables., Comment: (Gaia IDs corrected, no change in results) 23 pages (including appendix), 20 figures, 2 tables; Submitted to PASP, Comments are Welcome!

We study distributed training of Graph Neural Networks (GNNs) on billion-scale graphs that are partitioned across machines. Efficient training in this setting relies on min-edge-cut partitioning algorithms, which minimize cross-machine communication due to GNN neighborhood sampling. Yet, min-edge-cut partitioning over large graphs remains a challenge: State-of-the-art (SoTA) offline methods (e.g., METIS) are effective, but they require orders of magnitude more memory and runtime than GNN training itself, while computationally efficient algorithms (e.g., streaming greedy approaches) suffer from increased edge cuts. Thus, in this work we introduce Armada, a new end-to-end system for distributed GNN training whose key contribution is GREM, a novel min-edge-cut partitioning algorithm that can efficiently scale to large graphs. GREM builds on streaming greedy approaches with one key addition: prior vertex assignments are continuously refined during streaming, rather than frozen after an initial greedy selection. Our theoretical analysis and experimental results show that this refinement is critical to minimizing edge cuts and enables GREM to reach partition quality comparable to METIS but with 8-65x less memory and 8-46x faster. Given a partitioned graph, Armada leverages a new disaggregated architecture for distributed GNN training to further improve efficiency; we find that on common cloud machines, even with zero communication, GNN neighborhood sampling and feature loading bottleneck training. Disaggregation allows Armada to independently allocate resources for these operations and ensure that expensive GPUs remain saturated with computation. We evaluate Armada against SoTA systems for distributed GNN training and find that the disaggregated architecture leads to runtime improvements up to 4.5x and cost reductions up to 3.1x.

Tidal disruption events (TDEs) that are spatially offset from the nucleus of their host galaxies offer a new probe of massive black hole (MBH) wanderers, binaries, triples, and recoiling MBHs. Here we present AT2024tvd, the first off-nuclear TDE identified through optical sky surveys. High-resolution imaging with the Hubble Space Telescope shows that AT2024tvd is $0.914\pm 0.010$ arcsec offset from the apparent center of its host galaxy, corresponding to a projected distance of $0.808\pm 0.009$ kpc at $z=0.045$. AT2024tvd exhibits typical properties of nuclear TDEs, including a persistent hot UV/optical component that peaks at $L_{ bb}\sim 6\times 10^{43}\,erg\,s^{-1}$, broad hydrogen lines in its optical spectra, and delayed brightening of luminous ($L_{ X,peak}\sim 3\times 10^{43}\,erg\,s^{-1}$), highly variable soft X-ray emission. The MBH mass of AT2024tvd is $10^{6\pm1}\,M_\odot$, at least 10 times lower than its host galaxy's central black hole mass ($\gtrsim 10^8\,M_\odot$). The MBH in AT2024tvd has two possible origins: a wandering MBH from the lower-mass galaxy in a minor merger during the dynamical friction phase or a recoiling MBH ejected by triple interactions. Combining AT2024tvd with two previously known off-nuclear TDEs discovered in X-rays (3XMM\,J2150 and EP240222a), which likely involve intermediate-mass black holes in satellite galaxies, we find that the parent galaxies of all three events are very massive ($\sim 10^{10.9}\,M_\odot$). This result aligns with expectations from cosmological simulations that the number of offset MBHs scales linearly with the host halo mass., Comment: 26 pages, 16 figures, 4 tables. Submitted to journal on 24 Feb 2025. Comments welcome

Coaches are vital for effective collaboration, but cost and resource constraints often limit their availability during real-world tasks. This limitation poses serious challenges in life-critical domains that rely on effective teamwork, such as healthcare and disaster response. To address this gap, we propose and realize an innovative application of AI: task-time team coaching. Specifically, we introduce Socratic, a novel AI system that complements human coaches by providing real-time guidance during task execution. Socratic monitors team behavior, detects misalignments in team members' shared understanding, and delivers automated interventions to improve team performance. We validated Socratic through two human subject experiments involving dyadic collaboration. The results demonstrate that the system significantly enhances team performance with minimal interventions. Participants also perceived Socratic as helpful and trustworthy, supporting its potential for adoption. Our findings also suggest promising directions both for AI research and its practical applications to enhance human teamwork., Comment: Extended version of an identically-titled paper accepted at AAMAS 2025

Machine-learning-based variational Monte Carlo simulations are a promising approach for targeting quantum many body ground states, especially in two dimensions and in cases where the ground state is known to have a non-trivial sign structure. While many state-of-the-art variational energies have been reached with these methods for finite-size systems, little work has been done to use these results to extract information about the target state in the thermodynamic limit. In this work, we employ recurrent neural networks (RNNs) as a variational ans\"{a}tze, and leverage their recurrent nature to simulate the ground states of progressively larger systems through iterative retraining. This transfer learning technique allows us to simulate spin-$\frac{1}{2}$ systems on lattices with more than 1,000 spins without beginning optimization from scratch for each system size, thus reducing the demands for computational resources. In this study, we focus on the square-lattice antiferromagnetic Heisenberg model (SLAHM), where it is possible to carefully benchmark our results. We show that we are able to systematically improve the accuracy of the results from our simulations by increasing the training time, and obtain results for finite-sized lattices that are in good agreement with the literature values. Furthermore, we use these results to extract accurate estimates of the ground-state properties in the thermodynamic limit. This work demonstrates that RNN wavefunctions can be used to accurately study quantum many-body physics in the thermodynamic limit., Comment: 18 pages, 13 figures, 6 tables

Standard language model evaluations can fail to capture risks that emerge only at deployment scale. For example, a model may produce safe responses during a small-scale beta test, yet reveal dangerous information when processing billions of requests at deployment. To remedy this, we introduce a method to forecast potential risks across orders of magnitude more queries than we test during evaluation. We make forecasts by studying each query's elicitation probability -- the probability the query produces a target behavior -- and demonstrate that the largest observed elicitation probabilities predictably scale with the number of queries. We find that our forecasts can predict the emergence of diverse undesirable behaviors -- such as assisting users with dangerous chemical synthesis or taking power-seeking actions -- across up to three orders of magnitude of query volume. Our work enables model developers to proactively anticipate and patch rare failures before they manifest during large-scale deployments.

We introduce the Byzantine Selection Problem, living at the intersection of game theory and fault-tolerant distributed computing. Here, an event organizer is presented with a group of $n$ agents, and wants to select $\ell < n$ of them to form a team. For these purposes, each agent $i$ self-reports a positive skill value $v_i$, and a team's value is the sum of its members' skill values. Ideally, the value of the team should be as large as possible, which can be easily achieved by selecting agents with the highest $\ell$ skill values. However, an unknown subset of at most $t < n$ agents are byzantine and hence not to be trusted, rendering their true skill values as $0$. In the spirit of the distributed computing literature, the identity of the byzantine agents is not random but instead chosen by an adversary aiming to minimize the value of the chosen team. Can we still select a team with good guarantees in this adversarial setting? As it turns out, deterministically, it remains optimal to select agents with the highest $\ell$ values. Yet, if $t \geq \ell$, the adversary can choose to make all selected agents byzantine, leading to a team of value zero. To provide meaningful guarantees, one hence needs to allow for randomization, in which case the expected value of the selected team needs to be maximized, assuming again that the adversary plays to minimize it. For this case, we provide linear-time randomized algorithms that maximize the expected value of the selected team., Comment: Appearing in AAMAS'25

We consider voting on multiple independent binary issues. In addition, a weighting vector for each voter defines how important they consider each issue. The most natural way to aggregate the votes into a single unified proposal is issue-wise majority (IWM): taking a majority opinion for each issue. However, in a scenario known as Ostrogorski's Paradox, an IWM proposal may not be a Condorcet winner, or it may even fail to garner majority support in a special case known as Anscombe's Paradox. We show that it is co-NP-hard to determine whether there exists a Condorcet-winning proposal even without weights. In contrast, we prove that the single-switch condition provides an Ostrogorski-free voting domain under identical weighting vectors. We show that verifying the condition can be achieved in linear time and no-instances admit short, efficiently computable proofs in the form of forbidden substructures. On the way, we give the simplest linear-time test for the voter/candidate-extremal-interval condition in approval voting and the simplest and most efficient algorithm for recognizing single-crossing preferences in ordinal voting. We then tackle Anscombe's Paradox. Under identical weight vectors, we can guarantee a majority-supported proposal agreeing with IWM on strictly more than half of the overall weight, while with two distinct weight vectors, such proposals can get arbitrarily far from IWM. The severity of such examples is controlled by the maximum average topic weight $\tilde{w}_{max}$: a simple bound derived from a partition-based approach is tight on a large portion of the range $\tilde{w}_{max} \in (0,1)$. Finally, we extend Wagner's rule to the weighted setting: an average majority across topics of at least $\frac{3}{4}$'s precludes Anscombe's paradox from occurring., Comment: To appear in AAMAS'25

Interconnected quantum devices are the building blocks of quantum networks, where state transduction plays a central role. The frequency conversion of photons into the telecommunication C-band is decisive in taking advantage of current low-loss transmission lines. Here, we report the difference frequency conversion of $637.2$ nm fluorescent light from a cluster of NV centers in diamond to tunable wavelengths between $1559.6$ nm and $1565.2$ nm. In order to avoid detrimental noise from spontaneous emissions, we use a two-step conversion device based on a single-pumped periodic poled lithium niobate waveguide. We observed a total external (internal) conversion efficiency of $3.0\pm0.1$ ($20.5\pm0.8$) $\%$ with a noise rate of $2.4\pm0.8$ ($16\pm5$) cps/GHz., Comment: 10 pages, 7 figures

Einstein's general theory of relativity is based on the principle of equivalence - in essence, dating back to Galileo - which asserts that, locally, the effect of a gravitational field is equivalent to that of an accelerating reference frame, so that the local gravitational field is eliminated in a freely-falling frame. Einstein's theory enables this principle to extend to a global description of relativistic space-time, at the expense of allowing space-time to become curved, realising a consistent frame-independent description of nature at the classical level. Einstein's theory has been confirmed to great accuracy for astrophysical bodies. However, in the quantum domain the equivalence principle has been predicted to take a unique form involving a gauge phase that is observable if the wavefunction - fundamental to quantum descriptions - allows an object to interfere with itself after being simultaneously at rest in two differently accelerating frames, one being the laboratory (Newtonian) frame and the other in the freely-falling (Einsteinian) frame. To measure this gauge phase we realise a novel cold-atom interferometer in which one wave packet stays static in the laboratory frame while the other is in free fall. We follow the relative-phase evolution of the wave packets in the two frames, confirming the equivalence principle in the quantum domain. Our observation is yet another fundamental test of the interface between quantum theory and gravity. The new interferometer also opens the door for further probing of the latter interface, as well as to searches for new physics.

We measure radial stellar age gradients in the relatively isolated gas-rich dwarf irregular WLM, combining JWST NIRCam and NIRISS imaging with six archival Hubble fields over semi-major axis equivalent distances of 0$\lesssim$R$_{SMA}$$\lesssim$4 kpc ($\lesssim$3R$_{hl}$). Fitting lifetime star formation histories (SFHs) to resolved color-magnitude diagrams (CMDs), radial age gradients are quantified using $\tau_{90}$ and $\tau_{50}$, the lookback times to form 90\% and 50\% of the cumulative stellar mass. We find that globally, the outskirts of WLM are older on average, with ($\delta$$\tau_{90}$, $\delta$$\tau_{50}$)/$\delta$R$_{SMA}=$(0.82$^{+0.10}_{-0.10}$, 1.60$^{+0.23}_{-0.22}$) Gyr/kpc (stat.), in good agreement with simulations. However, we also detect an azimuthal dependence of radial stellar age gradients, finding that stars on the leading edge of WLM (relative to its proper motion) are both younger and have a flatter age gradient compared to the trailing edge. This difference persists over 0.6$\lesssim$R$_{SMA}$$\lesssim$3.2 kpc ($\sim$0.5$-$2.5R$_{hl}$) and lookback times up to $\sim$8 Gyr, and is robust to assumed stellar evolutionary model. Our results are consistent with star formation triggered by ram pressure stripping from a circumgalactic and/or intergalactic medium, suggested by recent HI observations. If confirmed, processes typifying dense environments, such as ram pressure stripping, may be more relevant to the evolution of isolated galaxies than previously thought., Comment: ApJ in press. 23 pages, 8 figures, 3 tables

While neural vocoders have made significant progress in high-fidelity speech synthesis, their application on polyphonic music has remained underexplored. In this work, we propose DisCoder, a neural vocoder that leverages a generative adversarial encoder-decoder architecture informed by a neural audio codec to reconstruct high-fidelity 44.1 kHz audio from mel spectrograms. Our approach first transforms the mel spectrogram into a lower-dimensional representation aligned with the Descript Audio Codec (DAC) latent space before reconstructing it to an audio signal using a fine-tuned DAC decoder. DisCoder achieves state-of-the-art performance in music synthesis on several objective metrics and in a MUSHRA listening study. Our approach also shows competitive performance in speech synthesis, highlighting its potential as a universal vocoder., Comment: Accepted at ICASSP 2025

Consider a graph $G = (V, E)$ and some commuters, each specified by a tuple $(u, v, b)$ consisting of two nodes in the graph $u, v \in V$ and a non-negative real number $b$, specifying their budget. The goal is to find a pricing function $p$ of the edges of $G$ that maximizes the revenue generated by the commuters. Here, each commuter $(u, v, b)$ either pays the lowest-cost of a $u$-$v$ path under the pricing $p$, or 0, if this exceeds their budget $b$. We study this problem for the case where $G$ is a bounded-width grid graph and give a polynomial-time approximation algorithm with approximation ratio $O(\log |E|)$. Our approach combines existing ideas with new insights. Most notably, we employ a rather seldom-encountered technique that we coin under the name 'assume-implement dynamic programming.' This technique involves dynamic programming where some information about the future decisions of the dynamic program is guessed in advance and 'assumed' to hold, and then subsequent decisions are forced to 'implement' the guess. This enables computing the cost of the current transition by using information that would normally only be available in the future.

Given the low throughput of blockchains like Bitcoin and Ethereum, scalability -- the ability to process an increasing number of transactions -- has become a central focus of blockchain research. One promising approach is the parallelization of transaction execution across multiple threads. However, achieving efficient parallelization requires a redesign of the incentive structure within the fee market. Currently, the fee market does not differentiate between transactions that access multiple high-demand resources versus a single low-demand one, as long as they require the same computational effort. Addressing this discrepancy is crucial for enabling more effective parallel execution. In this work, we aim to bridge the gap between the current fee market and the need for parallel execution by exploring alternative fee market designs. To this end, we propose a framework consisting of two key components: a Gas Computation Mechanism (GCM), which quantifies the load a transaction places on the network in terms of parallelization and computation, measured in units of gas, and a Transaction Fee Mechanism (TFM), which assigns a price to each unit of gas. We also introduce a set of desirable properties for a GCM, present multiple candidate mechanisms, and evaluate them against the properties. One promising candidate emerges: the weighted area GCM. Notably, this mechanism can be seamlessly composed with existing TFMs, such as EIP-1559. While our exploration primarily focuses on the execution component of the fee, which directly relates to parallel execution, we also outline how it could be integrated with fees associated with other factors, such as storage and data bandwidth, by drawing a parallel to a multi-dimensional fee market.

This paper studies distributionally robust optimization for a large class of risk measures with ambiguity sets defined by $\phi$-divergences. The risk measures are allowed to be non-linear in probabilities, are represented by a Choquet integral possibly induced by a probability weighting function, and include many well-known examples (for example, CVaR, Mean-Median Deviation, Gini-type). Optimization for this class of robust risk measures is challenging due to their rank-dependent nature. We show that for many types of probability weighting functions including concave, convex and inverse $S$-shaped, the robust optimization problem can be reformulated into a rank-independent problem. In the case of a concave probability weighting function, the problem can be further reformulated into a convex optimization problem with finitely many constraints that admits explicit conic representability for a collection of canonical examples. While the number of constraints in general scales exponentially with the dimension of the state space, we circumvent this dimensionality curse and provide two types of upper and lower bounds algorithms. They yield tight upper and lower bounds on the exact optimal value and are formally shown to converge asymptotically. This is illustrated numerically in two examples given by a robust newsvendor problem and a robust portfolio choice problem., Comment: 69 pages

We present the optical discovery and multiwavelength follow-up observations of AT2024kmq, a likely tidal disruption event (TDE) associated with a supermassive ($M_{\rm BH}\sim 10^{8} M_\odot$) black hole in a massive galaxy at $z=0.192$. The optical light curve of AT2024kmq exhibits two distinct peaks: an early fast (timescale 1 d) and luminous ($M\approx-20$ mag) red peak, then a slower (timescale 1 month) blue peak with a higher optical luminosity ($M\approx-22$ mag) and featureless optical spectra. The second component is similar to the spectroscopic class of "featureless TDEs" in the literature, and during this second component we detect highly variable, luminous ($L_X\approx 10^{44}$ erg s$^{-1}$), and hard ($f_\nu \propto \nu^{-1.5}$) X-ray emission. Luminous ($10^{29} $erg s$^{-1}$ Hz$^{-1}$ at 10 GHz) but unchanging radio emission likely arises from an underlying active galactic nucleus. The luminosity, timescale, and color of the early red optical peak can be explained by synchrotron emission, or alternatively by thermal emission from material at a large radius ($R\approx\mathrm{few}\times10^{15}$ cm). Possible physical origins for this early red component include an off-axis relativistic jet, and shocks from self-intersecting debris leading to the formation of the accretion disk. Late-time radio observations will help distinguish between the two possibilities., Comment: 23 pages, 7 figures, 6 tables. Submitted to journal on 11 Feb 2025. Comments welcome

Deep wide spectral line surveys with the Square Kilometre Array (SKA) will expand the cosmic frontiers of neutral atomic hydrogen (HI) in galaxies. However, at cosmologically significant redshifts ($z \gtrsim 0.5$), detections will typically be spatially unresolved and limited to the highest mass systems. Gravitational lensing could potentially alleviate these limitations, enabling lower mass systems to be studied at higher redshift and spatially resolved dynamical studies of some HI discs. Additionally, lensed HI systems would select foreground dark matter haloes using a different, more extended baryonic tracer compared to other lens surveys. This may result in a wider selected range of foreground dark matter halo properties, such as the concentration parameter. This paper uses the distortion of the observed HI mass function (HIMF) produced by strong gravitational lensing to find a flux density criterion for selecting lensed HI sources in future SKA-Mid spectral line surveys. This selection approach could yield lensed HI source densities in the range of $\sim 0.1$--$10$ galaxies per square degree out to a redshift of $z \simeq 3$ covered by SKA-MID Band 1. Although the sample sizes are modest, even with the proposed SKA-Mid surveys, the selection approach is straightforward and should have a 50% efficiency without any additional information, such as low-impact-factor or lower-redshift massive galaxies. The efficiency of selecting high-redshift, neutral-hydrogen-rich, lensed galaxies should then be greatly enhanced by using SKA-MID data in concert with the Vera C. Rubin Large Survey of Space and Time., Comment: 10 pages, 6 figures, 1 table; Accepted for publication in MNRAS

We measured the covariance matrix of the fields generated in an integrated third-order optical parametric oscillator operating above threshold. We observed up to $(2.3 \pm 0.3)$ dB of squeezing in amplitude difference, inferred $(4.9 \pm 0.7)$ dB of on-chip squeezing, while an excess of noise for the sum of conjugated quadratures hinders the entanglement. The degradation of amplitude correlations and state purity for the increasing of the pump power is consistent with the observed growth of the phase noise of the fields, showing the necessity of strategies for phase noise control aiming at entanglement generation in these systems., Comment: main text: 4 pages, 6 figures; supplemental material: 13 pages, 15 figures

In 1950 Arrow famously showed that there is no social welfare function satisfying four basic conditions. In 1976, on the other hand, Gibbard and Sonnenschein showed that there does exist a unique probabilistic social welfare method that satisfies a different set of strictly stronger conditions. In this paper we discuss a deterministic electoral method satisfying those same stronger conditions in an appropriate sense; it is not a counterexample to either of these theorems. We attach a simple reference implementation written in C with executables for Linux and Windows., Comment: 9 pages. Reference implementation attached - see section 6 of the paper

Many training methods, such as Adam(W) and Shampoo, learn a positive-definite curvature matrix and apply an inverse root before preconditioning. Recently, non-diagonal training methods, such as Shampoo, have gained significant attention; however, they remain computationally inefficient and are limited to specific types of curvature information due to the costly matrix root computation via matrix decomposition. To address this, we propose a Riemannian optimization approach that dynamically adapts spectral-factorized positive-definite curvature estimates, enabling the efficient application of arbitrary matrix roots and generic curvature learning. We demonstrate the efficacy and versatility of our approach in positive-definite matrix optimization and covariance adaptation for gradient-free optimization, as well as its efficiency in curvature learning for neural net training., Comment: technical report

We present Cryoscope -- a new 50 sq. deg field-of-view, 1.2 m aperture, K-dark survey telescope to be located at Dome C, Antarctica. Cryoscope has an innovative optical-thermal design wherein the entire telescope is cryogenically cooled. Cryoscope also explores new detector technology to cost-effectively tile the full focal plane. Leveraging the dark Antarctic sky and minimizing telescope thermal emission, Cryoscope achieves unprecedented deep, wide, fast and red observations, matching and exceeding volumetric survey speeds from the Ultraviolet Explorer, Vera Rubin Observatory, and Nancy Grace Roman Space Telescope. By providing coverage beyond wavelengths of 2 $\mu$m, we aim to create the most comprehensive dynamic movie of the most obscured reaches of the Universe. Cryoscope will be a dedicated discovery engine for electromagnetic emission from coalescing compact binaries, Earth-like exoplanets orbiting cold stars, and multiple facets of time-domain, stellar and solar system science. In this paper, we describe the scientific drivers and technical innovations for this new discovery engine operating in the K-dark passband, why we choose to deploy it in Antarctica, and the status of a fifth-scale prototype designed as a Pathfinder to retire technological risks prior to full-scale implementation., Comment: 36 pages, 20 figures, 4 tables; submitted to PASP on 2025-02-09

The recent development of powerful AI systems has highlighted the need for robust risk management frameworks in the AI industry. Although companies have begun to implement safety frameworks, current approaches often lack the systematic rigor found in other high-risk industries. This paper presents a comprehensive risk management framework for the development of frontier AI that bridges this gap by integrating established risk management principles with emerging AI-specific practices. The framework consists of four key components: (1) risk identification (through literature review, open-ended red-teaming, and risk modeling), (2) risk analysis and evaluation using quantitative metrics and clearly defined thresholds, (3) risk treatment through mitigation measures such as containment, deployment controls, and assurance processes, and (4) risk governance establishing clear organizational structures and accountability. Drawing from best practices in mature industries such as aviation or nuclear power, while accounting for AI's unique challenges, this framework provides AI developers with actionable guidelines for implementing robust risk management. The paper details how each component should be implemented throughout the life-cycle of the AI system - from planning through deployment - and emphasizes the importance and feasibility of conducting risk management work prior to the final training run to minimize the burden associated with it.

In this work, we investigate the computational aspects of asynchronous cellular automata (ACAs), a modification of cellular automata in which cells update independently, following an asynchronous schedule. We introduce flip automata networks (FAN), a simple modification of automata networks that remain robust under any asynchronous update schedule. We show that asynchronous automata can efficiently simulate their synchronous counterparts with a linear memory overhead, which improves upon the previously established quadratic bound. Additionally, we address the universality gap for (a)synchronous cellular automata -- the boundary separating universal and non-universal automata, which is still not fully understood. We tighten this boundary by proving that all one-way asynchronous automata lack universal computational power. Conversely, we establish the existence of a universal 6-state first-neighbor automaton in one dimension and a 3-state von Neumann automaton in two dimensions, which represent the smallest known universal constructions to date.

In stable matching, one must find a matching between two sets of agents, commonly men and women, or job applicants and job positions. Each agent has a preference ordering over who they want to be matched with. Moreover a matching is said to be stable if no pair of matched agents prefer each other compared to their current matching. We consider solving stable matching in a distributed synchronous setting, where each agent is its own process. Moreover, we assume up to $t_L$ agents on one side and $t_R$ on the other side can be byzantine. After properly defining the stable matching problem in this setting, we study its solvability. When there are as many agents on each side with fully-ordered preference lists, we give necessary and sufficient conditions for stable matching to be solvable in the synchronous setting. These conditions depend on the communication model used, i.e., if parties on the same side are allowed to communicate directly, and on the presence of a cryptographic setup, i.e., digital signatures.

White dwarfs (WDs) showing transits from orbiting planetary debris provide significant insights into the structure and dynamics of debris disks. This is a rare class of objects with only eight published systems. In this work, we perform a systematic search for such systems within 500 pc in the Gaia-eDR3 catalog of WDs using the light curves from the Zwicky Transient Facility (ZTF) and present six new candidates. Our selection process targets the top 1% most photometrically variable sources identified using a combined variability metric from ZTF and Gaia eDR3 photometry, boosted by a metric space we define using von Neumann statistics and Pearson-Skew as a novel discovery tool to identify these systems. This is followed by optical spectroscopic observations of visually selected variables to confirm metal pollution. Four of the six systems show long-timescale photometric variability spanning several months to years, resulting either from long-term evolution of transit activity or dust and debris clouds at wide orbits. Among them, WD J1013-0427 shows an indication of reddening during the long-duration dip. Interpreting this as dust extinction makes it the first system to indicate an abundance of small dust grains (radius $\lesssim$$0.3~{\rm \mu m}$) in the occulting material. The same object also shows metal emission lines that map an optically thick eccentric gas disk orbiting within the star's Roche limit. For each candidate, we infer the abundances of the photospheric metals and estimate accretion rates. We show that transiting debris systems tend to have higher inferred accretion rates compared to the general population of metal-polluted WDs. Growing the number of these systems will further illuminate such comparative properties in the near future. Separately, we also serendipitously discovered an AM CVn showing a very long-duration outburst $-$ only the fourth such system to be known., Comment: 28 pages, 16 figures, 6 tables. Version after first PASP review. Resubmitting in a week. Comments are Welcome!

Nonequilibrium plasmas generated by spark discharges induce chemical, thermal, and flow dynamic effects that are beneficial in many applications such as plasmalysis, plasma-assisted combustion, and plasma flow control. Among the flow dynamic effects generated by these discharges, vorticity is of particular interest because it enhances the mixing of the discharge products with the surrounding environment and accelerates the cooling of the kernel. This article provides a comprehensive examination of the vorticity produced by nanosecond discharges in air. Using computational fluid dynamic simulations of the blast wave resulting from energy deposition in the interelectrode volume during the nanosecond pulse, we analyze the various sources of vorticity during the post-discharge period. The non-uniform strength of the leading shock of the blast wave is found to be the primary promoter of vorticity. Other sources, such as the barocline torque, play a secondary role in explaining the overall recirculation pattern. In addition, the discharge cooling mechanisms are also investigated. The cooling regimes and their efficiencies are classified according to the discharge parameters, such as the inter-electrode gap, the initial kernel temperature, or the frequency of repetitive pulses. Finally, a physics-based, non-dimensional number PI* , equal to the ratio of the initial kernel temperature to the ambient temperature, is introduced. A numerical analysis shows that the transition between the recirculating and non-recirculating flow regimes occurs for PI* on the order of 10. This criterion is validated against experimental and numerical results from the literature., Comment: Journal of Physics D: Applied Physics, In press

Despite incredible progress, many neural architectures fail to properly generalize beyond their training distribution. As such, learning to reason in a correct and generalizable way is one of the current fundamental challenges in machine learning. In this respect, logic puzzles provide a great testbed, as we can fully understand and control the learning environment. Thus, they allow to evaluate performance on previously unseen, larger and more difficult puzzles that follow the same underlying rules. Since traditional approaches often struggle to represent such scalable logical structures, we propose to model these puzzles using a graph-based approach. Then, we investigate the key factors enabling the proposed models to learn generalizable solutions in a reinforcement learning setting. Our study focuses on the impact of the inductive bias of the architecture, different reward systems and the role of recurrent modeling in enabling sequential reasoning. Through extensive experiments, we demonstrate how these elements contribute to successful extrapolation on increasingly complex puzzles.These insights and frameworks offer a systematic way to design learning-based systems capable of generalizable reasoning beyond interpolation., Comment: The first two authors contributed equally to this work. Accepted as workshop paper at NEURMAD@AAAI25

Creativity assessment in science and engineering is increasingly based on both human and AI judgment, but the cognitive processes and biases behind these evaluations remain poorly understood. We conducted two experiments examining how including example solutions with ratings impact creativity evaluation, using a finegrained annotation protocol where raters were tasked with explaining their originality scores and rating for the facets of remoteness (whether the response is "far" from everyday ideas), uncommonness (whether the response is rare), and cleverness. In Study 1, we analyzed creativity ratings from 72 experts with formal science or engineering training, comparing those who received example solutions with ratings (example) to those who did not (no example). Computational text analysis revealed that, compared to experts with examples, no-example experts used more comparative language (e.g., "better/worse") and emphasized solution uncommonness, suggesting they may have relied more on memory retrieval for comparisons. In Study 2, parallel analyses with state-of-the-art LLMs revealed that models prioritized uncommonness and remoteness of ideas when rating originality, suggesting an evaluative process rooted around the semantic similarity of ideas. In the example condition, while LLM accuracy in predicting the true originality scores improved, the correlations of remoteness, uncommonness, and cleverness with originality also increased substantially - to upwards of 0.99 - suggesting a homogenization in the LLMs evaluation of the individual facets. These findings highlight important implications for how humans and AI reason about creativity and suggest diverging preferences for what different populations prioritize when rating., Comment: CogSci 2025

We are given a set of elements in a metric space. The distribution of the elements is arbitrary, possibly adversarial. Can we weigh the elements in a way that is resistant to such (adversarial) manipulations? This problem arises in various contexts. For instance, the elements could represent data points, requiring robust domain adaptation. Alternatively, they might represent tasks to be aggregated into a benchmark; or questions about personal political opinions in voting advice applications. This article introduces a theoretical framework for dealing with such problems. We propose clone-proof representation functions as a solution concept. These functions distribute importance across elements of a set such that similar objects (``clones'') share (some of) their weights, thus avoiding a potential bias introduced by their multiplicity. Our framework extends the maximum uncertainty principle to accommodate general metric spaces and includes a set of axioms - symmetry, continuity, and clone-proofness - that guide the construction of representation functions. Finally, we address the existence of representation functions satisfying our axioms in the significant case of Euclidean spaces and propose a general method for their construction., Comment: v1

In this work, we study multivalued byzantine agreement (BA) in an asynchronous network of $n$ parties where up to $t < \frac{n}{3}$ parties are byzantine. In this setting, we present a novel reduction from multivalued BA to binary BA. This reduction allows one to achieve BA on $\ell$-bit inputs with one instance of binary BA, one instance of weak agreement (WA) on $\ell$-bit inputs and $\Theta(\ell n + n^2)$ bits of additional communication. Afterwards, as our reduction uses multivalued WA, we design two new efficient WA protocols for $\ell$-bit inputs. In the first one, we use almost-universal hashing to achieve statistical security with probability $1 - 2^{-\lambda}$ against $t < \frac{n}{3}$ failures with $\Theta(n^2(\lambda + \log n + \log \ell))$ bits of communication. Then, we replace the hashes with error correcting code symbols and add a step based on the synchronous multivalued BA protocol COOL [DISC '21] to obtain our second protocol, which for any fixed $\varepsilon > 0$ achieves perfect security against $t \leq \frac{n}{3 + \varepsilon}$ failures with $\Theta(\ell n + n^2)$ bits of communication. Used in our reduction, our WA protocols extend binary BA to multivalued BA with a constant round overhead, a quadratic-in-$n$ communication overhead, and near-optimal security., Comment: 21 pages, 0 figures, preprint

We study the problem of broadcasting multiple messages in the CONGEST model. In this problem, a dedicated node $s$ possesses a set $M$ of messages with every message being of the size $O(\log n)$ where $n$ is the total number of nodes. The objective is to ensure that every node in the network learns all messages in $M$. The execution of an algorithm progresses in rounds and we focus on optimizing the round complexity of broadcasting multiple messages. Our primary contribution is a randomized algorithm designed for networks modeled as random graphs. The algorithm succeeds with high probability and achieves round complexity that is optimal up to a polylogarithmic factor. It leverages a multi-COBRA primitive, which uses multiple branching random walks running in parallel. To the best of our knowledge, this approach has not been applied in distributed algorithms before. A crucial aspect of our method is the use of these branching random walks to construct an optimal (up to a polylogarithmic factor) tree packing of a random graph, which is then used for efficient broadcasting. This result is of independent interest. We also prove the problem to be NP-hard in a centralized setting and provide insights into why straightforward lower bounds, namely graph diameter and $\frac{|M|}{minCut}$, can not be tight.

We show how to identify the distributions of the error components in the two-way dyadic model $y_{ij}=c+\alpha_i+\eta_j+\varepsilon_{ij}$. To this end, we extend the lemma of Kotlarski (1967), mimicking the arguments of Evdokimov and White (2012). We allow the characteristic functions of the error components to have real zeros, as long as they do not overlap with zeros of their first derivatives.

We present observations of the Type IIP supernova (SN) 2024jlf, including spectroscopy beginning just 0.7 days ($\sim$17 hours) after first light. Rapid follow-up was enabled by the new $\texttt{BTSbot-nearby}$ program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby ($D<60$ Mpc) galaxies and identified by the $\texttt{BTSbot}$ machine learning model. Early photometry and non-detections shortly prior to first light show that SN 2024jlf initially brightened by $>$4 mag/day, quicker than $\sim$90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived ($1.3 < \tau ~\mathrm{[d]} < 1.8$) emission lines of H$\alpha$, He II, and C IV. Assuming a wind velocity of $v_w=50$ km s$^{-1}$, these properties indicate that the red supergiant progenitor exhibited enhanced mass-loss in the last year before explosion. We constrain the mass-loss rate to $10^{-4} < \dot{M}~\mathrm{[M_\odot~yr^{-1}]} < 10^{-3}$ by matching observations to model grids from two independent radiative hydrodynamics codes. $\texttt{BTSbot-nearby}$ automation minimizes spectroscopic follow-up latency, enabling the observation of ephemeral early-time phenomena exhibited by transients., Comment: 23 pages, 9 figures

The rapid advancement of high-throughput sequencing and other assay technologies has resulted in the generation of large and complex multi-omics datasets, offering unprecedented opportunities for advancing precision medicine strategies. However, multi-omics data integration presents significant challenges due to the high dimensionality, heterogeneity, experimental gaps, and frequency of missing values across data types. Computational methods have been developed to address these issues, employing statistical and machine learning approaches to uncover complex biological patterns and provide deeper insights into our understanding of disease mechanisms. Here, we comprehensively review state-of-the-art multi-omics data integration methods with a focus on deep generative models, particularly variational autoencoders (VAEs) that have been widely used for data imputation and augmentation, joint embedding creation, and batch effect correction. We explore the technical aspects of loss functions and regularisation techniques including adversarial training, disentanglement and contrastive learning. Moreover, we discuss recent advancements in foundation models and the integration of emerging data modalities, while describing the current limitations and outlining future directions for enhancing multi-modal methodologies in biomedical research., Comment: 43 pages, 4 figures

This paper presents the first effort to Extend the Investigation of Stellar Populations In RElics (E-INSPIRE). We present a catalogue of 430 spectroscopically-confirmed ultra-compact massive galaxies (UCMGs) from the Sloan Digital Sky Survey at redshifts $0.0125\%$) of their stellar mass at $z<2$. We confirm that an efficient method of finding relics is to select UCMGs with large velocity dispersion values but we believe that the most efficient way is to select high velocity dispersion objects that also have super-solar metallicities and high [Mg/Fe]., Comment: 19 pages, 21 figures, submitted to MNRAS - minor revision requested by the referee

We consider methods for optimizing the bandgap calculation of 3D materials, considering 340 sample materials. Examined are the effects of the choice of the pseudopotential to describe core electrons, the plane-wave basis set cutoff energy, and the Brillouin zone integration. Cost-saving calculations in which the structure is optimized using reduced-quality Brillouin zone integrations and cutoff energies were found to lead to experimentally significant errors exceeding 0.1 eV in 18% of cases using the PBE functional and 21% of cases using PBE0. Such cost-savings approaches are therefore not recommended for general applications. Also, the current practice of using unoptimized grids to perform the Brillouin-zone integrations in bandgap calculations is found to be unreliable for 16% of materials using PBE and for 23% using PBE0. A k-space optimization scheme is introduced that interpolates extensive PBE results to determine a generally useful approach that when used in PBE0 calculations is found to be inadequate for only 1.6% of the materials studied.

Several factors make clustering of functional data challenging, including the infinite-dimensional space to which observations belong and the lack of a defined probability density function for the functional random variable. To overcome these barriers, researchers either assume that observations belong to a finite-dimensional space spanned by basis functions or apply nonparametric smoothing methods to the functions prior to clustering. Although extensive literature describes clustering methods for functional data, few studies have explored the clustering of measurement error--prone function-valued data. In this work, we consider clustering methods for functional data prone to complex, heteroscedastic measurement errors. Two stage-based methods using mixed-effects models are first applied to adjust for measurement error bias, followed by cluster analysis of the measurement error--adjusted curves. Through simulations, we investigate how varying sample size, the magnitude of measurement error, and the presence of complex heteroscedastic measurement errors influence the cluster analysis of functional data. Our results indicate that failing to account for measurement errors and the correlation structures associated with frequently collected functional data reduces the accuracy of identifying the true latent groups or clusters. The method consistently produces better results regardless of the initial clustering values used. Moreover, it is flexible and can be applied to various clustering approaches, based on the specific distribution of the data. The developed methods are applied to two data sets: a school-based study of energy expenditure among elementary school-aged children in Texas and data from the National Health and Nutrition Examination Survey on participants' physical activity monitored by wearable devices at frequent intervals.

We propose a unified framework for Singing Voice Synthesis (SVS) and Conversion (SVC), addressing the limitations of existing approaches in cross-domain SVS/SVC, poor output musicality, and scarcity of singing data. Our framework enables control over multiple aspects, including language content based on lyrics, performance attributes based on a musical score, singing style and vocal techniques based on a selector, and voice identity based on a speech sample. The proposed zero-shot learning paradigm consists of one SVS model and two SVC models, utilizing pre-trained content embeddings and a diffusion-based generator. The proposed framework is also trained on mixed datasets comprising both singing and speech audio, allowing singing voice cloning based on speech reference. Experiments show substantial improvements in timbre similarity and musicality over state-of-the-art baselines, providing insights into other low-data music tasks such as instrumental style transfer. Examples can be found at: everyone-can-sing.github.io.

The KOTO II experiment is proposed to measure the branching ratio of the decay $K_L\to\pi^0\nu\bar{\nu}$ at J-PARC. With a beamline to extract long-lived neutral kaons at 5 degrees from a production target, the single event sensitivity of the decay is $8.5\times 10^{-13}$, which is much smaller than the Standard Model prediction $3\times 10^{-11}$. This allows searches for new physics beyond the Standard Model and the first discovery of the decay with a significance exceeding $5\sigma$. As the only experiment proposed in the world dedicated to rare kaon decays, KOTO II will be indispensable in the quest for a complete understanding of flavor dynamics in the quark sector. Moreover, by combining efforts from the kaon community worldwide, we plan to develop the KOTO II detector further and expand the physics reach of the experiment to include measurements of the branching ratio of the $K_L\to\pi^0\ell^+\ell^-$ decays, studies of other $K_L$ decays, and searches for dark photons, axions, and axion-like particles. KOTO II will therefore obtain a comprehensive understanding of $K_L$ decays, providing further constraints on new physics scenarios with existing $K^+$ results., Comment: Submitted to the J-PARC PAC. arXiv admin note: substantial text overlap with arXiv:2110.04462

Open-source projects that aim to make their offerings public have competed against for-profit, proprietary companies in a number of domains. These open-source projects often arise in response to the offerings of proprietary companies in markets where products have already been commercialized. We assess what impact an open-source project might have when it enters the market for quantum computing - a market where the core technology is still being developed. We argue that an open-source quantum computer might alleviate market frictions that have impeded the development of a fault-tolerant quantum computer by providing the market with a possible mechanism for: 1.) benchmarking, 2.) the development of hardware agnostic technology, and 3.) improved liquidity on the supply side of the market for labor. Should these outcomes be realized, they may not only benefit the open-source project, but also the for-profit, proprietary companies operating in the ecosystem. In this respect, an open-source quantum project may play a more complementary role to the proprietary firms in the ecosystem, rather than a more competitive role. Our collective insights may have implications for other settings where an open-source project enters into a market where the core technology has yet to be commercialized., Comment: 11 pages, 3 figures

Docking marine surface vessels remains a largely manual task due to its safety-critical nature. In this paper, we develop a hierarchical symbolic control architecture for autonomous docking maneuvers of a dynamic positioning vessel, to provide formal safety guarantees. At the upper-level, we treat the vessel's desired surge, sway, and yaw velocities as control inputs and synthesize a symbolic controller in real-time. The desired velocities are then transmitted to and executed by the vessel's low-level velocity feedback control loop. Given a synthesized symbolic controller, we investigate methods to optimize the performance of the proposed control scheme for the docking task. The efficacy of this methodology is evaluated on a low-fidelity simulation model of a marine surface vessel in the presence of static and dynamic obstacles and, for the first time, through physical experiments on a scaled model vessel.