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Scaling up quantum computing devices requires solving ever more complex quantum control tasks. Machine learning has been proposed as a promising approach to tackle the resulting challenges. However, experimental implementations are still scarce. In this work, we demonstrate experimentally a neural-network-based preparation of Schr\"odinger cat states in a cavity coupled dispersively to a qubit. We show that it is possible to teach a neural network to output optimized control pulses for a whole family of quantum states. After being trained in simulations, the network takes a description of the target quantum state as input and rapidly produces the pulse shape for the experiment, without any need for time-consuming additional optimization or retraining for different states. Our experimental results demonstrate more generally how deep neural networks and transfer learning can produce efficient simultaneous solutions to a range of quantum control tasks, which will benefit not only state preparation but also parametrized quantum gates., Comment: 20 pages, appendix included

As artificial intelligence (AI) continues to rapidly advance, there is a growing demand to integrate AI capabilities into existing business applications. However, a significant gap exists between the rapid progress in AI and how slowly AI is being embedded into business environments. Deploying well-performing lab models into production settings, especially in on-premise environments, often entails specialized expertise and imposes a heavy burden of model management, creating significant barriers to implementing AI models in real-world applications. KModels leverages proven libraries and platforms (Kubeflow Pipelines, KServe) to streamline AI adoption by supporting both AI developers and consumers. It allows model developers to focus solely on model development and share models as transportable units (Templates), abstracting away complex production deployment concerns. KModels enables AI consumers to eliminate the need for a dedicated data scientist, as the templates encapsulate most data science considerations while providing business-oriented control. This paper presents the architecture of KModels and the key decisions that shape it. We outline KModels' main components as well as its interfaces. Furthermore, we explain how KModels is highly suited for on-premise deployment but can also be used in cloud environments. The efficacy of KModels is demonstrated through the successful deployment of three AI models within an existing Work Order Management system. These models operate in a client's data center and are trained on local data, without data scientist intervention. One model improved the accuracy of Failure Code specification for work orders from 46% to 83%, showcasing the substantial benefit of accessible and localized AI solutions.

Presented here is a transcription of the lecture notes from Professor Allan N. Kaufman's graduate statistical mechanics course at Berkeley from the 1972-1973 academic year. Part 1 addresses equilibrium statistical mechanics with topics: fundamentals, classical fluids and other systems, chemical equilibrium, and long-range interactions. Part 2 addresses non-equilibrium statistical mechanics with topics: fundamentals, Brownian motion, Liouville and Klimontovich equations, Landau equation, Markov processes and Fokker-Planck equation, linear response and transport theory, and an introduction to non-equilibrium quantum statistical mechanics., Comment: 156 pages, lecture notes, a dozen or less figures

Context. On 2022 January 20, the Energetic Particle Detector (EPD) on board Solar Orbiter measured a solar energetic particle (SEP) event showing unusual first arriving particles from the anti-Sun direction. Near-Earth spacecraft separated 17{\deg} in longitude to the west from Solar Orbiter measured classic antisunward-directed fluxes. STEREO-A and MAVEN, separated 18{\deg} to the east and 143{\deg} to the west from Solar Orbiter respectively, also observed the event, suggesting that particles spread over at least 160{\deg} in the heliosphere. Results. Solar Orbiter was embedded in a MC erupting on 16 January from the same active region as the one related to the SEP event on 20 January. The SEP event is related to a M5.5 flare and a fast CME-driven shock of 1433 km/s, which injected particles within and outside the MC. The hard SEP spectra, the presence of a Type II radio burst, and the co-temporal Type III radio bursts being observed from 80 MHz that seems to emanate from the Type II, points to the shock as the relevant accelerator of the particles. Conclusions. The detailed analysis of the SEP event strongly suggest that the energetic particles are injected mainly by a CME-driven shock into and outside of a previous MC present in the heliosphere at the time of the particle onset. The sunward propagating SEPs measured by Solar Orbiter are produced by the injection of particles along the longer (western) leg of the MC still connected to the Sun at the time of the release of the particles. The determined electron propagation path length inside the MC is around 30% longer than the estimated length of the loop leg of the MC itself (based on the graduated cylindrical shell model) consistent with a low number of field line rotations., Comment: 23 pages, 19 figures

To take full advantage of a specific hardware target, performance engineers need to gain control on compilers in order to leverage their domain knowledge about the program and hardware. Yet, modern compilers are poorly controlled, usually by configuring a sequence of coarse-grained monolithic black-box passes, or by means of predefined compiler annotations/pragmas. These can be effective, but often do not let users precisely optimize their varying compute loads. As a consequence, performance engineers have to resort to implementing custom passes for a specific optimization heuristic, requiring compiler engineering expert knowledge. In this paper, we present a technique that provides fine-grained control of general-purpose compilers by introducing the Transform dialect, a controllable IR-based transformation system implemented in MLIR. The Transform dialect empowers performance engineers to optimize their various compute loads by composing and reusing existing - but currently hidden - compiler features without the need to implement new passes or even rebuilding the compiler. We demonstrate in five case studies that the Transform dialect enables precise, safe composition of compiler transformations and allows for straightforward integration with state-of-the-art search methods.

We propose a novel framework for representing neural fields on triangle meshes that is multi-resolution across both spatial and frequency domains. Inspired by the Neural Fourier Filter Bank (NFFB), our architecture decomposes the spatial and frequency domains by associating finer spatial resolution levels with higher frequency bands, while coarser resolutions are mapped to lower frequencies. To achieve geometry-aware spatial decomposition we leverage multiple DiffusionNet components, each associated with a different spatial resolution level. Subsequently, we apply a Fourier feature mapping to encourage finer resolution levels to be associated with higher frequencies. The final signal is composed in a wavelet-inspired manner using a sine-activated MLP, aggregating higher-frequency signals on top of lower-frequency ones. Our architecture attains high accuracy in learning complex neural fields and is robust to discontinuities, exponential scale variations of the target field, and mesh modification. We demonstrate the effectiveness of our approach through its application to diverse neural fields, such as synthetic RGB functions, UV texture coordinates, and vertex normals, illustrating different challenges. To validate our method, we compare its performance against two alternatives, showcasing the advantages of our multi-resolution architecture.

Adiabatic passage in systems of interacting bosons is substantially affected by interactions and inter-particle entanglement. We consider STIRAP-like schemes in Bose-Hubbard chains that exhibit low-dimensional chaos (a 3 site chain), and high-dimensional chaos (more than 3 sites). The dynamics that is generated by a transfer protocol exhibits striking classical and quantum chaos fingerprints that are manifest in the mean-field classical treatment, in the truncated-Wigner semiclassical treatment, and in the full many-body quantum simulations., Comment: 13 pages, 14 figures

How do language models use information provided as context when generating a response? Can we infer whether a particular generated statement is actually grounded in the context, a misinterpretation, or fabricated? To help answer these questions, we introduce the problem of context attribution: pinpointing the parts of the context (if any) that led a model to generate a particular statement. We then present ContextCite, a simple and scalable method for context attribution that can be applied on top of any existing language model. Finally, we showcase the utility of ContextCite through three applications: (1) helping verify generated statements (2) improving response quality by pruning the context and (3) detecting poisoning attacks. We provide code for ContextCite at https://github.com/MadryLab/context-cite.

MICADO is a first light instrument for the Extremely Large Telescope (ELT), set to start operating later this decade. It will provide diffraction limited imaging, astrometry, high contrast imaging, and long slit spectroscopy at near-infrared wavelengths. During the initial phase operations, adaptive optics (AO) correction will be provided by its own natural guide star wavefront sensor. In its final configuration, that AO system will be retained and complemented by the laser guide star multi-conjugate adaptive optics module MORFEO (formerly known as MAORY). Among many other things, MICADO will study exoplanets, distant galaxies and stars, and investigate black holes, such as Sagittarius A* at the centre of the Milky Way. After their final design phase, most components of MICADO have moved on to the manufacturing and assembly phase. Here we summarize the final design of the instrument and provide an overview about its current manufacturing status and the timeline. Some lessons learned from the final design review process will be presented in order to help future instrumentation projects to cope with the challenges arising from the substantial differences between projects for 8-10m class telescopes (e.g. ESO-VLT) and the next generation Extremely Large Telescopes (e.g. ESO-ELT). Finally, the expected performance will be discussed in the context of the current landscape of astronomical observatories and instruments. For instance, MICADO will have similar sensitivity as the James Webb Space Telescope (JWST), but with six times the spatial resolution., Comment: Proceedings of the SPIE, Volume 13096, id. 1309611 11 pp. (2024)

The Virgo Cluster is the nearest substantial cluster of galaxies to the Milky Way and a cornerstone of the extragalactic distance scale. Here, we present JWST/NIRCam observations that simultaneously cover the cores and halos of ten galaxies in and around the Virgo Cluster and are designed to perform simultaneous measurements of the tip of the red giant branch (TRGB) and surface brightness fluctuations (SBF). Seven of the targets are within the Virgo Cluster and where we are able to resolve some of the cluster's substructure, while an additional three provide important constraints on Virgo infall. The seven galaxies within Virgo itself all have SBF measurements from the Advanced Camera for Surveys Virgo Cluster Survey (ACSVCS). After adjusting the ACSVCS measurements for the offset from our new JWST TRGB measurements, we determine a distance to the Virgo Cluster of d $=$ 16.17 $\pm$ 0.25 (stat) $\pm$ 0.47 (sys) Mpc. The work presented here is part of a larger program to develop a Population II distance scale through the TRGB and SBF that is completely independent of the prominent Cepheid + Type Ia supernova ladder. The TRGB distances to the galaxies presented here, when combined with future SBF measurements, will provide a crucial step forward for determining whether or not systematic errors can explain the well-known "Hubble tension'' or if there is significant evidence for cracks in the $\Lambda$CDM model., Comment: submitted to ApJ. comments welcome and appreciated. program name has changed from Paper I's "The Population II Extragalactic Distance Scale" to "The TRGB-SBF Project"

The study of transmission and progression of human papillomavirus (HPV) is crucial for understanding the incidence of cervical cancers, and has been identified as a priority worldwide. The complexity of the disease necessitates a detailed model of HPV transmission and its progression to cancer; to infer properties of the above we require a careful process that can match to imperfect or incomplete observational data. In this paper, we describe the HPVsim simulator to satisfy the former requirement; to satisfy the latter we couple this stochastic simulator to a process of emulation and history matching using the R package hmer. With these tools, we are able to obtain a comprehensive collection of parameter combinations that could give rise to observed cancer data, and explore the implications of the variability of these parameter sets as it relates to future health interventions., Comment: 21 pages, 15 figures; submitted to Epidemics

While Large Language Models (LLMs) are being quickly adapted to many domains, including healthcare, their strengths and pitfalls remain under-explored. In our study, we examine the effects of prompt engineering to guide Large Language Models (LLMs) in delivering parts of a Problem-Solving Therapy (PST) session via text, particularly during the symptom identification and assessment phase for personalized goal setting. We present evaluation results of the models' performances by automatic metrics and experienced medical professionals. We demonstrate that the models' capability to deliver protocolized therapy can be improved with the proper use of prompt engineering methods, albeit with limitations. To our knowledge, this study is among the first to assess the effects of various prompting techniques in enhancing a generalist model's ability to deliver psychotherapy, focusing on overall quality, consistency, and empathy. Exploring LLMs' potential in delivering psychotherapy holds promise with the current shortage of mental health professionals amid significant needs, enhancing the potential utility of AI-based and AI-enhanced care services., Comment: Accepted for AMIA 2024 proceedings

In this paper, we investigate a variant of the classical stochastic Multi-armed Bandit (MAB) problem, where the payoff received by an agent (either cost or reward) is both delayed, and directly corresponds to the magnitude of the delay. This setting models faithfully many real world scenarios such as the time it takes for a data packet to traverse a network given a choice of route (where delay serves as the agent's cost); or a user's time spent on a web page given a choice of content (where delay serves as the agent's reward). Our main contributions are tight upper and lower bounds for both the cost and reward settings. For the case that delays serve as costs, which we are the first to consider, we prove optimal regret that scales as $\sum_{i:\Delta_i > 0}\frac{\log T}{\Delta_i} + d^*$, where $T$ is the maximal number of steps, $\Delta_i$ are the sub-optimality gaps and $d^*$ is the minimal expected delay amongst arms. For the case that delays serves as rewards, we show optimal regret of $\sum_{i:\Delta_i > 0}\frac{\log T}{\Delta_i} + \bar{d}$, where $\bar d$ is the second maximal expected delay. These improve over the regret in the general delay-dependent payoff setting, which scales as $\sum_{i:\Delta_i > 0}\frac{\log T}{\Delta_i} + D$, where $D$ is the maximum possible delay. Our regret bounds highlight the difference between the cost and reward scenarios, showing that the improvement in the cost scenario is more significant than for the reward. Finally, we accompany our theoretical results with an empirical evaluation.

Taylor's power law (TL) or fluctuation scaling has been verified empirically for the abundances of many species, human and non-human, and in many other fields including physics, meteorology, computer science, and finance. TL asserts that the variance is directly proportional to a power of the mean, exactly for population moments and, whether or not population moments exist, approximately for sample moments. In many papers, linear regression of log variance as a function of log mean is used to estimate TL's parameters. We provide some statistical guarantees with large-sample asymptotics for this kind of inference under general conditions, and we derive confidence intervals for the parameters. In many ecological applications, the means and variances are estimated over time or across space from arrays of abundance data collected at different locations and time points. When the ratio between the time-series length and the number of spatial points converges to a constant as both become large, the usual normalized statistics are asymptotically biased. We provide a bias correction to get correct confidence intervals. TL, widely studied in multiple sciences, is a source of challenging new statistical problems in a nonstationary spatiotemporal framework. We illustrate our results with both simulated and real data sets.

A firm seeks to analyze a dataset and to release the results. The dataset contains information about individual people, and the firm is subject to some regulation that forbids the release of the dataset itself. The regulation also imposes conditions on the release of the results. What properties should the regulation satisfy? We restrict our attention to regulations tailored to controlling the downstream effects of the release specifically on the individuals to whom the data relate. A particular example of interest is an anonymization rule, where a data protection regulation limiting the disclosure of personally identifiable information does not restrict the distribution of data that has been sufficiently anonymized. In this paper, we develop a set of technical requirements for anonymization rules and related regulations. The requirements are derived by situating within a simple abstract model of data processing a set of guiding general principles put forth in prior work. We describe an approach to evaluating such regulations using these requirements -- thus enabling the application of the general principles for the design of mechanisms. As an exemplar, we evaluate competing interpretations of regulatory requirements from the EU's General Data Protection Regulation.

These notes are based on a lecture delivered by NC on March 2021, as part of an advanced course in Princeton University on the mathematical understanding of deep learning. They present a theory (developed by NC, NR and collaborators) of linear neural networks -- a fundamental model in the study of optimization and generalization in deep learning. Practical applications born from the presented theory are also discussed. The theory is based on mathematical tools that are dynamical in nature. It showcases the potential of such tools to push the envelope of our understanding of optimization and generalization in deep learning. The text assumes familiarity with the basics of statistical learning theory. Exercises (without solutions) are included., Comment: Lecture notes

Interpretable clustering algorithms aim to group similar data points while explaining the obtained groups to support knowledge discovery and pattern recognition tasks. While most approaches to interpretable clustering construct clusters using decision trees, the interpretability of trees often deteriorates on complex problems where large trees are required. In this work, we introduce the Neural Clustering Additive Model (NeurCAM), a novel approach to the interpretable clustering problem that leverages neural generalized additive models to provide fuzzy cluster membership with additive explanations of the obtained clusters. To promote sparsity in our model's explanations, we introduce selection gates that explicitly limit the number of features and pairwise interactions leveraged. Additionally, we demonstrate the capacity of our model to perform text clustering that considers the contextual representation of the texts while providing explanations for the obtained clusters based on uni- or bi-word terms. Extensive experiments show that NeurCAM achieves performance comparable to black-box methods on tabular datasets while remaining interpretable. Additionally, our approach significantly outperforms other interpretable clustering approaches when clustering on text data., Comment: Accepted to ECAI 2024; Official code implementation found at https://github.com/optimal-uoft/NeurCAM

The low elastic modulus of soft materials, combined with geometric nonlinearity and rate dependence, presents significant challenges in the characterization of their mechanical response. We introduce a novel method for measuring the mechanical properties of soft materials under large deformations via cylindrical cavity expansion. In this method, a cylindrical cavity is fabricated in the material and expanded by volume-controlled injection of an incompressible fluid with simultaneous measurement of the applied pressure at the cavity wall. The relationship between applied pressure and deformation at the cavity wall is then employed to characterize the nonlinear mechanical properties. We demonstrate the feasibility of the proposed method and validate it by measuring the mechanical properties of synthetic polydimethylsiloxane (PDMS) and comparing with reported values in the literature. Results indicate that the cylindrical cavitation method effectively captures the response of PDMS over a wide range of stiffness (shear modulus ranging from 5 kPa to 300 kPa) and exhibit high repeatability. The proposed method overcomes limitations in characterization of ultra-soft materials using traditional testing methods, such as challenges with fabrication and clamping in unaxial tension testing and friction and adhesion effects in compression and indentation testing, thus enabling accurate and precise characterization. It also offers improved accuracy and repeatability over other needle induced cavity expansion methods due to precise control over the initial cavity dimension and shape at the cost of increased invasiveness of testing.

We introduce a new and broader formulation of the stable marriage problem (SMP), called the stable polygamy problem (SPP), where multiple individuals from a larger group $L$ of $|L|$ individuals can be matched with a single individual from a smaller group $S$ of $|S|$ individuals. Each individual $\ell \in L$ possesses a social constraint set $C_{\ell}$ that contains a subset of $L$ with whom they cannot coexist harmoniously. We define a generalized concept of stability based on the preference and constraints of the individuals. We explore two common settings: common utility, where the utility of a match is the same for individuals from both sets, and preference ranking, where each individual has a preference ranking for every other individual in the opposite set. Our analysis is conducted using a novel graph-theoretical framework. The classic SMP has been investigated in recent years for spectrum access to match cells or users to channels, where only one-to-one matching is allowed. By contrast, the new SPP formulation allows us to solve more general models with channel reuse, where multiple users may access the same channel simultaneously. Interestingly, we show that classic algorithms, such as propose and reject (P&R), and Hungarian method are no longer efficient in the polygamy setting. We develop efficient algorithms to solve the SPP in polynomial time, tailored for implementations in spectrum access with channel reuse. We analytically show that our algorithm always solves the SPP with common utility. While the SPP with preference ranking cannot be solved by any algorithm in all cases, we prove that our algorithm effectively solves it in specific graph structures representing strong and weak interference regimes. Simulation results demonstrate the efficiency of our algorithms across various spectrum access scenarios.

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.

T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and echo triggering, face challenges with patient compliance and arrhythmias, limiting their effectiveness. Image registration can enable motion-robust T1 mapping, but inherent intensity differences between time points pose a challenge. We introduce MBSS-T1, a self-supervised model for motion correction in cardiac T1 mapping, constrained by physical and anatomical principles. The physical constraints ensure expected signal decay behavior, while the anatomical constraints maintain realistic deformations. The unique combination of these constraints ensures accurate T1 mapping along the longitudinal relaxation axis. MBSS-T1 outperformed baseline deep-learning-based image registration approaches in a 5-fold experiment on a public dataset of 210 patients (STONE sequence) and an internal dataset of 19 patients (MOLLI sequence). MBSS-T1 excelled in model fitting quality ($R^2$: 0.975 vs. 0.941, 0.946), anatomical alignment (Dice score: 0.89 vs. 0.84, 0.88), and expert visual quality assessment for the presence of visible motion artifacts (4.33 vs. 3.38, 3.66). MBSS-T1 has the potential to enable motion-robust T1 mapping for a broader range of patients, overcoming challenges such as arrhythmias and suboptimal compliance, and allowing for free-breathing T1 mapping without requiring large training datasets. Our code will be publicly available upon acceptance.

Code-LLMs, LLMs pre-trained on large code corpora, have shown great progress in learning rich representations of the structure and syntax of code, successfully using it to generate or classify code fragments. At the same time, understanding if they are able to do so because they capture code semantics, and how well, is still an open question. In this paper, we tackle this problem by introducing SeqCoBench, a benchmark for systematically assessing how Code-LLMs can capture code functional equivalence. SeqCoBench contains over 20 code transformations that either preserve or alter the semantics of Python programs. We conduct extensive evaluations in different settings, including zero-shot and parameter-efficient finetuning methods on state-of-the-art (Code-)LLMs to see if they can discern semantically equivalent or different pairs of programs in SeqCoBench. We find that the performance gap between these LLMs and classical match-based retrieval scores is minimal, with both approaches showing a concerning lack of depth in understanding code semantics., Comment: 37 pages

Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather. By explicitly resolving convective dynamics within the atmosphere they afford meteorologists the nuance needed to provide outlook on hazard. Deep learning models have thus far not proven skilful at km-scale atmospheric simulation, despite being competitive at coarser resolution with state-of-the-art global, medium-range weather forecasting. We present a generative diffusion model called StormCast, which emulates the high-resolution rapid refresh (HRRR) model-NOAA's state-of-the-art 3km operational CAM. StormCast autoregressively predicts 99 state variables at km scale using a 1-hour time step, with dense vertical resolution in the atmospheric boundary layer, conditioned on 26 synoptic variables. We present evidence of successfully learnt km-scale dynamics including competitive 1-6 hour forecast skill for composite radar reflectivity alongside physically realistic convective cluster evolution, moist updrafts, and cold pool morphology. StormCast predictions maintain realistic power spectra for multiple predicted variables across multi-hour forecasts. Together, these results establish the potential for autoregressive ML to emulate CAMs -- opening up new km-scale frontiers for regional ML weather prediction and future climate hazard dynamical downscaling.

We use a sample of 73 simulated satellite and central dwarf galaxies spanning a stellar mass range of $10^{5.3}-10^{9.1} M_\odot$ to investigate the origin of their stellar age gradients. We find that dwarf galaxies often form their stars "inside-out," i.e., the stars form at successively larger radii over time. However, the oldest stars get reshuffled beyond the star forming radius by fluctuations in the gravitational potential well caused by stellar feedback (the same mechanisms that cause dwarfs to form dark matter cores). The result is that many dwarfs appear to have an "outside-in" age gradient at $z=0$, with younger stellar populations more centrally concentrated. However, for the reshuffled galaxies with the most extended star formation, young stars can form out to the large radii to which the old stars have been reshuffled, erasing the age gradient. We find that major mergers do not play a significant role in setting the age gradients of dwarfs. We find similar age gradient trends in satellites and field dwarfs, suggesting environment plays only a minor role, if any. Finally, we find that the age gradient trends are imprinted on the galaxies at later times, suggesting that the stellar reshuffling dominates after the galaxies have formed 50% of their stellar mass. The later reshuffling is at odds with results from the FIRE-2 simulations. Hence, age gradients offer a test of current star formation and feedback models that can be probed via observations of resolved stellar populations., Comment: 25 pages, 9 figures

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index \gamma. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, \gamma_run(f) = \gamma + \beta \ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter \beta consistent with no running, \beta \in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero \beta. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA., Comment: 17 pages, 4 figures, 2 tables

A distance oracle (DO) with stretch $(\alpha, \beta)$ for a graph $G$ is a data structure that, when queried with vertices $s$ and $t$, returns a value $\widehat{d}(s,t)$ such that $d(s,t) \le \widehat{d}(s,t) \le \alpha \cdot d(s,t) + \beta$. An $f$-edge fault-tolerant distance sensitivity oracle ($f$-DSO) additionally receives a set $F$ of up to $f$ edges and estimates the $s$-$t$-distance in $G{-}F$. Our first contribution is a new distance oracle with subquadratic space for undirected graphs. Introducing a small additive stretch $\beta > 0$ allows us to make the multiplicative stretch $\alpha$ arbitrarily small. This sidesteps a known lower bound of $\alpha \ge 3$ (for $\beta = 0$ and subquadratic space) [Thorup & Zwick, JACM 2005]. We present a DO for graphs with edge weights in $[0,W]$ that, for any positive integer $t$ and any $c \in (0, \ell/2]$, has stretch $(1{+}\frac{1}{\ell}, 2W)$, space $\widetilde{O}(n^{2-\frac{c}{t}})$, and query time $O(n^c)$. These are the first subquadratic-space DOs with $(1+\epsilon, O(1))$-stretch generalizing Agarwal and Godfrey's results for sparse graphs [SODA 2013] to general undirected graphs. Our second contribution is a framework that turns a $(\alpha,\beta)$-stretch DO for unweighted graphs into an $(\alpha (1{+}\varepsilon),\beta)$-stretch $f$-DSO with sensitivity $f = o(\log(n)/\log\log n)$ and retains subquadratic space. This generalizes a result by Bil\`o, Chechik, Choudhary, Cohen, Friedrich, Krogmann, and Schirneck [STOC 2023, TheoretiCS 2024] for the special case of stretch $(3,0)$ and $f = O(1)$. By combining the framework with our new distance oracle, we obtain an $f$-DSO that, for any $\gamma \in (0, (\ell{+}1)/2]$, has stretch $((1{+}\frac{1}{\ell}) (1{+}\varepsilon), 2)$, space $n^{ 2- \frac{\gamma}{(\ell+1)(f+1)} + o(1)}/\varepsilon^{f+2}$, and query time $\widetilde{O}(n^{\gamma} /{\varepsilon}^2)$., Comment: An extended abstract of this work appeared at FOCS 2024

Quantum transport is often characterized not just by mean observables like the particle or energy current, but by their fluctuations and higher moments, which can act as detailed probes of the physical mechanisms at play. However, relatively few theoretical methods are able to access the full counting statistics (FCS) of transport processes through electronic junctions in strongly correlated regimes. While most experiments are concerned with the steady state properties, most accurate theoretical methods rely on computationally expensive propagation from a tractable initial state. Here, we propose a simple approach for computing the FCS through a junction directly at the steady state, utilizing the propagator noncrossing approximation (NCA). Compared to time propagation, our method offers reduced computational cost at the same level of approximation; but the idea can also be used within other approximations or as a basis for numerically exact techniques. We demonstrate the method's capabilities by investigating the impact of lead dimensionality on electronic transport in the nonequilibrium Anderson impurity model at the onset of Kondo physics. Our results reveal a distinct signature of one dimensional leads in the noise and Fano factor not present for other dimensionalities, showing the potential of FCS measurements as a probe of the environment surrounding a quantum dot.

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

Modern manipulators are acclaimed for their precision but often struggle to operate in confined spaces. This limitation has driven the development of hyper-redundant and continuum robots. While these present unique advantages, they face challenges in, for instance, weight, mechanical complexity, modeling and costs. The Minimally Actuated Serial Robot (MASR) has been proposed as a light-weight, low-cost and simpler alternative where passive joints are actuated with a Mobile Actuator (MA) moving along the arm. Yet, Inverse Kinematics (IK) and a general motion planning algorithm for the MASR have not be addressed. In this letter, we propose the MASR-RRT* motion planning algorithm specifically developed for the unique kinematics of MASR. The main component of the algorithm is a data-based model for solving the IK problem while considering minimal traverse of the MA. The model is trained solely using the forward kinematics of the MASR and does not require real data. With the model as a local-connection mechanism, MASR-RRT* minimizes a cost function expressing the action time. In a comprehensive analysis, we show that MASR-RRT* is superior in performance to the straight-forward implementation of the standard RRT*. Experiments on a real robot in different environments with obstacles validate the proposed algorithm.

Master equations provide researchers with the ability to track the distribution over possible states of a system. From these equations, we can summarize the temporal dynamics through a method of moments. These distributions and their moments capture the stochastic nature of a system, which is essential to study infectious diseases. In this paper, we define the states of the system to be the number of infected cells of a given type in the epithelium, the hollow organ tissue in the human body. Epithelium found in the cervix provides a location for viral infections to live and persist, such as human papillomavirus (HPV). HPV is a highly transmissible disease which most commonly affects biological females and has the potential to progress into cervical cancer. By defining a master equation model which tracks the infected cell layer dynamics, information on disease extinction, progression, and viral output can be derived from the method of moments. From this methodology and the outcomes we glean from it, we aim to inform differing states of HPV infected cells, and assess the effects of structural information for each outcome., Comment: 15 pages, 8 figures

Is there a way to design powerful AI systems based on machine learning methods that would satisfy probabilistic safety guarantees? With the long-term goal of obtaining a probabilistic guarantee that would apply in every context, we consider estimating a context-dependent bound on the probability of violating a given safety specification. Such a risk evaluation would need to be performed at run-time to provide a guardrail against dangerous actions of an AI. Noting that different plausible hypotheses about the world could produce very different outcomes, and because we do not know which one is right, we derive bounds on the safety violation probability predicted under the true but unknown hypothesis. Such bounds could be used to reject potentially dangerous actions. Our main results involve searching for cautious but plausible hypotheses, obtained by a maximization that involves Bayesian posteriors over hypotheses. We consider two forms of this result, in the iid case and in the non-iid case, and conclude with open problems towards turning such theoretical results into practical AI guardrails.

In this paper we argue that a jailbroken GenAI model can cause substantial harm to GenAI-powered applications and facilitate PromptWare, a new type of attack that flips the GenAI model's behavior from serving an application to attacking it. PromptWare exploits user inputs to jailbreak a GenAI model to force/perform malicious activity within the context of a GenAI-powered application. First, we introduce a naive implementation of PromptWare that behaves as malware that targets Plan & Execute architectures (a.k.a., ReAct, function calling). We show that attackers could force a desired execution flow by creating a user input that produces desired outputs given that the logic of the GenAI-powered application is known to attackers. We demonstrate the application of a DoS attack that triggers the execution of a GenAI-powered assistant to enter an infinite loop that wastes money and computational resources on redundant API calls to a GenAI engine, preventing the application from providing service to a user. Next, we introduce a more sophisticated implementation of PromptWare that we name Advanced PromptWare Threat (APwT) that targets GenAI-powered applications whose logic is unknown to attackers. We show that attackers could create user input that exploits the GenAI engine's advanced AI capabilities to launch a kill chain in inference time consisting of six steps intended to escalate privileges, analyze the application's context, identify valuable assets, reason possible malicious activities, decide on one of them, and execute it. We demonstrate the application of APwT against a GenAI-powered e-commerce chatbot and show that it can trigger the modification of SQL tables, potentially leading to unauthorized discounts on the items sold to the user., Comment: Website, see https://sites.google.com/view/promptware

We have explored receding contact line dynamics on superhydrophobic surfaces, composed of micropillars arrays. We present here dynamic receding contact angle measurements of water on such surfaces, covering contact line speeds spanning over five decades. We have studied the effect of pillars fraction on dynamical receding contact angles. We compared these measurements to those on smooth surfaces with the same chemical nature and also with similar systems reported in the literature. We show that superhydrophobic surfaces exhibit a significantly lower dependence of contact angle on contact line speed compared to smooth surfaces. Additionally, we observed that a higher surface fraction of pillars leads to a greater dependence of the contact angle on contact line speed, approaching the dependence of the angle on smooth surface. Interestingly, we show that the exact texuration of the surface does not play a fundamental role in the angle-velocity relationships as long as microtextures present the same type of periodic pattern (pillar arrays or microgrid). These results are interpreted in terms of viscous friction reduction on superhydrophobic surfaces, shedding light on the underlying mechanisms governing their unique dynamic behavior. In addition we show that contact angles follow same laws for two different geometries (milimetric sessile drop and a centimetric capillary bridge).

The transport properties (electrical resistivity, thermal conductivity, and viscosity) of iron-hydrogen alloy are of great significance in the stability and evolution of planetary magnetic fields. Here, we investigate the thermal transport properties of iron doped with varying hydrogen content as functions of pressure (P) and temperature (T) for the top and bottom of Earth's outer core and beyond, corresponding to pressures of about 130 to 300 GPa and temperatures of 4000 to 7000 K. Using first-principles density functional theory molecular dynamic simulations (FPMD), we verify that crystalline FeH$_x$ is superionic with H diffusing freely. We find a low frequency viscosity of 10-11 mPa$\cdot$s for liquid Fe-H alloys at Earth's outer core conditions by the linear response Green-Kubo formula. Using the KKR method within density functional theory (DFT) plus Dynamical mean-field Theory (DMFT), we find saturation of electrical resistivity with increasing temperatures in liquid iron at outer core conditions. The effect of H on electrical and thermal transport we find is small, so that the exact H content of the core is not needed. The primary effect of H is on the equation of state, decreasing the density at constant P and T. We find the Lorenz number is smaller than the ideal value, and obtain for X(H)= 0.20, or 0.45 wt% H , thermal conductivity $\kappa$ of $\sim$105 and $\sim$190 $Wm^{-1}K^{-1}$, respectively, at conditions near the core-mantle and inner-outer core boundary.

Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1,000-10,000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part I, we construct an ensemble weather forecasting system based on Spherical Fourier Neural Operators (SFNO), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. Using large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states, and the ML ensemble thus passes a crucial spectral test in the literature. The IFS and ML ensembles have similar Extreme Forecast Indices, and we show that the ML extreme weather forecasts are reliable and discriminating.

The Sun drives a supersonic wind which inflates a giant plasma bubble in our very local interstellar neighborhood, the heliosphere. It is bathed in an extremely variable background of energetic ions and electrons which originate from a number of sources. Solar energetic particles (SEPs) are accelerated in the vicinity of the Sun, whereas shocks driven by solar disturbances are observed to accelerate energetic storm particles (ESPs). Moreover, a dilute population with a distinct composition forms the anomalous cosmic rays (ACRs) which are of a mixed interstellar-heliospheric origin. Particles are also accelerated at planetary bow shocks. We will present recent observations of energetic particles by Solar Orbiter and Parker Solar Probe, as well as other spacecraft that allow us to study the acceleration and transport of energetic particles at multiple locations in the inner heliosphere., Comment: 10 pages, one figure, proceedings IAU Symposium 388

Recent advances in large language models (LLMs) have enabled autonomous agents with complex reasoning and task-fulfillment capabilities using a wide range of tools. However, effectively identifying the most relevant tools for a given task becomes a key bottleneck as the toolset size grows, hindering reliable tool utilization. To address this, we introduce Re-Invoke, an unsupervised tool retrieval method designed to scale effectively to large toolsets without training. Specifically, we first generate a diverse set of synthetic queries that comprehensively cover different aspects of the query space associated with each tool document during the tool indexing phase. Second, we leverage LLM's query understanding capabilities to extract key tool-related context and underlying intents from user queries during the inference phase. Finally, we employ a novel multi-view similarity ranking strategy based on intents to pinpoint the most relevant tools for each query. Our evaluation demonstrates that Re-Invoke significantly outperforms state-of-the-art alternatives in both single-tool and multi-tool scenarios, all within a fully unsupervised setting. Notably, on the ToolE datasets, we achieve a 20% relative improvement in nDCG@5 for single-tool retrieval and a 39% improvement for multi-tool retrieval., Comment: EMNLP Findings 2024

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, an alignment is presented according to a specific mapping criteria and methodology which prioritizes structural and semantic considerations. The ontology alignment is evaluated by checking its logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the mapping criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles., Comment: 28 pages, 10 figures

In Part I, we created an ensemble based on Spherical Fourier Neural Operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires several orders of magnitude fewer computational resources. Here in Part II, we generate a huge ensemble (HENS), with 7,424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. For extreme climate statistics, HENS samples events 4$\sigma$ away from the ensemble mean. At each grid cell, HENS improves the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.

ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting.

Bipolaronic superconductivity is an exotic pairing mechanism proposed for materials like Ba$_{1-x}$K$_x$BiO$_3$ (BKBO); however, conclusive experimental evidence for a (bi)polaron metallic state in this material remains elusive. Here, we combine resonant inelastic x-ray and neutron total scattering techniques with advanced modelling to study the local lattice distortions, electronic structure, and electron-phonon coupling ($e$-ph) in BKBO as a function of doping. Data for the parent compound ($x = 0$) indicates that the electronic gap opens in predominantly oxygen-derived states strongly coupled to a long-range ordered breathing distortion of the oxygen sublattice. Upon doping, short-range breathing distortions and sizable ($e$-ph) coupling persist into the superconducting regime ($x = 0.4$). Comparisons with exact diagonalization and determinant quantum Monte Carlo calculations further support this conclusion. Our results provide compelling evidence that BKBO's metallic phase hosts a liquid of small (bi)polarons derived from local breathing distortions of the lattice, with implications for understanding the low-temperature superconducting instability

To address the rural broadband challenge and to leverage the unique opportunities that rural regions provide for piloting advanced wireless applications, we design and implement the ARA wireless living lab for research and innovation in rural wireless systems and their applications in precision agriculture, community services, and so on. ARA focuses on the unique community, application, and economic context of rural regions, and it features the first-of-its-kind, real-world deployment of long-distance, high-capacity wireless x-haul and access platforms across a rural area of diameter over 30 km. With both software-defined radios and programmable COTS systems and through effective orchestration of these wireless resources with fiber as well as compute resources embedded end-to-end across user equipment, base stations, edge, and cloud, ARA offers programmability, performance, robustness, and heterogeneity at the same time, thus enabling rural-focused co-evolution of wireless and applications while helping advance the frontiers of wireless systems in domains such as O-RAN, NextG, and agriculture applications. Here we present the design principles and implementation strategies of ARA, characterize its performance and heterogeneity, and highlight example wireless and application experiments uniquely enabled by ARA., Comment: 17 pages, 18 figures

Diffusion models have opened the path to a wide range of text-based image editing frameworks. However, these typically build on the multi-step nature of the diffusion backwards process, and adapting them to distilled, fast-sampling methods has proven surprisingly challenging. Here, we focus on a popular line of text-based editing frameworks - the ``edit-friendly'' DDPM-noise inversion approach. We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength. We trace the artifacts to mismatched noise statistics between inverted noises and the expected noise schedule, and suggest a shifted noise schedule which corrects for this offset. To increase editing strength, we propose a pseudo-guidance approach that efficiently increases the magnitude of edits without introducing new artifacts. All in all, our method enables text-based image editing with as few as three diffusion steps, while providing novel insights into the mechanisms behind popular text-based editing approaches., Comment: Project page: https://turboedit-paper.github.io/

Pulsar-timing-array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive--black-hole binaries. Those analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings--Downs cross-correlation pattern among the gravitational-wave--induced residuals across pulsars. These assumptions are idealizations that may not be realized in actuality. We test them in the NANOGrav 15 yr data set using Bayesian posterior predictive checks: after fitting our fiducial model to real data, we generate a population of simulated data-set replications, and use them to assess whether the optimal-statistic significance, inter-pulsar correlations, and spectral coefficients assume extreme values for the real data when compared to the replications. We confirm that the NANOGrav 15 yr data set is consistent with power-law and Hellings--Downs assumptions. We also evaluate the evidence for the stochastic background using posterior-predictive versions of the frequentist optimal statistic and of Bayesian model comparison, and find comparable significance (3.2\ $\sigma$ and 3\ $\sigma$ respectively) to what was previously reported for the standard statistics. We conclude with novel visualizations of the reconstructed gravitational waveforms that enter the residuals for each pulsar. Our analysis strengthens confidence in the identification and characterization of the gravitational-wave background as reported by NANOGrav., Comment: 20 pages, 14 Figures

In this paper we model the high-energy tail of observed solar energetic proton energy distributions with a kappa distribution function. We employ a technique for deriving the thermodynamic parameters of solar energetic proton populations measured by the Parker Solar Probe (PSP) Integrated Science Investigation of the Sun (IS$\odot$IS) EPI-Hi high energy telescope (HET), over energies from 10 - 60 MeV. With this technique we explore, for the first time, the characteristic thermodynamic properties of the solar energetic protons associated with an interplanetary coronal mass ejection (ICME) and its driven shock. We find that (1) the spectral index, or equivalently, the thermodynamic parameter kappa of solar energetic protons ($\kappa_{\rm EP}$) gradually increases starting from the pre-ICME region (upstream of the CME-driven shock), reaching a maximum in the CME ejecta ($\kappa_{\rm EP} \approx 3.5$), followed by a gradual decrease throughout the trailing portion of the CME; (2) solar energetic proton temperature and density ($T_{\rm EP}$ and $n_{\rm EP}$) appear anti-correlated, a behavior consistent to sub-isothermal polytropic processes; and (3) values of $T_{\rm EP}$ and $\kappa_{\rm EP}$ appear are positively correlated, indicating an increasing entropy with time. Therefore, these proton populations are characterized by a complex and evolving thermodynamic behavior, consisting of multiple sub-isothermal polytropic processes, and a large-scale trend of increasing temperature, kappa, and entropy. This study and its companion study by Livadiotis et al. (2024) open a new set of procedures for investigating the thermodynamic behavior of energetic particles and their shared thermal properties., Comment: 16 pages, 6 figures, 1 table

Understanding how and when galaxies formed stars over the history of the Universe is fundamental to the study of galaxy evolution. The star formation histories (SFHs) of galaxies in the local Universe can be measured with high precision using deep imaging with space telescopes. Such resolved SFHs are based on modelling the observed color-magnitude diagram (CMD) with stellar evolution models and rely on age-sensitive features like the main sequence turn-off to measure a galaxy's star formation rate as a function of time. There are many other population-level parameters that factor into these measurements, such as the stellar initial mass function (IMF), binary fraction, and metallicity, to name a few. We present and release StarFormationHistories.jl, a modular, open-source Julia package for measuring resolved SFHs with a focus on model flexibility for these types of population parameters. The code can model unresolved photometric binaries and supports arbitrary IMFs. Random uncertainties in the SFH measurements can be quantified with Monte Carlo posterior sampling methods. We illustrate the performance of the package on JWST/NIRCAM data of the Local Group dwarf irregular galaxy WLM $\left(M_v\approx-14.2\right)$, which exhibits a complex, well-sampled CMD, and HST/ACS data of the ultra-faint Milky Way satellite dwarf galaxy Horologium I $\left(M_v\approx-3.7\right)$, which has a much simpler but sparser CMD., Comment: 28 pages, 11 figures, 1 appendix

In the rapidly evolving landscape of wireless networks, achieving enhanced throughput with low latency for data transmission is crucial for future communication systems. While low complexity OSPF-type solutions have shown effectiveness in lightly-loaded networks, they often falter in the face of increasing congestion. Recent approaches have suggested utilizing backpressure and deep learning techniques for route optimization. However, these approaches face challenges due to their high implementation and computational complexity, surpassing the capabilities of networks with limited hardware devices. A key challenge is developing algorithms that improve throughput and reduce latency while keeping complexity levels compatible with OSPF. In this collaborative research between Ben-Gurion University and Ceragon Networks Ltd., we address this challenge by developing a novel approach, dubbed Regularized Routing Optimization (RRO). The RRO algorithm offers both distributed and centralized implementations with low complexity, making it suitable for integration into 5G and beyond technologies, where no significant changes to the existing protocols are needed. It increases throughput while ensuring latency remains sufficiently low through regularized optimization. We analyze the computational complexity of RRO and prove that it converges with a level of complexity comparable to OSPF. Extensive simulation results across diverse network topologies demonstrate that RRO significantly outperforms existing methods.

The increasing deployment of AI is shaping the future landscape of the internet, which is set to become an integrated ecosystem of AI agents. Orchestrating the interaction among AI agents necessitates decentralized, self-sustaining mechanisms that harmonize the tension between individual interests and social welfare. In this paper we tackle this challenge by synergizing reinforcement learning with principal-agent theory from economics. Taken separately, the former allows unrealistic freedom of intervention, while the latter struggles to scale in sequential settings. Combining them achieves the best of both worlds. We propose a framework where a principal guides an agent in a Markov Decision Process (MDP) using a series of contracts, which specify payments by the principal based on observable outcomes of the agent's actions. We present and analyze a meta-algorithm that iteratively optimizes the policies of the principal and agent, showing its equivalence to a contraction operator on the principal's Q-function, and its convergence to subgame-perfect equilibrium. We then scale our algorithm with deep Q-learning and analyze its convergence in the presence of approximation error, both theoretically and through experiments with randomly generated binary game-trees. Extending our framework to multiple agents, we apply our methodology to the combinatorial Coin Game. Addressing this multi-agent sequential social dilemma is a promising first step toward scaling our approach to more complex, real-world instances.

Spinal cord injury (SCI) is a devastating incidence leading to permanent paralysis and loss of sensory-motor functions potentially resulting in the formation of lesions within the spinal cord. Imaging biomarkers obtained from magnetic resonance imaging (MRI) scans can predict the functional recovery of individuals with SCI and help choose the optimal treatment strategy. Currently, most studies employ manual quantification of these MRI-derived biomarkers, which is a subjective and tedious task. In this work, we propose (i) a universal tool for the automatic segmentation of intramedullary SCI lesions, dubbed \texttt{SCIsegV2}, and (ii) a method to automatically compute the width of the tissue bridges from the segmented lesion. Tissue bridges represent the spared spinal tissue adjacent to the lesion, which is associated with functional recovery in SCI patients. The tool was trained and validated on a heterogeneous dataset from 7 sites comprising patients from different SCI phases (acute, sub-acute, and chronic) and etiologies (traumatic SCI, ischemic SCI, and degenerative cervical myelopathy). Tissue bridges quantified automatically did not significantly differ from those computed manually, suggesting that the proposed automatic tool can be used to derive relevant MRI biomarkers. \texttt{SCIsegV2} and the automatic tissue bridges computation are open-source and available in Spinal Cord Toolbox (v6.4 and above) via the \texttt{sct\_deepseg -task seg\_sc\_lesion\_t2w\_sci} and \texttt{sct\_analyze\_lesion} functions, respectively., Comment: Accepted at MICCAI AMAI 2024 workshop

This paper formalises the Canadian Traveller problem as a positional two-player game on graphs. We consider two variants depending on whether an edge is blocked. In the locally-informed variant, the traveller learns if an edge is blocked upon reaching one of its endpoints, while in the uninformed variant, they discover this only when the edge is supposed to appear. We provide a polynomial algorithm for each shortest path variant in the uninformed case. This algorithm also solves the case of directed acyclic non-temporal graphs. In the locally-informed case, we prove that finding a winning strategy is PSPACE-complete. Moreover, we establish that the problem is polynomial-time solvable when $k=1$ but NP-hard for $k\geq 2$. Additionally, we show that the standard (non-temporal) Canadian Traveller Problem is NP-hard when there are $k\geq 4$ blocked edges, which is, to the best of our knowledge, the first hardness result for CTP for a constant number of blocked edges.