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We show that it is consistent from an inaccessible cardinal that classical Namba forcing has the weak $\omega_1$-approximation property. In fact, this is the case if $\aleph_1$-preserving forcings do not add cofinal branches to $\aleph_1$-sized trees. The exact statement we obtain is similar to Hamkins' Key Lemma. It follows as a corollary that Martin's Maximum implies that there are stationarily many indestructibly weaky guessing models that are not internally unbounded. This answers questions of Cox and Krueger. Our result on Martin's Maximum shortens the proof of Cox and Krueger's main result by removing their use of higher Namba forcings, but we find another application of their ideas by answering a question of Adolf, Apter, and Koepke on preservation of successive cardinals by singularizing forcings.

The dielectric loss of silicon nitride (Si$_3$N$_4$) limits the performance of microwave-frequency devices that rely on this material for sensing, signal processing, and quantum communication. Using superconducting resonant circuits, we measure the cryogenic loss tangent of either as-deposited or high-temperature annealed stoichiometric Si$_3$N$_4$ as a function of drive strength and temperature. The internal loss behavior of the electrical resonators is largely consistent with the standard tunneling model of two-level systems (TLS), including damping caused by resonant energy exchange with TLS and by the relaxation of non-resonant TLS. We further supplement the TLS model with a self-heating effect to explain an increase in the loss observed in as-deposited films at large drive powers. Critically, we demonstrate that annealing remedies this anomalous power-induced loss, reduces the relaxation-type damping by more than two orders of magnitude, and reduces the resonant-type damping by a factor of three. Employing infrared absorption spectroscopy, we find that annealing reduces the concentration of hydrogen in the Si$_3$N$_4$, suggesting that hydrogen impurities cause substantial dissipation., Comment: 11 pages, 7 figures

This paper presents a method for optimal motion planning of continuum robots by employing Bernstein surfaces to approximate the system's dynamics and impose complex constraints, including collision avoidance. The main contribution is the approximation of infinite-dimensional continuous problems into their discrete counterparts, facilitating their solution using standard optimization solvers. This discretization leverages the unique properties of Bernstein surface, providing a framework that extends previous works which focused on ODEs approximated by Bernstein polynomials. Numerical validations are conducted through several numerical scenarios. The presented methodology offers a promising direction for solving complex optimal control problems in the realm of soft robotics.

Purpose of Review: This review provides an overview of the state of the art in bioinspired soft robotics with by examining advancements in actuation, functionality, modeling, and control. Recent Findings: Recent research into actuation methods, such as artificial muscles, have expanded the functionality and potential use of bioinspired soft robots. Additionally, the application of finite dimensional models has improved computational efficiency for modeling soft continuum systems, and garnered interest as a basis for controller formulation. Summary: Bioinspiration in the field of soft robotics has led to diverse approaches to problems in a range of task spaces. In particular, new capabilities in system simplification, miniaturization, and untethering have each contributed to the field's growth. There is still significant room for improvement in the streamlining of design and manufacturing for these systems, as well as in their control.

We provide a systematic investigation of using physics-informed neural networks to compute Lyapunov functions. We encode Lyapunov conditions as a partial differential equation (PDE) and use this for training neural network Lyapunov functions. We analyze the analytical properties of the solutions to the Lyapunov and Zubov PDEs. In particular, we show that employing the Zubov equation in training neural Lyapunov functions can lead to approximate regions of attraction close to the true domain of attraction. We also examine approximation errors and the convergence of neural approximations to the unique solution of Zubov's equation. We then provide sufficient conditions for the learned neural Lyapunov functions that can be readily verified by satisfiability modulo theories (SMT) solvers, enabling formal verification of both local stability analysis and region-of-attraction estimates in the large. Through a number of nonlinear examples, ranging from low to high dimensions, we demonstrate that the proposed framework can outperform traditional sums-of-squares (SOS) Lyapunov functions obtained using semidefinite programming (SDP)., Comment: The current version has been submitted for publication; corrected some minor typos from v2

The Spatial Pattern Matching (SPM) query allows for the retrieval of Points of Interest (POIs) based on spatial patterns defined by keywords and distance criteria. However, it does not consider the connectivity between POIs. In this study, we introduce the Qualitative and Quantitative Spatial Pattern Matching (QQ-SPM) query, an extension of the SPM query that incorporates qualitative connectivity constraints. To answer the proposed query type, we propose the QQESPM algorithm, which adapts the state-of-the-art ESPM algorithm to handle connectivity constraints. Performance tests comparing QQESPM to a baseline approach demonstrate QQESPM's superiority in addressing the proposed query type., Comment: DBLP Entry: https://dblp.org/rec/conf/geoinfo/MinervinoCOS23.html Conference Repository: http://urlib.net/ibi/8JMKD3MGPDW34P/4ADBK2H Accepted for the Brazilian Symposium on Geoinformatics (GEOINFO 2023)

In physics, density $\rho(\cdot)$ is a fundamentally important scalar function to model, since it describes a scalar field or a probability density function that governs a physical process. Modeling $\rho(\cdot)$ typically scales poorly with parameter space, however, and quickly becomes prohibitively difficult and computationally expensive. One promising avenue to bypass this is to leverage the capabilities of denoising diffusion models often used in high-fidelity image generation to parameterize $\rho(\cdot)$ from existing scientific data, from which new samples can be trivially sampled from. In this paper, we propose $\rho$-Diffusion, an implementation of denoising diffusion probabilistic models for multidimensional density estimation in physics, which is currently in active development and, from our results, performs well on physically motivated 2D and 3D density functions. Moreover, we propose a novel hashing technique that allows $\rho$-Diffusion to be conditioned by arbitrary amounts of physical parameters of interest., Comment: 6 pages, 2 figures, accepted for publication at the NeurIPS 2023 workshop "Machine Learning and the Physical Sciences"

Fine-tuning language models~(LMs) on human-generated data remains a prevalent practice. However, the performance of such models is often limited by the quantity and diversity of high-quality human data. In this paper, we explore whether we can go beyond human data on tasks where we have access to scalar feedback, for example, on math problems where one can verify correctness. To do so, we investigate a simple self-training method based on expectation-maximization, which we call ReST$^{EM}$, where we (1) generate samples from the model and filter them using binary feedback, (2) fine-tune the model on these samples, and (3) repeat this process a few times. Testing on advanced MATH reasoning and APPS coding benchmarks using PaLM-2 models, we find that ReST$^{EM}$ scales favorably with model size and significantly surpasses fine-tuning only on human data. Overall, our findings suggest self-training with feedback can substantially reduce dependence on human-generated data., Comment: Accepted to TMLR. Camera-ready version. First three authors contributed equally

A known source of decoherence in superconducting qubits is the presence of broken Cooper pairs, or quasiparticles. These can be generated by high-energy radiation, either present in the environment or purposefully introduced, as in the case of some hybrid quantum devices. Here, we systematically study the properties of a transmon qubit under illumination by focused infrared radiation with various powers, durations, and spatial locations. Despite the high energy of incident photons, our observations agree well with a model of low-energy quasiparticle dynamics dominated by trapping. This technique can be used for understanding and potentially mitigating the effects of high-energy radiation on superconducting circuits with a variety of geometries and materials., Comment: 15 pages, 11 figures

Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadImageNet is highly resource-intensive. To address these limitations, we introduce RadImageGAN, the first multi-modal radiologic data generator, which was developed by training StyleGAN-XL on the real RadImageNet dataset of 102,774 patients. RadImageGAN can generate high-resolution synthetic medical imaging datasets across 12 anatomical regions and 130 pathological classes in 3 modalities. Furthermore, we demonstrate that RadImageGAN generators can be utilized with BigDatasetGAN to generate multi-class pixel-wise annotated paired synthetic images and masks for diverse downstream segmentation tasks with minimal manual annotation. We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning. This shows that RadImageGAN combined with BigDatasetGAN can improve model performance and address data scarcity while reducing the resources needed for annotations for segmentation tasks.

Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for novel computing hardware in order to truly unlock the potential for AI. Physics-based hardware, such as thermodynamic computing, has the potential to provide a fast, low-power means to accelerate AI primitives, especially generative AI and probabilistic AI. In this work, we present the first continuous-variable thermodynamic computer, which we call the stochastic processing unit (SPU). Our SPU is composed of RLC circuits, as unit cells, on a printed circuit board, with 8 unit cells that are all-to-all coupled via switched capacitances. It can be used for either sampling or linear algebra primitives, and we demonstrate Gaussian sampling and matrix inversion on our hardware. The latter represents the first thermodynamic linear algebra experiment. We also illustrate the applicability of the SPU to uncertainty quantification for neural network classification. We envision that this hardware, when scaled up in size, will have significant impact on accelerating various probabilistic AI applications., Comment: 26 pages, 22 figures

Recent advances in theoretical biology suggest that basal cognition and sentient behaviour are emergent properties of in vitro cell cultures and neuronal networks, respectively. Such neuronal networks spontaneously learn structured behaviours in the absence of reward or reinforcement. In this paper, we characterise this kind of self-organisation through the lens of the free energy principle, i.e., as self-evidencing. We do this by first discussing the definitions of reactive and sentient behaviour in the setting of active inference, which describes the behaviour of agents that model the consequences of their actions. We then introduce a formal account of intentional behaviour, that describes agents as driven by a preferred endpoint or goal in latent state-spaces. We then investigate these forms of (reactive, sentient, and intentional) behaviour using simulations. First, we simulate the aforementioned in vitro experiments, in which neuronal cultures spontaneously learn to play Pong, by implementing nested, free energy minimising processes. The simulations are then used to deconstruct the ensuing predictive behaviour, leading to the distinction between merely reactive, sentient, and intentional behaviour, with the latter formalised in terms of inductive planning. This distinction is further studied using simple machine learning benchmarks (navigation in a grid world and the Tower of Hanoi problem), that show how quickly and efficiently adaptive behaviour emerges under an inductive form of active inference., Comment: 33 pages, 9 figures

The unprecedented medium-resolution (R~1500-3500) near- and mid-infrared (1-18um) spectrum provided by JWST for the young (140+/-20Myr) low-mass (12-20MJup) L-T transition (L7) companion VHS1256b gives access to a catalogue of molecular absorptions. In this study, we present a comprehensive analysis of this dataset utilizing a forward modelling approach, applying our Bayesian framework, ForMoSA. We explore five distinct atmospheric models to assess their performance in estimating key atmospheric parameters: Teff, log(g), [M/H], C/O, gamma, fsed, and R. Our findings reveal that each parameter's estimate is significantly influenced by factors such as the wavelength range considered and the model chosen for the fit. This is attributed to systematic errors in the models and their challenges in accurately replicating the complex atmospheric structure of VHS1256b, notably the complexity of its clouds and dust distribution. To propagate the impact of these systematic uncertainties on our atmospheric property estimates, we introduce innovative fitting methodologies based on independent fits performed on different spectral windows. We finally derived a Teff consistent with the spectral type of the target, considering its young age, which is confirmed by our estimate of log(g). Despite the exceptional data quality, attaining robust estimates for chemical abundances [M/H] and C/O, often employed as indicators of formation history, remains challenging. Nevertheless, the pioneering case of JWST's data for VHS1256b has paved the way for future acquisitions of substellar spectra that will be systematically analyzed to directly compare the properties of these objects and correct the systematics in the models., Comment: 32 pages, 16 figures, 6 tables, 2 appendices

Theoretical results from discrete geometry suggest that normed spaces can abstractly embed finite metric spaces with surprisingly low theoretical bounds on distortion in low dimensions. In this paper, inspired by this theoretical insight, we highlight normed spaces as a more flexible and computationally efficient alternative to several popular Riemannian manifolds for learning graph embeddings. Normed space embeddings significantly outperform several popular manifolds on a large range of synthetic and real-world graph reconstruction benchmark datasets while requiring significantly fewer computational resources. We also empirically verify the superiority of normed space embeddings on growing families of graphs associated with negative, zero, and positive curvature, further reinforcing the flexibility of normed spaces in capturing diverse graph structures as graph sizes increase. Lastly, we demonstrate the utility of normed space embeddings on two applied graph embedding tasks, namely, link prediction and recommender systems. Our work highlights the potential of normed spaces for geometric graph representation learning, raises new research questions, and offers a valuable tool for experimental mathematics in the field of finite metric space embeddings. We make our code and data publically available., Comment: 23 pages,7 figures,9 tables | The first two authors contributed equally

The two-particle finite-volume scattering formalism derived by L\"uscher and generalized in many subsequent works does not hold for energies far enough below the two-particle threshold to reach the nearest left-hand cut. The breakdown of the formalism is signaled by the fact that a real scattering amplitude is predicted in a regime where it should be complex. In this work, we address this limitation by deriving an extended formalism that includes the nearest branch cut, arising from single particle exchange. We focus on two-nucleon ($NN \to NN$) scattering, for which the cut arises from pion exchange, but give expressions for any system with a single channel of identical particles. The new result takes the form of a modified quantization condition that can be used to constrain an intermediate K-matrix in which the cut is removed. In a second step, integral equations, also derived in this work, must be used to convert the K-matrix to the physical scattering amplitude. We also show how the new formalism reduces to the standard approach when the $N \to N \pi$ coupling is set to zero., Comment: 62 pages, 9 figures, v2: fix typos and improve discussion of integral equations, additional figure to better explain time-ordered perturbation theory, v3: published version including comparison to previous work

The Gemini Planet Imager (GPI) has excelled in imaging debris disks in the near-infrared. The GPI Exoplanet Survey (GPIES) imaged twenty-four debris disks in polarized $H$-band light, while other programs observed half of these disks in polarized $J$- and/or $K1$-bands. Using these data, we present a uniform analysis of the morphology of each disk to find asymmetries suggestive of perturbations, particularly those due to planet-disk interactions. The multi-wavelength surface brightness, the disk color and geometry permit identification of any asymmetries such as warps or disk offsets from the central star. We find that nineteen of the disks in this sample exhibit asymmetries in surface brightness, disk color, disk geometry, or a combination of the three, suggesting that for this sample, perturbations, as seen in scattered light, are common. The relationship between these perturbations and potential planets in the system are discussed. We also explore correlations among stellar temperatures, ages, disk properties, and observed perturbations. We find significant trends between the vertical aspect ratio and the stellar temperature, disk radial extent, and the dust grain size distribution power-law, $q$. We also confirm a trend between the disk color and stellar effective temperature, where the disk becomes increasingly red/neutral with increasing temperature. Such results have important implications on the evolution of debris disk systems around stars of various spectral types., Comment: 46 pages, 20 figures, 6 tables, accepted for publication in ApJ

String theory models generically predict the existence of multiple axion-like particle (ALP) fields, yet the majority of both theoretical and experimental works have assumed only one ALP. In this paper, we discuss the phenomenology of systems with multiple ALPs that can undergo oscillations akin to neutrino oscillations. Motivated by this effect, we extend the 'anarchy' framework, which has been used to predict neutrino oscillation parameters, to generate the parameters of many ALP systems. We explore the phenomenology of these ALP anarchy models in some of the leading ALP search strategies, including the CERN Axion Solar Telescope, magnetic white dwarfs and the gamma-ray spectra of distant blazars. We include both the ALP-photon and the ALP-electron coupling. We find that ALP anarchy models predict drastically different results than single ALP models., Comment: 19 pages, 7 figures; Typo corrected in eq(6.5); Additional details added to section headers; Results Unchanged

Thermodynamic computing exploits fluctuations and dissipation in physical systems to efficiently solve various mathematical problems. For example, it was recently shown that certain linear algebra problems can be solved thermodynamically, leading to an asymptotic speedup scaling with the matrix dimension. The origin of this "thermodynamic advantage" has not yet been fully explained, and it is not clear what other problems might benefit from it. Here we provide a new thermodynamic algorithm for exponentiating a real matrix, with applications in simulating linear dynamical systems. We describe a simple electrical circuit involving coupled oscillators, whose thermal equilibration can implement our algorithm. We also show that this algorithm also provides an asymptotic speedup that is linear in the dimension. Finally, we introduce the concept of thermodynamic parallelism to explain this speedup, stating that thermodynamic noise provides a resource leading to effective parallelization of computations, and we hypothesize this as a mechanism to explain thermodynamic advantage more generally., Comment: 14 pages, 5 figures

Non-Contact sensing is an emerging technology with applications across many industries from driver monitoring in vehicles to patient monitoring in healthcare. Current state-of-the-art implementations focus on RGB video, but this struggles in varying/noisy light conditions and is almost completely unfeasible in the dark. Near Infra-Red (NIR) video, however, does not suffer from these constraints. This paper aims to demonstrate the effectiveness of an alternative Convolution Attention Network (CAN) architecture, to regress photoplethysmography (PPG) signal from a sequence of NIR frames. A combination of two publicly available datasets, which is split into train and test sets, is used for training the CAN. This combined dataset is augmented to reduce overfitting to the 'normal' 60 - 80 bpm heart rate range by providing the full range of heart rates along with corresponding videos for each subject. This CAN, when implemented over video cropped to the subject's head, achieved a Mean Average Error (MAE) of just 0.99 bpm, proving its effectiveness on NIR video and the architecture's feasibility to regress an accurate signal output., Comment: 4 pages, 3 figures, 3 tables, Irish Machine Vision and Image Processing Conference 2023

The fundamental theorem behind financial markets is that stock prices are intrinsically complex and stochastic. One of the complexities is the volatility associated with stock prices. Volatility is a tendency for prices to change unexpectedly [1]. Price volatility is often detrimental to the return economics, and thus, investors should factor it in whenever making investment decisions, choices, and temporal or permanent moves. It is, therefore, crucial to make necessary and regular short and long-term stock price volatility forecasts for the safety and economics of investors returns. These forecasts should be accurate and not misleading. Different models and methods, such as ARCH GARCH models, have been intuitively implemented to make such forecasts. However, such traditional means fail to capture the short-term volatility forecasts effectively. This paper, therefore, investigates and implements a combination of numeric and probabilistic models for short-term volatility and return forecasting for high-frequency trades. The essence is that one-day-ahead volatility forecasts were made with Gaussian Processes (GPs) applied to the outputs of a Numerical market prediction (NMP) model. Firstly, the stock price data from NMP was corrected by a GP. Since it is not easy to set price limits in a market due to its free nature and randomness, a Censored GP was used to model the relationship between the corrected stock prices and returns. Forecasting errors were evaluated using the implied and estimated data., Comment: 25 pages

Let $n\geq 8$ be divisible by 4. The Clifford-cyclotomic gate set $\mathcal{G}_n$ is the universal gate set obtained by extending the Clifford gates with the $z$-rotation $T_n = \mathrm{diag}(1,\zeta_n)$, where $\zeta_n$ is a primitive $n$-th root of unity. In this note, we show that, when $n$ is a power of 2, a multiqubit unitary matrix $U$ can be exactly represented by a circuit over $\mathcal{G}_n$ if and only if the entries of $U$ belong to the ring $\mathbb{Z}[1/2,\zeta_n]$. We moreover show that $\log(n)-2$ ancillas are always sufficient to construct a circuit for $U$. Our results generalize prior work to an infinite family of gate sets and show that the limitations that apply to single-qubit unitaries, for which the correspondence between Clifford-cyclotomic operators and matrices over $\mathbb{Z}[1/2,\zeta_n]$ fails for all but finitely many values of $n$, can be overcome through the use of ancillas.

We define a graph to be $S$-regular if it contains an equitable partition given by a matrix $S$. These graphs are generalizations of both regular and bipartite, biregular graphs. An $S$-regular matrix is defined then as a matrix on an $S$-regular graph consistent with the graph's equitable partition. In this paper we derive the limiting spectral density for large, random $S$-regular matrices as well as limiting functions of certain statistics for their eigenvector coordinates as a function of eigenvalue. These limiting functions are defined in terms of spectral measures on $S$-regular trees. In general, these spectral measures do not have a closed-form expression; however, we provide a defining system of polynomials for them. Finally, we explore eigenvalue bounds of $S$-regular graph, proving an expander mixing lemma, Alon-Bopana bound, and other eigenvalue inequalities in terms of the eigenvalues of the matrix $S$., Comment: 24 pages, 3 figures

Breathing rate is a vital health metric that is an invaluable indicator of the overall health of a person. In recent years, the non-contact measurement of health signals such as breathing rate has been a huge area of development, with a wide range of applications from telemedicine to driver monitoring systems. This paper presents an investigation into a method of non-contact breathing rate detection using a motion detection algorithm, optical flow. Optical flow is used to successfully measure breathing rate by tracking the motion of specific points on the body. In this study, the success of optical flow when using different sets of points is evaluated. Testing shows that both chest and facial movement can be used to determine breathing rate but to different degrees of success. The chest generates very accurate signals, with an RMSE of 0.63 on the tested videos. Facial points can also generate reliable signals when there is minimal head movement but are much more vulnerable to noise caused by head/body movements. These findings highlight the potential of optical flow as a non-invasive method for breathing rate detection and emphasize the importance of selecting appropriate points to optimize accuracy., Comment: In Proceedings of Irish Machine Vision and Image Processing Conference 2023 (IMVIP2023), Galway, Ireland, August 2023

Exploiting the power of quantum computation to realise superior machine learning algorithmshas been a major research focus of recent years, but the prospects of quantum machine learning (QML) remain dampened by considerable technical challenges. A particularly significant issue is that generic QML models suffer from so-called barren plateaus in their training landscapes -- large regions where cost function gradients vanish exponentially in the number of qubits employed, rendering large models effectively untrainable. A leading strategy for combating this effect is to build problem-specific models which take into account the symmetries of their data in order to focus on a smaller, relevant subset of Hilbert space. In this work, we introduce a family of rotationally equivariant QML models built upon the quantum Fourier transform, and leverage recent insights from the Lie-algebraic study of QML models to prove that (a subset of) our models do not exhibit barren plateaus. In addition to our analytical results we numerically test our rotationally equivariant models on a dataset of simulated scanning tunnelling microscope images of phosphorus impurities in silicon, where rotational symmetry naturally arises, and find that they dramatically outperform their generic counterparts in practice.

The Nancy Grace Roman Space Telescope Coronagraph Instrument will enable the polarimetric imaging of debris disks and inner dust belts in the optical and near-infrared wavelengths, in addition to the high-contrast polarimetric imaging and spectroscopy of exoplanets. The Coronagraph uses two Wollaston prisms to produce four orthogonally polarized images and is expected to measure the polarization fraction with measurement errors < 3% per spatial resolution element. To simulate the polarization observations through the Hybrid Lyot Coronagraph (HLC) and Shaped Pupil Coronagraph (SPC), we model disk scattering, the coronagraphic point-response function, detector noise, speckles, jitter, and instrumental polarization and calculate the Stokes parameters. To illustrate the potential for discovery and a better understanding of known systems with both the HLC and SPC modes, we model the debris disks around Epsilon Eridani and HR 4796A, respectively. For Epsilon Eridani, using astrosilicates with 0.37+/-0.01 as the peak input polarization fraction in one resolution element, we recover the peak disk polarization fraction of 0.33+/-0.01. Similarly, for HR 4796A, for a peak input polarization fraction of 0.92+/-0.01, we obtain the peak output polarization fraction as 0.80+/-0.03. The Coronagraph design meets the required precision, and forward modeling is needed to accurately estimate the polarization fraction., Comment: 21 pages, 13 figures, Accepted in Publications of the Astronomical Society of the Pacific

We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic questions posed in natural language, with an arbitrary adversarial string inserted before the question is complete. Even in the simple setting of 1-digit addition problems, it is easy to find adversarial prompts that make all tested models (including PaLM2, GPT4, Claude2) misbehave, and even to steer models to a particular wrong answer. We additionally provide a simple algorithm for finding successful attacks by querying those same models, which we name "prompt inversion rejection sampling" (PIRS). We finally show that models can be partially hardened against these attacks via reinforcement learning and via agentic constitutional loops. However, we were not able to make a language model fully robust against adversarial arithmetic attacks.

Context. Recent observations of close detached eclipsing M and K dwarf binaries have provided substantial support for magnetic saturation when stars rotate sufficiently fast, leading to a magnetic braking (MB) torque proportional to the spin of the star. Aims. We investigated here how strong MB torques need to be to reproduce the observationally-inferred relative numbers of white dwarf plus M dwarf post-common-envelope binaries under the assumption of magnetic saturation. Methods. We carried out binary population simulations with the BSE code adopting empirically-derived inter-correlated main-sequence binary distributions as initial binary populations and compared the simulation outcomes with observations. Results. We found that the dearth of extreme mass ratio binaries in the inter-correlated initial distributions is key to reproduce the large fraction of post-common-envelope binaries hosting low-mass M dwarfs (${\sim0.1-0.2}$ M$_\odot$). In addition, orbital angular momentum loss rates due to MB should be high for M dwarfs with radiative cores and orders of magnitude smaller for fully convective stars to explain the observed dramatic change of the fraction of short-period binaries at the fully convective boundary. Conclusions. We conclude that saturated but disrupted, that is, dropping drastically at the fully convective boundary, MB can explain the observations of both close main-sequence binaries containing M and K dwarfs and post-common-envelope binaries. Whether a similar prescription can explain the spin down rates of single stars and of binaries containing more massive stars needs to be tested., Comment: Accepted for publication in A&A

Individual-based models of contagious processes are useful for predicting epidemic trajectories and informing intervention strategies. In such models, the incorporation of contact network information can capture the non-randomness and heterogeneity of realistic contact dynamics. In this paper, we consider Bayesian inference on the spreading parameters of an SIR contagion on a known, static network, where information regarding individual disease status is known only from a series of tests (positive or negative disease status). When the contagion model is complex or information such as infection and removal times is missing, the posterior distribution can be difficult to sample from. Previous work has considered the use of Approximate Bayesian Computation (ABC), which allows for simulation-based Bayesian inference on complex models. However, ABC methods usually require the user to select reasonable summary statistics. Here, we consider an inference scheme based on the Mixture Density Network compressed ABC (MDN-ABC), which minimizes the expected posterior entropy in order to learn informative summary statistics. This allows us to conduct Bayesian inference on the parameters of a partially observed contagious process while also circumventing the need for manual summary statistic selection. This methodology can be extended to incorporate additional simulation complexities, including behavioral change after positive tests or false test results., Comment: 27 pages, 7 figures

Meta Reinforcement Learning (Meta RL) trains agents that adapt to fast-changing environments and tasks. Current strategies often lose adaption efficiency due to the passive nature of model exploration, causing delayed understanding of new transition dynamics. This results in particularly fast-evolving tasks being impossible to solve. We propose a novel approach, Hypothesis Network Planned Exploration (HyPE), that integrates an active and planned exploration process via the hypothesis network to optimize adaptation speed. HyPE uses a generative hypothesis network to form potential models of state transition dynamics, then eliminates incorrect models through strategically devised experiments. Evaluated on a symbolic version of the Alchemy game, HyPE outpaces baseline methods in adaptation speed and model accuracy, validating its potential in enhancing reinforcement learning adaptation in rapidly evolving settings.

Recollision processes are fundamental to strong-field physics and attoscience, thus models connecting recolliding trajectories to quantum amplitudes are a crucial part in furthering understanding of these processes. We report developments in the semiclassical path-integral-based Coulomb quantum-orbit strong-field approximation model for strong-field ionization by including an additional phase known as Maslov's phase and implementing a new solution strategy via Monte-Carlo-style sampling of the initial momenta. In doing so, we obtain exceptional agreement with solutions to the time-dependent Schr\"odinger equation for hydrogen, helium, and argon. We provide an in-depth analysis of the resulting photoelectron momentum distributions for these targets, facilitated by the quantum-orbits arising from the solutions to the saddle-point equations. The analysis yields a new class of rescattered trajectories that includes the well-known laser-driven long and short trajectories, along with novel Coulomb-driven rescattered trajectories. By virtue of the precision of the model, it opens the door to detailed investigations of a plethora of strong-field phenomena such as photoelectron holography, laser-induced electron diffraction and high-order above threshold ionization., Comment: 15 pages, 7 figures

The success of self-supervised contrastive learning hinges on identifying positive data pairs, such that when they are pushed together in embedding space, the space encodes useful information for subsequent downstream tasks. Constructing positive pairs is non-trivial as the pairing must be similar enough to reflect a shared semantic meaning, but different enough to capture within-class variation. Classical approaches in vision use augmentations to exploit well-established invariances to construct positive pairs, but invariances in the time-series domain are much less obvious. In our work, we propose a novel method of using a learned measure for identifying positive pairs. Our Retrieval-Based Reconstruction (REBAR) measure measures the similarity between two sequences as the reconstruction error that results from reconstructing one sequence with retrieved information from the other. Then, if the two sequences have high REBAR similarity, we label them as a positive pair. Through validation experiments, we show that the REBAR error is a predictor of mutual class membership. Once integrated into a contrastive learning framework, our REBAR method learns an embedding that achieves state-of-the-art performance on downstream tasks across various modalities., Comment: ICLR 2024 | Code available at: https://github.com/maxxu05/rebar

Simulating the evolution of the gravitational N-body problem becomes extremely computationally expensive as N increases since the problem complexity scales quadratically with the number of bodies. We study the use of Artificial Neural Networks (ANNs) to replace expensive parts of the integration of planetary systems. Neural networks that include physical knowledge have grown in popularity in the last few years, although few attempts have been made to use them to speed up the simulation of the motion of celestial bodies. We study the advantages and limitations of using Hamiltonian Neural Networks to replace computationally expensive parts of the numerical simulation. We compare the results of the numerical integration of a planetary system with asteroids with those obtained by a Hamiltonian Neural Network and a conventional Deep Neural Network, with special attention to understanding the challenges of this problem. Due to the non-linear nature of the gravitational equations of motion, errors in the integration propagate. To increase the robustness of a method that uses neural networks, we propose a hybrid integrator that evaluates the prediction of the network and replaces it with the numerical solution if considered inaccurate. Hamiltonian Neural Networks can make predictions that resemble the behavior of symplectic integrators but are challenging to train and in our case fail when the inputs differ ~7 orders of magnitude. In contrast, Deep Neural Networks are easy to train but fail to conserve energy, leading to fast divergence from the reference solution. The hybrid integrator designed to include the neural networks increases the reliability of the method and prevents large energy errors without increasing the computing cost significantly. For this problem, the use of neural networks results in faster simulations when the number of asteroids is >70., Comment: Accepted for publication in the Journal of Computational Physics

We provide a novel reduction from swap-regret minimization to external-regret minimization, which improves upon the classical reductions of Blum-Mansour [BM07] and Stolz-Lugosi [SL05] in that it does not require finiteness of the space of actions. We show that, whenever there exists a no-external-regret algorithm for some hypothesis class, there must also exist a no-swap-regret algorithm for that same class. For the problem of learning with expert advice, our result implies that it is possible to guarantee that the swap regret is bounded by {\epsilon} after $\log(N)^{O(1/\epsilon)}$ rounds and with $O(N)$ per iteration complexity, where $N$ is the number of experts, while the classical reductions of Blum-Mansour and Stolz-Lugosi require $O(N/\epsilon^2)$ rounds and at least $\Omega(N^2)$ per iteration complexity. Our result comes with an associated lower bound, which -- in contrast to that in [BM07] -- holds for oblivious and $\ell_1$-constrained adversaries and learners that can employ distributions over experts, showing that the number of rounds must be $\tilde\Omega(N/\epsilon^2)$ or exponential in $1/\epsilon$. Our reduction implies that, if no-regret learning is possible in some game, then this game must have approximate correlated equilibria, of arbitrarily good approximation. This strengthens the folklore implication of no-regret learning that approximate coarse correlated equilibria exist. Importantly, it provides a sufficient condition for the existence of correlated equilibrium which vastly extends the requirement that the action set is finite, thus answering a question left open by [DG22; Ass+23]. Moreover, it answers several outstanding questions about equilibrium computation and learning in games.

Squeezing of optical fields, used as a powerful resource for many applications, and the radiation properties in the process of high harmonic generation have thus far been considered separately. In this Letter, we want to clarify that the joint quantum state of all the optical field modes in the process of high harmonic generation is in general entangled and squeezed. We show that this is already the case in the simplest scenario of driving uncorrelated atoms by a classical laser light field. The previous observation of product coherent states after the high harmonic generation process is a consequence of the assumption that the ground state depletion can be neglected, which is related to vanishing dipole moment correlations. Furthermore, we analyze how the resulting quadrature squeezing in the fundamental laser mode after the interaction can be controlled and explicitly show that all field modes are entangled., Comment: 4 pages (2 figures)

Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object localization capabilities. This paper studies the following question: Can we augment CLIP training with task-specific vision models from model zoos to improve its visual representations? Towards this end, we leverage open-source task-specific vision models to generate pseudo-labels for an uncurated and noisy image-text dataset. Subsequently, we train CLIP models on these pseudo-labels in addition to the contrastive training on image and text pairs. This simple setup shows substantial improvements of up to 16.3% across different vision tasks, including segmentation, detection, depth estimation, and surface normal estimation. Importantly, these enhancements are achieved without compromising CLIP's existing capabilities, including its proficiency in promptable zero-shot classification.

Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known as sycophancy. We investigate the prevalence of sycophancy in models whose finetuning procedure made use of human feedback, and the potential role of human preference judgments in such behavior. We first demonstrate that five state-of-the-art AI assistants consistently exhibit sycophancy across four varied free-form text-generation tasks. To understand if human preferences drive this broadly observed behavior, we analyze existing human preference data. We find that when a response matches a user's views, it is more likely to be preferred. Moreover, both humans and preference models (PMs) prefer convincingly-written sycophantic responses over correct ones a non-negligible fraction of the time. Optimizing model outputs against PMs also sometimes sacrifices truthfulness in favor of sycophancy. Overall, our results indicate that sycophancy is a general behavior of state-of-the-art AI assistants, likely driven in part by human preference judgments favoring sycophantic responses., Comment: 32 pages, 20 figures

We consider electromagnetic finite-volume effects through order $1/L^3$ in different formulations of QED, where $L$ is the periodicity of the spatial volume. An inherent problem at this order is the appearance of structure-dependent quantities related to form factors and the analytical structure of the correlation functions. The non-local constraint of the widely used QED$_{\textrm{L}}$ regularization gives rise to structure-dependent effects that are difficult to evaluate analytically and can act as a precision bottleneck in lattice calculations. For this reason, we consider general volume expansions relevant for the mass spectrum as well as leptonic decay rates in QED$_{\textrm{C}}$, QED$_{\textrm{L}}$ and QED$_{\textrm{L}}^{\textrm{IR}}$, the latter being a class of non-local formulations generalising QED$_{\textrm{L}}$. One choice within this class is QED$_{\textrm{r}}$, first introduced at this conference, and we show that the effects of non-locality for the $1/L^3$ term in the expansion can be removed. We observe that there are still $1/L^3$ contributions unrelated to the (non-)locality of the studied QED formulations, but rather to collinear singularities in the physical amplitudes., Comment: Proceedings The 40th International Symposium on Lattice Field Theory (Lattice 2023), July 31st - August 4th, 2023, Fermi National Accelerator Laboratory. 7 pages