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Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for example, when data are private or sensitive. In this work, we investigate methods to mitigate memorization: three regularizer-based, three finetuning-based, and eleven machine unlearning-based methods, with five of the latter being new methods that we introduce. We also introduce TinyMem, a suite of small, computationally-efficient LMs for the rapid development and evaluation of memorization-mitigation methods. We demonstrate that the mitigation methods that we develop using TinyMem can successfully be applied to production-grade LMs, and we determine via experiment that: regularizer-based mitigation methods are slow and ineffective at curbing memorization; fine-tuning-based methods are effective at curbing memorization, but overly expensive, especially for retaining higher accuracies; and unlearning-based methods are faster and more effective, allowing for the precise localization and removal of memorized information from LM weights prior to inference. We show, in particular, that our proposed unlearning method BalancedSubnet outperforms other mitigation methods at removing memorized information while preserving performance on target tasks.

We calculate higher-order soft-gluon corrections for the associated production of a top-antitop quark pair and a $Z$ boson ($t{\bar t}Z$ production) in proton-proton collisions. We find that these corrections are numerically dominant and large. We present approximate NNLO (aNNLO) and approximate N$^3$LO (aN$^3$LO) cross sections that add, respectively, second-order and third-order soft-gluon corrections to the exact NLO QCD result. We also add electroweak (EW) corrections through NLO. We estimate uncertainties that arise from scale dependence, which are smaller at higher orders, and from parton distribution functions. We compare our aN$^3$LO QCD + NLO EW theoretical results to measurements of $t{\bar t}Z$ cross sections from the LHC and find good agreement. We also calculate single-particle-inclusive differential distributions in top-quark transverse momentum and rapidity., Comment: 10 pages, 3 figures. arXiv admin note: text overlap with arXiv:2312.00861

Scanning Probe Microscopy (SPM) techniques have shown great potential in fabricating nanoscale structures endowed with exotic quantum properties achieved through various manipulations of atoms and molecules. However, the selection of proper manipulation parameters requires extensive domain knowledge, which is not necessarily transferable to new systems. Therefore, efficient and autonomous SPM techniques are needed to reduce the reliance on user supervision and learn optimal strategies for new systems, in particular for the challenge of controlling chemical reactions. In this paper, we developed a software infrastructure named AutoOSS (Autonomous On-Surface Synthesis) to automate bromine removal from Zn(II)-5,15- bis(4-bromo-2,6-dimethylphenyl)porphyrin (ZnBr2Me4DPP) on Au(111), using neural network models to interpret STM outputs and deep reinforcement learning models to optimize manipulation parameters. This is further supported by Bayesian Optimization Structure Search (BOSS) and Density Functional Theory (DFT) computations to explore 3D structures and reaction mechanisms based on STM images.

Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. The binding affinity, which refers to the strength of this interaction, is central to many important problems in bioinformatics such as drug design. An extensive amount of work has been devoted to predicting binding affinity over the past decades due to its significance. In this paper, we review all significant recent works, focusing on the methods, features, and benchmark datasets. We have observed a rising trend in the use of traditional machine learning and deep learning models for predicting binding affinity, accompanied by an increasing amount of data on proteins and small drug-like molecules. While prediction results are constantly improving, we also identify several open questions and potential directions that remain unexplored in the field. This paper could serve as an excellent starting point for machine learning researchers who wish to engage in the study of binding affinity, or for anyone with general interests in machine learning, drug discovery, and bioinformatics.

The scaling of superconducting nanowire-based devices to larger arrays is often limited by the cabling required to interface with each device. Cryogenic integrated circuits constructed from nanowire cryotrons, or nanocryotrons, can address this limitation by performing signal processing on chip. In this study, we characterize key performance metrics of the nanocryotron to elucidate its potential as a logical element in cryogenic integrated circuits and develop an electro-thermal model to connect material parameters with device performance. We find that the performance of the nanocryotron depends significantly on the device geometry, and trade-offs are associated with optimizing the gain, jitter, and energy dissipation. We demonstrate that nanocryotrons fabricated on niobium nitride can achieve a grey zone less than 210 nA wide for a 5 ns long input pulse corresponding to a maximum achievable gain of 48 dB, an energy dissipation of less than 20 aJ per operation, and a jitter of less than 60 ps., Comment: 5 pages, 5 figures

This paper addresses the capacitated periodic review inventory control problem, focusing on a retailer managing multiple products with limited shared resources, such as storage or inbound labor at a facility. Specifically, this paper is motivated by the questions of (1) what does it mean to backtest a capacity control mechanism, (2) can we devise and backtest a capacity control mechanism that is compatible with recent advances in deep reinforcement learning for inventory management? First, because we only have a single historic sample path of Amazon's capacity limits, we propose a method that samples from a distribution of possible constraint paths covering a space of real-world scenarios. This novel approach allows for more robust and realistic testing of inventory management strategies. Second, we extend the exo-IDP (Exogenous Decision Process) formulation of Madeka et al. 2022 to capacitated periodic review inventory control problems and show that certain capacitated control problems are no harder than supervised learning. Third, we introduce a `neural coordinator', designed to produce forecasts of capacity prices, guiding the system to adhere to target constraints in place of a traditional model predictive controller. Finally, we apply a modified DirectBackprop algorithm for learning a deep RL buying policy and a training the neural coordinator. Our methodology is evaluated through large-scale backtests, demonstrating RL buying policies with a neural coordinator outperforms classic baselines both in terms of cumulative discounted reward and capacity adherence (we see improvements of up to 50% in some cases).

Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks assume simple two-tier network topologies where end devices are directly connected to the aggregation server. While this is a practical mental model, it does not exploit the inherent topology of real-world distributed systems like the Internet-of-Things. We present Flight, a novel FL framework that supports complex hierarchical multi-tier topologies, asynchronous aggregation, and decouples the control plane from the data plane. We compare the performance of Flight against Flower, a state-of-the-art FL framework. Our results show that Flight scales beyond Flower, supporting up to 2048 simultaneous devices, and reduces FL makespan across several models. Finally, we show that Flight's hierarchical FL model can reduce communication overheads by more than 60%.

We report measurements of ten custom-made high-homogeneity LYSO crystals. The investigation is motivated by the need for a compact, high-resolution, and fast electromagnetic calorimeter for a new rare pion decay experiment. Each $2.5\times 2.5 \times 18$ cm$^3$ crystal was first characterized for general light yield properties and then its longitudinal response uniformity and energy resolution were measured using low-energy gamma sources. The ten crystals were assembled as an array and subjected to a 30 - 100 MeV positron beam with excellent momentum definition. The energy and timing resolutions were measured as a function of energy, and the spatial resolution was determined at 70 MeV. An additional measurement using monoenergetic 17.6 MeV gammas produced through a p-Li resonance was later made after the photosensors used in positron testing were improved. As an example of the results, the energy resolution at 70 MeV of 1.80 $\pm$ 0.05% is more than two times better than reported results using previous generation LYSO crystals.

Dreams offer a unique window into the cognitive and affective dynamics of the sleeping and the waking mind. Recent quantitative linguistic approaches have shown promise in obtaining corpus-level measures of dream sentiment and topic occurrence. However, it is currently unclear how the affective content of individual dreams relates to their semantic content and structure. Here, we combine word embedding, topic modeling, and network analysis to investigate this relationship. By applying Discourse Atom Topic Modeling (DATM) to the DreamBank corpus of >18K dream reports, we represent the latent themes arising within dreams as a sparse dictionary of topics and identify the affective associations of those topics. We show that variation in dream affect (valence and arousal) is associated with changes in topical content. By representing each dream report as a network of topics, we demonstrate that the affective content of dreams is also coupled to semantic structure. Specifically, positively valenced dreams exhibit more coherent, structured, and linear narratives, whilst negatively valenced dreams have more narrative loops and dominant topics. Additionally, topic networks of high arousal dreams are structurally dominated by few high arousal topics and incoherent topical connections, whereas low arousal dreams contain more loops. These findings suggest that affective processes are associated with both the content and structure of dreams. Our approach showcases the potential of integrating natural language processing and network analysis with psychology to elucidate the interplay of affect, cognition and narrative in dreams. This methodology has broad applications for the study of narrated experience and psychiatric symptomatology.

We prove that a tree with a vertex of degree at least five must be missing a connected partition of some type and therefore its chromatic symmetric function cannot be $e$-positive. We prove that this also holds for a tree with a vertex of degree four as long as it is not adjacent to any leaf. This brings us very close to the conjecture by Dahlberg, She, and van Willigenburg of non-$e$-positivity for all trees with a vertex of degree at least four. We also prove that spiders with four legs cannot have an $e$-positive chromatic symmetric function., Comment: 14 pages

The signature is a collection of iterated integrals describing the "shape" of a path. It appears naturally in the Taylor expansions of controlled differential equations and, as a consequence, is arguably the central object within rough path theory. In this paper, we will consider the signature of Brownian motion with time, and present both new and recently developed approximations for some of its integrals. Since these integrals (or equivalent L\'{e}vy areas) are nonlinear functions of the Brownian path, they are not Gaussian and known to be challenging to simulate. To conclude the paper, we will present some applications of these approximations to the high order numerical simulation of stochastic differential equations (SDEs)., Comment: 69 pages, 12 figures

We conduct a systematic search for astrophysical transients using data from the Atacama Cosmology Telescope (ACT). The data were taken from 2017 to 2022 in three frequency bands spanning 77 GHz to 277 GHz. In this paper we present a pipeline for transient detection using single observation maps where each pixel of a map contains one observation with an integration time of approximately four minutes. We find 34 transient events at 27 unique locations. All but two of the transients are associated with Galactic stars and exhibit a wide range of properties. We also detect an event coincident with the classical nova, YZ Ret and one event consistent with a flaring active galactic nucleus. We notably do not detect any reverse shock emission from gamma ray bursts, a non-detection which is in tension with current models., Comment: 23 pages, 10 figures, 10 tables. First and second author share equal contributions. Article and accompanying data submitted to ApJ. Data tables will be made available upon publication

We demonstrate an exact equivalence between classical population dynamics and Lindbladian evolution admitting a dark state and obeying a set of certain local symmetries. We then introduce {\em quantum population dynamics} as models in which this local symmetry condition is relaxed. This allows for non-classical processes in which animals behave like Schr\"odinger's cat and enter superpositions of live and dead states, thus resulting in coherent superpositions of different population numbers. We develop a field theory treatment of quantum population models as a synthesis of Keldysh and third quantization techniques and draw comparisons to the stochastic Doi-Peliti field theory description of classical population models. We apply this formalism to study a prototypical ``Schr\"odigner cat'' population model on a $d$-dimensional lattice, which exhibits a phase transition between a dark extinct phase and an active phase that supports a stable quantum population. Using a perturbative renormalization group approach, we find a critical scaling of the Schr\"odinger cat population distinct from that observed in both classical population dynamics and usual quantum phase transitions.

The advent of back-illuminated complementary metal-oxide-semiconductor (CMOS) sensors and their well-known advantages over charge-coupled devices (CCDs) make them an attractive technology for future X-ray missions. However, numerous challenges remain, including improving their depletion depth and identifying effective methods to calculate per-pixel gain conversion. We have tested a commercial Sony IMX290LLR CMOS sensor under X-ray light using an $^{55}$Fe radioactive source and collected X-ray photons for $\sim$15 consecutive days under stable conditions at regulated temperatures of 21{\deg}C and 26{\deg}C. At each temperature, the data set contained enough X-ray photons to produce one spectrum per pixel consisting only of single-pixel events. We determined the gain dispersion of its 2.1 million pixels using the peak fitting and the Energy Calibration by Correlation (ECC) methods. We measured a gain dispersion of 0.4\% at both temperatures and demonstrated the advantage of the ECC method in the case of spectra with low statistics. The energy resolution at 5.9 keV after the per-pixel gain correction is improved by $\gtrsim$10 eV for single-pixel and all event spectra, with single-pixel event energy resolution reaching $123.6\pm 0.2$ eV, close to the Fano limit of silicon sensors at room temperature. Finally, our long data acquisition demonstrated the excellent stability of the detector over more than 30 days under a flux of $10^4$ photons per second., Comment: 15 pages, 7 figures, accepted for publication in JATIS

We describe the new design and current performance of the Mid-InfraRed Spectrometer and Imager (MIRSI) on the NASA Infrared Telescope Facility (IRTF). The system has been converted from a liquid nitrogen/liquid helium cryogen system to one that uses a closed-cycle cooler, which allows it to be kept on the telescope at operating temperature and available for observing on short notice, requiring less effort by the telescope operators and day crew to maintain operating temperature. Several other enhancements have been completed, including new detector readout electronics, an IRTF-style standard instrument user interface, new stepper motor driver electronics, and an optical camera that views the same field as the mid-IR instrument using a cold dichroic mirror, allowing for guiding and/or simultaneous optical imaging. The instrument performance is presented, both with an engineering-grade array used from 2021-2023, and a science-grade array installed in the fall of 2023. Some sample astronomical results are also shown. The upgraded MIRSI is a facility instrument at the IRTF available to all users., Comment: 15 pages, 17 figures, accepted to PASP

Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows in principle straightforward extraction of any other observable of interest, besides the energy, but in practice this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning ans\"atze (deep QMC) to obtain highly accurate wave functions free of basis set errors, and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties., Comment: 12 pages, 9 figures in the main text

Model execution allows us to prototype and analyse software engineering models by stepping through their possible behaviours, using techniques like animation and simulation. On the other hand, deductive verification allows us to construct formal proofs demonstrating satisfaction of certain critical properties in support of high-assurance software engineering. To ensure coherent results between execution and proof, we need unifying semantics and automation. In this paper, we mechanise Interaction Trees (ITrees) in Isabelle/HOL to produce an execution and verification framework. ITrees are coinductive structures that allow us to encode infinite labelled transition systems, yet they are inherently executable. We use ITrees to create verification tools for stateful imperative programs, concurrent programs with message passing in the form of the CSP and \Circus languages, and abstract system models in the style of the Z and B methods. We demonstrate how ITrees can account for diverse semantic presentations, such as structural operational semantics, a relational program model, and CSP's failures-divergences trace model. Finally, we demonstrate how ITrees can be executed using the Isabelle code generator to support the animation of models., Comment: 40 pages, under revision for ACM TOSEM. arXiv admin note: text overlap with arXiv:2105.05133

Sedimenting flows occur in a range of society-critical systems, such as circulating fluidized bed reactors and pyroclastic density currents (PDCs), the most hazardous volcanic process. In these systems, mass loading is sufficiently high ($\gg\mathcal{O}(1)$) and momentum coupling between the phases gives rise to mesoscale behavior, such as formation of coherent structures. This heterogeneity has been demonstrated to generate and sustain turbulence in the carrier phase and directly impact large-scale quantities of interest, such as settling time. While contemporary work has explored the physical processes underpinning these multiphase phenomenon for monodisperse assemblies of particles, polydispersity flow behavior has been largely understudied. Since all real-world flows are polydisperse, understanding the role of polydispersity in gas-solid systems is critical for informing closures that are accurate and robust. This work characterizes the sedimentation behavior of two polydispersed gas-solid flows, with statistical and physical properties of the particulate phase sampled from historical PDC ejecta. Highly resolved data for both polydisperse distributions of particles at two volume fractions (1% and 10%) is collected using an Euler-Lagrange framework and is compared with monodisperse configurations of particles with diameters equivalent to the arithmetic mean of the polydisperse configurations. From this data, we find that polydispersity has an important impact on cluster formation and structure and that this is most pronounced for more dilute flows. At higher volume fraction, the effect of polydispersity is less pronounced. We also propose a new metric for predicting the degree of clustering, termed `surface loading', and a model for the coefficient of drag that produces accurate predictions for settling velocity given the high-fidelity data presented.

The urgent need for data democratization in scientific research was the focal point of a panel discussion at SC23 in Denver, Colorado, from November 12 to 17, 2023. This article summarizes the outcomes of that discussion and subsequent conversations. We advocate for strategic investments in financial, human, and technological resources for sustainable data democratization. Emphasizing that data is central to scientific discovery and AI deployment, we highlight barriers such as limited access, inadequate financial incentives for cross-domain collaboration, and a shortage of workforce development initiatives. Our recommendations aim to guide decision-makers in fostering an inclusive research community, breaking down research silos, and developing a skilled workforce to advance scientific discovery., Comment: 5 pages

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. Our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

We measure the ionised gas velocity dispersions of star-forming galaxies in the MAGPI survey ($z\sim0.3$) and compare them with galaxies in the SAMI ($z\sim0.05$) and KROSS ($z\sim1$) surveys to investigate how the ionised gas velocity dispersion evolves. For the first time, we use a consistent method that forward models galaxy kinematics from $z=0$ to $z=1$. This method accounts for spatial substructure in emission line flux and beam smearing. We investigate the correlation between gas velocity dispersion and galaxy properties to understand the mechanisms that drive gas turbulence. We find that in both MAGPI and SAMI galaxies, the gas velocity dispersion more strongly correlates with the star-formation rate surface density ($\Sigma_{\rm SFR}$) than with a variety of other physical properties, and the average gas velocity dispersion is similar, at the same $\Sigma_{\rm SFR}$, for SAMI, MAGPI and KROSS galaxies. The results indicate that mechanisms related to $\Sigma_{\rm SFR}$ could be the dominant driver of gas turbulence from $z\sim1$ to $z\sim0$, for example, stellar feedback and/or gravitational instability. The gas velocity dispersion of MAGPI galaxies is also correlated with the non-rotational motion of the gas, illustrating that in addition to star-formation feedback, gas transportation and accretion may also contribute to the gas velocity dispersion for galaxies at $z\sim 0.3$. KROSS galaxies only have a moderate correlation between gas velocity dispersion and $\Sigma_{\rm SFR}$ and a higher scatter of gas velocity dispersion with respect to $\Sigma_{\rm SFR}$, in agreement with the suggestion that other mechanisms, such as gas transportation and accretion, are relatively more important at higher redshift galaxies., Comment: 15 pages, 10 figures, accepted for publication in MNRAS

The development of systems to measure and optimize emerging energetic material performance is critical for CWA defeat. This study documents a combination of two spectroscopic systems designed to monitor decomposition of a CWA simulant and temperature. The first system is a custom benchtop Polygonal Rotating Mirror Infrared Spectrometer (PRiMIRS) incorporating a fully customizable sample cell to observe decomposition of DIMP as it interacts with combusting composite metal particles. The second is TDLAS used to monitor increases in background gas temperatures as the composite metal powders combust. The PRiMIRS system demonstrates a very high SNR at Hz, reasonable SNR when operating at 100 Hz, and capabilities of resolving spectral features with a FWHM resolution of 15 cm^-1. TDLAS was able to monitor temperature rises between room temperature and 230C +/- 5C at 100 Hz.For testing, liquid DIMP was inserted in a preheated SS cell to generate DIMP vapor and (Al-8Mg):Zr metal powders were ignited in a SS mount with a resistively heated nichrome wire at one end of the cell. The ignited particles propagated across the cell containing DIMP vapor. The path averaged gas temperature in the preheated SS cell rises rapidly (100ms) and decays slowly (< 5 s) but remains below 230C during particle combustion, a temperature at which the thermal decomposition of DIMP is not observed over similarly short timescales (seconds). However, when combusting particles were introduced to the DIMP vapor (heterogenous environment), spectral signatures indicative of decomposition products such as IMP and IPA were observed within seconds., Comment: 9 Figures, 14 pages, 1 Supplemental Figure

The integration of topology into photonics has generated a new design framework for constructing robust and unidirectional waveguides, which are not feasible with traditional photonic devices. Here, we overcome current barriers to the successful integration of quantum emitters such as quantum dots (QDs) into valley-Hall (VH) topological waveguides, utilising photonic defects at the topological interface to stabilise the local charge environment and inverse design for efficient topological-conventional mode conversion. By incorporating QDs within defects of VH-photonic crystals, we demonstrate the first instances of single-photon resonant fluorescence and resonant transmission spectroscopy of a quantum emitter at a topological waveguide interface. Our results bring together topological photonics with optical nonlinear effects at the single-photon level, offering a new avenue to investigate the interaction between topology and quantum nonlinear systems.

Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application designed to achieve a computational goal. Task-based execution frameworks abstract the parallel execution of an application's tasks on arbitrary hardware. Research into these task executors has accelerated as computational sciences increasingly need to take advantage of parallel compute and/or heterogeneous hardware. However, the lack of evaluation standards makes it challenging to compare and contrast novel systems against existing implementations. Here, we introduce TaPS, the Task Performance Suite, to support continued research in parallel task executor frameworks. TaPS provides (1) a unified, modular interface for writing and evaluating applications using arbitrary execution frameworks and data management systems and (2) an initial set of reference synthetic and real-world science applications. We discuss how the design of TaPS supports the reliable evaluation of frameworks and demonstrate TaPS through a survey of benchmarks using the provided reference applications., Comment: To appear in the Proceedings of 20th IEEE International Conference on e-Science

We study the dynamic behaviour of (weighted) composition operators on the space of holomorphic functions on a plane domain. Any such operator is hypercyclic if and only if it is topologically mixing, and when the symbol is automorphic, such an operator is supercyclic if and only if it is mixing. When the domain is a punctured plane, a composition operator is supercyclic if and only if it satisfies the Frequent Hypercyclity Criterion, and when the domain is conformally equivalent to a punctured disc, such an operator is hypercyclic if and only if it satisfies the Frequent Hypercyclicity Criterion. When the domain is finitely connected and either conformally equivalent to an annulus or having two or more holes, no weighted composition operator can be supercyclic., Comment: 19 pages

Studies of the internal mass structure of galaxies have observed a `conspiracy' between the dark matter and stellar components, with total (stars $+$ dark) density profiles showing remarkable regularity and low intrinsic scatter across various samples of galaxies at different redshifts. Such homogeneity suggests the dark and stellar components must somehow compensate for each other in order to produce such regular mass structures. We test the conspiracy using a sample of 22 galaxies from the `Middle Ages Galaxy Properties with Integral field spectroscopy' (MAGPI) Survey that targets massive galaxies at $ z \sim 0.3$. We use resolved, 2D stellar kinematics with the Schwarzschild orbit-based modelling technique to recover intrinsic mass structures, shapes, and dark matter fractions. This work is the first implementation of the Schwarzschild modelling method on a sample of galaxies at a cosmologically significant redshift. We find that the variability of structure for combined mass (baryonic and dark) density profiles is greater than that of the stellar components alone. Furthermore, we find no significant correlation between enclosed dark matter fractions at the half-light radius and the stellar mass density structure. Rather, the total density profile slope, $\gamma_{\mathrm{tot}}$, strongly correlates with the dark matter fraction within the half-light radius, as $\gamma_{\mathrm{tot}} = (1.3 \pm 0.2) f_{\mathrm{DM}} - (2.44 \pm 0.04)$. Our results refute the bulge-halo conspiracy and suggest that stochastic processes dominate in the assembly of structure for massive galaxies., Comment: 21 pages, 18 figures

NASA's return to the Moon presents unparalleled opportunities to advance high-impact scientific capabilities. At the cutting edge of these possibilities are extremely high-resolution interferometric observations at visible and ultraviolet wavelengths. Such technology can resolve the surfaces of stars, explore the inner accretion disks of nascent stars and black holes, and eventually enable us to observe surface features and weather patterns on nearby exoplanets. We have been awarded Phase 1 support from NASA's Innovative Advanced Concepts (NIAC) program to explore the feasibility of constructing a high-resolution, long-baseline UV/optical imaging interferometer on the lunar surface, in conjunction with the Artemis Program. A 1996 study comparing interferometers on the Moon versus free-flyers in space concluded that, without pre-existing lunar infrastructure, free-flyers were preferable. However, with the advent of the Artemis Program, it is now crucial to revisit the potential of building lunar interferometers. Our objective is to conduct a study with the same level of rigor applied to large baseline, free-flying interferometers during the 2003-2005 NASA Vision Missions Studies. This preparation is essential for timely and effective utilization of the forthcoming lunar infrastructure. In this paper, we highlight the groundbreaking potential of a lunar surface-based interferometer. This concept study will be a huge step forward to larger arrays on both the moon and free-flying in space, over a wide variety of wavelengths and science topics. Our Phase 1 study began in April 2024, and here we present a concise overview of our vision and the progress made so far., Comment: 6 pages, 3 figures, SPIE Astronomical Telescopes + Instrumentation 2024 (Invited Paper)

The workshop focused on the application of ANAs to particle physics keeping in mind the ultimate goal of a collider at the energy frontier (10\,TeV, e$^+$/e$^-$, e$^-$/e$^-$, or $\gamma\gamma$). The development of ANAs is conducted at universities and national laboratories worldwide. The community is thematically broad and diverse, in particular since lasers suitable for ANA research (multi-hundred-terawatt peak power, a few tens of femtosecond-long pulses) and acceleration of electrons to hundreds of mega electron volts to multi giga electron volts became commercially available. The community spans several continents (Europe, America, Asia), including more than 62 laboratories in more than 20 countries. It is among the missions of the ICFA-ANA panel to feature the amazing progress made with ANAs, to provide international coordination and to foster international collaborations towards a future HEP collider. The scope of this edition of the workshop was to discuss the recent progress and necessary steps towards realizing a linear collider for particle physics based on novel-accelerator technologies (laser or beam driven in plasma or structures). Updates on the relevant aspects of the European Strategy for Particle Physics (ESPP) Roadmap Process as well as of the P5 (in the US) were presented, and ample time was dedicated to discussions. The major outcome of the workshop is the decision for ALEGRO to coordinate efforts in Europe, in the US, and in Asia towards a pre-CDR for an ANA-based, 10\,TeV CM collider. This goal of this coordination is to lead to a funding proposal to be submitted to both EU and EU/US funding agencies. This document presents a summary of the workshop, as seen by the co-chairs, as well as short 'one-pagers' written by the presenters at the workshop., Comment: 72 pages

A rapid, exploratory review to establish a shared understanding of what constitutes good quality EdTech and implementation. The review: (1) examined existing frameworks and standards; and (2) identified the characteristics, quality components, essential conditions and evaluation criteria for the design and implementation of EdTech. The report documents: (1) the strength of the evidence base against sector-led standards and frameworks for EdTech product quality; and (2) a single view of the evidence base which could underpin the design of a future framework.

Rapid identification and accurate documentation of interfering and high-risk behaviors in ASD, such as aggression, self-injury, disruption, and restricted repetitive behaviors, are important in daily classroom environments for tracking intervention effectiveness and allocating appropriate resources to manage care needs. However, having a staff dedicated solely to observing is costly and uncommon in most educational settings. Recently, multiple research studies have explored developing automated, continuous, and objective tools using machine learning models to quantify behaviors in ASD. However, the majority of the work was conducted under a controlled environment and has not been validated for real-world conditions. In this work, we demonstrate that the latest advances in video-based group activity recognition techniques can quantify behaviors in ASD in real-world activities in classroom environments while preserving privacy. Our explainable model could detect the episode of problem behaviors with a 77% F1-score and capture distinctive behavior features in different types of behaviors in ASD. To the best of our knowledge, this is the first work that shows the promise of objectively quantifying behaviors in ASD in a real-world environment, which is an important step toward the development of a practical tool that can ease the burden of data collection for classroom staff.

We present the detection and characterization of fluctuations in linearly polarized emission from the atmosphere above the South Pole. These measurements make use of Austral winter survey data from the SPT-3G receiver on the South Pole Telescope in three frequency bands centered at 95, 150, and 220 GHz. We use the cross-correlation between detectors to produce an unbiased estimate of the power in Stokes I, Q, and U parameters on large angular scales. Our results are consistent with the polarized signal being produced by the combination of Rayleigh scattering of thermal radiation from the ground and thermal emission from a population of horizontally aligned ice crystals with an anisotropic distribution described by Kolmogorov turbulence. The signal is most significant at large angular scales, high observing frequency, and low elevation angle. Polarized atmospheric emission has the potential to significantly impact observations on the large angular scales being targeted by searches for inflationary B-mode CMB polarization. We present the distribution of measured angular power spectrum amplitudes in Stokes Q and I for 4 years of winter observations, which can be used to simulate the impact of atmospheric polarization and intensity fluctuations at the South Pole on a specified experiment and observation strategy. For the SPT-3G data, downweighting the small fraction of significantly contaminated observations is an effective mitigation strategy. In addition, we present a strategy for further improving sensitivity on large angular scales where maps made in the 220 GHz band are used to measure and subtract the polarized atmosphere signal from the 150 GHz band maps. In observations with the SPT-3G instrument at the South Pole, the polarized atmospheric signal is a well-understood and sub-dominant contribution to the measured noise after implementing the mitigation strategies described here., Comment: 32 pages, 28 figures

Carbon abundances in first-ascent giant stars are usually lower than those of their main-sequence counterparts. At moderate metallicities, stellar evolution of single stars cannot account for the existence of red-giant branch stars with enhanced carbon abundances. The phenomenon is usually interpreted as resulting from past mass transfer from an evolved binary companion now in the white dwarf evolutionary stage. Aims: We aim to confirm the links between [C/O] enhancement, s-process element enhancement and binary fraction using large-scale catalogues of stellar abundances and probable binary stars. Methods: We use a large data set from the 17 data release of the SDSS-IV/APOGEE~2 survey to identify carbon-enhanced stars in the Galactic disk. We identify a continuum of carbon enrichment throughout three different sub-populations of disk stars and explore links between the degree of carbon enrichment and binary frequency, metallicity and chemical compositions. Results: We verify a clear correlation between binary frequency and enhancement in the abundances of both carbon and cerium, lending support to the scenario whereby carbon-enhanced stars are the result of mass transfer by an evolved binary companion. In addition, we identify clustering in the carbon abundances of high-$\alpha$ disk stars, suggesting that those on the high metallicity end are likely younger, in agreement with theoretical predictions for the presence of a starburst population following the gas-rich merger of the Gaia-Enceladus/Sausage system., Comment: 20 pages

Traditional cosmic ray filtering algorithms used in X-ray imaging detectors aboard space telescopes perform event reconstruction based on the properties of activated pixels above a certain energy threshold, within 3x3 or 5x5 pixel sliding windows. This approach can reject up to 98% of the cosmic ray background. However, the remaining unrejected background constitutes a significant impediment to studies of low surface brightness objects, which are especially prevalent in the high-redshift universe. The main limitation of the traditional filtering algorithms is their ignorance of the long-range contextual information present in image frames. This becomes particularly problematic when analyzing signals created by secondary particles produced during interactions of cosmic rays with body of the detector. Such signals may look identical to the energy deposition left by X-ray photons, when one considers only the properties within the small sliding window. Additional information is present, however, in the spatial and energy correlations between signals in different parts of the frame, which can be accessed by modern machine learning (ML) techniques. In this work, we continue the development of an ML-based pipeline for cosmic ray background mitigation. Our latest method consist of two stages: first, a frame classification neural network is used to create class activation maps (CAM), localizing all events within the frame; second, after event reconstruction, a random forest classifier, using features obtained from CAMs, is used to separate X-ray and cosmic ray features. The method delivers >40% relative improvement over traditional filtering in background rejection in standard 0.3-10keV energy range, at the expense of only a small (<2%) level of lost X-ray signal. Our method also provides a convenient way to tune the cosmic ray rejection threshold to adapt to a user's specific scientific needs., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

The Advanced X-ray Imaging Satellite (AXIS) is a NASA probe class mission concept designed to deliver arcsecond resolution with an effective area ten times that of Chandra (at launch). The AXIS focal plane features an MIT Lincoln Laboratory (MIT-LL) X-ray charge-coupled device (CCD) detector working in conjunction with an application specific integrated circuit (ASIC), denoted the Multi-Channel Readout Chip (MCRC). While this readout ASIC targets the AXIS mission, it is applicable to a range of potential X-ray missions with comparable readout requirements. Designed by the X-ray astronomy and Observational Cosmology (XOC) group at Stanford University, the MCRC ASIC prototype (MCRC-V1.0) uses a 350 nm technology node and provides 8 channels of high speed, low noise, low power consumption readout electronics. Each channel implements a current source to bias the detector output driver, a preamplifier to provide gain, and an output buffer to interface directly to an analog-to-digital (ADC) converter. The MCRC-V1 ASIC exhibits comparable performance to our best discrete electronics implementations, but with ten times less power consumption and a fraction of the footprint area. In a total ionizing dose (TID) test, the chip demonstrated a radiation hardness equal or greater to 25 krad, confirming the suitability of the process technology and layout techniques used in its design. The next iteration of the ASIC (MCRC-V2) will expand the channel count and extend the interfaces to external circuits, advancing its readiness as a readout-on-a-chip solution for next generation X-ray CCD-like detectors. This paper summarizes our most recent characterization efforts, including the TID radiation campaign and results from the first operation of the MCRC ASIC in combination with a representative MIT-LL CCD., Comment: To appear in SPIE Astronomical Telescopes + Instrumentation proceedings 2024

Bringing artificial intelligence (AI) alongside next-generation X-ray imaging detectors, including CCDs and DEPFET sensors, enhances their sensitivity to achieve many of the flagship science cases targeted by future X-ray observatories, based upon low surface brightness and high redshift sources. Machine learning algorithms operating on the raw frame-level data provide enhanced identification of background vs. astrophysical X-ray events, by considering all of the signals in the context within which they appear within each frame. We have developed prototype machine learning algorithms to identify valid X-ray and cosmic-ray induced background events, trained and tested upon a suite of realistic end-to-end simulations that trace the interaction of cosmic ray particles and their secondaries through the spacecraft and detector. These algorithms demonstrate that AI can reduce the unrejected instrumental background by up to 41.5 per cent compared with traditional filtering methods. Alongside AI algorithms to reduce the instrumental background, next-generation event reconstruction methods, based upon fitting physically-motivated Gaussian models of the charge clouds produced by events within the detector, promise increased accuracy and spectral resolution of the lowest energy photon events., Comment: Proceedings of the SPIE, Astronomical Telescopes and Instrumentation, Space Telescopes and Instrumentation 2024: Ultraviolet to Gamma Ray

The single photoelectron performance characteristics of a Large Area Picosecond PhotoDetector (LAPPD) were studied using a picosecond optical laser source. We verify that the LAPPD is capable of achieving transit time spreads of less than 70 ps and spatial resolutions on the order of millimetres when illuminated with single photons. Isolation of the single photoelectron peak is easily possible in the pulse height distribution and the mean single photoelectron gain is measured to be greater than $10^{6}$ with gains above $10^{7}$ possible in certain voltage configurations. We judge the performance of the LAPPD sufficient for application in large-scale water or scintillator-based neutrino experiments, with additional work required to develop the digital signal processing algorithms necessary for processing photon hits in high photon occupancy environments., Comment: 13 pages, 11 figures

Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision making task by learning from demonstrations, and has been widely applied to robotics, autonomous driving, and autoregressive text generation. The simplest approach to IL, behavior cloning (BC), is thought to incur sample complexity with unfavorable quadratic dependence on the problem horizon, motivating a variety of different online algorithms that attain improved linear horizon dependence under stronger assumptions on the data and the learner's access to the expert. We revisit the apparent gap between offline and online IL from a learning-theoretic perspective, with a focus on general policy classes up to and including deep neural networks. Through a new analysis of behavior cloning with the logarithmic loss, we show that it is possible to achieve horizon-independent sample complexity in offline IL whenever (i) the range of the cumulative payoffs is controlled, and (ii) an appropriate notion of supervised learning complexity for the policy class is controlled. Specializing our results to deterministic, stationary policies, we show that the gap between offline and online IL is not fundamental: (i) it is possible to achieve linear dependence on horizon in offline IL under dense rewards (matching what was previously only known to be achievable in online IL); and (ii) without further assumptions on the policy class, online IL cannot improve over offline IL with the logarithmic loss, even in benign MDPs. We complement our theoretical results with experiments on standard RL tasks and autoregressive language generation to validate the practical relevance of our findings.

Language model alignment methods, such as reinforcement learning from human feedback (RLHF), have led to impressive advances in language model capabilities, but existing techniques are limited by a widely observed phenomenon known as overoptimization, where the quality of the language model plateaus or degrades over the course of the alignment process. Overoptimization is often attributed to overfitting to an inaccurate reward model, and while it can be mitigated through online data collection, this is infeasible in many settings. This raises a fundamental question: Do existing offline alignment algorithms make the most of the data they have, or can their sample-efficiency be improved further? We address this question with a new algorithm for offline alignment, $\chi^2$-Preference Optimization ($\chi$PO). $\chi$PO is a one-line change to Direct Preference Optimization (DPO; Rafailov et al., 2023), which only involves modifying the logarithmic link function in the DPO objective. Despite this minimal change, $\chi$PO implicitly implements the principle of pessimism in the face of uncertainty via regularization with the $\chi^2$-divergence -- which quantifies uncertainty more effectively than KL-regularization -- and provably alleviates overoptimization, achieving sample-complexity guarantees based on single-policy concentrability -- the gold standard in offline reinforcement learning. $\chi$PO's simplicity and strong guarantees make it the first practical and general-purpose offline alignment algorithm that is provably robust to overoptimization.

The high contrast and spatial resolution requirements for directly imaging exoplanets requires effective coordination of wavefront control, coronagraphy, observation techniques, and post-processing algorithms. However, even with this suite of tools, identifying and retrieving exoplanet signals embedded in resolved scattered light regions can be extremely challenging due to the increased noise from scattered light off the circumstellar disk and the potential misinterpretation of the true nature of the detected signal. This issue pertains not only to imaging terrestrial planets in habitable zones within zodiacal and exozodiacal emission but also to young planets embedded in circumstellar, transitional, and debris disks. This is particularly true for H{\alpha} detection of exoplanets in transitional disks. This work delves into recent H{\alpha} observations of three transitional disks systems with MagAO-X, an extreme adaptive optics system for the 6.5-meter Magellan Clay telescope. We employed angular differential imaging (ADI) and simultaneous spectral differential imaging (SSDI) in combination with KLIP, a PCA algorithm in post-processing, for optimal starlight suppression and quasi-static noise removal. We discuss the challenges in protoplanet identification with MagAO-X in environments rich with scattered and reflected light from disk structures and explore a potential solution for removing noise contributions from real astronomical objects with current observation and post-processing techniques.