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The R package panelPomp supports analysis of panel data via a general class of partially observed Markov process models (PanelPOMP). This package tutorial describes how the mathematical concept of a PanelPOMP is represented in the software and demonstrates typical use-cases of panelPomp. Monte Carlo methods used for POMP models require adaptation for PanelPOMP models due to the higher dimensionality of panel data. The package takes advantage of recent advances for PanelPOMP, including an iterated filtering algorithm, Monte Carlo adjusted profile methodology and block optimization methodology to assist with the large parameter spaces that can arise with panel models. In addition, tools for manipulation of models and data are provided that take advantage of the panel structure.

Wireless electromagnetic sensing is a passive, non-destructive technique for measuring physical and chemical changes through permittivity or conductivity changes. However, the readout is limited by the sensitivity of the materials, often requiring complex sampling, particularly in the GHz range. We report capacitive dielectric temperature sensors based on polydimethylsiloxne (PDMS) loaded with 10 vol% of inexpensive, commercially available conductive fillers including copper powder (Cu), graphite powder (GP) and milled carbon fibre powder (CF). The sensors are tested in the range of 20{\deg}C to 110{\deg}C, with enhanced sensitivity from 20 to 60{\deg}C, and relative response of up to 85.5% at 200 MHz for CF loaded capacitors. Additionally, we demonstrate that operating frequency influences the relative sensing response by as much as 15.0% in loaded composite capacitors. Finally, we demonstrate the suitability of PDMS-CF capacitors as a sensing element in wirelessly coupled chipless resonant coils tuned to 6.78 MHz with a readout response, in the resonant frequency of the sensor. The wireless readout of the PDMS-CF chipless system exhibited an average sensitivity of 0.38 %{\deg}C-1 which is a 40x improvement over a pristine PDMS-based capacitive sensor and outperforms state of the art frequency-domain radio frequency (RF) temperature sensors including carbon-based composites at higher loadings. Exploiting the high sensitivity, we interrogate the sensor wirelessly using a low-cost and portable open-source NanoVNA demonstrating a relative response in the resonant frequency of the reader coil of 48.5%, with the response in good agreement with the instrumentation-grade vector network analyzers (VNAs), demonstrating that the sensors could be integrated into an inexpensive and portable measurement setup that does not rely on specialty equipment or highly trained operators., Comment: Main body 24 pages, 8 figures, one table. Supplemental information 10 pages, 11 figures, two tables

Progress in gravitational-wave astronomy depends upon having sensitive detectors with good data quality. Since the end of the LIGO-Virgo-KAGRA third Observing run in March 2020, detector-characterization efforts have lead to increased sensitivity of the detectors, swifter validation of gravitational-wave candidates and improved tools used for data-quality products. In this article, we discuss these efforts in detail and their impact on our ability to detect and study gravitational-waves. These include the multiple instrumental investigations that led to reduction in transient noise, along with the work to improve software tools used to examine the detectors data-quality. We end with a brief discussion on the role and requirements of detector characterization as the sensitivity of our detectors further improves in the future Observing runs., Comment: 35 pages, 18 figures

Domain adaptation is a widely used method in natural language processing (NLP) to improve the performance of a language model within a specific domain. This method is particularly common in the biomedical domain, which sees regular publication of numerous scientific articles. PubMed, a significant corpus of text, is frequently used in the biomedical domain. The primary objective of this study is to explore whether refining a pre-training dataset using specific quality metrics for scientific papers can enhance the performance of the resulting model. To accomplish this, we employ two straightforward journal impact metrics and conduct experiments by continually pre-training BERT on various subsets of the complete PubMed training set, we then evaluate the resulting models on biomedical language understanding tasks from the BLURB benchmark. Our results show that pruning using journal impact metrics is not efficient. But we also show that pre-training using fewer abstracts (but with the same number of training steps) does not necessarily decrease the resulting model's performance.

Consider a monic polynomial of degree $n$ whose subleading coefficients are independent, identically distributed, nondegenerate random variables having zero mean, unit variance, and finite moments of all orders, and let $m \geq 0$ be a fixed integer. We prove that such a random monic polynomial has exactly $m$ real zeros with probability $n^{-b+o(1)}$ as $n\to \infty$ through integers of the same parity as $m$, where $b \approx 0.76$ is a positive constant. More generally, we determine conditions under which a similar asymptotic formula describes the corresponding probability for families of random real polynomials with multiple fixed coefficients. Our work extends well-known universality results of Dembo, Poonen, Shao, and Zeitouni, whose considered the family of real polynomials with all coefficients random. As a number-theoretic consequence of these results, we deduce that an algebraic integer $\alpha$ of degree $n$ has exactly $m$ real Galois conjugates with probability $n^{-b+o(1)}$, when such $\alpha$ are ordered by the heights of their minimal polynomials., Comment: 23 pages

The online ordered Ramsey game is played between two players, Builder and Painter, on an infinite sequence of vertices with ordered graphs $(G_1,G_2)$, which have linear orderings on their vertices. On each turn, Builder first selects an edge before Painter colors it red or blue. Builder's objective is to construct either an ordered red copy of $G_1$ or an ordered blue copy of $G_2$, while Painter's objective is to delay this for as many turns as possible. The online ordered Ramsey number $r_o(G_1,G_2)$ is the number of turns Builder takes to win in the case that both players play optimally. Few lower bounds are known for this quantity. In this paper, we introduce a succinct proof of a new lower bound based on the maximum left- and right-degrees in the ordered graphs. We also upper bound $r_o(G_1,G_2)$ in two cases: when $G_1$ is a cycle and $G_2$ a complete bipartite graph, and when $G_1$ is a tree and $G_2$ a clique., Comment: 8 pages

The interplay between the $^{22}$Ne$(\alpha,\gamma)^{26}$Mg and the competing $^{22}$Ne$(\alpha,n)^{25}$Mg reactions determines the efficiency of the latter as a neutron source at the temperatures of stellar helium burning. In both cases, the rates are dominated by the $\alpha$-cluster resonance at 830 keV. This resonance plays a particularly important role in determining the strength of the neutron flux for both the weak and main $s$-process as well as the $n$-process. Recent experimental studies based on transfer reactions suggest that the neutron and $\gamma$-ray strengths for this resonance are approximately equal. In this study, the $^{22}$Ne$(\alpha,n)^{25}$Mg resonance strength has been remeasured and found to be similar to the previous direct studies. This reinforces an 830 keV resonance strength that is approximately a factor of three larger for the $^{22}$Ne$(\alpha,n)^{25}$Mg reaction than for the $^{22}$Ne$(\alpha,\gamma)^{26}$Mg reaction.

Chromium ditelluride, CrTe$_2$, is an attractive candidate van der Waals material for hosting 2D magnetism. However, how the room-temperature ferromagnetism of the bulk evolves as the sample is thinned to the single-layer limit has proved controversial. This, in part, reflects its metastable nature, vs. a series of more stable self-intercalation compounds with higher relative Cr:Te stoichiometry. Here, exploiting a recently-developed method for enhancing nucleation in molecular beam epitaxy growth of transition-metal chalcogenides, we demonstrate the selective stabilisation of high-coverage CrTe$_2$ and Cr$_{2+\varepsilon}$Te$_3$ epitaxial monolayers. Combining X-ray magnetic circular dichroism, scanning tunnelling microscopy, and temperature-dependent angle-resolved photoemission, we demonstrate that both compounds order magnetically with a similar Tc. We find, however, that monolayer CrTe$_2$ forms as an anti-ferromagnetic metal, while monolayer Cr$_{2+\varepsilon}$Te$_3$ hosts an intrinsic ferromagnetic semiconducting state. This work thus demonstrates that control over the self-intercalation of metastable Cr-based chalcogenides provides a powerful route for tuning both their metallicity and magnetic structure, establishing the Cr-Te system as a flexible materials class for future 2D spintronics.

Obtaining reliable and precise estimates of wildlife species abundance and distribution is essential for the conservation and management of animal populations and natural reserves. Remote sensors such as camera traps are increasingly employed to gather data on uniquely identifiable individuals. Spatial capture-recapture (SCR) models provide estimates of population and spatial density from such data. These models introduce spatial correlation between observations of the same individual through a latent activity center. However SCR models assume that observations are independent over time and space, conditional on their given activity center, so that observed sightings at a given time and location do not influence the probability of being seen at future times and/or locations. With detectors like camera traps, this is ecologically unrealistic given the smooth movement of animals over space through time. We propose a new continuous-time modeling framework that incorporates both an individual's (latent) activity center and (known) previous location and time of detection. We demonstrate that standard SCR models can produce substantially biased density estimates when there is correlation in the times and locations of detections, and that our new model performs substantially better than standard SCR models on data simulated through a movement model as well as in a real camera trap study of American martens where an improvement in model fit is observed when incorporating the observed locations and times of previous observations., Comment: 20 pages, 4 figures

Tokenising continuous speech into sequences of discrete tokens and modelling them with language models (LMs) has led to significant success in text-to-speech (TTS) synthesis. Although these models can generate speech with high quality and naturalness, their synthesised samples can still suffer from artefacts, mispronunciation, word repeating, etc. In this paper, we argue these undesirable properties could partly be caused by the randomness of sampling-based strategies during the autoregressive decoding of LMs. Therefore, we look at maximisation-based decoding approaches and propose Temporal Repetition Aware Diverse Beam Search (TRAD-BS) to find the most probable sequences of the generated speech tokens. Experiments with two state-of-the-art LM-based TTS models demonstrate that our proposed maximisation-based decoding strategy generates speech with fewer mispronunciations and improved speaker consistency.

In this work, we present the elastic electron scattering longitudinal form factors of $A\le 10$ nuclei computed in a variational Monte Carlo approach. We employ the Norfolk family of local chiral interactions and a consistent electromagnetic charge operator. Our calculations are compared both to data and past theoretical evaluations. This work represents, to our knowledge, the first exact many-body calculation of longitudinal form factors in the $7\,\leq\,A\,\leq\,10$ mass range. Finally, we identify $^9$Be and $^{10}$B as candidate targets for renewed experimental interest, as they exhibit the potential to provide more stringent constraints on the theoretical models., Comment: 12 pages, 6 figures

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

Random spin systems at low temperatures are glassy and feature computational hardness in finding low-energy states. We study the random all-to-all interacting fermionic Sachdev--Ye--Kitaev (SYK) model and prove that, in contrast, (I) the low-energy states have polynomial circuit depth, yet (II) the annealed and quenched free energies agree to inverse-polynomially low temperatures, ruling out a glassy phase transition in this sense. These results are derived by showing that fermionic and spin systems significantly differ in their \emph{commutation index}, which quantifies the non-commutativity of Hamiltonian terms. Our results suggest that low-temperature strongly interacting fermions, unlike spins, belong in a classically nontrivial yet quantumly easy phase., Comment: Fixed author name

Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multi-agent scenarios at extremely high ($\geq 150mph$) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human racecar driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team's approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multi-agent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system the Dallara AV-21 platform and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement., Comment: Published in Journal of Field Robotics

The Tor anonymity network allows users such as political activists and those under repressive governments to protect their privacy when communicating over the internet. At the same time, Tor has been demonstrated to be vulnerable to several classes of deanonymizing attacks that expose user behavior and identities. Prior work has shown that these threats can be mitigated by leveraging trusted execution environments (TEEs). However, previous proposals assume that all relays in the network will be TEE-based-which as a practical matter is unrealistic. In this work, we introduce ParTEETor, a Tor-variant system, which leverages partial deployments of TEEs to thwart known attacks. We study two modes of operation: non-policy and policy. Non-policy mode uses the existing Tor relay selection algorithm to provide users incident security. Policy mode extends the relay selection algorithm to address the classes of attacks by enforcing a specific TEE circuit configuration. We evaluate ParTEETor for security, performance, and privacy. Our evaluation demonstrates that at even a small TEE penetration (e.g., 10% of relays are TEE-based), users can reach performance of Tor today while enforcing a security policy to guarantee protection from at least two classes of attacks. Overall, we find that partial deployments of TEEs can substantially improve the security of Tor, without a significant impact on performance or privacy.

As user content and queries become increasingly multi-modal, the need for effective multi-modal search systems has grown. Traditional search systems often rely on textual and metadata annotations for indexed images, while multi-modal embeddings like CLIP enable direct search using text and image embeddings. However, embedding-based approaches face challenges in integrating contextual features such as user locale and recency. Building a scalable multi-modal search system requires fine-tuning several components. This paper presents a multi-modal search architecture and a series of AB tests that optimize embeddings and multi-modal technologies in Adobe Express template search. We address considerations such as embedding model selection, the roles of embeddings in matching and ranking, and the balance between dense and sparse embeddings. Our iterative approach demonstrates how utilizing sparse, dense, and contextual features enhances short and long query search, significantly reduces null rates (over 70\%), and increases click-through rates (CTR). Our findings provide insights into developing robust multi-modal search systems, thereby enhancing relevance for complex queries., Comment: CIKM 2024 (International Conference on Information and Knowledge Management), Multimodal Search and Recommendations Workshop

The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting well-defined mutations. However, the field is still in its infancy, and personalised treatments are far from being standard of care. Personalised medicine is often associated with the utilisation of omics data. Yet, implementation of multi-omics data has proven difficult, due to the variety and scale of the information within the data, as well as the complexity behind the myriad of interactions taking place within the cell. An alternative approach to precision medicine is to employ a function-based profile of the cell. This involves screening a range of drugs against patient derived cells. Here we demonstrate a proof-of-concept, where a collection of drug screens against a highly diverse set of patient-derived cell lines, are leveraged to identify putative treatment options for a 'new patient'. We show that this methodology is highly efficient in ranking the drugs according to their activity towards the target cells. We argue that this approach offers great potential, as activities can be efficiently imputed from various subsets of the drug treated cell lines that do not necessarily originate from the same tissue type., Comment: 3 figures and 5 tables

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

We present the initial data for the ($J = 3 \to 2$) transition of $^{13}$CO obtained from the Central Molecular Zone (CMZ) of the Milky Way as part of the CO Heterodyne Inner Milky Way Plane Survey 2 (CHIMPS2). Covering $359^\circ \leq l \leq 1^\circ$ and $|b| \leq 0.5^\circ$ with an angular resolution of 19 arcsec, velocity resolution of 1 km s$^{-1}$, and rms $T_A^* = 0.59$ K at these resolutions, our observations unveil the complex structure of the CMZ molecular gas in improved detail. Complemented by the $^{12}$CO CHIMPS2 data, we estimate a median optical depth of $\tau_{13} = 0.087$. The preliminary analysis yields a median $^{13}$CO column density range equal to $N(^{13}\text{CO})= 2$--$5 \times 10^{18}$ cm$^{-2}$, median H$_2$ column density equal to $N(\text{H}_2)= 4 \times 10^{22}$ cm$^{-2}$ to $1 \times 10^{23}$ cm$^{-2}$. We derive $N(\text{H}_2)$-based total mass estimates of $M(\text{H}_2)= 2$--$6 \times 10^7\, M_{\odot}$, in agreement with previous studies. We analyze the relationship between the integrated intensity of $^{13}$CO and the surface density of compact sources identified by Herschel Hi-GAL, and find that younger Hi-GAL sources detected at 500 $\mu$m but not at 70 $\mu$m follow the dense gas of the CMZ more closely than those that are bright at 70 $\mu$m. The latter, actively star-forming sources, appear to be more associated with material in the foreground spiral arms., Comment: 13 pages, 9 figures

Quantification of cardiac biomarkers from cine cardiovascular magnetic resonance (CMR) data using deep learning (DL) methods offers many advantages, such as increased accuracy and faster analysis. However, only a few studies have focused on the scan-rescan precision of the biomarker estimates, which is important for reproducibility and longitudinal analysis. Here, we propose a cardiac biomarker estimation pipeline that not only focuses on achieving high segmentation accuracy but also on improving the scan-rescan precision of the computed biomarkers, namely left and right ventricular ejection fraction, and left ventricular myocardial mass. We evaluate two approaches to improve the apical-basal resolution of the segmentations used for estimating the biomarkers: one based on image interpolation and one based on segmentation interpolation. Using a database comprising scan-rescan cine CMR data acquired from 92 subjects, we compare the performance of these two methods against ground truth (GT) segmentations and DL segmentations obtained before interpolation (baseline). The results demonstrate that both the image-based and segmentation-based interpolation methods were able to narrow Bland-Altman scan-rescan confidence intervals for all biomarkers compared to the GT and baseline performances. Our findings highlight the importance of focusing not only on segmentation accuracy but also on the consistency of biomarkers across repeated scans, which is crucial for longitudinal analysis of cardiac function., Comment: 11 pages, 3 figures, MICCAI STACOM 2024

The cutting edge of applying AI to science is the closed-loop automation of scientific research: robot scientists. We have previously developed two robot scientists: `Adam' (for yeast functional biology), and `Eve' (for early-stage drug design)). We are now developing a next generation robot scientist Genesis. With Genesis we aim to demonstrate that an area of science can be investigated using robot scientists unambiguously faster, and at lower cost, than with human scientists. Here we report progress on the Genesis project. Genesis is designed to automatically improve system biology models with thousands of interacting causal components. When complete Genesis will be able to initiate and execute in parallel one thousand hypothesis-led closed-loop cycles of experiment per-day. Here we describe the core Genesis hardware: the one thousand computer-controlled $\mu$-bioreactors. For the integrated Mass Spectrometry platform we have developed AutonoMS, a system to automatically run, process, and analyse high-throughput experiments. We have also developed Genesis-DB, a database system designed to enable software agents access to large quantities of structured domain information. We have developed RIMBO (Revisions for Improvements of Models in Biology Ontology) to describe the planned hundreds of thousands of changes to the models. We have demonstrated the utility of this infrastructure by developed two relational learning bioinformatic projects. Finally, we describe LGEM+ a relational learning system for the automated abductive improvement of genome-scale metabolic models.

SPIDER is a balloon-borne instrument designed to map the cosmic microwave background at degree-angular scales in the presence of Galactic foregrounds. SPIDER has mapped a large sky area in the Southern Hemisphere using more than 2000 transition-edge sensors (TESs) during two NASA Long Duration Balloon flights above the Antarctic continent. During its first flight in January 2015, SPIDER observed in the 95 GHz and 150 GHz frequency bands, setting constraints on the B-mode signature of primordial gravitational waves. Its second flight in the 2022-23 season added new receivers at 280 GHz, each using an array of TESs coupled to the sky through feedhorns formed from stacks of silicon wafers. These receivers are optimized to produce deep maps of polarized Galactic dust emission over a large sky area, providing a unique data set with lasting value to the field. In this work, we describe the instrument's performance during SPIDER's second flight., Comment: Submitted to SPIE Astronomical Telescopes + Instrumentation 2024, JATIS

Searching on bipartite graphs serves as a fundamental task for various real-world applications, such as recommendation systems, database retrieval, and document querying. Conventional approaches rely on similarity matching in continuous Euclidean space of vectorized node embeddings. To handle intensive similarity computation efficiently, hashing techniques for graph-structured data have emerged as a prominent research direction. However, despite the retrieval efficiency in Hamming space, previous studies have encountered catastrophic performance decay. To address this challenge, we investigate the problem of hashing with Graph Convolutional Network for effective Top-N search. Our findings indicate the learning effectiveness of incorporating hashing techniques within the exploration of bipartite graph reception fields, as opposed to simply treating hashing as post-processing to output embeddings. To further enhance the model performance, we advance upon these findings and propose Bipartite Graph Contrastive Hashing (BGCH+). BGCH+ introduces a novel dual augmentation approach to both intermediate information and hash code outputs in the latent feature spaces, thereby producing more expressive and robust hash codes within a dual self-supervised learning paradigm. Comprehensive empirical analyses on six real-world benchmarks validate the effectiveness of our dual feature contrastive learning in boosting the performance of BGCH+ compared to existing approaches.

Dose prediction plays a key role in knowledge-based planning (KBP) by automatically generating patient-specific dose distribution. Recent advances in deep learning-based dose prediction methods necessitates collaboration among data contributors for improved performance. Federated learning (FL) has emerged as a solution, enabling medical centers to jointly train deep-learning models without compromising patient data privacy. We developed the FedKBP framework to evaluate the performances of centralized, federated, and individual (i.e. separated) training of dose prediction model on the 340 plans from OpenKBP dataset. To simulate FL and individual training, we divided the data into 8 training sites. To evaluate the effect of inter-site data variation on model training, we implemented two types of case distributions: 1) Independent and identically distributed (IID), where the training and validating cases were evenly divided among the 8 sites, and 2) non-IID, where some sites have more cases than others. The results show FL consistently outperforms individual training on both model optimization speed and out-of-sample testing scores, highlighting the advantage of FL over individual training. Under IID data division, FL shows comparable performance to centralized training, underscoring FL as a promising alternative to traditional pooled-data training. Under non-IID division, larger sites outperformed smaller sites by up to 19% on testing scores, confirming the need of collaboration among data owners to achieve better prediction accuracy. Meanwhile, non-IID FL showed reduced performance as compared to IID FL, posing the need for more sophisticated FL method beyond mere model averaging to handle data variation among participating sites., Comment: Under review by SPIE Medical Imaging 2025 Conference

We study a model for the initial state of the universe based on a gravitational path integral that includes connected geometries which simultaneously produce bra and ket of the wave function. We argue that a natural object to describe this state is the Wigner distribution, which is a function on a classical phase space obtained by a certain integral transform of the density matrix. We work with Lorentzian de Sitter Jackiw-Teitelboim gravity in which we find semiclassical saddle-points for pure gravity, as well as when we include matter components such as a CFT and a classical inflaton field. We also discuss different choices of fixing time reparametrizations. In the regime of large universes our connected geometry dominates over the Hartle-Hawking saddle and gives a distribution that has a meaningful probabilistic interpretation for local observables. It does not, however, give a normalizable probability measure on the entire phase space of the theory.

We introduce Decoded Quantum Interferometry (DQI), a quantum algorithm for reducing classical optimization problems to classical decoding problems by exploiting structure in the Fourier spectrum of the objective function. DQI reduces sparse max-XORSAT to decoding LDPC codes, which can be achieved using powerful classical algorithms such as Belief Propagation (BP). As an initial benchmark, we compare DQI using belief propagation decoding against classical optimization via simulated annealing. In this setting we present evidence that, for a certain family of max-XORSAT instances, DQI with BP decoding achieves a better approximation ratio on average than simulated annealing, although not better than specialized classical algorithms tailored to those instances. We also analyze a combinatorial optimization problem corresponding to finding polynomials that intersect the maximum number of points. There, DQI efficiently achieves a better approximation ratio than any polynomial-time classical algorithm known to us, thus realizing an apparent exponential quantum speedup. Finally, we show that the problem defined by Yamakawa and Zhandry in order to prove an exponential separation between quantum and classical query complexity is a special case of the optimization problem efficiently solved by DQI.

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

Hyperspectral imaging techniques have a unique ability to probe the inhomogeneity of material properties whether driven by compositional variation or other forms of phase segregation. In the doped cuprates, iridates, and related materials, scanning tunneling microscopy/spectroscopy (STM/STS) measurements have found the emergence of pseudogap 'puddles' from the macroscopically Mott insulating phase with increased doping. However, categorizing this hyperspectral data by electronic order is not trivial, and has often been done with ad hoc methods. In this paper we demonstrate the utility of $k$-means, a simple and easy-to-use unsupervised clustering method, as a tool for classifying heterogeneous scanning tunneling spectroscopy data by electronic order for Rh-doped Sr$_2$IrO$_{4}$, a cuprate-like material. Applied to STM data acquired within the Mott phase, $k$-means successfully identified areas of Mott order and of pseudogap order. The unsupervised nature of $k$-means limits avenues for bias, and provides clustered spectral shapes without a priori knowledge of the physics. Additionally, we demonstrate successful use of $k$-means as a preprocessing tool to constrain phenomenological function fitting. Clustering the data allows us to reduce the fitting parameter space, limiting over-fitting. We suggest $k$-means as a fast, simple model for processing hyperspectral data on materials of mixed electronic order., Comment: 6 pages, 4 figures

Detecting exoplanet transits at X-ray wavelengths would provide a window into the effects of high energy irradiation on the upper atmospheres of planets. However, stars are relatively dim in the X-ray, making exoplanet transit detections difficult with current X-ray telescopes. To date, only one exoplanet (HD~189733~b) has an X-ray transit detection. In this study, we investigate the capability of future X-ray observatories to detect more exoplanet transits, focusing on both the NewAthena-WFI instrument and the proposed Advanced X-ray Imaging Satellite (AXIS), which provide more light-collecting power than current instruments. We examined all the transiting exoplanet systems in the NASA Exoplanet Archive and gathered X-ray flux measurements or estimates for each host star. We then predicted the stellar count rates for both AXIS and NewAthena and simulated light curves, using null-hypothesis testing to identify the top 15 transiting planets ranked by potential detection significance. We also evaluate transit detection probabilities when the apparent X-ray radius is enlarged due to atmospheric escape, finding that $\geq 5$ of these planetary systems may be detectable on the $>4\sigma$ level in this scenario. Finally, we note that the assumed host star coronal temperature, which affects the shape of an X-ray transit, can also significantly affect our ability to detect the planet., Comment: Accepted for publication in AAS Journals

We report the measurement of the final-state products of negative pion and muon nuclear capture at rest on argon by the LArIAT experiment at the Fermilab Test Beam Facility. We measure a population of isolated MeV-scale energy depositions, or blips, in 296 LArIAT events containing tracks from stopping low-momentum pions and muons. The average numbers of visible blips are measured to be 0.74 $\pm$ 0.19 and 1.86 $\pm$ 0.17 near muon and pion track endpoints, respectively. The 3.6$\sigma$ statistically significant difference in blip content between muons and pions provides the first demonstration of a new method of pion-muon discrimination in neutrino liquid argon time projection chamber experiments. LArIAT Monte Carlo simulations predict substantially higher average blip counts for negative muon (1.22 $\pm$ 0.08) and pion (2.34 $\pm$ 0.09) nuclear captures. We attribute this difference to Geant4's inaccurate simulation of the nuclear capture process.

We summarise and implement a numerical method for evaluating a numerical method for modelling the frequency dependent power-law absorption within ultrasound using the first order linear wave equations with a loss taking the form of a fractional time derivative. The (Caputo) fractional time derivative requires the full problem history which is contained within an iterative procedure with the resulting numerical method requiring a static memory at across all time steps without loss of accuracy. The Spatial domain is treated by the Fourier k-space method, with derivatives on a uniform grid. Numerically comparisons are made against a model for the same power-law absorption with loss described by the fractional- Laplacian operator. One advantage of the fractional time derivative over the Fractional Laplacian is the local treatment of the power-law, allowing for a spatially varying frequency power-law.

Artificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these methods have been shown to be subject to race bias, i.e. they exhibit different levels of performance for different races depending on the (im)balance of the data used to train the AI model. In this paper we investigate the source of this bias, seeking to understand its root cause(s) so that it can be effectively mitigated. We perform a series of classification and segmentation experiments on short-axis cine CMR images acquired from Black and White subjects from the UK Biobank and apply AI interpretability methods to understand the results. In the classification experiments, we found that race can be predicted with high accuracy from the images alone, but less accurately from ground truth segmentations, suggesting that the distributional shift between races, which is often the cause of AI bias, is mostly image-based rather than segmentation-based. The interpretability methods showed that most attention in the classification models was focused on non-heart regions, such as subcutaneous fat. Cropping the images tightly around the heart reduced classification accuracy to around chance level. Similarly, race can be predicted from the latent representations of a biased segmentation model, suggesting that race information is encoded in the model. Cropping images tightly around the heart reduced but did not eliminate segmentation bias. We also investigate the influence of possible confounders on the bias observed.

Vision-language-action models have gained significant attention for their ability to model trajectories in robot learning. However, most existing models rely on Transformer models with vanilla causal attention, which we find suboptimal for processing segmented multi-modal sequences. Additionally, the autoregressive generation approach falls short in generating multi-dimensional actions. In this paper, we introduce Actra, an optimized Transformer architecture featuring trajectory attention and learnable action queries, designed for effective encoding and decoding of segmented vision-language-action trajectories in robot imitation learning. Furthermore, we devise a multi-modal contrastive learning objective to explicitly align different modalities, complementing the primary behavior cloning objective. Through extensive experiments conducted across various environments, Actra exhibits substantial performance improvement when compared to state-of-the-art models in terms of generalizability, dexterity, and precision.

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

Quantum information processing with neutral atoms relies on Rydberg excitation for entanglement generation. While the use of heavy divalent or open-shell elements, such as strontium or ytterbium, has benefits due to their optically active core and a variety of possible qubit encodings, their Rydberg structure is generally complex. For some isotopes in particular, hyperfine interactions are relevant even for highly excited electronic states. We employ multi-channel quantum defect theory to infer the Rydberg structure of isotopes with non-zero nuclear spin and perform non-perturbative Rydberg-pair interaction calculations. We find that due to the high level density and sensitivities to external fields, experimental parameters must be precisely controlled. Specifically in ${}^{87}$Sr, we study an intrinsic F\"orster resonance, unique to divalent atoms with hyperfine-split thresholds, which simultaneously provides line stability with respect to external field fluctuations and enhanced long-range interactions. Additionally, we provide parameters for pair states that can be effectively described by single-channel Rydberg series. The explored pair states provide exciting opportunities for applications in the blockade regime as well as for more exotic long-range interactions such as largely flat, distance-independent potentials., Comment: 12 pages, 7 figures

We present shadow Hamiltonian simulation, a framework for simulating quantum dynamics using a compressed quantum state that we call the "shadow state". The amplitudes of this shadow state are proportional to the expectations of a set of operators of interest. The shadow state evolves according to its own Schr\"odinger equation, and under broad conditions can be simulated on a quantum computer. We analyze a number of applications of this framework to quantum simulation problems. This includes simulating the dynamics of exponentially large systems of free fermions, or exponentially large systems of free bosons, the latter example recovering a recent algorithm for simulating exponentially many classical harmonic oscillators. Shadow Hamiltonian simulation can be extended to simulate expectations of more complex operators such as two-time correlators or Green's functions, and to study the evolution of operators themselves in the Heisenberg picture.

To enhance the spatial resolution of the M\"ONCH 25 \textmu m pitch hybrid pixel detector, deep learning models have been trained using both simulation and measurement data. Challenges arise when comparing simulation-based deep learning models to measurement-based models for electrons, as the spatial resolution achieved through simulations is notably inferior to that from measurements. Discrepancies are also observed when directly comparing X-ray simulations with measurements, particularly in the spectral output of single pixels. These observations collectively suggest that current simulations require optimization. To address this, a Monte Carlo simulation was conducted to model the dynamics of charge carriers within the silicon sensor, aiming to refine charge transport modeling. The simulation encompasses the initial generation of the charge cloud, charge cloud drift, charge diffusion and repulsion, and electronic noise. The simulation results were validated with measurements from the M\"ONCH detector for X-rays, and the agreement between measurements and simulations was significantly improved by accounting for the charge repulsion., Comment: Prepared for submission to JINST as a proceeding for 25th International Workshops on Radiation Imaging Detectors

As consumer flexibility becomes expected, it is important that the market mechanisms which attain that flexibility are perceived as fair. We set out fairness issues in energy markets today, and propose a market design to address them. Consumption is categorised as either essential or flexible with different prices and reliability levels for each. Prices are generated by an Automatic Market Maker (AMM) based on instantaneous scarcity and resource is allocated using a novel Fair Play algorithm. We empirically show the performance of the system over 1 year for 101 UK households and benchmark its performance against more classical approaches., Comment: Under review for inclusion in Special Issue of Applied Energy on `(R)Evolution of Electricity Markets: Designing Smart Electricity Markets for a Decarbonized World'

With an increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate high-resolution wind data. Conventional downscaling methods for generating these data have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method, using generative adversarial networks (GANs), for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). We achieve results comparable in historical accuracy and spatiotemporal variability to conventional downscaling by training a GAN model with ERA5 low-resolution input and high-resolution targets from the Wind Integration National Dataset, while reducing computational costs over dynamical downscaling by two orders of magnitude. Spatiotemporal cross-validation shows low error and high correlations with observations and excellent agreement with holdout data across distributions of physical metrics. We apply this approach to downscale 30-km hourly ERA5 data to 2-km 5-minute wind data for January 2000 through December 2023 at multiple hub heights over Eastern Europe. Uncertainty is estimated over the period with observational data by additionally downscaling the members of the European Centre for Medium-Range Weather Forecasting Ensemble of Data Assimilations. Comparisons against observational data from the Meteorological Assimilation Data Ingest System and multiple wind farms show comparable performance to the CONUS validation. This 24-year data record is the first member of the super resolution for renewable energy resource data with wind from reanalysis data dataset (Sup3rWind)., Comment: 58 pages, 31 figures

We study heuristic algorithms for job shop scheduling problems. We compare classical approaches, such as the shifting bottleneck heuristic with novel strategies using decision diagrams. Balas' local refinement is used to improve feasible solutions. Heuristic approaches are combined with Mixed Integer Programming and Constraint Programming approaches. We discuss our results via computational experiments.

Critical parameters are the key to superconductivity research, and reliable instrumentations can facilitate the study. Traditionally, one has to use several different measurement techniques to measure critical parameters separately. In this work, we develop the use of a single species of quantum sensor to determine and estimate several critical parameters with the help of independent simulation data. We utilize the nitrogen-vacancy (NV) center in the diamond, which recently emerged as a promising candidate for probing exotic features in condensed matter physics. The non-invasive and highly stable nature provides extraordinary opportunities to solve scientific problems in various systems. Using a high-quality single-crystalline YBa$_{2}$Cu$_{4}$O$_{8}$ (YBCO) as a platform, we demonstrate the use of diamond particles and a bulk diamond to probe the Meissner effect. The evolution of the vector magnetic field, the $H-T$ phase diagram, and the map of fluorescence contour are studied via NV sensing. Our results reveal different critical parameters, including lower critical field $H_{c1}$, upper critical field $H_{c2}$, and critical current density $j_{c}$, as well as verifying the unconventional nature of this high-temperature superconductor YBCO. Therefore, NV-based quantum sensing techniques have huge potential in condensed matter research.

We consider the road-field reaction-diffusion model introduced by Berestycki, Roquejoffre, and Rossi. By performing a "thin-front limit," we are able to deduce a Hamilton-Jacobi equation with a suitable effective Hamiltonian on the road that governs the front location of the road-field model. Our main motivation is to apply the theory of strong (flux-limited) viscosity solutions in order to determine a control formulation interpretation of the front location. In view of the ecological meaning of the road-field model, this is natural as it casts the invasion problem as one of finding optimal paths that balance the positive growth rate in the field with the fast diffusion on the road. Our main contribution is a nearly complete picture of the behavior on two-road conical domains. When the diffusivities on each road are the same, we show that the propagation speed in each direction in the cone can be computed via those associated with one-road half-space problem. When the diffusivities differ, we show that the speed along the faster road is unchanged, while the speed along the slower road can be enhanced. Along the way we provide a new proof of known results on the one-road half-space problem via our approach., Comment: 50 pages, 7 figures

The $^{244}$Pu($^{50}$Ti,$xn$)$^{294-x}$Lv reaction was investigated at Lawrence Berkeley National Laboratory's 88-Inch Cyclotron facility. The experiment was aimed at the production of a superheavy element with $Z\ge 114$ by irradiating an actinide target with a beam heavier than $^{48}$Ca. Produced Lv ions were separated from the unwanted beam and nuclear reaction products using the Berkeley Gas-filled Separator and implanted into a newly commissioned focal plane detector system. Two decay chains were observed and assigned to the decay of $^{290}$Lv. The production cross section was measured to be $\sigma_{\rm prod}=0.44(^{+58}_{-28})$~pb at a center-of-target center-of-mass energy of 220(3)~MeV. This represents the first published measurement of the production of a superheavy element near the `Island-of-Stability', with a beam of $^{50}$Ti and is an essential precursor in the pursuit of searching for new elements beyond $Z=118$., Comment: Submitted to Physical Review Letters

We investigate the possibility that primordial black holes (PBHs) can be formed from large curvature perturbations generated during the waterfall phase transition in a supersymmetric scenario where sneutrino is the inflaton in a hybrid inflationary framework. We obtain a spectral index ($n_s \simeq 0.966$), and a tensor-to-scalar ratio ($r\simeq 0.0056-10^{-11}$), consistent with the current Planck data satisfying PBH as dark matter (DM) and detectable Gravitational Wave (GW) signal. Our findings show that the mass of PBH and the peak in the GW spectrum is correlated with the right-handed (s)neutrino mass. We identify parameter space where PBHs can be the entire DM candidate of the universe (with mass $10^{-13}\, M_\odot$) or a fraction of it. This can be tested in future observatories, for example, with amplitude $\Omega_{\rm GW}h^2$ $\sim 10^{-9}$ and peak frequency $f\sim 0.1$ Hz in LISA and $\Omega_{\rm GW}h^2 \sim 10^{-11}$ and peak frequency of $\sim 10$ Hz in ET via second-order GW signals. We study two models of sneutrino inflation: Model$-1$ involves canonical sneutrino kinetic term which predicts the sub-Planckian mass parameter $M$, while the coupling between a gauge singlet and the waterfall field, $\beta$, needs to be quite large whereas, for the model$-2$ involving $\alpha-$attractor canonical sneutrino kinetic term, $\beta$ can take a natural value. Estimating explicitly, we show that both models have mild fine-tuning. We also derive an analytical expression for the power spectrum in terms of the microphysics parameters of the model like (s)neutrino mass, etc. that fits well with the numerical results. The typical reheat temperature for both the models is around $10^{7}-10^{8}$~GeV suitable for non-thermal leptogenesis., Comment: In the revised version texts modified, all results including figures and conclusions remain intact

Purpose: To describe OCTolyzer: an open-source toolkit for retinochoroidal analysis in optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) images. Method: OCTolyzer has two analysis suites, for SLO and OCT images. The former enables anatomical segmentation and feature measurement of the en face retinal vessels. The latter leverages image metadata for retinal layer segmentations and deep learning-based choroid layer segmentation to compute retinochoroidal measurements such as thickness and volume. We introduce OCTolyzer and assess the reproducibility of its OCT analysis suite for choroid analysis. Results: At the population-level, choroid region metrics were highly reproducible (Mean absolute error/Pearson/Spearman correlation for macular volume choroid thickness (CT):6.7$\mu$m/0.9933/0.9969, macular B-scan CT:11.6$\mu$m/0.9858/0.9889, peripapillary CT:5.0$\mu$m/0.9942/0.9940). Macular choroid vascular index (CVI) had good reproducibility (volume CVI:0.0271/0.9669/0.9655, B-scan CVI:0.0130/0.9090/0.9145). At the eye-level, measurement error in regional and vessel metrics were below 5% and 20% of the population's variability, respectively. Major outliers were from poor quality B-scans with thick choroids and invisible choroid-sclera boundary. Conclusions: OCTolyzer is the first open-source pipeline to convert OCT/SLO data into reproducible and clinically meaningful retinochoroidal measurements. OCT processing on a standard laptop CPU takes under 2 seconds for macular or peripapillary B-scans and 85 seconds for volume scans. OCTolyzer can help improve standardisation in the field of OCT/SLO image analysis and is freely available here: https://github.com/jaburke166/OCTolyzer., Comment: Main paper: 15 pages, 8 figures, 3 tables. Supplementary material: 6 pages, 6 figures, 6 tables. Submitted to "New Frontiers in Optical Coherence Tomography" Special Issue at ARVO Translational Vision Science & Technology