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Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential of LVLMs to achieve high performance in medical imaging use cases (e.g., radiology report generation), but there remain barriers that hinder the ability to deploy these solutions broadly. These include the cost of extensive computational requirements for developing large scale models, expertise in the development of sophisticated AI models, and the difficulty in accessing substantially large, high-quality datasets that adequately represent the population in which the LVLM solution is to be deployed. The HOPPR Medical-Grade Platform addresses these barriers by providing powerful computational infrastructure, a suite of foundation models on top of which developers can fine-tune for their specific use cases, and a robust quality management system that sets a standard for evaluating fine-tuned models for deployment in clinical settings. The HOPPR Platform has access to millions of imaging studies and text reports sourced from hundreds of imaging centers from diverse populations to pretrain foundation models and enable use case-specific cohorts for fine-tuning. All data are deidentified and securely stored for HIPAA compliance. Additionally, developers can securely host models on the HOPPR platform and access them via an API to make inferences using these models within established clinical workflows. With the Medical-Grade Platform, HOPPR's mission is to expedite the deployment of LVLM solutions for medical imaging and ultimately optimize radiologist's workflows and meet the growing demands of the field., Comment: 6 pages, 3 figures

Foundation models are trained on massive amounts of data to distinguish complex patterns and can be adapted to a wide range of downstream tasks with minimal computational resources. Here, we develop a foundation model for prostate cancer digital pathology called HistoEncoder by pre-training on 48 million prostate tissue tile images. We demonstrate that HistoEncoder features extracted from tile images with similar histological patterns map closely together in the feature space. HistoEncoder outperforms models pre-trained with natural images, even without fine-tuning or with 1000 times less training data. We describe two use cases that leverage the capabilities of HistoEncoder by fine-tuning the model with a limited amount of data and computational resources. First, we show how HistoEncoder can be used to automatically annotate large-scale datasets with high accuracy. Second, we combine histomics with commonly used clinical nomograms, significantly improving prostate cancer-specific death survival models. Foundation models such as HistoEncoder can allow organizations with limited resources to build effective clinical software tools without needing extensive datasets or significant amounts of computing.

Offline reinforcement learning can enable policy learning from pre-collected, sub-optimal datasets without online interactions. This makes it ideal for real-world robots and safety-critical scenarios, where collecting online data or expert demonstrations is slow, costly, and risky. However, most existing offline RL works assume the dataset is already labeled with the task rewards, a process that often requires significant human effort, especially when ground-truth states are hard to ascertain (e.g., in the real-world). In this paper, we build on prior work, specifically RL-VLM-F, and propose a novel system that automatically generates reward labels for offline datasets using preference feedback from a vision-language model and a text description of the task. Our method then learns a policy using offline RL with the reward-labeled dataset. We demonstrate the system's applicability to a complex real-world robot-assisted dressing task, where we first learn a reward function using a vision-language model on a sub-optimal offline dataset, and then we use the learned reward to employ Implicit Q learning to develop an effective dressing policy. Our method also performs well in simulation tasks involving the manipulation of rigid and deformable objects, and significantly outperform baselines such as behavior cloning and inverse RL. In summary, we propose a new system that enables automatic reward labeling and policy learning from unlabeled, sub-optimal offline datasets., Comment: 7 pages. Accepted at the LangRob Workshop 2024 @ CoRL, 2024

Human excrement composting (HEC) is a sustainable strategy for human excrement (HE) management that recycles nutrients and mitigates health risks while reducing reliance on freshwater, fossil fuels, and fertilizers. We present a comprehensive microbial time series analysis of HEC and show that the initial gut-like microbiome of HEC systems transitions to a microbiome similar to soil and traditional compost in fifteen biological replicates tracked weekly for one year., Comment: Main text: 9 pages, 2 figures; Extended data: 10 figures; Supplemental Text: 32 pages, 8 figures, 2 tables

This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms. The ability of an EMG classifier to handle inputs drawn from a different distribution than the training distribution is critical for real-world deployment as a control interface. By predicting the user's intended gesture using EMG signals, we can create a wearable solution to control assistive technologies, such as computers, prosthetics, and mobile manipulator robots. This new out-of-distribution benchmark consists of two major tasks that have utility for building robust and adaptable control interfaces: 1) intersubject classification and 2) adaptation using train-test splits for time-series. This benchmark spans nine datasets--the largest collection of EMG datasets in a benchmark. Among these, a new dataset is introduced, featuring a novel, easy-to-wear high-density EMG wearable for data collection. The lack of open-source benchmarks has made comparing accuracy results between papers challenging for the EMG research community. This new benchmark provides researchers with a valuable resource for analyzing practical measures of out-of-distribution performance for EMG datasets. Our code and data from our new dataset can be found at emgbench.github.io.

Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who are unable to complete activities of daily living (ADLs). Speech interfaces, especially ones that utilize Large Language Models (LLMs), can enable individuals to effectively and naturally communicate high-level commands and nuanced preferences to robots. In this work, we demonstrate an LLM-based speech interface for a commercially available assistive feeding robot. Our system is based on an iteratively designed framework, from the paper "VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots," that incorporates human-centric elements for integrating LLMs as interfaces for robots. It has been evaluated through a user study with 11 older adults at an independent living facility. Videos are located on our project website: https://sites.google.com/andrew.cmu.edu/voicepilot/.

The time delay between multiple images of strongly lensed quasars has been used to infer the Hubble constant. The primary systematic uncertainty for time-delay cosmography is the mass-sheet transform (MST), which preserves the lensing observables while altering the inferred $H_0$. The TDCOSMO collaboration used velocity dispersion measurements of lensed quasars and lensed galaxies to infer that mass sheets are present, which decrease the inferred $H_0$ by 8$\%$. Here, we test the assumption that the density profiles of galaxy-galaxy and galaxy-quasar lenses are the same. We use a composite star-plus-dark-matter mass profile for the parent deflector population and model the selection function for galaxy-galaxy and galaxy-quasar lenses. We find that a power-law density profile with an MST is a good approximation to a two-component mass profile around the Einstein radius, but we find that galaxy-galaxy lenses have systematically higher mass-sheet components than galaxy-quasar lenses. For individual systems, $\lambda_\mathrm{int}$ correlates with the ratio of the half-light radius and Einstein radius of the lens. By propagating these results through the TDCOSMO methodology, we find that $H_0$ is lowered by a further $\sim$3\%. Using the velocity dispersions from \citet{slacs9} and our fiducial model for selection biases, we infer $H_0 = 66\pm4 \ \mathrm{(stat)} \pm 1 \ \mathrm{(model \ sys)} \pm 2 \ \mathrm{(measurement \ sys)} \ \mathrm{km} \ \mathrm{s}^{-1} \ \mathrm{Mpc}^{-1}$ for the TDCOSMO plus SLACS dataset. The first residual systematic error is due to plausible alternative choices in modeling the selection function, and the second is an estimate of the remaining systematic error in the measurement of velocity dispersions for SLACS lenses. Accurate time-delay cosmography requires precise velocity dispersion measurements and accurate calibration of selection biases.

This paper presents an innovative framework for analyzing the regularity of solutions to the stochastic Navier-Stokes equations by integrating Sobolev-Besov hybrid spaces with fractional operators and quantum-inspired dynamics. We propose new regularity theorems that address the multiscale and chaotic nature of fluid flows, offering novel insights into energy dissipation mechanisms. The introduction of a Schr\"odinger-like operator into the fluid dynamics model captures quantum-scale turbulence effects, enhancing our understanding of energy redistribution across different scales. The results also include the development of anisotropic stochastic models that account for direction-dependent viscosity, improving the representation of real-world turbulent flows. These advances in stochastic modeling and regularity analysis provide a comprehensive toolset for tackling complex fluid dynamics problems. The findings are applicable to fields such as engineering, quantum turbulence, and environmental sciences. Future directions include the numerical implementation of the proposed models and the use of machine learning techniques to optimize parameters for enhanced simulation accuracy., Comment: 14 pages

Strongly lensed quasars can be used to constrain cosmological parameters through time-delay cosmography. Models of the lens masses are a necessary component of this analysis. To enable time-delay cosmography from a sample of $\mathcal{O}(10^3)$ lenses, which will soon become available from surveys like the Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Euclid Wide Survey, we require fast and standardizable modeling techniques. To address this need, we apply neural posterior estimation (NPE) for modeling galaxy-scale strongly lensed quasars from the Strong Lensing Insights into the Dark Energy Survey (STRIDES) sample. NPE brings two advantages: speed and the ability to implicitly marginalize over nuisance parameters. We extend this method by employing sequential NPE to increase precision of mass model posteriors. We then fold individual lens models into a hierarchical Bayesian inference to recover the population distribution of lens mass parameters, accounting for out-of-distribution shift. After verifying our method using simulated analogs of the STRIDES lens sample, we apply our method to 14 Hubble Space Telescope single-filter observations. We find the population mean of the power-law elliptical mass distribution slope, $\gamma_{\text{lens}}$, to be $\mathcal{M}_{\gamma_{\text{lens}}}=2.13 \pm 0.06$. Our result represents the first population-level constraint for these systems. This population-level inference from fully automated modeling is an important stepping stone towards cosmological inference with large samples of strongly lensed quasars.

Visual correspondence is a crucial step in key computer vision tasks, including camera localization, image registration, and structure from motion. The most effective techniques for matching keypoints currently involve using learned sparse or dense matchers, which need pairs of images. These neural networks have a good general understanding of features from both images, but they often struggle to match points from different semantic areas. This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. Therefore, the learned descriptors do not require image pairs at inference time, allowing feature caching and fast matching using similarity search, unlike learned matchers. We present adapted versions of six existing descriptors, with an average increase in performance of 29% in camera localization, with comparable accuracy to existing matchers as LightGlue and LoFTR in two existing benchmarks. Both code and trained models are available at https://www.verlab.dcc.ufmg.br/descriptors/reasoning_accv24, Comment: Accepted in ACCV 2024

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Shared autonomy holds promise for improving the usability and accessibility of assistive robotic arms, but current methods often rely on costly expert demonstrations and lack the ability to adapt post-deployment. This paper introduces ILSA, an Incrementally Learned Shared Autonomy framework that continually improves its assistive control policy through repeated user interactions. ILSA leverages synthetic kinematic trajectories for initial pretraining, reducing the need for expert demonstrations, and then incrementally finetunes its policy after each manipulation interaction, with mechanisms to balance new knowledge acquisition with existing knowledge retention during incremental learning. We validate ILSA for complex long-horizon tasks through a comprehensive ablation study and a user study with 20 participants, demonstrating its effectiveness and robustness in both quantitative performance and user-reported qualitative metrics. Code and videos are available at https://ilsa-robo.github.io/.

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but it requires nonvolatile photonic memory capable of rapid updates during on-chip training sessions or when new information becomes available during deployment. Phase-change materials (PCMs) are promising for providing compact, nonvolatile optical weighting; however, they face limitations in terms of bit precision, programming speed, and cycling endurance. Here, we propose a novel photonic memory cell that merges nonvolatile photonic weighting using PCMs with high-speed, volatile tuning enabled by an integrated PN junction. Our experiments demonstrate that the same PN modulator, fabricated via a foundry compatible process, can achieve dual functionality. It supports coarse programmability for setting initial optical weights and facilitates high-speed fine-tuning to adjust these weights dynamically. The result showcases a 400-fold increase in volatile tuning speed and a 10,000-fold enhancement in efficiency. This multifunctional photonic memory with volatile and nonvolatile capabilities could significantly advance the performance and versatility of photonic memory cells, providing robust solutions for dynamic computing environments.

Eating disorders are complex mental health conditions that affect millions of people around the world. Effective interventions on social media platforms are crucial, yet testing strategies in situ can be risky. We present a novel LLM-driven experimental testbed for simulating and assessing intervention strategies in ED-related discussions. Our framework generates synthetic conversations across multiple platforms, models, and ED-related topics, allowing for controlled experimentation with diverse intervention approaches. We analyze the impact of various intervention strategies on conversation dynamics across four dimensions: intervention type, generative model, social media platform, and ED-related community/topic. We employ cognitive domain analysis metrics, including sentiment, emotions, etc., to evaluate the effectiveness of interventions. Our findings reveal that civility-focused interventions consistently improve positive sentiment and emotional tone across all dimensions, while insight-resetting approaches tend to increase negative emotions. We also uncover significant biases in LLM-generated conversations, with cognitive metrics varying notably between models (Claude-3 Haiku $>$ Mistral $>$ GPT-3.5-turbo $>$ LLaMA3) and even between versions of the same model. These variations highlight the importance of model selection in simulating realistic discussions related to ED. Our work provides valuable information on the complex dynamics of ED-related discussions and the effectiveness of various intervention strategies., Comment: 9 pages, 5 figures

Given a finite simple graph $G$, let $\operatorname{M}(G)$ denote its 2-distance graph, in which two vertices are adjacent if and only if they have distance 2 in $G$. In this paper, we consider the periodic behavior of the sequence $G, \operatorname{M}(G), \operatorname{M}^2(G), \operatorname{M}^3(G), \ldots$ obtained by iterating the 2-distance operation. In particular, we classify the connected graphs with period 3, and we partially characterize those with period 2. We then study two families of graphs whose 2-distance sequence is eventually periodic: namely, generalized Petersen graphs and complete $m$-ary trees. For each family, we show that the eventual period is 2, and we determine the pre-period and the two limit graphs of the sequence., Comment: 34 pages, 13 figures

Translating written sentences from oral languages to a sequence of manual and non-manual gestures plays a crucial role in building a more inclusive society for deaf and hard-of-hearing people. Facial expressions (non-manual), in particular, are responsible for encoding the grammar of the sentence to be spoken, applying punctuation, pronouns, or emphasizing signs. These non-manual gestures are closely related to the semantics of the sentence being spoken and also to the utterance of the speaker's emotions. However, most Sign Language Production (SLP) approaches are centered on synthesizing manual gestures and do not focus on modeling the speakers expression. This paper introduces a new method focused in synthesizing facial expressions for sign language. Our goal is to improve sign language production by integrating sentiment information in facial expression generation. The approach leverages a sentence sentiment and semantic features to sample from a meaningful representation space, integrating the bias of the non-manual components into the sign language production process. To evaluate our method, we extend the Frechet Gesture Distance (FGD) and propose a new metric called Frechet Expression Distance (FED) and apply an extensive set of metrics to assess the quality of specific regions of the face. The experimental results showed that our method achieved state of the art, being superior to the competitors on How2Sign and PHOENIX14T datasets. Moreover, our architecture is based on a carefully designed graph pyramid that makes it simpler, easier to train, and capable of leveraging emotions to produce facial expressions.

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Cooling of atomic motion is a crucial tool for many branches of atomic physics, ranging from fundamental physics explorations to quantum information and sensing. For trapped ions, electromagnetically-induced-transparency (EIT) cooling has received attention for the relative speed, low laser power requirements, and broad cooling bandwidth of the technique. However, in applications where the ion used for cooling has hyperfine structure to enable long coherence times, it is difficult to find a closed three-level system in which to perform standard EIT cooling. Here, we demonstrate successful EIT cooling on 25Mg+ by the addition of an extra laser frequency; this method can be applied to any ion with non-zero nuclear spin. Furthermore, we demonstrate simultaneous EIT cooling of all axial modes in mixed-species crystals 9Be+ - 25Mg+ and 9Be+ - 25Mg+ - 9Be+ through the 25Mg+ ion., Comment: 10 pages, 6 figures, 2 tables

Trust is a key factor in ensuring acceptable human-robot interaction, especially in settings where robots may be assisting with critical activities of daily living. When practically deployed, robots are bound to make occasional mistakes, yet the degree to which these errors will impact a care recipient's trust in the robot, especially in performing physically assistive tasks, remains an open question. To investigate this, we conducted experiments where participants interacted with physically assistive robots which would occasionally make intentional mistakes while performing two different tasks: bathing and feeding. Our study considered the error response of two populations: younger adults at a university (median age 26) and older adults at an independent living facility (median age 83). We observed that the impact of errors on a users' trust in the robot depends on both their age and the task that the robot is performing. We also found that older adults tend to evaluate the robot on factors unrelated to the robot's performance, making their trust in the system more resilient to errors when compared to younger adults. Code and supplementary materials are available on our project webpage., Comment: 8 pages, 5 figures, in proceedings for IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) 2024

We construct a relativistic quantum communication channel between two localized qubit systems, mediated by a relativistic quantum field, that can achieve the theoretical maximum for the quantum capacity in arbitrary curved spacetimes using the Unruh-DeWitt detector formalism. Using techniques from algebraic quantum field theory, we express the quantum capacity of the quantum communication channel purely in terms of the correlation functions of the field and the causal propagator for the wave equation. Consequently, the resulting quantum channel, and hence the quantum capacity, are by construction manifestly covariant, respect the causal structure of spacetime, and are independent of the details of the background geometry, topology, and the choice of Hilbert space (quasifree) representations of the field., Comment: 15+4 pages, 1 figure; revtex4-2; v3: fixed typos and added clarifications

The symmetric group $\mathfrak{S}_n$ (and more generally, any Coxeter group) admits an associative operation known as the Demazure product. In this paper, we first extend the Demazure product to the (infinite) set of all biwords on $\{1, \ldots, n\}$, or equivalently, the set of all $n \times n$ nonnegative integer matrices. We define this product diagrammatically, via braid-like graphs we call kelp beds, since they significantly generalize the seaweeds introduced by Tiskin (2015). Our motivation for this extended Demazure product arises from optimization theory, in particular the semigroup of all $(n+1) \times (n+1)$ simple nonnegative integer Monge matrices equipped with the distance (i.e., min-plus) product. As our main result, we show that this semigroup of Monge matrices is isomorphic to the semigroup of biwords equipped with the extended Demazure product. We exploit this isomorphism to write down generating functions for the growth series of the Monge matrices with respect to certain natural matrix norms., Comment: 18 pages

The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on rigid objects cannot fully address. The increasing interest within the community in textile perception and manipulation has led to new methods that aim to address challenges in modeling, perception, and control, resulting in significant progress. However, this progress is often tailored to one specific textile or a subcategory of these textiles. To understand what restricts these methods and hinders current approaches from generalizing to a broader range of real-world textiles, this review provides an overview of the field, focusing specifically on how and to what extent textile variations are addressed in modeling, perception, benchmarking, and manipulation of textiles. We finally conclude by identifying key open problems and outlining grand challenges that will drive future advancements in the field., Comment: 30 pages, 3 figures, 2 tables. Submitted to Annual Review of Control, Robotics, and Autonomous Systems

The PROSPECT experiment is designed to perform precise searches for antineutrino disappearance at short distances (7 - 9~m) from compact nuclear reactor cores. This Letter reports results from a new neutrino oscillation analysis performed using the complete data sample from the PROSPECT-I detector operated at the High Flux Isotope Reactor in 2018. The analysis uses a multi-period selection of inverse beta decay neutrino interactions with reduced backgrounds and enhanced statistical power to set limits on electron-flavor disappearance caused by mixing with sterile neutrinos with 0.2 - 20 eV$^2$ mass splittings. Inverse beta decay positron energy spectra from six different reactor-detector distance ranges are found to be statistically consistent with one another, as would be expected in the absence of sterile neutrino oscillations. The data excludes at 95% confidence level the existence of sterile neutrinos in regions above 3~eV$^2$ previously unexplored by terrestrial experiments, including all space below 10~eV$^2$ suggested by the recently strengthened Gallium Anomaly. The best-fit point of the Neutrino-4 reactor experiment's claimed observation of short-baseline oscillation is ruled out at more than five standard deviations., Comment: 6 pages, 4 figures

We produce a flat $\Lambda$-module of $\Lambda$-adic critical slope overconvergent modular forms, producing a Hida-type theory that interpolates such forms over $p$-adically varying integer weights. This provides a Hida-theoretic explanation for an observation of Coleman that the rank of such forms is locally constant in the weight. The key to the interpolation is to use Coleman's presentation of de Rham cohomology in terms of overconvergent forms to link critical slope overconvergent modular forms with the ordinary part of 1st coherent cohomology of modular curves interpolated by Boxer--Pilloni's higher Hida theory. We also set up a Galois deformation theory designed to conform to these critical overconvergent forms and prove "$R = T$" for it. As applications, we (1) produce a "formal" $\Lambda$-adic interpolation of the ordinary part of de Rham cohomology, (2) produce a bi-ordinary complex whose Hecke algebra $T$ is a natural candidate for an "$R = T$" theorem where $R$ is a deformation ring for 2-dimensional $p$-adic representations of ${\rm Gal}(\bar{\bf Q}/{\bf Q})$ that become reducible and decomposable upon restriction to a decomposition group at $p$, (3) produce a degree-shifting Hecke action on the cohomology of the bi-ordinary complex, and (4) specialize this degree-shifting action to weight 1 and apply the critical "$R = T$" theorem to find, under a supplemental assumption, an action of a Stark unit on the part of weight 1 coherent cohomology over ${\bf Z}_p$ that is isotypic for an ordinary eigenform with complex multiplication., Comment: 72 pages

The PROSPECT-I detector has several features that enable measurement of the direction of a compact neutrino source. In this paper, a detailed report on the directional measurements made on electron antineutrinos emitted from the High Flux Isotope Reactor is presented. With an estimated true neutrino (reactor to detector) direction of $\phi = 40.8\unicode{xB0} \pm 0.7\unicode{xB0}$ and $\theta = 98.6\unicode{xB0} \pm 0.4\unicode{xB0}$, the PROSPECT-I detector is able to reconstruct an average neutrino direction of $\phi = 39.4\unicode{xB0} \pm 2.9\unicode{xB0}$ and $\theta = 97.6\unicode{xB0} \pm 1.6\unicode{xB0}$. This measurement is made with approximately 48000 Inverse Beta Decay signal events and is the most precise directional reconstruction of reactor antineutrinos to date.

The earth mover's distance (EMD), also known as the 1-Wasserstein metric, measures the minimum amount of work required to transform one probability distribution into another. The EMD can be naturally generalized to measure the "distance" between any number (say $d$) of distributions. In previous work (2021), we found a recursive formula for the expected value of the generalized EMD, assuming the uniform distribution on the standard $n$-simplex. This recursion, however, was computationally expensive, requiring $\binom{d+n}{d}$ many iterations. The main result of the present paper is a nonrecursive formula for this expected value, expressed as the integral of a certain polynomial of degree at most $dn$. As a secondary result, we resolve an unanswered problem by giving a formula for the generalized EMD in terms of pairwise EMDs; this can be viewed as an analogue of the Cayley-Menger determinant formula that gives the hypervolume of a simplex in terms of its edge lengths., Comment: 20 pages; the second half of this paper supersedes my preprint arXiv:2306.12030, and thus there is some overlap of exposition

For Cartan geometries admitting automorphisms with isotropies satisfying a particular, loosely dynamical property on their model geometries, we demonstrate the existence of an open subset of the geometry with trivial holonomy. This property, which generalizes characteristics of isotropies corresponding to isolated higher-order fixed points in parabolic geometries that are known to require a nearby open subset to have vanishing curvature, only relies upon the behavior of the isotropy in the model geometry, and therefore applies regardless of initial curvature assumptions, such as regularity or normality. Along the way to proving our main results, we also derive a couple of results for working with holonomy, relating to limits of sequences of developments and the existence of antidevelopments, that are useful in their own right. To showcase the effectiveness of the techniques developed, we use them to completely characterize all almost c-projective and almost quaternionic structures that admit a nontrivial automorphism with a higher-order fixed point, as well as all nondegenerate partially integrable almost CR structures that admit a higher-order fixed point with non-null isotropy., Comment: 27 pages, 4 figures. Simplifies results from original version of arXiv:2303.00561

We introduce sets $\mathcal{R}(\sigma)$ consisting of semistandard tableaux of shape $\sigma$, subject to a certain restriction on the initial column. These $\mathcal{R}(\sigma)$ are generalizations of the rational, symplectic, or orthogonal tableaux (which furnish a weight basis for the irreducible finite-dimensional representations $U_\sigma$ of the classical groups $H = \operatorname{GL}_k$, $\operatorname{Sp}_{2k}$, or $\operatorname{O}_k$, respectively). As our main result, we show that the cardinality of $\mathcal{R}(\sigma)$ gives the Bernstein degree (i.e., multiplicity) of the modules of covariants (of type $U_\sigma$) of each classical group $H$. Via Howe duality, these modules can also be viewed as $(\mathfrak{g},K)$-modules of unitary highest weight representations of a real reductive group $G_{\mathbb{R}}$. Notably, our result using $\mathcal{R}(\sigma)$ is valid regardless of the rank of $H$, whereas the previous result of Nishiyama-Ochiai-Taniguchi (expressing the Bernstein degree in terms of $\dim U_\sigma$) holds only when $k$ is at most the real rank of $G_{\mathbb{R}}$. In effect, as $k$ increases beyond this range, $|\mathcal{R}(\sigma)|$ interpolates between $\dim U_\sigma$ and the dimension of a certain "limiting" $K$-module., Comment: 16 pages; certain parts of the exposition overlap with our paper arXiv:2312.16749, which contains further details

Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators. Unlocking the full potential of such systems toward this goal requires flexible initial state preparation, precise time evolution, and extensive probes for final state characterization. We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. Emulating a two-dimensional (2D) XY quantum magnet, we leverage a wide range of measurement techniques to study quantum states after ramps from an antiferromagnetic initial state. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions attributed to the interplay between quantum and classical coarsening of the correlated domains. This interpretation is corroborated by injecting variable energy density into the initial state, which enables studying the effects of the eigenstate thermalization hypothesis (ETH) in targeted parts of the eigenspectrum. Finally, we digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization. These results establish the efficacy of superconducting analog-digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Chromia (Cr2O3) is a magnetoelectric oxide which permits voltage-control of the antiferromagnetic (AFM) order, but it suffers technological constraints due to its low Neel Temperature (TN ~307 K) and the need of a symmetry breaking applied magnetic field to achieve reversal of the Neel vector. Recently, boron (B) doping of Cr2O3 films led to an increase TN > 400 K and allowed the realization of voltage magnetic-field free controlled N\'eel vector rotation. Here, we directly image the impact of B doping on the formation of AFM domains in Cr2O3 thin films and elucidate the mechanism of voltage-controlled manipulation of the spin structure using nitrogen vacancy (NV) scanning probe magnetometry. We find a stark reduction and thickness dependence of domain size in B-doped Cr2O3 (B:Cr2O3) films, explained by the increased germ density, likely associated with the B doping. By reconstructing the surface magnetization from the NV stray-field maps, we find a qualitative distinction between the undoped and B-doped Cr2O3 films, manifested by the histogram distribution of the AFM ordering, i.e., 180 degree domains for pure films, and 90 degree domains for B:Cr2O3 films. Additionally, NV imaging of voltage-controlled B-doped Cr2O3 devices corroborate the 90 degeree rotation of the AFM domains observed in magnetotransport measurement.

Rare-earth iron garnets have distinctive spin-wave (SW) properties such as low magnetic damping and long SW coherence length making them ideal candidates for magnonics. Among them, thulium iron garnet (TmIG) is a ferrimagnetic insulator with unique magnetic properties including perpendicular magnetic anisotropy (PMA) and topological hall effect at room temperature when grown down to a few nanometers, extending its application to magnon spintronics. Here, the SW propagation properties of TmIG films (thickness of 7-34 nm) grown on GGG and sGGG substrates are studied at room temperature. Magnetic measurements show in-plane magnetic anisotropy for TmIG films grown on GGG and out-of-plane magnetic anisotropy for films grown on sGGG substrates with PMA. SW electrical transmission spectroscopy measurements on TmIG/GGG films unveil magnetostatic surface spin waves (MSSWs) propagating up to 80 um with a SW group velocity of 2-8 km s^-1. Intriguingly, these MSSWs exhibit nonreciprocal propagation, opening new applications in SW functional devices. TmIG films grown on sGGG substrates exhibit forward volume spin waves with a reciprocal propagation behavior up to 32 um.

Intraoperative ultrasound (iUS) imaging has the potential to improve surgical outcomes in brain surgery. However, its interpretation is challenging, even for expert neurosurgeons. In this work, we designed the first patient-specific framework that performs brain tumor segmentation in trackerless iUS. To disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical objective, a patient-specific real-time network is trained using synthetic ultrasound data generated by simulating virtual iUS sweep acquisitions in pre-operative MR data. Extensive experiments performed in real ultrasound data demonstrate the effectiveness of the proposed approach, allowing for adapting to the surgeon's definition of surgical targets and outperforming non-patient-specific models, neurosurgeon experts, and high-end tracking systems. Our code is available at: \url{https://github.com/ReubenDo/MHVAE-Seg}., Comment: Early accept at MICCAI 2024 - code available at: https://github.com/ReubenDo/MHVAE-Seg

Efficient and compact single photon emission platforms operating at room temperature with ultrafast speed and high brightness will be fundamental components of the emerging quantum communications and computing fields. However, so far, it is very challenging to design practical deterministic single photon emitters based on nanoscale solid-state materials that meet the fast emission rate and strong brightness demands. Here, a solution is provided to this longstanding problem by using metallic nanocavities integrated with hexagonal boron nitride (hBN) flakes with defects acting as nanoscale single photon emitters (SPEs) at room temperature. The presented hybrid nanophotonic structure creates a rapid speedup and large enhancement in single photon emission at room temperature. Hence, the nonclassical light emission performance is substantially improved compared to plain hBN flakes and hBN on gold-layered structures without nanocavity. Extensive theoretical calculations are also performed to accurately model the new hybrid nanophotonic system and prove that the incorporation of plasmonic nanocavity is key to efficient SPE performance. The proposed quantum nanocavity single photon source is expected to be an element of paramount importance to the envisioned room-temperature integrated quantum photonic networks.

Topologically protected magnetic skyrmions in magnetic materials are stabilized by interfacial or bulk Dzyaloshinskii-Moriya interaction (DMI). Interfacial DMI decays with increase of the magnetic layer thickness in just a few nanometers and bulk DMI typically stabilizes magnetic skyrmions at low temperatures. Consequently, more flexibility in manipulation of DMI is required for utilizing nanoscale skyrmions in energy efficient memory and logic devices at room temperature (RT). Here, we demonstrate the observation of RT skyrmions stabilized by gradient DMI (g-DMI) in composition gradient engineered CoPt single layer films by employing topological Hall effect, magnetic force microscopy, and nitrogen vacancy scanning magnetometry. Skyrmions remain stable at a wide range of applied magnetic fields and are confirmed to be nearly Bloch-type from micromagnetic simulation and analytical magnetization reconstruction. Furthermore, we observe skyrmion pairs which may be explained by skyrmion-antiskyrmion interactions. Our findings expand the family of magnetic materials hosting RT magnetic skyrmions by tuning g-DMI via gradient polarity and choice of magnetic elements.

We present the design of a portable coronagraph, CATEcor, that incorporates a novel "shaded truss" style of external occultation and serves as a proof-of-concept for that family of coronagraphs. The shaded truss design style has the potential for broad application in various scientific settings. We conceived CATEcor itself as a simple instrument to observe the corona during the darker skies available during a partial solar eclipse, or for students or interested amateurs to detect the corona under ideal non-eclipsed conditions. CATEcor is therefore optimized for simplicity and accessibility to the public. It is implemented using an existing dioptric telescope and an adapter rig that mounts in front of the objective lens, restricting the telescope aperture and providing external occultation. The adapter rig, including occulter, is fabricated using fusion deposition modeling (FDM; colloquially "3D printing"), greatly reducing cost. The structure is designed to be integrated with moderate care and may be replicated in a university or amateur setting. While CATEcor is a simple demonstration unit, the design concept, process, and trades are useful for other more sophisticated coronagraphs in the same general family, which might operate under normal daytime skies outside the annular-eclipse conditions used for CATEcor., Comment: 27pp; 12 figures; accepted to Solar Physics

Liquids and granular media are pervasive throughout human environments, yet remain particularly challenging for robots to sense and manipulate precisely. In this work, we present a systematic approach at integrating capacitive sensing within robotic end effectors to enable robust sensing and precise manipulation of liquids and granular media. We introduce the parallel-jaw RoboCAP Gripper with embedded capacitive sensing arrays that enable a robot to directly sense the materials and dynamics of liquids inside of diverse containers, including some visually opaque. When coupled with model-based control, we demonstrate that the proposed system enables a robotic manipulator to achieve state-of-the-art precision pouring accuracy for a range of substances with varying dynamics properties. Code, designs, and build details are available on the project website.

Robotics presents a promising opportunity for enhancing bathing assistance, potentially to alleviate labor shortages and reduce care costs, while offering consistent and gentle care for individuals with physical disabilities. However, ensuring flexible and efficient cleaning of the human body poses challenges as it involves direct physical contact between the human and the robot, and necessitates simple, safe, and effective control. In this paper, we introduce a soft, expandable robotic manipulator with embedded capacitive proximity sensing arrays, designed for safe and efficient bathing assistance. We conduct a thorough evaluation of our soft manipulator, comparing it with a baseline rigid end effector in a human study involving 12 participants across $96$ bathing trails. Our soft manipulator achieves an an average cleaning effectiveness of 88.8% on arms and 81.4% on legs, far exceeding the performance of the baseline. Participant feedback further validates the manipulator's ability to maintain safety, comfort, and thorough cleaning.

Deterministic and nondeterministic finite automata (DFAs and NFAs) are abstract models of computation commonly taught in introductory computing theory courses. These models have important applications (such as fast regular expression matching), and are used to introduce formal language theory. Undergraduate students often struggle with understanding these models at first, due to the level of abstraction. As a result, various pedagogical tools have been developed to allow students to practice with these models. We introduce the FSM Builder, a new pedagogical tool enabling students to practice constructing DFAs and NFAs with a graphical editor, giving personalized feedback and partial credit. The algorithms used for generating these are heavily inspired by previous works. The key advantages to its competitors are greater flexibility and scalability. This is because the FSM Builder is implemented using efficient algorithms from an open source package, allowing for easy extension and question creation. We discuss the implementation of the tool, how it stands out from previous tools, and takeaways from experiences of using the tool in multiple large courses. Survey results indicate the interface and feedback provided by the tool were useful to students., Comment: 7 pages

We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method, dubbed XFeat (Accelerated Features), revisits fundamental design choices in convolutional neural networks for detecting, extracting, and matching local features. Our new model satisfies a critical need for fast and robust algorithms suitable to resource-limited devices. In particular, accurate image matching requires sufficiently large image resolutions - for this reason, we keep the resolution as large as possible while limiting the number of channels in the network. Besides, our model is designed to offer the choice of matching at the sparse or semi-dense levels, each of which may be more suitable for different downstream applications, such as visual navigation and augmented reality. Our model is the first to offer semi-dense matching efficiently, leveraging a novel match refinement module that relies on coarse local descriptors. XFeat is versatile and hardware-independent, surpassing current deep learning-based local features in speed (up to 5x faster) with comparable or better accuracy, proven in pose estimation and visual localization. We showcase it running in real-time on an inexpensive laptop CPU without specialized hardware optimizations. Code and weights are available at www.verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24., Comment: CVPR 2024; Source code available at www.verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24