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This paper presents a rationale for using professional and continuing education (PaCE) units at post-secondary institutions throughout California to design and implement talent-pipelines, research and development collaborations, and other knowledge ecosystems where emerging and returning professionals can acquire the knowledge, skills, and abilities (KSAs), as well as the experience, they need to address the challenges of the Fourth Industrial Revolution (4IR). The paper provides an analysis of the reasons why PaCE units are uniquely positioned to address the needs of industry and job seekers, and on a timetable that keeps pace with 4IR velocity.

In this paper we examine academic recovery in 12 mid- to large-sized school districts across 10 states during the 2021-22 school year. Our findings highlight the challenges that recovery efforts faced during the 2021-22 school year. Although, on average, math and reading test score gains during the school year reached the pace of pre-pandemic school years, they were not accelerated beyond that pace. This is not surprising given that we found that districts struggled to implement recovery programs at the scale they had planned. In the districts where we had detailed data on student participation in academic interventions, we found that recovery efforts often fell short of original expectations for program scale, intensity of treatment, and impact. Interviews with a subsample of district leaders revealed several implementation challenges, including difficulty engaging targeted students consistently across schools, issues with staffing and limitations to staff capacity, challenges with scheduling, and limited engagement of parents as partners in recovery initiatives. Our findings on the pace and trajectory of recovery and the challenges of implementing recovery initiatives raise important questions about the scale of district recovery efforts. [The working paper received additional funding from Kenneth C. Griffin and AIR Equity Initiative.]

Modern machine learning methods have recently demonstrated remarkable capability to generalize under task shift, where latent knowledge is transferred to a different, often more difficult, task under a similar data distribution. We investigate this phenomenon in an overparameterized linear regression setting where the task shifts from classification during training to regression during evaluation. In the zero-shot case, wherein no regression data is available, we prove that task shift is impossible in both sparse signal and random signal models for any Gaussian covariate distribution. In the few-shot case, wherein limited regression data is available, we propose a simple postprocessing algorithm which asymptotically recovers the ground-truth predictor. Our analysis leverages a fine-grained characterization of individual parameters arising from minimum-norm interpolation which may be of independent interest. Our results show that while minimum-norm interpolators for classification cannot transfer to regression a priori, they experience surprisingly structured attenuation which enables successful task shift with limited additional data., Comment: AISTATS 2025

We describe a concrete device roadmap towards a fault-tolerant quantum computing architecture based on noise-resilient, topologically protected Majorana-based qubits. Our roadmap encompasses four generations of devices: a single-qubit device that enables a measurement-based qubit benchmarking protocol; a two-qubit device that uses measurement-based braiding to perform single-qubit Clifford operations; an eight-qubit device that can be used to show an improvement of a two-qubit operation when performed on logical qubits rather than directly on physical qubits; and a topological qubit array supporting lattice surgery demonstrations on two logical qubits. Devices that enable this path require a superconductor-semiconductor heterostructure that supports a topological phase, quantum dots and coupling between those quantum dots that can create the appropriate loops for interferometric measurements, and a microwave readout system that can perform fast, low-error single-shot measurements. We describe the key design components of these qubit devices, along with the associated protocols for demonstrations of single-qubit benchmarking, Clifford gate execution, quantum error detection, and quantum error correction, which differ greatly from those in more conventional qubits. Finally, we comment on implications and advantages of this architecture for utility-scale quantum computation., Comment: 11+6 pages, 8+5 figures

We present MicroBooNE's first search for dark sector $e^+e^-$ explanations of the long-standing MiniBooNE anomaly. The MiniBooNE anomaly has garnered significant attention over the past 20 years including previous MicroBooNE investigations into both anomalous electron and photon excesses, but its origin still remains unclear. In this letter, we provide the first direct test of dark sector models in which dark neutrinos, produced through neutrino-induced scattering, decay into missing energy and visible $e^+e^-$ pairs comprising the MiniBooNE anomaly. Many such models have recently gained traction as a viable solution to the anomaly while evading past bounds. Using an exposure of $6.87 \times 10^{20}$ protons-on-target in the Booster Neutrino Beam, we implement a selection targeting forward-going, coherently produced $e^+e^-$ events. After unblinding, we observe 95 events, which we compare with the constrained background-only prediction of $69.7 \pm 17.3$. This analysis sets the world's first direct limits on these dark sector models and, at the 95\% confidence level, excludes the majority of the parameter space viable as a solution to the MiniBooNE anomaly., Comment: 7 pages, 5 figures, Supplemental Materials included in Ancillary files

Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two mode-squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution., Comment: 17 pages, 8 figures

The interplay between electronic topology and superconductivity is the subject of great current interest in condensed matter physics. For example, superconductivity induced on the surface of topological insulators is predicted to be triplet in nature, while the interplay between electronic correlations and topology may lead to unconventional superconductivity as in twisted bilayer graphene. Here, we unveil an unconventional two-dimensional superconducting state in the recently discovered Dirac nodal line semimetal ZrAs2 which is exclusively confined to the top and bottom surfaces within the crystal's ab plane. As a remarkable consequence of this emergent state, we observe a Berezinskii-Kosterlitz-Thouless (BKT) transition, the hallmark of two-dimensional superconductivity. Notably, this is the first observation of a BKT transition on the surface of a three-dimensional system. Furthermore, employing angle-resolved photoemission spectroscopy and first-principles calculations, we find that these same surfaces also host a two-dimensional van Hove singularity near the Fermi energy. The proximity of van Hove singularity to the Fermi level leads to enhanced electronic correlations contributing to the stabilization of superconductivity at the surface of ZrAs2, a unique phenomenon among topological semimetals. The surface-confined nature of the van Hove singularity, and associated superconductivity, realized for the first time, opens new avenues to explore the interplay between low-dimensional quantum topology, correlations, and superconductivity in a bulk material without resorting to the superconducting proximity effect., Comment: in press

Chirality or handedness manifests in all fields of science, ranging from cell biology, molecular interaction, and catalysis to different branches of physics. In condensed matter physics, chirality is intrinsic to enigmatic quantum phases, such as chiral charge density waves and chiral superconductivity. Here, the underlying chiral response is subtle and leads to broken symmetries in the ground state. Detection of subtle broken symmetries is the key to understand these quantum states but they are extremely challenging to expose leading to debate and controversy. Here, using second-order optical response, we uncover the broken symmetries of a chiral charge density wave in the Kagome lattice KV3Sb5, revealing the relevant broken symmetries of its charge order. KV3Sb5 undergoes a phase transition to a charge-ordered state at low temperatures. Our polarization-dependent mid-infrared photocurrent microscopy reveals an intrinsic, longitudinal helicity-dependent photocurrent associated with the charge order. Our measurements, supported by our theoretical analysis, provide direct evidence for broken inversion and mirror symmetries at the charge order transition, indicating a chiral charge ordered state. On the other hand, we do not observe a circular photogalvanic effect along the direction perpendicular to that of the incident light, imposing stringent constraints on the rotational and point group symmetries of the charge order. Our study not only visualizes the chiral nature of the Kagome charge order revealing its broken symmetries, but also highlights the nonlinear photogalvanic effect as a sensitive probe for detecting subtle symmetry breakings., Comment: in press

Large reasoning models (LRMs) tackle complex reasoning problems by following long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking, and self-validation. However, the training techniques and data requirements to elicit Long CoT remain poorly understood. In this work, we find that a Large Language model (LLM) can effectively learn Long CoT reasoning through data-efficient supervised fine-tuning (SFT) and parameter-efficient low-rank adaptation (LoRA). With just 17k long CoT training samples, the Qwen2.5-32B-Instruct model achieves significant improvements on a wide range of math and coding benchmarks, including 56.7% (+40.0%) on AIME 2024 and 57.0% (+8.1%) on LiveCodeBench, competitive to the proprietary o1-preview model's score of 44.6% and 59.1%. More importantly, we find that the structure of Long CoT is critical to the learning process, whereas the content of individual reasoning steps has minimal impact. Perturbations affecting content, such as training on incorrect samples or removing reasoning keywords, have little impact on performance. In contrast, structural modifications that disrupt logical consistency in the Long CoT, such as shuffling or deleting reasoning steps, significantly degrade accuracy. For example, a model trained on Long CoT samples with incorrect answers still achieves only 3.2% lower accuracy compared to training with fully correct samples. These insights deepen our understanding of how to elicit reasoning capabilities in LLMs and highlight key considerations for efficiently training the next generation of reasoning models. This is the academic paper of our previous released Sky-T1-32B-Preview model. Codes are available at https://github.com/NovaSky-AI/SkyThought.

Upcoming telescopes like the Vera Rubin Observatory (VRO) and the Argus Array will image large fractions of the sky multiple times per night yielding numerous Near Earth Object (NEO) discoveries. When asteroids are measured with short observation time windows, the dominant uncertainty in orbit construction is due to distance uncertainty to the NEO. One approach to recover distances is from topocentric parallax, which is a technique that leverages the rotation of the Earth, causing a small but detectable sinusoidal additive signal to the Right Ascension (RA) of the NEO following a period of 1 day. In this paper, we further develop and evaluate this technique to recover distances in as quickly as a single night. We first test the technique on synthetic data of 19 different asteroids ranging from $\sim0.05 \,\text{AU}$ to $\sim2.4 \,\text{AU}$. We modify previous algorithms and quantify the limitations of the method, recovering distances with uncertainties as low as the $\sim1.3\%$ level for more nearby objects ($\lesssim$ 0.3 AU) assuming typical astrometric uncertainties. We then acquire our own observations of two asteroids within a single night with $\sim0.1''$ uncertainties on RA, and we find we are able to recover distances to the $3\%$ level. We forecast likely scenarios with the VRO and the Argus Array with varying levels of astrometric precision and expected pointings per night. Our analysis indicates that distances to NEOs on the scale of $\sim0.5$ AU can be constrained to below the percent level within a single night, depending on spacing of observations from one observatory. In a follow-up paper, we will compare these constraints with synchronous and asynchronous observations from two separate observatories to measure parallax even more efficiently, an exciting and likely possibility over the upcoming decade., Comment: Submitting to the Astronomical Journal, comments welcome

Simulation has been pivotal in recent robotics milestones and is poised to play a prominent role in the field's future. However, recent robotic advances often rely on expensive and high-maintenance platforms, limiting access to broader robotics audiences. This work introduces Wheeled Lab, a framework for the low-cost, open-source wheeled platforms that are already widely established in education and research. Through integration with Isaac Lab, Wheeled Lab introduces modern techniques in Sim2Real, such as domain randomization, sensor simulation, and end-to-end learning, to new user communities. To kickstart education and demonstrate the framework's capabilities, we develop three state-of-the-art policies for small-scale RC cars: controlled drifting, elevation traversal, and visual navigation, each trained in simulation and deployed in the real world. By bridging the gap between advanced Sim2Real methods and affordable, available robotics, Wheeled Lab aims to democratize access to cutting-edge tools, fostering innovation and education in a broader robotics context. The full stack, from hardware to software, is low cost and open-source., Comment: Under Review

We demonstrate the potential of new adaptive optical technology to expand the detection horizon of gravitational-wave observatories. Achieving greater quantum-noise-limited sensitivity to spacetime strain hinges on achieving higher circulating laser power, in excess of 1~MW, in conjunction with highly-squeezed quantum states of light. The new technology will enable significantly higher levels of laser power and squeezing in gravitational-wave detectors, by providing high-precision, low-noise correction of limiting sources of thermal distortions directly to the core interferometer optics. In simulated projections for LIGO~A+, assuming an input laser power of 125~W and an effective injected squeezing level of 9~dB entering the interferometer, an initial concept of this technology can reduce the noise floor of the detectors by up to 20\% from 200~Hz to 5~kHz, corresponding to an increment of 4~Mpc in the sky-averaged detection range for binary neutron star mergers. This work lays the foundation for one of the key technology improvements essential to fully utilize the scientific potential of the existing 4-km LIGO facilities, to observe black hole merger events past a redshift of~5, and opens a realistic pathway towards a next-generation 40-km gravitational-wave observatory in the United States, Cosmic~Explorer., Comment: 8 pages, 5 figures

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

This article presents the first search for neutrino-induced neutral current coherent single-photon production (NC coherent 1$\gamma$). The search makes use of data from the MicroBooNE 85-tonne active volume liquid argon time projection chamber detector, situated in the Fermilab Booster Neutrino Beam (BNB), with an average neutrino energy of $\langle E_{\nu}\rangle \sim 0.8$ GeV. A targeted selection of candidate neutrino interactions with a single photon-like electromagnetic shower in the final state and no visible vertex activity was developed to search for the NC coherent 1$\gamma$ process, along with two auxiliary selections used to constrain the dominant background from NC$\pi^0$ production. With an integrated exposure of $6.87 \times 10^{20}$ protons on target delivered by the BNB, we set the world's first limit for this rare process, corresponding to an upper limit on the flux-averaged cross section of $\sigma<1.49 \times 10^{-41}\text{cm}^2$ at 90\% C.L., Comment: 20 pages, 17 figures

We present an inclusive search for anomalous production of single-photon events from neutrino interactions in the MicroBooNE experiment. The search and its signal definition are motivated by the previous observation of a low-energy excess of electromagnetic shower events from the MiniBooNE experiment. We use the Wire-Cell reconstruction framework to select a sample of inclusive single-photon final-state interactions with a final efficiency and purity of 7.0% and 40.2%, respectively. We leverage simultaneous measurements of sidebands of charged current $\nu_{\mu}$ interactions and neutral current interactions producing $\pi^{0}$ mesons to constrain signal and background predictions and reduce uncertainties. We perform a blind analysis using a dataset collected from February 2016 to July 2018, corresponding to an exposure of $6.34\times10^{20}$ protons on target from the Booster Neutrino Beam (BNB) at Fermilab. In the full signal region, we observe agreement between the data and the prediction, with a goodness-of-fit $p$-value of 0.11. We then isolate a sub-sample of these events containing no visible protons, and observe $93\pm22\text{(stat.)}\pm35\text{(syst.)}$ data events above prediction, corresponding to just above $2\sigma$ local significance, concentrated at shower energies below 600 MeV., Comment: 9 pages, 6 figures

Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC). By integrating context retrieval into content generation, RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks. However, despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including robustness issues, privacy concerns, adversarial attacks, and accountability issues. Addressing these risks is critical for future applications of RAG systems, as they directly impact their trustworthiness. Although various methods have been developed to improve the trustworthiness of RAG methods, there is a lack of a unified perspective and framework for research in this topic. Thus, in this paper, we aim to address this gap by providing a comprehensive roadmap for developing trustworthy RAG systems. We place our discussion around five key perspectives: reliability, privacy, safety, fairness, explainability, and accountability. For each perspective, we present a general framework and taxonomy, offering a structured approach to understanding the current challenges, evaluating existing solutions, and identifying promising future research directions. To encourage broader adoption and innovation, we also highlight the downstream applications where trustworthy RAG systems have a significant impact.

We report results from an updated search for neutral current (NC) resonant $\Delta$(1232) baryon production and subsequent $\Delta$ radiative decay (NC $\Delta\rightarrow N \gamma$). We consider events with and without final state protons; events with a proton can be compared with the kinematics of a $\Delta(1232)$ baryon decay, while events without a visible proton represent a more generic phase space. In order to maximize sensitivity to each topology, we simultaneously make use of two different reconstruction paradigms, Pandora and Wire-Cell, which have complementary strengths, and select mostly orthogonal sets of events. Considering an overall scaling of the NC $\Delta\rightarrow N \gamma$ rate as an explanation of the MiniBooNE anomaly, our data exclude this hypothesis at 94.4% CL. When we decouple the expected correlations between NC $\Delta\rightarrow N \gamma$ events with and without final state protons, and allow independent scaling of both types of events, our data exclude explanations in which excess events have associated protons, and do not exclude explanations in which excess events have no associated protons.

We report the masses, sizes, and orbital properties of 86 planets orbiting 55 stars observed by NASA's K2 Mission with follow-up Doppler measurements by the HIRES spectrometer at the W. M. Keck Observatory and the Automated Planet Finder at Lick Observatory. Eighty-one of the planets were discovered from their transits in the K2 photometry, while five were found based on subsequent Doppler measurements of transiting planet host stars. The sizes of the transiting planets range from Earth-size to larger than Jupiter (1-3 REarth is typical), while the orbital periods range from less than a day to a few months. For 32 of the planets, the Doppler signal was detected with significance greater than 5-sigma (51 were detected with >3-sigma significance). An important characteristic of this catalog is the use of uniform analysis procedures to determine stellar and planetary properties. This includes the transit search and fitting procedures applied to the K2 photometry, the Doppler fitting techniques applied to the radial velocities, and the spectral modeling to determine bulk stellar parameters. Such a uniform treatment will make the catalog useful for statistical studies of the masses, densities, and system architectures of exoplanetary systems. This work also serves as a data release for all previously unpublished RVs and associated stellar activity indicators obtained by our team for these systems, along with derived stellar and planet parameters., Comment: 156 pages, 86 planets, 55 stars, 104 figures, 48 tables. Accepted to ApJS

We study a method that involves principally convex feasibility-seeking and makes secondary efforts of objective function value reduction. This is the well-known superiorization method (SM), where the iterates of an asymptotically convergent iterative feasibility-seeking algorithm are perturbed by objective function nonascent steps. We investigate the question under what conditions a sequence generated by an SM algorithm asymptotically converges to a feasible point whose objective function value is superior (meaning smaller or equal) to that of a feasible point reached by the corresponding unperturbed one (i.e., the exactly same feasibility-seeking algorithm that the SM algorithm employs.) This question is yet only partially answered in the literature. We present a condition under which an SM algorithm that uses negative gradient descent steps in its perturbations fails to yield such a superior outcome. The significance of the discovery of this negative condition is that it necessitates that the inverse of this condition will have to be assumed to hold in any future guarantee result for the SM. The condition is important for practitioners who use the SM because it is avoidable in experimental work with the SM, thus increasing the success rate of the method in real-world applications., Comment: 17 pages. Accepted for publication in the journal "Optimization Letters". arXiv admin note: text overlap with arXiv:2212.14724

The Breakthrough Starshot project aims to send a tiny 2 gram spacecraft to Proxima Centauri propelled by a light sail and powerful Earth-based lasers. We provide two derivations of the laser energy required to propel the spacecraft and give the reader the opportunity to decide which one is correct before providing the answer. In the second part of this paper we point out that one of the formulae in Einstein's amazing 1905 paper is correct only in certain limits, but Einstein fails to mention that. This has caused some confusion in the Breakthrough Starshot literature.

Robot learning requires a considerable amount of high-quality data to realize the promise of generalization. However, large data sets are costly to collect in the real world. Physics simulators can cheaply generate vast data sets with broad coverage over states, actions, and environments. However, physics engines are fundamentally misspecified approximations to reality. This makes direct zero-shot transfer from simulation to reality challenging, especially in tasks where precise and force-sensitive manipulation is necessary. Thus, fine-tuning these policies with small real-world data sets is an appealing pathway for scaling robot learning. However, current reinforcement learning fine-tuning frameworks leverage general, unstructured exploration strategies which are too inefficient to make real-world adaptation practical. This paper introduces the Simulation-Guided Fine-tuning (SGFT) framework, which demonstrates how to extract structural priors from physics simulators to substantially accelerate real-world adaptation. Specifically, our approach uses a value function learned in simulation to guide real-world exploration. We demonstrate this approach across five real-world dexterous manipulation tasks where zero-shot sim-to-real transfer fails. We further demonstrate our framework substantially outperforms baseline fine-tuning methods, requiring up to an order of magnitude fewer real-world samples and succeeding at difficult tasks where prior approaches fail entirely. Last but not least, we provide theoretical justification for this new paradigm which underpins how SGFT can rapidly learn high-performance policies in the face of large sim-to-real dynamics gaps. Project webpage: https://weirdlabuw.github.io/sgft/{weirdlabuw.github.io/sgft}

As part of the Deciphering the Interplay between the Interstellar medium, Stars, and the Circumgalactic medium (DIISC) survey, we present the UV metal absorption features in the Circumgalactic Medium (CGM) near the H I gas disk ($<$4.5$R_\mathrm{HI}$) of 31 nearby galaxies through quasar absorption line spectroscopy. Of the ions under study, Si III $\lambda1206$ was most frequently detected (18 of 31 sight lines), while C II $\lambda1334$ and Si II $\lambda1260$ were detected in 17 and 15 of 31 sight lines, respectively. Many components were consistent with photoionization equilibrium models, most of the cold and cool gas phase clouds were found to have lengths smaller than 2 kpc. Sight lines with smaller impact parameters ($\rho$) normalized by the galaxy's virial radius ($R_\mathrm{vir}$) and H I radius ($R_\mathrm{HI}$) tend to have more components and larger rest-frame equivalent widths ($W_r$) than those that probe the CGM at larger radii. In particular, we find that the location of metals are better traced by $\rho$ / $R_\mathrm{HI}$ rather than the traditional $\rho$ / $R_\mathrm{vir}$. Larger covering fractions are found closer to galaxies, with a radial decline that depends on the $W_r$ limit used. Our results provide new insights into the spatial distribution of metals around the H I disks of low-redshift galaxies., Comment: 20 pages, 15 figures, accepted for publication in ApJ

Monin Obukhov Similarity Theory (MOST) has long served as the basis for parameterizations of turbulence exchange between the surface and the atmospheric boundary layer in models for weather and climate prediction. Decades of research, however, has illuminated some of the limitations of MOST based surface layer parameterizations, particularly when MOST's foundational assumptions of flat and horizontally homogeneous terrain are violated. Recent work has leveraged the anisotropy of turbulence as an additional non-dimensional term to extend and generalize MOST to complex terrain. In this work, we examine the performance of this generalized MOST for the scaling of velocity variances, refit these scalings, and study key characteristics of turbulence anisotropy across the 47 towers in the wide ranging National Ecological Observation Network (NEON). NEON in particular covers a diverse selection of ecosystems, from the arctic circle to tropical islands, and as such expands the previous generalized MOST to vegetated canopies and other environments not examined in previous studies. The work finds that anisotropy generalized MOST readily extends to these new environments, with robust performance over a wide range of canopy and terrain configurations. Results also further illuminate velocity variance scaling across degrees and forms (one-component and two-component) of anisotropy, relate the degree of anisotropy to key environmental characteristics, and evaluate scaling differences between canopies and non-vegetated surfaces. Overall, this study expands our understanding of the complexity of turbulence while paving the way for improved surface layer parameterizations., Comment: Submitted for Publication in Quarterly Journal of the Royal Meteorological Society

The randomized SVD is a method to compute an inexpensive, yet accurate, low-rank approximation of a matrix. The algorithm assumes access to the matrix through matrix-vector products (matvecs). Therefore, when we would like to apply the randomized SVD to a matrix function, $f(A)$, one needs to approximate matvecs with $f(A)$ using some other algorithm, which is typically treated as a black-box. Chen and Hallman (SIMAX 2023) argued that, in the common setting where matvecs with $f(A)$ are approximated using Krylov subspace methods (KSMs), more efficient low-rank approximation is possible if we open this black-box. They present an alternative approach that significantly outperforms the naive combination of KSMs with the randomized SVD, although the method lacked theoretical justification. In this work, we take a closer look at the method, and provide strong and intuitive error bounds that justify its excellent performance for low-rank approximation of matrix functions.

In real-world decision making, outcomes are often delayed, meaning individuals must make multiple decisions before receiving any feedback. Moreover, feedback can be presented in different ways: it may summarize the overall results of multiple decisions (aggregated feedback) or report the outcome of individual decisions after some delay (clustered feedback). Despite its importance, the timing and presentation of delayed feedback has received little attention in cognitive modeling of decision-making, which typically focuses on immediate feedback. To address this, we conducted an experiment to compare the effect of delayed vs. immediate feedback and aggregated vs. clustered feedback. We also propose a Hierarchical Instance-Based Learning (HIBL) model that captures how people make decisions in delayed feedback settings. HIBL uses a super-model that chooses between sub-models to perform the decision-making task until an outcome is observed. Simulations show that HIBL best predicts human behavior and specific patterns, demonstrating the flexibility of IBL models., Comment: Conference on Human Computer Interaction International

This paper details the world's first live 3D holographic (diffractive) video call using off-the-shelf hardware. We introduce a novel pipeline that facilitates the capture, processing, and transmission of RGBZ data, using an iPhone for image and depth capture with VividQ's SDK for hologram generation and hardware for display., Comment: Published and Presented at Session 63: Emerging Approaches for AR/VR/MR, SID Display Week 2022. 4 pages, 9 figures

Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential delays or inaccuracies in identifying clinically significant cancers. This leads to numerous unnecessary biopsies and risks of missing clinically significant cancers. Here we present prostate vision contrastive network (ProViCNet), prostate organ-specific vision foundation models for Magnetic Resonance Imaging (MRI) and Trans-Rectal Ultrasound imaging (TRUS) for comprehensive cancer detection. ProViCNet was trained and validated using 4,401 patients across six institutions, as a prostate cancer detection model on radiology images relying on patch-level contrastive learning guided by biopsy confirmed radiologist annotations. ProViCNet demonstrated consistent performance across multiple internal and external validation cohorts with area under the receiver operating curve values ranging from 0.875 to 0.966, significantly outperforming radiologists in the reader study (0.907 versus 0.805, p<0.001) for mpMRI, while achieving 0.670 to 0.740 for TRUS. We also integrated ProViCNet with standard PSA to develop a virtual screening test, and we showed that we can maintain the high sensitivity for detecting clinically significant cancers while more than doubling specificity from 15% to 38% (p<0.001), thereby substantially reducing unnecessary biopsies. These findings highlight that ProViCNet's potential for enhancing prostate cancer diagnosis accuracy and reduce unnecessary biopsies, thereby optimizing diagnostic pathways., Comment: 44pages

Measuring the properties of planets younger than about 50 Myr helps to test different planetary formation and evolution models. NASA's Transiting Exoplanet Survey Satellite (TESS) has observed nearly the entire sky, including a wide range of star-forming regions and young stellar clusters, expanding our census of the newborn planet population. In this work, we present the discovery of the TIC 88785435 planetary system located in the Upper-Centaurus Lupus (UCL) region of the Scorpius-Centaurus OB association (Sco-Cen) and a preliminary survey of the planet population within Sco-Cen. TIC 88785435 is a pre-main sequence, K7V dwarf ($M_\star = 0.72M_\odot$, $R_\star = 0.91R_\odot$, $T_\mathrm{eff}$ = 3998K, V = 11.7 mag) located within the bounds of UCL. We investigate the distribution of rotation periods measured from the TESS long-cadence data and the Halpha and Li abundances from the spectra of TIC 88785435. TESS long-candence data reveal that TIC 88785435 hosts a transiting super-Neptune ($R_b = 5.03R_\oplus$, P = 10.51 days), TIC 88785435 b. Ground-based follow-up validates the planetary nature of TIC 88785435 b. Using the TESS data, we perform a preliminary survey to investigate how TIC 88785435 b compares to the population of newly born planets located within Sco-Cen., Comment: 15 pages, 9 Figures, submitted to AJ

Vision-language models can connect the text description of an object to its specific location in an image through visual grounding. This has potential applications in enhanced radiology reporting. However, these models require large annotated image-text datasets, which are lacking for PET/CT. We developed an automated pipeline to generate weak labels linking PET/CT report descriptions to their image locations and used it to train a 3D vision-language visual grounding model. Our pipeline finds positive findings in PET/CT reports by identifying mentions of SUVmax and axial slice numbers. From 25,578 PET/CT exams, we extracted 11,356 sentence-label pairs. Using this data, we trained ConTEXTual Net 3D, which integrates text embeddings from a large language model with a 3D nnU-Net via token-level cross-attention. The model's performance was compared against LLMSeg, a 2.5D version of ConTEXTual Net, and two nuclear medicine physicians. The weak-labeling pipeline accurately identified lesion locations in 98% of cases (246/251), with 7.5% requiring boundary adjustments. ConTEXTual Net 3D achieved an F1 score of 0.80, outperforming LLMSeg (F1=0.22) and the 2.5D model (F1=0.53), though it underperformed both physicians (F1=0.94 and 0.91). The model achieved better performance on FDG (F1=0.78) and DCFPyL (F1=0.75) exams, while performance dropped on DOTATE (F1=0.58) and Fluciclovine (F1=0.66). The model performed consistently across lesion sizes but showed reduced accuracy on lesions with low uptake. Our novel weak labeling pipeline accurately produced an annotated dataset of PET/CT image-text pairs, facilitating the development of 3D visual grounding models. ConTEXTual Net 3D significantly outperformed other models but fell short of the performance of nuclear medicine physicians. Our study suggests that even larger datasets may be needed to close this performance gap.

The Swift Burst Alert Telescope (BAT) is a coded aperture gamma-ray instrument with a large field of view that was designed to detect and localize transient events. When a transient is detected, either on-board or externally, the BAT saves time-tagged event (TTE) data which provides the highest quality information of the locations of the photons on the detector plane and their energies. This data can be used to produce spectra, lightcurves, and sky images of a transient event. While these data products are produced by the Swift Data Center and can be produced by current software, they are often preset to certain time and energy intervals which has limited their use in the current time domain and multi-messenger environment. Here, we introduce a new capability for the BatAnalysis python package to download and process TTE data under an open-source pythonic framework that allows for easy interfacing with other python packages. The new capabilities of the BatAnalysis software allows for TTE data to be used by the community in a variety of advanced customized analyses of astrophysical sources which BAT may have TTE data for, such as Fast Radio Bursts (FRBs), Gamma-ray Bursts (GRBs), Low Mass X-ray Binaries (LMXB), Soft Gamma-ray Repeaters, magnetars, and many other sources. We highlight the usefulness of the BatAnalysis package in analyzing TTE data produced by an on-board GRB trigger, a FRB external trigger, a sub-threshold detection of the LMXB EXO 0748-676, and an external trigger of a GRB that BAT detected during a slew., Comment: 13 pages, 8 figures, submitted to ApJ but comments welcome, BatAnalysis github link is: https://github.com/parsotat/BatAnalysis

Astronomical radio interferometers achieve exquisite angular resolution by cross-correlating signal from a cosmic source simultaneously observed by distant pairs of radio telescopes to produce a Fourier-type measurement called a visibility. Million Points of Light (MPoL) is a Python library supporting feed-forward modeling of interferometric visibility datasets for synthesis imaging and parametric Bayesian inference, built using the autodifferentiable machine learning framework PyTorch. Neural network components provide a rich set of modular and composable building blocks that can be used to express the physical relationships between latent model parameters and observed data following the radio interferometric measurement equation. Industry-grade optimizers make it straightforward to simultaneously solve for the synthesized image and calibration parameters using stochastic gradient descent., Comment: Submitted to JOSS. 1559 words, 1 figure

Massive quantum oscillators are finding increasing applications in proposals for high-precision quantum sensors and interferometric detection of weak forces. Although optimal estimation of certain properties of massive quantum oscillators such as phase shifts and displacements have strict counterparts in the theory of quantum estimation of the electromagnetic field, the phase space anisotropy of the massive oscillator is characterized by a length scale parameter that is an independent target for quantum estimation methods. We show that displaced squeezed states and excited eigenstates of a massive oscillator exhibit Heisenberg scaling of the quantum Fisher information for the length scale with respect to excitation number, and discuss asymptotically unbiased and efficient estimation allowing to achieve the predicted sensitivity. We construct a sequence of entangled states of two massive oscillators that provides a boost in length scale sensitivity equivalent to appending a third massive oscillator to a non-entangled system, and a state of $N$ oscillators exhibiting Heisenberg scaling with respect to the total energy.

A central challenge in transfer learning is designing algorithms that can quickly adapt and generalize to new tasks without retraining. Yet, the conditions of when and how algorithms can effectively transfer to new tasks is poorly characterized. We introduce a geometric characterization of transfer in Hilbert spaces and define three types of inductive transfer: interpolation within the convex hull, extrapolation to the linear span, and extrapolation outside the span. We propose a method grounded in the theory of function encoders to achieve all three types of transfer. Specifically, we introduce a novel training scheme for function encoders using least-squares optimization, prove a universal approximation theorem for function encoders, and provide a comprehensive comparison with existing approaches such as transformers and meta-learning on four diverse benchmarks. Our experiments demonstrate that the function encoder outperforms state-of-the-art methods on four benchmark tasks and on all three types of transfer.

Utilising JWST, ALMA and the VLA we present high angular resolution (0.06''- 0.42''), multi-wavelength (4 micron - 3cm) observations of the VLA 1623-2417 protostellar system to characterise the origin, morphology and, properties of the continuum emission. JWST observations at 4.4 micron reveal outflow cavities for VLA 1623 A and, for the first time, VLA 1623 B, as well as scattered light from the upper layers of the VLA 1623 W disk. We model the millimetre-centimetre spectral energy distributions to quantify the relative contributions of dust and ionised gas emission, calculate dust masses, and use spectral index maps to determine where optical depth hinders this analysis. In general, all objects appear to be optically thick down to ~90 GHz, show evidence for significant amounts (10's - 100's M_Earth) of large (>1 mm) dust grains, and are dominated by ionised gas emission for frequencies ~<15 GHz. In addition, we find evidence of unsettled millimetre dust in the inclined disk of VLA 1623 B possibly attributed to instabilities within the circumstellar disk, adding to the growing catalogue of unsettled Class 0/I disks. Our results represent some of the highest resolution observations possible with current instrumentation, particularly in the case of the VLA. However, our interpretation is still limited at low frequencies (~<22 GHz) and thus motivates the need for next-generation interferometers operating at centimetre wavelengths., Comment: Accepted for publication in ApJ. 35 pages, 10 figures

We describe a randomized variant of the block conjugate gradient method for solving a single positive-definite linear system of equations. Our method provably outperforms preconditioned conjugate gradient with a broad-class of Nystr\"om-based preconditioners, without ever explicitly constructing a preconditioner. In analyzing our algorithm, we derive theoretical guarantees for new variants of Nystr\"om preconditioned conjugate gradient which may be of separate interest. We also describe how our approach yields state-of-the-art algorithms for key data-science tasks such as computing the entire ridge regression regularization path and generating multiple independent samples from a high-dimensional Gaussian distribution.

High-quality electrostatic gating is a fundamental ingredient for successful semiconducting device physics, and a key element of realizing clean quantum transport. Inspired by the widespread improvement of transport quality when two-dimensional van der Waals (vdW) materials are gated exclusively by other vdW materials, we have developed a method for gating non-vdW materials with an all-vdW gate stack, consisting of a hexagonal boron nitride dielectric layer and a few-layer graphite gate electrode. We demonstrate this gating approach on MOVPE-grown InSb nanoribbons (NRs), a novel variant of the InSb nanowire, with a flattened cross-section. In our all-vdW gated NR devices we observe conductance features that are reproducible and have low- to near-zero gate hysteresis. We also report quantized conductance, which persists to lower magnetic fields and longer channel lengths than typical InSb nanowire devices reported to date. Additionally, we observe level splitting that is highly anisotropic in an applied magnetic field, which we attribute to the ribbon cross-section. The performance of our devices is consistent with the reduced disorder expected from the all-vdW gating scheme, and marks the first report of ballistic, few-modes quantum transport in a non-vdW material with an all-vdW gate. Our results establish all-vdW gating as a promising approach for high-quality gating of non-vdW materials for quantum transport, which is in principle applicable generically, beyond InSb systems. In addition, the work showcases the specific potential of all-vdW gate/InSb NR devices for enabling clean quantum devices that may be relevant for spintronics and topological superconductivity studies., Comment: 25 pages, 4 figures, plus supplementary material

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. We present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

Snapping shrimp produce bubbles that emit light when they collapse. When a bubble collapses so strongly that it emits light, the light emission is usually called sonoluminescence; in the case of the shrimp, it is called "shrimpoluminescence". The bubble collapses so fast that no heat can escape and the gas trapped in the bubble becomes hot enough to ionize. Light is emitted through electron-ion bremsstrahlung, electron-atom bremsstrahlung, and electron-ion recombination. In this paper, we study the dynamics of a sonoluminescing bubble and learn how to calculate the spectrum of emitted light, allowing us to explain the physical mechanisms of shrimpoluminescence., Comment: Submitted to the physics department at the University of Colorado in Feb. 2022 to satisfy qualifying exam requirements during my PhD

We present a comprehensive pipeline, augmented by a visual analytics system named ``GapMiner'', that is aimed at exploring and exploiting untapped opportunities within the empty areas of high-dimensional datasets. Our approach begins with an initial dataset and then uses a novel Empty Space Search Algorithm (ESA) to identify the center points of these uncharted voids, which are regarded as reservoirs containing potentially valuable novel configurations. Initially, this process is guided by user interactions facilitated by GapMiner. GapMiner visualizes the Empty Space Configurations (ESC) identified by the search within the context of the data, enabling domain experts to explore and adjust ESCs using a linked parallel-coordinate display. These interactions enhance the dataset and contribute to the iterative training of a connected deep neural network (DNN). As the DNN trains, it gradually assumes the task of identifying high-potential ESCs, diminishing the need for direct user involvement. Ultimately, once the DNN achieves adequate accuracy, it autonomously guides the exploration of optimal configurations by predicting performance and refining configurations, using a combination of gradient ascent and improved empty-space searches. Domain users were actively engaged throughout the development of our system. Our findings demonstrate that our methodology consistently produces substantially superior novel configurations compared to conventional randomization-based methods. We illustrate the effectiveness of our method through several case studies addressing various objectives, including parameter optimization, adversarial learning, and reinforcement learning.

On the kagome lattice, electrons benefit from the simultaneous presence of band topology, flat electronic bands, and van Hove singularities, forming competing or cooperating orders. Understanding the interrelation between these distinct order parameters remains a significant challenge, leaving much of the associated physics unexplored. In the kagome superconductor KV3Sb5, which exhibits a charge density wave (CDW) state below T = 78 K, we uncover an unpredicted field-induced phase transition below 6 K. The observed transition is marked by a hysteretic anomaly in the resistivity, nonlinear electrical transport, and a change in the symmetry of the electronic response as probed via the angular dependence of the magnetoresistivity. These observations surprisingly suggest the emergence of an unanticipated broken symmetry state coexisting with the original CDW. To understand this experimental observation, we developed a theoretical minimal model for the normal state inside the high-temperature parent CDW phase where an incommensurate CDW order emerges as an instability sub-leading to superconductivity. The incommensurate CDW emerges when superconducting fluctuations become fully suppressed by large magnetic fields. Our results suggest that, in kagome superconductors, quantum states can either coexist or are nearly degenerate in energy, indicating that these are rich platforms to expose new correlated phenomena.

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"{o}dinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to address the titular question, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. This work lays the foundation to establish accurate and reproducible FN-DMC estimates for all future studies across applications in materials science, physics, chemistry, and biology.

This paper proposes a cutting mechanics-based machine learning (CMML) modeling method to discover governing equations of machining dynamics. The main idea of CMML design is to integrate existing physics in cutting mechanics and unknown physics in data to achieve automated model discovery, with the potential to advance machining modeling. Based on existing physics in cutting mechanics, CMML first establishes a general modeling structure governing machining dynamics, that is represented by a set of unknown differential algebraic equations. CMML can therefore achieve data-driven discovery of these unknown equations through effective cutting mechanics-based nonlinear learning function space design and discrete optimization-based learning algorithm. Experimentally verified time domain simulation of milling is used to validate the proposed modeling method. Numerical results show CMML can discover the exact milling dynamics models with process damping and edge force from noisy data. This indicates that CMML has the potential to be used for advancing machining modeling in practice with the development of effective metrology systems.

Attention can be used to inform choice selection in contextual bandit tasks even when context features have not been previously experienced. One example of this is in dimensional shifts, where additional feature values are introduced and the relationship between features and outcomes can either be static or variable. Attentional mechanisms have been extensively studied in contextual bandit tasks where the feedback of choices is provided immediately, but less research has been done on tasks where feedback is delayed or in counterfactual feedback cases. Some methods have successfully modeled human attention with immediate feedback based on reward prediction errors (RPEs), though recent research raises questions of the applicability of RPEs onto more general attentional mechanisms. Alternative models suggest that information theoretic metrics can be used to model human attention, with broader applications to novel stimuli. In this paper, we compare two different methods for modeling how humans attend to specific features of decision making tasks, one that is based on calculating an information theoretic metric using a memory of past experiences, and another that is based on iteratively updating attention from reward prediction errors. We compare these models using simulations in a contextual bandit task with both intradimensional and extradimensional domain shifts, as well as immediate, delayed, and counterfactual feedback. We find that calculating an information theoretic metric over a history of experiences is best able to account for human-like behavior in tasks that shift dimensions and alter feedback presentation. These results indicate that information theoretic metrics of attentional mechanisms may be better suited than RPEs to predict human attention in decision making, though further studies of human behavior are necessary to support these results.