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Precise control of electrochemical reactions can greatly enhance the performance of electrochemical energy storage and conversion systems. However, electrochemical reactions are extremely sensitive to the physical and chemical environment. Thus, controlling the electrolyte ionic composition and the electrochemical potential of specific ions in the vicinity of the electrodes can enhance their selectivity towards the desired products and modify the overpotential of redox reactions. Here, we show that ion pumps based on a ratcheting mechanism can be used to tune the overpotentials of electrochemical reactions. Flashing ratchets are devices that utilize the temporal modulation of a spatially asymmetric electric field to drive a non-zero time-averaged current. We have recently demonstrated experimentally first-of-their-kind ratchet-based ion pumps (RBIPs). Since the ion pumping modifies the electrochemical potential of ions, and redox reaction rates are determined by the electrochemical potential, ratchet-based ion pumping provides an additional degree of freedom in tuning electrochemical reaction rates and overpotentials. RBIPs were fabricated by coating the two surfaces of nano-porous alumina wafers with gold, forming nano-porous capacitors. The electric field within the nano-pores was modulated by oscillating the capacitor voltage. When immersed in solution, ions within the pores experienced a modulating electric field, resulting in ratchet-based ion pumping. The RBIP was placed as a membrane between two Pt electrodes immersed in solution. The application of an input signal to the ratchet was shown to accelerate or inhibit redox reactions on the surface of the Pt electrodes according to the ratchet input signal properties. Ion pumping may allow tuning of the electrolyte content at the vicinity of the electrodes, thus providing another degree of freedom for the electrochemical process.

This paper proposes a framework for selecting policies that maximize expected benefit in the presence of estimation uncertainty, by controlling for estimation risk and incorporating risk aversion. The proposed method explicitly balances the size of the estimated benefit against the uncertainty inherent in its estimation, ensuring that chosen policies meet a reporting guarantee, namely that the actual benefit of the implemented policy is guaranteed not to fall below the reported estimate with a pre-specified confidence level. This approach applies to a variety of settings, including the selection of policy rules that allocate individuals to treatments based on observed characteristics, using both experimental and non-experimental data; and the allocation of limited budgets among competing social programs; as well as many others. Across these applications, the framework offers a principled and robust method for making data-driven policy choices under uncertainty. In broader terms, it focuses on policies that are on the efficient decision frontier, describing policies that offer maximum estimated benefit for a given acceptable level of estimation risk., Comment: 41 pages, 3 figures

T, p flash calculations determine the correct number of phases at phase equilibrium and their compositions for fixed temperature and pressure. They are essential for chemical process simulation and optimization. The convex envelope method (CEM) is an existing approach that employs the tangent plane criterion to determine liquid phase equilibria for chemical systems with an arbitrary number of components without providing the number of phases beforehand. This work extends the CEM to include also vapor and solid phases. Thus, any phase equilibrium of a given system with an arbitrary number of components and phases can be calculated over the whole composition space. The CEM results are presented for various vapor-liquid and solid-liquid phase equilibria examples of up to four components. We show how the CEM can be used for parameter fitting of gE-models. As an outlook, we demonstrate how the CEM can be combined with a machine learning-based tool for property prediction to construct phase equilibria.

Using the framework relating hypergeometric motives to modular forms, we define an explicit family of weight 2 Hecke eigenforms with complex multiplication. We use the theory of ${}_2F_1(1)$ hypergeometric series and Ramanujan's theory of alternative bases to compute the exact central $L$-value of these Hecke eigenforms in terms of special beta values. We also show the integral Fourier coefficients can be written in terms of Jacobi sums, reflecting a motivic relation between the hypergeometric series and the modular forms., Comment: 15 pages

This paper introduces a deep learning (DL)-based framework for task-based ultrasound (US) beamforming, aiming to enhance clinical outcomes by integrating specific clinical tasks directly into the beamforming process. Task-based beamforming optimizes the beamformer not only for image quality but also for performance on a particular clinical task, such as lesion classification. The proposed framework explores two approaches: (1) a Joint Beamformer and Classifier (JBC) that classifies the US images generated by the beamformer to provide feedback for image quality improvement; and (2) a Channel Data Classifier Beamformer (CDCB) that incorporates classification directly at the channel data representation within the beamformer's bottleneck layer. Additionally, we introduce channel data augmentations to address challenges posed by noisy and limited in-vivo data. Numerical evaluations demonstrate that training with channel data augmentations significantly improves image quality. The proposed methods were evaluated against conventional Delay-and-Sum (DAS) and Minimum Variance (MV) beamforming techniques, demonstrating superior performance in terms of both image contrast and clinical relevance. Among all methods, the CDCB approach achieves the best results, outperforming others in terms of image quality and clinical relevance. These approaches exhibit significant potential for improving clinical relevance and image quality in ultrasound imaging.

This paper presents the first effort to Extend the Investigation of Stellar Populations In RElics (E-INSPIRE). We present a catalogue of 430 spectroscopically-confirmed ultra-compact massive galaxies (UCMGs) from the Sloan Digital Sky Survey at redshifts $0.0125\%$) of their stellar mass at $z<2$. We confirm that an efficient method of finding relics is to select UCMGs with large velocity dispersion values but we believe that the most efficient way is to select high velocity dispersion objects that also have super-solar metallicities and high [Mg/Fe]., Comment: 19 pages, 21 figures, submitted to MNRAS - minor revision requested by the referee

This study investigated discrepancies between measured and simulated x-ray drive in Inertial Confinement Fusion (ICF) hohlraums at the National Ignition Facility (NIF). Despite advances in radiation-hydrodynamic simulations, a consistent "drive deficit" remains. Experimentally measured ICF capsule bang-times are systematically 400-700 ps later than simulations predict. The Build-A-Hohlraum (BAH) campaign explored potential causes for this discrepancy by systematically varying hohlraum features, including laser entrance hole (LEH) windows, capsules, and gas fills. Overall, the agreement between simulated and experimental x-ray drive was found to be largely unaffected by these changes. The data allows us to exclude some hypotheses put forward to potentially explain the discrepancy. Errors in the local thermodynamic equilibrium (LTE) atomic modeling, errors in the modeling of LEH closure and errors due to a lack of plasma species mix physics in simulations are shown to be inconsistent with our measurements. Instead, the data supports the hypothesis that errors in NLTE emission modeling are a significant contributor to the discrepancy. X-ray emission in the 2 - 4 keV range is found to be approximately 30% lower than in simulations. This is accompanied by higher than predicted electron temperatures in the gold bubble region, pointing to errors in non-LTE modeling. Introducing an opacity multiplier of 0.87 on energy groups above 1.8 keV improves agreement with experimental data, reducing the bang-time discrepancy from 300 ps to 100 ps. These results underscore the need for refined NLTE opacity models to enhance the predictive power of hohlraum simulations.

In vertex recoloring, we are given $n$ vertices with their initial coloring, and edges arrive in an online fashion. The algorithm must maintain a valid coloring by recoloring vertices, at a cost. The problem abstracts a scenario of job placement in machines (possibly in the cloud), where vertices represent jobs, colors represent machines, and edges represent ``anti affinity'' (disengagement) constraints. Online recoloring is a hard problem. One family of instances which is fairly well-understood is bipartite graphs, in which two colors are sufficient to satisfy all constraints. In this case it is known that the competitive ratio of vertex recoloring is $\Theta(\log n)$. We propose a generalization of the problem, which allows using additional colors (possibly at a higher cost), to improve overall performance. We analyze the simple case of bipartite graphs of bounded largest \emph{bond} (a bond of a connected graph is an edge-cut that partitions the graph into two connected components). First, we propose two algorithms. One exhibits a trade-off for the uniform-cost case: given $\Omega(\log\beta)\le c\le O(\log n)$ colors, the algorithm guarantees that its cost is at most $O(\frac{\log n}{c})$ times the optimal offline cost for two colors, where $n$ is the number of vertices and $\beta$ is the size of the largest bond. The other algorithm is for the case where the additional colors come at a higher cost, $D>1$: given $\Delta$ additional colors, where $\Delta$ is the maximum degree in the graph, the algorithm guarantees $O(\log D)$ competitiveness. As to lower bounds, we show that if the cost of the extra colors is $D>1$, no (randomized) algorithm can achieve a competitive ratio of $o(\log D)$. We also show that for bipartite graphs of unbounded bond size, any deterministic online algorithm has competitive ratio $\Omega(\min(D,\log n))$.

Quantum-mechanical simulations can offer atomic-level insights into chemical processes on surfaces. This understanding is crucial for the rational design of new solid catalysts as well as materials to store energy and mitigate greenhouse gases. However, achieving the accuracy needed for reliable predictions has proven challenging. Density functional theory (DFT), the workhorse quantum-mechanical method, can often lead to inconsistent predictions, necessitating accurate methods from correlated wave-function theory (cWFT). However, the high computational demands and significant user intervention associated with cWFT have traditionally made it impractical to carry out for surfaces. In this work, we address this challenge, presenting an automated framework which leverages multilevel embedding approaches, to apply accurate cWFT methods to ionic surfaces with computational costs approaching DFT. With this framework, we have reproduced experimental adsorption enthalpies for a diverse set of 19 adsorbate-surface systems. Moreover, we resolve long-standing debates on the adsorption configuration of several systems, while offering valuable benchmarks to assess DFT. This framework is completely open-source, making it possible to now routinely apply cWFT to complex problems in ionic surface chemistry.

Multiphase flows, characterized by the presence of particles, bubbles, or droplets dispersed within a fluid, are ubiquitous in natural and industrial processes. Studying densely dispersed flows in 4D (3D + time) at very small scales without introducing perturbations is challenging, but crucial to understand their macroscopic behavior. The penetration power of X-rays and the flux provided by advanced X-ray sources, such as synchrotron-radiation facilities, offer an opportunity to address this need. However, current X-ray methods at these facilities require the rotation of the sample to obtain 4D information, thus disturbing the flow. Here, we demonstrate the potential of using X-ray Multi-Projection Imaging (XMPI), a novel technique to temporally resolve any dense particle suspension flows in 4D, while eliminating the need of sample rotation. By acquiring images of a microparticle-seeded flow from multiple viewing directions simultaneously, we can determine their instantaneous three-dimensional positions, both when flowing in a simple liquid and a highly dense and opaque complex fluid (e.g. blood). Along with the recent progress in AI-supported 4D reconstruction from sparse projections, this approach creates new opportunities for high-speed rotation-free 4D microtomography, opening a new spatiotemporal frontier. With XMPI, it is now feasible to track the movement of individual microparticles within dense suspensions, extending even to the chaotic realms of turbulent flows.

We examine the families of hypergeometric series associated to a special class of modular hypergeometric Galois representations recently studied by Allen, Grove, Long, and Tu. The underlying motives allow us to express a special $L$-value of the corresponding modular form in terms of these hypergeometric series. We use extra symmetries coming from the underlying modular forms to study the transcendence of families of periods, which have the form ${}_3F_2(1)$, on hypergeometric surfaces. For several hypergeometric surfaces with four periods, we show that the vector space generated by these elements has dimension at most 2. As an application of our results, we utilize a classical formula of ${}_3F_2(1)$ series due to Kummer to find relations between $L$-values of modular forms that differ by an even twist, as predicted in a theorem of Shimura., Comment: 37 pages, including 3 figures, 4 tables, references, and a table of contents

We uncover a high-field magnetic phase in i-MAX compounds exhibiting a canted antiferromagnetic (AFM) order with unprecedented properties, revealed through NMR and AC susceptibility. Intriguingly, as the atomic number of Rare Earth increases, the transition field of this canted AFM phase grows at the expense of the lower-field AFM state. Our findings point to the complexity of the magnetic structure in i-MAX compounds, demonstrating a non-trivial evolution of their phase diagram while increasing both the atomic number of the Rare Earth element and the external field., Comment: 8 pages, 5 figures

Metal-organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics., Comment: 17 pages, 8 Figures

Legacy software systems, written in outdated languages like MUMPS and mainframe assembly, pose challenges in efficiency, maintenance, staffing, and security. While LLMs offer promise for modernizing these systems, their ability to understand legacy languages is largely unknown. This paper investigates the utilization of LLMs to generate documentation for legacy code using two datasets: an electronic health records (EHR) system in MUMPS and open-source applications in IBM mainframe Assembly Language Code (ALC). We propose a prompting strategy for generating line-wise code comments and a rubric to evaluate their completeness, readability, usefulness, and hallucination. Our study assesses the correlation between human evaluations and automated metrics, such as code complexity and reference-based metrics. We find that LLM-generated comments for MUMPS and ALC are generally hallucination-free, complete, readable, and useful compared to ground-truth comments, though ALC poses challenges. However, no automated metrics strongly correlate with comment quality to predict or measure LLM performance. Our findings highlight the limitations of current automated measures and the need for better evaluation metrics for LLM-generated documentation in legacy systems., Comment: Abbreviated version submitted to LLM4Code 2025 (a workshop co-located with ICSE 2025), 13 pages, 3 figures

Collisions play a crucial role in governing particle and energy transport in plasmas confined in a magnetic mirror trap. Modern gyrokinetic codes are used to model transport in magnetic mirrors, but some of these codes utilize approximate model collision operators. This study focuses on a Pastukhov-style method of images calculation of particle and energy confinement times using a Lenard-Bernstein model collision operator. Prior work on parallel particle and energy balances used a different Fokker-Planck plasma collision operator and the method needs to be extended in non-trivial ways to study the Lenard-Bernstein operator. To assess the effectiveness of our approach, we compare our results with a modern finite element solver. Our findings reveal that the particle confinement time scales like $a \exp(a^2)$ using the Lenard-Bernstein operator, in contrast to the more accurate scaling that the Coulomb collision operator would yield $a^2 \exp(a^2)$, where $a^2$ is approximately proportional to the ambipolar potential. We propose that codes modeling collisional losses in a magnetic mirrors utilizing the Lenard-Bernstein or Dougherty collision operator scale their collision frequency of any electrostatically confined species. This study illuminates the intricate role the collision operator plays in the Pastukhov-style method of images calculation of collisional confinement., Comment: 21 pages, 4 figures, 3 tables, submitted to journal of plasma physics

Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, critical barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long scan duration. As these systems can operate in unusual environments, the level and spectral characteristics of the environmental electromagnetic inference (EMI) noise can change substantially across sites and scans, further reducing image quality. Methods for accelerating acquisition and boosting image quality are of critical importance to enable clinically actionable high-quality imaging in these systems. Despite the role that compressed sensing (CS) and artificial intelligence (AI)-based methods have had in improving image quality for high-field MRI, their adoption for low-field imaging is in its infancy, and it is unclear how robust these methods are in low SNR regimes. Here, we investigate and compare leading CS and AI-based methods for image reconstruction from subsampled data and perform a thorough analysis of their performance across a range of SNR values. We compare classical L1-wavelet CS with leading data-driven and model-driven AI methods. Experiments are performed using publicly available datasets and our own low-field and high-field experimental data. Specifically, we apply an unrolled AI network to low-field MRI, and find it outperforms competing reconstruction methods. We prospectively deploy our undersampling methods to accelerate imaging on a 6.5 mT MRI scanner. This work highlights the potential and pitfalls of advanced reconstruction techniques in low-field MRI, paving the way for broader clinical applications.

This paper presents a comparative analysis of hallucination detection systems for AI, focusing on automatic summarization and question answering tasks for Large Language Models (LLMs). We evaluate different hallucination detection systems using the diagnostic odds ratio (DOR) and cost-effectiveness metrics. Our results indicate that although advanced models can perform better they come at a much higher cost. We also demonstrate how an ideal hallucination detection system needs to maintain performance across different model sizes. Our findings highlight the importance of choosing a detection system aligned with specific application needs and resource constraints. Future research will explore hybrid systems and automated identification of underperforming components to enhance AI reliability and efficiency in detecting and mitigating hallucinations., Comment: 18 pags, 13 figures, 2 tables

Technological trends show that Radio Frequency Reinforcement Learning (RFRL) will play a prominent role in the wireless communication systems of the future. Applications of RFRL range from military communications jamming to enhancing WiFi networks. Before deploying algorithms for these purposes, they must be trained in a simulation environment to ensure adequate performance. For this reason, we previously created the RFRL Gym: a standardized, accessible tool for the development and testing of reinforcement learning (RL) algorithms in the wireless communications space. This environment leveraged the OpenAI Gym framework and featured customizable simulation scenarios within the RF spectrum. However, the RFRL Gym was limited to training a single RL agent per simulation; this is not ideal, as most real-world RF scenarios will contain multiple intelligent agents in cooperative, competitive, or mixed settings, which is a natural consequence of spectrum congestion. Therefore, through integration with Ray RLlib, multi-agent reinforcement learning (MARL) functionality for training and assessment has been added to the RFRL Gym, making it even more of a robust tool for RF spectrum simulation. This paper provides an overview of the updated RFRL Gym environment. In this work, the general framework of the tool is described relative to comparable existing resources, highlighting the significant additions and refactoring we have applied to the Gym. Afterward, results from testing various RF scenarios in the MARL environment and future additions are discussed., Comment: Accepted to IEEE CCNC 2025. Added revisions from paper reviews

Drug discovery typically consists of multiple steps, including identifying a target protein key to a disease's etiology, validating that interacting with this target could prevent symptoms or cure the disease, discovering a small molecule or biologic therapeutic to interact with it, and optimizing the candidate molecule through a complex landscape of required properties. Drug discovery related tasks often involve prediction and generation while considering multiple entities that potentially interact, which poses a challenge for typical AI models. For this purpose we present MAMMAL - Molecular Aligned Multi-Modal Architecture and Language - a method that we applied to create a versatile multi-task multi-align foundation model that learns from large-scale biological datasets (2 billion samples) across diverse modalities, including proteins, small molecules, and genes. We introduce a prompt syntax that supports a wide range of classification, regression, and generation tasks. It allows combining different modalities and entity types as inputs and/or outputs. Our model handles combinations of tokens and scalars and enables the generation of small molecules and proteins, property prediction, and transcriptomic lab test predictions. We evaluated the model on 11 diverse downstream tasks spanning different steps within a typical drug discovery pipeline, where it reaches new SOTA in 9 tasks and is comparable to SOTA in 2 tasks. This performance is achieved while using a unified architecture serving all tasks, in contrast to the original SOTA performance achieved using tailored architectures. The model code and pretrained weights are publicly available at https://github.com/BiomedSciAI/biomed-multi-alignment and https://huggingface.co/ibm/biomed.omics.bl.sm.ma-ted-458m.

The synthesis of binary nanocrystal superlattices (BNSLs) enables the targeted integration of orthogonal physical properties, like photoluminescence and magnetism, into a single superstructure, unlocking a vast design space for multifunctional materials. Yet, the formation mechanism of BNSLs remains poorly understood, restricting the use of simulation to predict the structure and properties of the superlattices. Here, we use a combination of in situ scattering and molecular simulation to elucidate the self-assembly of two common BNSLs through emulsion templating. Our self-assembly experiments reveal that no intermediate structures precede the formation of the final binary phases, indicating that their formation proceeds through classical nucleation. Using simulations, we find that, despite the formation of AlB2 and NaZn13 typically being attributed to entropy, their self-assembly is most consistent with the nanocrystals possessing short-range interparticle attraction, which we find can dramatically accelerate nucleation kinetics in BNSLs. We also find homogenous, classical nucleation in simulation, corroborating our experiments. These results establish a robust correspondence between experiment and theory, paving the way towards a priori prediction of BNSLs.

Stein variational inference (SVI) is a sample-based approximate Bayesian inference technique that generates a sample set by jointly optimizing the samples' locations to minimize an information-theoretic measure of discrepancy with the target probability distribution. SVI thus provides a fast and significantly more sample-efficient approach to Bayesian inference than traditional (random-sampling-based) alternatives. However, the optimization techniques employed in existing SVI methods struggle to address problems in which the target distribution is high-dimensional, poorly-conditioned, or non-convex, which severely limits the range of their practical applicability. In this paper, we propose a novel trust-region optimization approach for SVI that successfully addresses each of these challenges. Our method builds upon prior work in SVI by leveraging conditional independences in the target distribution (to achieve high-dimensional scaling) and second-order information (to address poor conditioning), while additionally providing an effective adaptive step control procedure, which is essential for ensuring convergence on challenging non-convex optimization problems. Experimental results show our method achieves superior numerical performance, both in convergence rate and sample accuracy, and scales better in high-dimensional distributions, than previous SVI techniques.

How embedded, actively accreting low-mass protostars accrete their mass is still greatly debated. Observations are now piecing together the puzzle of embedded protostellar accretion, in particular with new facilities in the near-infrared. However, high-resolution theoretical models are still lacking, with a stark paucity of detailed simulations of these early phases. Here we present high-resolution non-ideal magneto-hydrodynamic simulations of a Solar mass protostar accreting at rates exceeding 10$^{-6} M_{\odot}$ yr$^{-1}$. We show the results of the accretion flow for four different protostellar magnetic fields, 10 G, 500 G, 1 kG, and 2 kG, combined with a disk magnetic field. For weaker (10 G and 500 G) protostar magnetic fields, accretion occurs via a turbulent boundary layer mode, with disk material impacting across the protostellar surface. In the 500 G model, the presence of a magnetically dominated outflow focuses the accretion towards the equator, slightly enhancing and ordering the accretion. For kG magnetic fields, the disk becomes truncated due to the protostellar dipole and exhibits magnetospheric accretion, with the 2 kG model having accretion bursts induced by the interchange instability. We present bolometric light curves for the models and find that they reproduce observations of Class I protostars from YSOVAR, with high bursts followed by an exponential decay possibly being a signature of instability-driven accretion. Finally, we present the filling fractions of accretion and find that 90\% of the mass is accreted in a surface area fraction of 10-20\%. These simulations will be extended in future work for a broader parameter space, with their high resolution and high temporal spacing able to explore a wide range of interesting protostellar physics., Comment: Accepted to A&A

Arrays of coupled superconducting qubits are analog quantum simulators able to emulate a wide range of tight-binding models in parameter regimes that are difficult to access or adjust in natural materials. In this work, we use a superconducting qubit array to emulate a tight-binding model on the rhombic lattice, which features flat bands. Enabled by broad adjustability of the dispersion of the energy bands and of on-site disorder, we examine regimes where flat-band localization and Anderson localization compete. We observe disorder-induced localization for dispersive bands and disorder-induced delocalization for flat bands. Remarkably, we find a sudden transition between the two regimes and, in its vicinity, the semblance of quantum critical scaling., Comment: 9 pages, 5 figures, and Supplementary Information

We present a comprehensive survey on the use of annotations in information visualizations, highlighting their crucial role in improving audience understanding and engagement with visual data. Our investigation encompasses empirical studies on annotations, showcasing their impact on user engagement, interaction, comprehension, and memorability across various contexts. We also study the existing tools and techniques for creating annotations and their diverse applications, enhancing the understanding of both practical and theoretical aspects of annotations in data visualization. Additionally, we identify existing research gaps and propose potential future research directions, making our survey a valuable resource for researchers, visualization designers, and practitioners by providing a thorough understanding of the application of annotations in visualization.

This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can be used to perform global optimization for non-convex perception problems. Specifically, BM leverages the low-rank structure of typical semidefinite programs to dramatically reduce the computational cost of performing optimization. This paper discusses BM in certifiable perception, with three main objectives: (i) to consolidate information from the literature into a unified presentation, (ii) to elucidate the role of the linear independence constraint qualification (LICQ), a concept not yet well-covered in certifiable perception literature, and (iii) to share practical considerations that are discussed among practitioners but not thoroughly covered in the literature. Our general aim is to offer a practical primer for applying BM towards certifiable perception., Comment: Accepted to 2024 Robotics: Science and Systems (RSS) Safe Autonomy Workshop

The planted random subgraph detection conjecture of Abram et al. (TCC 2023) asserts the pseudorandomness of a pair of graphs $(H, G)$, where $G$ is an Erdos-Renyi random graph on $n$ vertices, and $H$ is a random induced subgraph of $G$ on $k$ vertices. Assuming the hardness of distinguishing these two distributions (with two leaked vertices), Abram et al. construct communication-efficient, computationally secure (1) 2-party private simultaneous messages (PSM) and (2) secret sharing for forbidden graph structures. We prove the low-degree hardness of detecting planted random subgraphs all the way up to $k\leq n^{1 - \Omega(1)}$. This improves over Abram et al.'s analysis for $k \leq n^{1/2 - \Omega(1)}$. The hardness extends to $r$-uniform hypergraphs for constant $r$. Our analysis is tight in the distinguisher's degree, its advantage, and in the number of leaked vertices. Extending the constructions of Abram et al, we apply the conjecture towards (1) communication-optimal multiparty PSM protocols for random functions and (2) bit secret sharing with share size $(1 + \epsilon)\log n$ for any $\epsilon > 0$ in which arbitrary minimal coalitions of up to $r$ parties can reconstruct and secrecy holds against all unqualified subsets of up to $\ell = o(\epsilon \log n)^{1/(r-1)}$ parties.

The accuracy of recommender systems influences their trust and decision-making when using them. Providing additional information, such as visualizations, offers context that would otherwise be lacking. However, the role of visualizations in influencing trust and decisions with recommender systems is under-explored. To bridge this gap, we conducted a two-part human-subject experiment to investigate the impact of scatterplots on recommender system decisions. Our first study focuses on high-level decisions, such as selecting which recommender system to use. The second study focuses on low-level decisions, such as agreeing or disagreeing with a specific recommendation. Our results show scatterplots accompanied by higher levels of accuracy influence decisions and that participants tended to trust the recommendations more when scatterplots were accompanied by descriptive accuracy (e.g., \textit{high}, \textit{medium}, or \textit{low}) instead of numeric accuracy (e.g., \textit{90\%}). Furthermore, we observed scatterplots often assisted participants in validating their decisions. Based on the results, we believe that scatterplots and visualizations, in general, can aid in making informed decisions, validating decisions, and building trust in recommendation systems.

We explore the dynamics of qubit-state purity in the presence of transverse noise that is anisotropically distributed in the Bloch-sphere XY plane. We perform Ramsey experiments with noise injected along a fixed laboratory-frame axis and observe oscillations in the purity at twice the qubit frequency arising from the intrinsic qubit Larmor precession. We probe the oscillation dependence on the noise anisotropy, orientation, and power spectral density, using a low-frequency fluxonium qubit. Our results elucidate the impact of transverse noise anisotropy on qubit decoherence and may be useful to disentangle charge and flux noise in superconducting quantum circuits.

Context: Mutual neutralization between cations and anions play an important role in determining the charge-balance in certain astrophysical environments. However, empirical data for such reactions involving complex molecular species has been lacking due to challenges in performing experimental studies, leaving the astronomical community to rely on decades old models with large uncertainties for describing these processes in the interstellar medium. Aims: To investigate the mutual neutralization (MN) reaction, C$_{60}^+$ + C$_{60}^-$ $\rightarrow$ C$_{60}^*$ + C$_{60}$, for collisions at interstellar-like conditions. Methods: The mutual neutralization reaction between C$_{60}^+$ and C$_{60}^-$ at collision energies of 100\,meV was studied using the Double ElectroStatic Ion Ring ExpEriment, DESIREE, and its merged-beam capabilities. To aid in the interpretation of the experimental results, semi-classical modeling based on the Landau-Zener approach was performed for the studied reaction. Results: We experimentally identify a narrow range of kinetic energies for the neutral reaction products. Modeling was used to calculate the quantum state-selective reaction probabilities, absolute cross sections, and rate coefficients of these MN reactions, using the experimental results as a benchmark. The MN cross sections are compared with model results for electron attachment to C$_{60}$ and electron recombination with C$_{60}^+$. Conclusions: The present results show that it is crucial to take mutual polarization effects, the finite sizes, and the final quantum states of both molecular ions into account for reliable predictions of MN rates expected to strongly influence the charge-balance and chemistry in, e.g., dense molecular clouds., Comment: 9 pages, 4 figures

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using virtual instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

We propose a method to realize microwave-activated CZ gates between two remote spin qubits in quantum dots using an offset-charge-sensitive transmon coupler. The qubits are longitudinally coupled to the coupler, so that the transition frequency of the coupler depends on the logical qubit states; a capacitive network model using first-quantized charge operators is developed to illustrate this. Driving the coupler transition then implements a conditional phase shift on the qubits. Two pulsing schemes are investigated: a rapid, off-resonant pulse with constant amplitude, and a pulse with envelope engineering that incorporates dynamical decoupling to mitigate charge noise. We develop non-Markovian time-domain simulations to accurately model gate performance in the presence of $1/f^\beta$ charge noise. Simulation results indicate that a CZ gate fidelity exceeding 90% is possible with realistic parameters and noise models., Comment: 19 pages, 8 figures

Let $S$ a be locally compact space with a countable base. Let $\cal Y$ be a transient symmetric Borel right process with state space $S$ and continuous strictly positive $p$--potential densities $u^p(x,y)$. Local and uniform moduli of continuity are obtained for the local times of both fully and partially rebirthed versions of $\cal Y$. A fully rebirthed version of $\cal Y$ is an extension of $\cal Y$ so that instead of terminating at the end of its lifetime it is immediately ``reborn'' with a probability measure $\mu $, on $S$. I.e., the process goes to the set $B\subset S$ with probability $\mu(B) $, after which it continues to evolve the way $\YY$ did, being reborn with probability $\mu $ each time it dies. This rebirthed version of $\cal Y$ is a recurrent Borel right process with state space $S$ and $p$-potential densities of form, \[ u^p(x,y)+h(x,y),\qquad x,y\in S,\,\, p>0, \] where $h(x,y)$ is not symmetric. The local times of the rebirthed process are given in terms of the local times of $\cal Y$ and isomorphism theorems in the spirit of Dynkin, Eisenbam and Kaspi are obtained that relate these local times to generalized chi--square processes formed by Gaussian processes with covariances $u^{q}(x,y)$ for different values of $q$. These isomorphisms allow one to obtain exact local and uniform moduli of continuity for the local times of the rebirthed process. Several explicit examples are given in which $\cal Y$ is either a modified L\'evy process or a diffusion. Analogous results are obtained for partially rebirthed versions of $\cal Y$. This is obtained by starting $\cal Y$ in $S$ and when it dies returning it to $S$ with a sub-probability measure $\Xi$. (With probability $1-|\Xi |$ it is sent to a disjoint state space $S'$, where it remains.)

This paper explores new methods for tracking learning growth using curriculum-embedded assessments. The key objective of the study was to examine new ways to capture students' performance data to help make ecologically-valid inferences about what learners know and can do long before benchmark or end-of-year assessments. Student learning outcomes are examined through the use of embedded assessments and learning growth mapping. A study of 450,934 students finds that continuous engagement with essential skills on an educational technology platform, BrainPOP, is strongly associated with statistically significant increases in learning gains in Literacy/English Language Arts, Math and Science. Educators might consider using embedded assessments to gain access to and provide early feedback for students. By identifying gaps in students' knowledge, teachers can offer timely support to their most vulnerable students.

This paper presents a synthesis of three collaboratively conducted studies exploring the relationship between career-focused advising and the postsecondary transition with an emphasis on students enrolled in Career and Technical Education (CTE). The studies included a survey administered to high school seniors in New York City schools, an impact study of career coaches in North Carolina, and a qualitative study looking at implementation of advising in New York City and North Carolina. Key themes coming from the three studies included: 1) postsecondary transition advising is a schoolwide phenomenon; 2) the content and intensity shifts over a student's time in high school; 3) advising tends to focus more on college with less attention paid to career opportunities except in settings with an explicit career emphasis; 4) students with more advantaged backgrounds tend to participate in advising at higher levels; 5) higher participation in advising is associated with an increase in CTE-related activities; 6) college-focused advising is associated with higher enrollment in four-year schools while career-focused advising is associated with higher enrollment in two-year and lower enrollment in four-year institutions; and 7) additional research on how advising outcomes differ by student characteristics is needed. This article also summarizes key methodological takeaways about doing research related to advising. [This paper was created by the Early College Research Center, UNC Greensboro.]