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Lithium ion batteries are widely used in many applications. Battery management systems control their optimal use and charging and predict when the battery will cease to deliver the required output on a planned duty or driving cycle. Such systems use a simulation of a mathematical model of battery performance. These models can be electrochemical or data-driven. Electrochemical models for batteries running at high currents are mathematically and computationally complex. In this work, we show that a well-regarded electrochemical model, the Pseudo Two Dimensional (P2D) model, can be replaced by a computationally efficient Convolutional Neural Network (CNN) surrogate model fit to accurately simulated data from a class of random driving cycles. We demonstrate that a CNN is an ideal choice for accurately capturing Lithium ion concentration profiles. Additionally, we show how the neural network model can be adjusted to correspond to battery changes in State of Health (SOH)., Comment: 18 pages, 5 figures

We determine the asymptotic number of regular multipartite hypergraphs, also known as multidimensional binary contingency tables, for all values of the parameters.

Background: Global viral threats underscore the need for effective genomic surveillance, but high costs and uneven resource distribution hamper its implementation. Targeting surveillance to international travelers in major travel hubs may offer a more efficient strategy for the early detection of SARS-CoV-2 variants. Methods: We developed and calibrated a multiple-strain metapopulation model of global SARS-CoV-2 transmission using extensive epidemiological, phylogenetic, and high-resolution air travel data. We then compared baseline surveillance with various resource-allocation approaches that prioritize travelers, focusing on Omicron BA.1/BA.2 retrospectively and on hypothetical future variants under different emergence, transmission and vaccine effectiveness scenarios. Findings: Focusing existing surveillance resources on travelers at key global hubs significantly shortened detection delays without increasing total surveillance efforts. In retrospective analyses of Omicron BA.1/BA.2, traveler-targeted approaches consistently outperformed baseline strategies, even when overall resources were reduced. Simulations indicate that focusing surveillance on key travel hubs outperform baseline practices in detecting future variants, across different possible origins, even with reduced resources. This approach also remains effective in future pandemic scenarios with varying reproductive numbers and vaccine effectiveness. Interpretation: These findings provide a quantitative, cost-effective framework for strengthening global genomic surveillance. By reallocating resources toward international travelers in select travel hubs, early detection of emerging variants can be enhanced, informing rapid public health interventions and bolstering preparedness for future pandemics.

Freezing of gait (FOG) is a debilitating feature of Parkinson's disease (PD), which is a cause of injurious falls among PD patients. Recent advances in wearable-based human activity recognition (HAR) technology have enabled the detection of FOG subtypes across benchmark datasets. Since FOG manifestation is heterogeneous, developing models that quantify FOG consistently across patients with varying demographics, FOG types, and PD conditions is important. Bias and fairness in FOG models remain understudied in HAR, with research focused mainly on FOG detection using single benchmark datasets. We evaluated the bias and fairness of HAR models for wearable-based FOG detection across demographics and PD conditions using multiple datasets and the effectiveness of transfer learning as a potential bias mitigation approach. Our evaluation using demographic parity ratio (DPR) and equalized odds ratio (EOR) showed model bias (DPR & EOR < 0.8) for all stratified demographic variables, including age, sex, and disease duration. Our experiments demonstrated that transfer learning from multi-site datasets and generic human activity representations significantly improved fairness (average change in DPR +0.027, +0.039, respectively) and performance (average change in F1-score +0.026, +0.018, respectively) across attributes, supporting the hypothesis that generic human activity representations learn fairer representations applicable to health analytics., Comment: Submitted to IMWUT 2025

AI's integration into education promises to equip teachers with data-driven insights and intervene in student learning. Despite the intended advancements, there is a lack of understanding of interactions and emerging dynamics in classrooms where various stakeholders including teachers, students, and AI, collaborate. This paper aims to understand how students perceive the implications of AI in Education in terms of classroom collaborative dynamics, especially AI used to observe students and notify teachers to provide targeted help. Using the story completion method, we analyzed narratives from 65 participants, highlighting three challenges: AI decontextualizing of the educational context; AI-teacher cooperation with bias concerns and power disparities; and AI's impact on student behavior that further challenges AI's effectiveness. We argue that for effective and ethical AI-facilitated cooperative education, future AIEd design must factor in the situated nature of implementation. Designers must consider the broader nuances of the education context, impacts on multiple stakeholders, dynamics involving these stakeholders, and the interplay among potential consequences for AI systems and stakeholders. It is crucial to understand the values in the situated context, the capacity and limitations of both AI and humans for effective cooperation, and any implications to the relevant ecosystem., Comment: 32 pages. Accepted by ACM CSCW Conference 2025, will be published in PACM HCI 2025

Accessing data collected by federal statistical agencies is essential for public policy research and improving evidence-based decision making, such as evaluating the effectiveness of social programs, understanding demographic shifts, or addressing public health challenges. Differentially private interactive systems, or validation servers, can form a crucial part of the data-sharing infrastructure. They may allow researchers to query targeted statistics, providing flexible, efficient access to specific insights, reducing the need for broad data releases and supporting timely, focused research. However, they have not yet been practically implemented. While substantial theoretical work has been conducted on the privacy and accuracy guarantees of differentially private mechanisms, prior efforts have not considered usability as an explicit goal of interactive systems. This work outlines and considers the barriers to developing differentially private interactive systems for informing public policy and offers an alternative way forward. We propose balancing three design considerations: privacy assurance, statistical utility, and system usability, we develop recommendations for making differentially private interactive systems work in practice, we present an example architecture based on these recommendations, and we provide an outline of how to conduct the necessary user-testing. Our work seeks to move the practical development of differentially private interactive systems forward to better aid public policy making and spark future research.

This study investigates students' perceptions of Generative Artificial Intelligence (AI), with a focus on Higher Education institutions in Northern Ireland and India. We collect quantitative Likert ratings and qualitative comments from 1,211 students on their awareness and perceptions of AI and investigate variations in attitudes toward AI across institutions and subject areas, as well as interactions between these variables with demographic variables (focusing on gender). We find that: (a) while perceptions varied across institutions, responses for Computer Sciences students were similar; and (b) after controlling for institution and subject area, we observed no effect of gender. These results are consistent with previous studies, which find that students' perceptions are predicted by prior experience. We consider the implications of this relation and some considerations for the role of experience.

Accurate diagnosis of gait impairments is often hindered by subjective or costly assessment methods, with current solutions requiring either expensive multi-camera equipment or relying on subjective clinical observation. There is a critical need for accessible, objective tools that can aid in gait assessment while preserving patient privacy. In this work, we present a mobile phone-based, privacy-preserving artificial intelligence (AI) system for classifying gait impairments and introduce a novel dataset of 743 videos capturing seven distinct gait patterns. The dataset consists of frontal and sagittal views of trained subjects simulating normal gait and six types of pathological gait (circumduction, Trendelenburg, antalgic, crouch, Parkinsonian, and vaulting), recorded using standard mobile phone cameras. Our system achieved 86.5% accuracy using combined frontal and sagittal views, with sagittal views generally outperforming frontal views except for specific gait patterns like Circumduction. Model feature importance analysis revealed that frequency-domain features and entropy measures were critical for classifcation performance, specifically lower limb keypoints proved most important for classification, aligning with clinical understanding of gait assessment. These findings demonstrate that mobile phone-based systems can effectively classify diverse gait patterns while preserving privacy through on-device processing. The high accuracy achieved using simulated gait data suggests their potential for rapid prototyping of gait analysis systems, though clinical validation with patient data remains necessary. This work represents a significant step toward accessible, objective gait assessment tools for clinical, community, and tele-rehabilitation settings, Comment: 21 pages, 4 Figures, 4 Tables, Submitted to PLOS Digital Health

We present the results of a Keck and NOEMA spectroscopic survey of 507 galaxies, where we confirm the presence of two massive overdensities at $z = 3.090 - 3.110$ and $z = 3.133 - 3.155$ in the neighborhood of the GOODS-N, each with over a dozen spectroscopically confirmed members. We find that both of these have galaxy overdensities of NIR-detected galaxies of $\delta_{\rm gal, obs} = 6 - 9$ within corrected volumes of $(6 - 7) \times 10^3~{\rm cMpc}^3$. We estimate the properties of the $z = 0$ descendants of these overdensities using a spherical collapse model and find that both should virialize by $z \simeq 0.5 - 0.8$, with total masses of $M_{\rm tot} \simeq (6 - 7) \times 10^{14}~{\rm M}_\odot$. The same spherical collapse calculations, as well as a clustering-of-clusters statistical analysis, suggest a >80% likelihood that the two overdensities will collapse into a single cluster with $M_{\rm tot} = (1.0 - 1.5) \times 10^{15}~{\rm M}_\odot$ by $z \sim 0.1-0.4$. The $z = 3.14$ substructure contains a core of four bright dusty star-forming galaxies with $\Sigma {\rm SFR} = 2700 \pm 700~{\rm M}_\odot~{\rm yr}^{-1}$ in a volume of only 280 ${\rm cMpc}^3$., Comment: 18 pages, 8 figures. Accepted to ApJ

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

We prove that the Ramsey number $R(5,5)$ is less than or equal to~$46$. The proof uses a combination of linear programming and checking a large number of cases by computer. All of the computations were independently replicated.

Comet C/2017 K2 (Pan-STARRS) provided a rare opportunity to investigate the evolution of coma composition and outgassing patterns over a transitional heliocentric distance (Rh) range where activity drivers in comets are thought to change from "hypervolatile" (CO, CH$_4$, C$_2$H$_6$, and/or CO$_2$)-dominated to H2O-dominated. We performed high-resolution, cross-dispersed, near-infrared spectroscopy of C/2017 K2 with iSHELL at the NASA Infrared Telescope Facility (IRTF) and NIRSPEC at the W. M. Keck 2 Observatory. We report gas rotational temperatures (Trot) and molecular production rates (Q; mol/s) or upper limits for the hypervolatile species CH$_4$, CO, and C$_2$H$_6$, together with less volatile ices (CH$_3$OH, H$_2$O, HCN, C$_2$H$_2$, NH$_3$, and OCS) over a range of pre-perihelion distances, Rh= 3.15 - 2.35 au. We also report (or stringently constrain) abundance ratios (mixing ratios) of the targeted species with respect to CO, C$_2$H$_6$, and (when detected) H$_2$O. All volatiles were enriched relative to water in C/2017 K2 when compared to their mean values among Oort Cloud comets, whereas abundances relative to C2H6 were consistent with their average values from other long-period comets., Comment: Submitted to the Astronomical Journal for publication

The logarithm of the number of Eulerian orientations, scaled by the number of vertices, is known as the residual entropy in studies of ice-type models on graphs. The spanning tree entropy depends similarly on the number of spanning trees. We demonstrate and investigate a remarkably strong, though non-deterministic, correlation between these two entropies. This leads us to propose a new heuristic estimate for the residual entropy of regular graphs that performs much better than previous heuristics. We also study the expansion properties and residual entropy of random graphs with given degrees.

Quantum error mitigation is a promising route to achieving quantum utility, and potentially quantum advantage in the near-term. Many state-of-the-art error mitigation schemes use knowledge of the errors in the quantum processor, which opens the question to what extent inaccuracy in the error model impacts the performance of error mitigation. In this work, we develop a methodology to efficiently compute upper bounds on the impact of error-model inaccuracy in error mitigation. Our protocols require no additional experiments, and instead rely on comparisons between the error model and the error-learning data from which the model is generated. We demonstrate the efficacy of our methodology by deploying it on an IBM Quantum superconducting qubit quantum processor, and through numerical simulation of standard error models. We show that our estimated upper bounds are typically close to the worst observed performance of error mitigation on random circuits. Our methodology can also be understood as an operationally meaningful metric to assess the quality of error models, and we further extend our methodology to allow for comparison between error models. Finally, contrary to what one might expect we show that observable error in noisy layered circuits of sufficient depth is not always maximized by a Clifford circuit, which may be of independent interest., Comment: 22 pages including references and appendices, 9 figures

Dynamic circuit operations -- measurements with feedforward -- are important components for future quantum computing efforts, but lag behind gates in the availability of characterization methods. Here we introduce a series of dynamic circuit benchmarking routines based on interleaving dynamic circuit operation blocks $F$ in one-qubit randomized benchmarking sequences of data qubits. $F$ spans between the set of data qubits and a measurement qubit and may include feedforward operations based on the measurement. We identify six candidate operation blocks, such as preparing the measured qubit in $|0\rangle$ and performing a $Z$-Pauli on the data qubit conditioned on a measurement of `1'. Importantly, these blocks provide a methodology to accumulate readout assignment errors in a long circuit sequence. We also show the importance of dynamic-decoupling in reducing ZZ crosstalk and measurement-induced phase errors during dynamic circuit blocks. When measured on an IBM Eagle device with appropriate dynamical decoupling, the results are consistent with measurement assignment error and the decoherence of the data qubit as the leading error sources.

We present a resonant-mode, transverse-field, radio-frequency (rf) neutron spin flipper design that uses high-temperature superconducting films to ensure sharp transitions between uniform magnetic field regions. Resonant mode allows for low power, high frequency operation but requires strict homogeneity of the magnetic fields inside the device. This design was found to efficiently flip neutrons at 96.6$\pm 0.6\%$ at an effective frequency of 4 MHz with a beam size of $2.5~\times~2.5$~cm and a wavelength of 0.4 nm. The high frequency and efficiency enable this device to perform high-resolution neutron spectroscopy with comparable performance to currently implemented rf flipper designs. The limitation of the maximum frequency was found due to the field homogeneity of the device. We numerically analyze the maximum possible efficiency of this design using a Bloch solver simulation with magnetic fields generated from finite-element simulations. We also discuss future improvements of the efficiency and frequency to the design based on the experimental and simulation results.

This paper considers a natural ruleset for playing a partisan combinatorial game on a directed graph, which we call Digraph Placement. Given a digraph $G$ with a not necessarily proper $2$-coloring of $V(G)$, the Digraph Placement game played on $G$ by the players Left and Right, who play alternately, is defined as follows. On her turn, Left chooses a blue vertex which is deleted along with all of its out-neighbours. On his turn Right chooses a red vertex, which is deleted along with all of its out-neighbours. A player loses if on their turn they cannot move. We show constructively that Digraph Placement is a universal partisan ruleset; for all partisan combinatorial games $X$ there exists a Digraph Placement game, $G$, such that $G = X$. Digraph Placement and many other games including Nim, Poset Game, Col, Node Kayles, Domineering, and Arc Kayles are instances of a class of placement games that we call conflict placement games. We prove that $X$ is a conflict placement game if and only if it has the same literal form as a Digraph Placement game. A corollary of this is that deciding the winner of a Digraph Placement game is PSPACE-hard. Next, for a game value $X$ we prove bounds on the order of a smallest Digraph Placement game $G$ such that $G = X$., Comment: 30 pages, 3 figures, 1 appendix

The Goos-H\"anchen (GH) shift describes a phenomenon in which a specularly-reflected beam is laterally translated along the reflecting surface such that the incident and reflected rays no longer intersect at the surface. Using a neutron spin-echo technique and a specially-designed magnetic multilayer mirror, we have measured the relative phase between the reflected up and down neutron spin states in total reflection. The relative GH shift calculated from this phase shows a strong resonant enhancement at a particular incident neutron wavevector, which is due to a waveguiding effect in one of the magnetic layers. Calculations based on the observed phase difference between the neutron states indicate a propagation distance along the waveguide layer of 0.65 mm for the spin-down state, which we identify with the magnitude of the giant GH shift. The existence of a physical GH shift is confirmed by the observation of neutron absorption in the waveguide layer. We propose ways in which our experimental method may be exploited for neutron quantum-enhanced sensing of thin magnetic layers.