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Mastery learning -- the process by which students must demonstrate proficiency with a single topic before moving on -- is well recognized as one of the best ways to learn, yet many teachers struggle or remain unsure about how to implement it into a classroom setting. This study leverages two field experiments to test the efficacy of a program designed to encourage greater mastery learning through technology and proactive continuous teacher support. Focusing on elementary and middle school mathematics, teachers receive weekly coaching in how to use Computer Assisted Learning (CAL) for students to follow a customized roadmap of incremental progress. Results indicate significant intent-to-treat effects on math performance of 0.12-0.22 standard deviations. Further analysis shows that these gains are concentrated among students in classrooms with at least an average of 35 minutes of practice per week. Teachers able to achieve high-dosage practice have a high degree of initial buy-in, a clear implementation strategy for when practice occurs, and a willingness to closely monitor progress and follow-up with struggling students. [The Wilson Sheehan Lab for Economic Opportunities (LEO) at Notre Dame provided additional support for this research.]

Aluminum scandium nitride (AlScN) has emerged as a highly promising material for high-temperature applications due to its robust piezoelectric, ferroelectric, and dielectric properties. This study investigates the behavior of Al0.7Sc0.3N thin films in extreme thermal environments, demonstrating functional stability up to 1000{\deg}C, making it suitable for use in aerospace, hypersonics, deep-well, and nuclear reactor systems. Tantalum silicide (TaSi2)/Al0.7Sc0.3N/TaSi2 capacitors were fabricated and characterized across a wide temperature range, revealing robust ferroelectric and dielectric properties, along with significant enhancement in piezoelectric performance. At 1000{\deg}C, the ferroelectric hysteresis loops showed a substantial reduction in coercive field from 4.3 MV/cm to 1.2 MV/cm, while the longitudinal piezoelectric coefficient increased nearly tenfold, reaching 75.1 pm/V at 800{\deg}C. Structural analysis via scanning and transmission electron microscopy confirmed the integrity of the TaSi2/Al0.7Sc0.3N interfaces, even after exposure to extreme temperatures. Furthermore, the electromechanical coupling coefficient was calculated to increase by over 500%, from 12.9% at room temperature to 82% at 700{\deg}C. These findings establish AlScN as a versatile material for high-temperature ferroelectric, piezoelectric, and dielectric applications, offering unprecedented thermal stability and functional enhancement.

Side channels have become an essential component of many modern information-theoretic schemes. The emerging field of cross technology communications (CTC) provides practical methods for creating intentional side channels between existing communications technologies. This paper describes a theoretical foundation for one such, recently proposed, CTC scheme: Ghost Modulation (GM). Designed to modulate a low-data-rate message atop an existing network stream, GM is particularly suited for transmitting identification or covert information. The implementation only requires firmware updates to existing hardware, making it a cost-effective solution. However, GM provides an interesting technical challenge due to a highly asymmetric binary crossover erasure channel (BCEC) that results from packet drops and network delays. In this work, we provide a mathematical description of the signal and channel models for GM. A heuristic decision rule based on maximum-likelihood principles for simplified channel models is proposed. We describe an algorithm for GM packet acquisition and timing synchronization, supported by simulation results. Several well known short block codes are applied, and bit error rate (BER) results are presented., Comment: 7 Pages, 5 Figures

Large Audio-Language Models (LALMs) have unclocked audio dialogue capabilities, where audio dialogues are a direct exchange of spoken language between LALMs and humans. Recent advances, such as GPT-4o, have enabled LALMs in back-and-forth audio dialogues with humans. This progression not only underscores the potential of LALMs but also broadens their applicability across a wide range of practical scenarios supported by audio dialogues. However, given these advancements, a comprehensive benchmark to evaluate the performance of LALMs in the open-ended audio dialogue understanding remains absent currently. To address this gap, we propose an Audio Dialogue Understanding Benchmark (ADU-Bench), which consists of 4 benchmark datasets. They assess the open-ended audio dialogue ability for LALMs in 3 general scenarios, 12 skills, 9 multilingual languages, and 4 categories of ambiguity handling. Notably, we firstly propose the evaluation of ambiguity handling in audio dialogues that expresses different intentions beyond the same literal meaning of sentences, e.g., "Really!?" with different intonations. In summary, ADU-Bench includes over 20,000 open-ended audio dialogues for the assessment of LALMs. Through extensive experiments conducted on 13 LALMs, our analysis reveals that there is still considerable room for improvement in the audio dialogue understanding abilities of existing LALMs. In particular, they struggle with mathematical symbols and formulas, understanding human behavior such as roleplay, comprehending multiple languages, and handling audio dialogue ambiguities from different phonetic elements, such as intonations, pause positions, and homophones.

We consider succinct data structures for representing a set of $n$ horizontal line segments in the plane given in rank space to support \emph{segment access}, \emph{segment selection}, and \emph{segment rank} queries. A segment access query finds the segment $(x_1, x_2, y)$ given its $y$-coordinate ($y$-coordinates of the segments are distinct), a segment selection query finds the $j$th smallest segment (the segment with the $j$th smallest $y$-coordinate) among the segments crossing the vertical line for a given $x$-coordinate, and a segment rank query finds the number of segments crossing the vertical line through $x$-coordinate $i$ with $y$-coordinate at most $y$, for a given $x$ and $y$. This problem is a central component in compressed data structures for persistent strings supporting random access. Our main result is data structure using $2n\lg{n} + O(n\lg{n}/\lg{\lg{n}})$ bits of space and $O(\lg{n}/\lg{\lg{n}})$ query time for all operations. We show that this space bound is optimal up to lower-order terms. We will also show that the query time for segment rank is optimal. The query time for segment selection is also optimal by a previous bound. To obtain our results, we present a novel segment wavelet tree data structure of independent interest. This structure is inspired by and extends the classic wavelet tree for sequences. This leads to a simple, succinct solution with $O(\log n)$ query times. We then extend this solution to obtain optimal query time. Our space lower bound follows from a simple counting argument, and our lower bound for segment rank is obtained by a reduction from 2-dimensional counting.

Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports and other sensor information, such as EEG, ECG, EMG and so on. Despite the common data standardisation challenge within medical image analysis domain, the future of deep learning in stroke outcome prediction lie in using multimodal information, including final infarct data, to achieve better prediction of long-term functional outcomes. This article provides a broad review of recent advances and applications of deep learning in the prediction of stroke outcomes, including (i) the data and models used, (ii) the prediction tasks and measures of success, (iii) the current challenges and limitations, and (iv) future directions and potential benefits. This comprehensive review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field., Comment: The paper is under consideration at Biomedical Engineering Letters (Springer)

We prove two results concerning percolation on general graphs. - We establish the converse of the classical Peierls argument: if the critical parameter for (uniform) percolation satisfies $p_c<1$, then the number of minimal cutsets of size $n$ separating a given vertex from infinity is bounded above exponentially in $n$. This resolves a conjecture of Babson and Benjamini from 1999. - We prove that $p_c<1$ for every uniformly transient graph. This solves a problem raised by Duminil-Copin, Goswami, Raoufi, Severo and Yadin, and provides a new proof that $p_c<1$ for every transitive graph of superlinear growth., Comment: 12 pages

Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both approaches for enhanced performance. Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate submodules while sharing a unified embedding space optimized jointly. To enable positive knowledge transfer, we design a loss function that enforces alignment and uniformity both within and across submodules. Experiments on three real-world datasets demonstrate that the proposed method significantly outperforms using either approach alone and achieves state-of-the-art results. Our implementations are publicly available at https://github.com/YuweiCao-UIC/GSAU.git., Comment: Under review

The accurate computational study of wavepacket nuclear dynamics is considered to be a classically intractable problem, particularly with increasing dimensions. Here we present two algorithms that, in conjunction with other methods developed by us, will form the basis for performing quantum nuclear dynamics in arbitrary dimensions. For one algorithm, we present a direct map between the Born-Oppenheimer Hamiltonian describing the wavepacket time-evolution and the control parameters of a spin-lattice Hamiltonian that describes the dynamics of qubit states in an ion-trap quantum computer. This map is exact for three qubits, and when implemented, the dynamics of the spin states emulate those of the nuclear wavepacket. However, this map becomes approximate as the number of qubits grow. In a second algorithm we present a general quantum circuit decomposition formalism for such problems using a method called the Quantum Shannon Decomposition. This algorithm is more robust and is exact for any number of qubits, at the cost of increased circuit complexity. The resultant circuit is implemented on IBM's quantum simulator (QASM) for 3-7 qubits. In both cases the wavepacket dynamics is found to be in good agreement with the classical result and the corresponding vibrational frequencies obtained from the wavepacket density time-evolution, are in agreement to within a few tenths of a wavenumbers.

Pb on Si(111)-(7x7) shows surprising nucleation and mass transport dynamics at odds with standard theories. To create local imbalances on stable Pb islands we use the tip of a scanning force microscope. We enforce a short, local contact between the island and our tip. The subsequent island height growth and the local contact potential difference are studied via scanning force microscopy and Kelvin probe force microscopy. Though the island has a large volume increase after the contact, we observe that its surrounding wetting layer shows the same Pb density decrease as the global wetting layer. This indicates a collective density thinning of the wetting layer., Comment: 9 pages, 2 figures in main text and 5 figures in the Appendix; Paper accepted by Physical Review B: https://journals.aps.org/prb/accepted/5e078Yf5E3015a8088ba499754fdeefbd71da71aa

We present the first multi-band centimeter detection of POX 52, a nearby dwarf galaxy believed to habor a robust intermediate mass black hole (IMBH). We conducted the deep observations using the Australia Telescope Compact Array (ATCA), spanning frequencies from 4.5 to 10 GHz, as well as the sensitive observations from the Karl G. Jansky Very Large Array (VLA) operating in its most extended A-configuration at S band (2--4 GHz) and C band (4--8 GHz). In the ATCA observations, the source shows a compact morphology, with only one direction marginally resolved. The higher resolution of the VLA allowed us to slightly resolve the source, fitting it well with a two-dimensional Gaussian model. The detected radio emission confirms the presence of Active Galactic Nucleus (AGN) activity, indicating either a low-power jet or AGN-driven winds/outflows. Our dual-epoch observations with ATCA and VLA, together with previous non-detection flux density upper limits, reveal radio emission variability spanning two decades. In addition, we find that POX 52 aligns well with the low-mass extension of the fundamental plane for high-accretion, radio-quiet massive AGNs., Comment: Submitted to ApJ; 11 pages, 3 figures

Most atomistic machine learning (ML) models rely on a locality ansatz, and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by long-range physical effects - most notably electrostatics. Many approaches have been proposed to overcome these limitations, but efforts to make them efficient and widely available are hampered by the need to incorporate an ad hoc implementation of methods to treat long-range interactions. We develop a framework aiming to bring some of the established algorithms to evaluate non-bonded interactions - including Ewald summation, classical particle-mesh Ewald (PME), and particle-particle/particle-mesh (P3M) Ewald - into atomistic ML. We provide a reference implementation for PyTorch as well as an experimental one for JAX. Beyond Coulomb and more general long-range potentials, we introduce purified descriptors which disregard the immediate neighborhood of each atom, and are more suitable for general long-ranged ML applications. Our implementations are fast, feature-rich, and modular: They provide an accurate evaluation of physical long-range forces that can be used in the construction of (semi)empirical baseline potentials; they exploit the availability of automatic differentiation to seamlessly combine long-range models with conventional, local ML schemes; and they are sufficiently flexible to implement more complex architectures that use physical interactions as building blocks. We benchmark and demonstrate our torch-pme and jax-pme libraries to perform molecular dynamics simulations, to train range-separated ML potentials, and to evaluate long-range equivariant descriptors of atomic structures.

In this paper, we introduce the first diffusion model designed to generate complete synthetic human genotypes, which, by standard protocols, one can straightforwardly expand into full-length, DNA-level genomes. The synthetic genotypes mimic real human genotypes without just reproducing known genotypes, in terms of approved metrics. When training biomedically relevant classifiers with synthetic genotypes, accuracy is near-identical to the accuracy achieved when training classifiers with real data. We further demonstrate that augmenting small amounts of real with synthetically generated genotypes drastically improves performance rates. This addresses a significant challenge in translational human genetics: real human genotypes, although emerging in large volumes from genome wide association studies, are sensitive private data, which limits their public availability. Therefore, the integration of additional, insensitive data when striving for rapid sharing of biomedical knowledge of public interest appears imperative.

We classify the shift operators for the symmetric Askey-Wilson polynomials and construct shift operators for the non-symmetric Askey-Wilson polynomials using two decompositions of non-symmetric Askey-Wilson polynomials in terms of symmetric ones. These shift operators are difference-reflection operators, and we discuss the conditions under which they restrict to shift operators for the symmetric Askey-Wilson polynomials. We use them to compute the norms of the non-symmetric Askey-Wilson polynomials and compute their specialisations for $q\to1$. These turn out to be shift operators for the non-symmetric Heckman-Opdam polynomials of type $BC_1$ that have recently been found., Comment: 57 pages

I present a novel formulation of substitution, where facts about substitution that previously required tens or hundreds of lines to justify in a proof assistant now follow immediately - they can be justified by writing the four letters "refl". The paper is an executable literate Agda script, and source of the paper is available as an artifact in the file Weaken.lagda.md. Not all consequences of the pandemic have been awful. For the last three years, I've had the great pleasure of meeting with Peter Thiemann and Jeremy Siek for a couple of hours every week, via Zoom, exploring topics including core calculi, gradual typing, and formalisation in Agda. The work reported here arose from those discussions, and is dedicated to Peter on the occasion of his 60th birthday., Comment: In Proceedings PT 2024, arXiv:2412.01856

Diffusion models have achieved impressive advancements in various vision tasks. However, these gains often rely on increasing model size, which escalates computational complexity and memory demands, complicating deployment, raising inference costs, and causing environmental impact. While some studies have explored pruning techniques to improve the memory efficiency of diffusion models, most existing methods require extensive retraining to retain the model performance. Retraining a modern large diffusion model is extremely costly and resource-intensive, which limits the practicality of these methods. In this work, we achieve low-cost diffusion pruning without retraining by proposing a model-agnostic structural pruning framework for diffusion models that learns a differentiable mask to sparsify the model. To ensure effective pruning that preserves the quality of the final denoised latent, we design a novel end-to-end pruning objective that spans the entire diffusion process. As end-to-end pruning is memory-intensive, we further propose time step gradient checkpointing, a technique that significantly reduces memory usage during optimization, enabling end-to-end pruning within a limited memory budget. Results on state-of-the-art U-Net diffusion models SDXL and diffusion transformers (FLUX) demonstrate that our method can effectively prune up to 20% parameters with minimal perceptible performance degradation, and notably, without the need for model retraining. We also showcase that our method can still prune on top of time step distilled diffusion models., Comment: Project page: https://yangzhang-v5.github.io/EcoDiff

The two-qubit controlled-not (C-NOT) gate is an essential component in the construction of a gate-based quantum computer. In fact, its operation, combined with single qubit rotations allows to realise any quantum circuit. Several strategies have been adopted in order to build quantum gates, among them the photonic one offers the dual advantage of excellent isolation from the external environment and ease of manipulation at the single qubit level. Here we adopt a scalable time-multiplexed approach in order to build a fully reconfigurable architecture capable of implementing a post-selected interferometric scheme that implements the C-NOT operation with a fidelity of $(93.8\pm1.4)\%$. We use our experimental platform to generate the four Bell states., Comment: 9 pages, 4 figures

The volume of data generated and stored in contemporary global data centers is experiencing exponential growth. This rapid data growth necessitates efficient processing and analysis to extract valuable business insights. In distributed data processing systems, data undergoes exchanges between the compute servers that contribute significantly to the total data processing duration in adequately large clusters, necessitating efficient data transport protocols. Traditionally, data transport frameworks such as JDBC and ODBC have used TCP/IP-over-Ethernet as their underlying network protocol. Such frameworks require serializing the data into a single contiguous buffer before handing it off to the network card, primarily due to the requirement of contiguous data in TCP/IP. In OLAP use cases, this serialization process is costly for columnar data batches as it involves numerous memory copies that hurt data transport duration and overall data processing performance. We study the serialization overhead in the context of a widely-used columnar data format, Apache Arrow, and propose leveraging RDMA to transport Arrow data over Infiniband in a zero-copy manner. We design and implement Thallus, an RDMA-based columnar data transport protocol for Apache Arrow based on the Thallium framework from the Mochi ecosystem, compare it with a purely Thallium RPC-based implementation, and show substantial performance improvements can be achieved by using RDMA for columnar data transport.

Inverse Reinforcement Learning (IRL) has demonstrated effectiveness in a variety of imitation tasks. In this paper, we introduce an IRL framework designed to extract rewarding features from expert trajectories affected by delayed disturbances. Instead of relying on direct observations, our approach employs an efficient off-policy adversarial training framework to derive expert features and recover optimal policies from augmented delayed observations. Empirical evaluations in the MuJoCo environment under diverse delay settings validate the effectiveness of our method. Furthermore, we provide a theoretical analysis showing that recovering expert policies from augmented delayed observations outperforms using direct delayed observations.

Code summarization facilitates program comprehension and software maintenance by converting code snippets into natural-language descriptions. Over the years, numerous methods have been developed for this task, but a key challenge remains: effectively evaluating the quality of generated summaries. While human evaluation is effective for assessing code summary quality, it is labor-intensive and difficult to scale. Commonly used automatic metrics, such as BLEU, ROUGE-L, METEOR, and BERTScore, often fail to align closely with human judgments. In this paper, we explore the potential of Large Language Models (LLMs) for evaluating code summarization. We propose CODERPE (Role-Player for Code Summarization Evaluation), a novel method that leverages role-player prompting to assess the quality of generated summaries. Specifically, we prompt an LLM agent to play diverse roles, such as code reviewer, code author, code editor, and system analyst. Each role evaluates the quality of code summaries across key dimensions, including coherence, consistency, fluency, and relevance. We further explore the robustness of LLMs as evaluators by employing various prompting strategies, including chain-of-thought reasoning, in-context learning, and tailored rating form designs. The results demonstrate that LLMs serve as effective evaluators for code summarization methods. Notably, our LLM-based evaluator, CODERPE , achieves an 81.59% Spearman correlation with human evaluations, outperforming the existing BERTScore metric by 17.27%.

In invasive breast cancer, cell clusters of varying sizes and shapes are embedded in the fibrous extracellular matrix (ECM). Although the prevailing view attributes this structure to increasing disorder resulting from loss of function and dedifferentiation, our findings reveal that it arises through a process of active self-organization driven by cancer cell motility. Simulations and histological analyses of tumours from over 2,000 breast cancer patients reveal that motile, aligned cancer cells within clusters move as active nematic aggregates through the surrounding highly aligned ECM fibres, which form a confining, passive nematic phase. Cellular motion leads to cluster splitting and coalescence. The degree of cluster activity, combined with heterogeneity in cell motility, is reflected in specific scaling behaviours for cluster shape, size distribution, and the distance between cluster boundaries and nematic defects in ECM alignment. Increased activity estimates correlate with tumour progression and are associated with a poorer prognosis for patients.

Enabling effective collaboration among LLMs is a crucial step toward developing autonomous systems capable of solving complex problems. While LLMs are typically used as single-model generators, where humans critique and refine their outputs, the potential for jointly-trained collaborative models remains largely unexplored. Despite promising results in multi-agent communication and debate settings, little progress has been made in training models to work together on tasks. In this paper, we present a first step toward "Multi-agent LLM training" (MALT) on reasoning problems. Our approach employs a sequential multi-agent setup with heterogeneous LLMs assigned specialized roles: a generator, verifier, and refinement model iteratively solving problems. We propose a trajectory-expansion-based synthetic data generation process and a credit assignment strategy driven by joint outcome based rewards. This enables our post-training setup to utilize both positive and negative trajectories to autonomously improve each model's specialized capabilities as part of a joint sequential system. We evaluate our approach across MATH, GSM8k, and CQA, where MALT on Llama 3.1 8B models achieves relative improvements of 14.14%, 7.12%, and 9.40% respectively over the same baseline model. This demonstrates an early advance in multi-agent cooperative capabilities for performance on mathematical and common sense reasoning questions. More generally, our work provides a concrete direction for research around multi-agent LLM training approaches., Comment: Preliminary work

This paper presents a unifying framework for Trefftz-like methods, which allows the analysis and construction of discretization methods based on the decomposition into, and coupling of, local and global problems. We apply the framework to provide a comprehensive error analysis for the Embedded Trefftz discontinuous Galerkin method, for a wide range of second-order scalar elliptic partial differential equations and a scalar reaction-advection problem. We also analyze quasi-Trefftz methods with our framework and build bridges to other methods that are similar in virtue., Comment: 43 pages, 6 figures

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation., Comment: Accepted to COLING 2025

While Transformers have revolutionized machine learning on various data, existing Transformers for temporal graphs face limitations in (1) restricted receptive fields, (2) overhead of subgraph extraction, and (3) suboptimal generalization capability beyond link prediction. In this paper, we rethink temporal graph Transformers and propose TGTOD, a novel end-to-end Temporal Graph Transformer for Outlier Detection. TGTOD employs global attention to model both structural and temporal dependencies within temporal graphs. To tackle scalability, our approach divides large temporal graphs into spatiotemporal patches, which are then processed by a hierarchical Transformer architecture comprising Patch Transformer, Cluster Transformer, and Temporal Transformer. We evaluate TGTOD on three public datasets under two settings, comparing with a wide range of baselines. Our experimental results demonstrate the effectiveness of TGTOD, achieving AP improvement of 61% on Elliptic. Furthermore, our efficiency evaluation shows that TGTOD reduces training time by 44x compared to existing Transformers for temporal graphs. To foster reproducibility, we make our implementation publicly available at https://github.com/kayzliu/tgtod., Comment: Preprint. Under review. Code available at https://github.com/kayzliu/tgtod

The aim of this paper is to extend worst risk minimization, also called worst average loss minimization, to the functional realm. This means finding a functional regression representation that will be robust to future distribution shifts on the basis of data from two environments. In the classical non-functional realm, structural equations are based on a transfer matrix $B$. In section~\ref{sec:sfr}, we generalize this to consider a linear operator $\mathcal{T}$ on square integrable processes that plays the the part of $B$. By requiring that $(I-\mathcal{T})^{-1}$ is bounded -- as opposed to $\mathcal{T}$ -- this will allow for a large class of unbounded operators to be considered. Section~\ref{sec:worstrisk} considers two separate cases that both lead to the same worst-risk decomposition. Remarkably, this decomposition has the same structure as in the non-functional case. We consider any operator $\mathcal{T}$ that makes $(I-\mathcal{T})^{-1}$ bounded and define the future shift set in terms of the covariance functions of the shifts. In section~\ref{sec:minimizer}, we prove a necessary and sufficient condition for existence of a minimizer to this worst risk in the space of square integrable kernels. Previously, such minimizers were expressed in terms of the unknown eigenfunctions of the target and covariate integral operators (see for instance \cite{HeMullerWang} and \cite{YaoAOS}). This means that in order to estimate the minimizer, one must first estimate these unknown eigenfunctions. In contrast, the solution provided here will be expressed in any arbitrary ON-basis. This completely removes any necessity of estimating eigenfunctions. This pays dividends in section~\ref{sec:estimation}, where we provide a family of estimators, that are consistent with a large sample bound. Proofs of all the results are provided in the appendix.

Br\"{a}nd\'{e}n and Claesson introduced the concept of mesh patterns in 2011, and since then, these patterns have attracted significant attention in the literature. Subsequently, in 2015, Hilmarsson \emph{et al.} initiated the first systematic study of avoidance of mesh patterns, while Kitaev and Zhang conducted the first systematic study of the distribution of mesh patterns in 2019. A permutation $\sigma = \sigma_1 \sigma_2 \cdots \sigma_n$ in the symmetric group $S_n$ is called a king permutation if $\left| \sigma_{i+1}-\sigma_i \right| > 1$ for each $1 \leq i \leq n-1$. Riordan derived a recurrence relation for the number of such permutations in 1965. The generating function for king permutations was obtained by Flajolet and Sedgewick in 2009. In this paper, we initiate a systematic study of the distribution of mesh patterns on king permutations by finding distributions for 22 mesh patterns of short length., Comment: 26 pages

Match-cuts are powerful cinematic tools that create seamless transitions between scenes, delivering strong visual and metaphorical connections. However, crafting match-cuts is a challenging, resource-intensive process requiring deliberate artistic planning. In MatchDiffusion, we present the first training-free method for match-cut generation using text-to-video diffusion models. MatchDiffusion leverages a key property of diffusion models: early denoising steps define the scene's broad structure, while later steps add details. Guided by this insight, MatchDiffusion employs "Joint Diffusion" to initialize generation for two prompts from shared noise, aligning structure and motion. It then applies "Disjoint Diffusion", allowing the videos to diverge and introduce unique details. This approach produces visually coherent videos suited for match-cuts. User studies and metrics demonstrate MatchDiffusion's effectiveness and potential to democratize match-cut creation., Comment: https://matchdiffusion.github.io

The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces significant challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled outcomes. In this context, we leverage a linked EHR dataset of approximately one million de-identified individuals from Bristol, North Somerset, and South Gloucestershire, UK, to characterize urinary tract infections (UTIs) and develop predictive models focused on data quality, fairness and transparency. A comprehensive data pre-processing and curation pipeline transforms the raw EHR data into a structured format suitable for AI modeling. Given the limited availability and biases of ground truth UTI outcomes, we introduce a UTI risk estimation framework informed by clinical expertise to estimate UTI risk across individual patient timelines. Using this framework, we built pairwise XGBoost models to differentiate UTI risk categories with explainable AI techniques to identify key predictors while ensuring interpretability. Our findings reveal differences in clinical and demographic factors across risk groups, offering insights into UTI risk stratification and progression. This study demonstrates the added value of AI-driven insights into UTI clinical decision-making while prioritizing interpretability, transparency, and fairness, underscoring the importance of sound data practices in advancing health outcomes.

Air quality has important climate and health effects. There is a need, therefore, to monitor air quality both indoors and outdoors. Methods of measuring air quality should be cost-effective if they are to be used widely, and one such method is low-cost sensors (LCS). This study reports on the use of LCSs in Ulaanbaatar, Mongolia, to measure $\mathrm{PM_{2.5}}$ concentrations inside yurts or "gers." Some of these gers were part of a non-government agency (NGO) initiative to improve the insulating properties of these housing structures. The goal of the NGO was to decrease particulate emissions inside the gers; a secondary result was to lower the use of coal and other biomass material. LCSs were installed in gers heated primarily by coal, and interior air quality was measured. Gers that were modified by increasing their insulating capacities showed a 17.5% reduction in $\mathrm{PM_{2.5}}$ concentrations, but these concentrations remained higher than levels recommended by health organizations. Gers that were insulated and used a combination of both coal and electricity showed a 19.1% reduction in $\mathrm{PM_{2.5}}$ concentrations. Insulated gers that used electricity for both heating and cooking showed a 48% reduction in $\mathrm{PM_{2.5}}$, though concentrations were still 6.4 times higher than those recommended by the World Health Organization (WHO). Nighttime and daytime trends followed similar patterns in $\mathrm{PM_{2.5}}$ concentrations with slight variations. It was found that, at nighttime, the outside $\mathrm{PM_{2.5}}$ concentrations were generally higher than the inside concentrations of the gers in this study. This suggests that $\mathrm{PM_{2.5}}$ would flow into the gers whenever the doors were opened, causing spikes in $\mathrm{PM_{2.5}}$ concentrations.

We present three biometric datasets (iCarB-Face, iCarB-Fingerprint, iCarB-Voice) containing face videos, fingerprint images, and voice samples, collected inside a car from 200 consenting volunteers. The data was acquired using a near-infrared camera, two fingerprint scanners, and two microphones, while the volunteers were seated in the driver's seat of the car. The data collection took place while the car was parked both indoors and outdoors, and different "noises" were added to simulate non-ideal biometric data capture that may be encountered in real-life driver recognition. Although the datasets are specifically tailored to in-vehicle biometric recognition, their utility is not limited to the automotive environment. The iCarB datasets, which are available to the research community, can be used to: (i) evaluate and benchmark face, fingerprint, and voice recognition systems (we provide several evaluation protocols); (ii) create multimodal pseudo-identities, to train/test multimodal fusion algorithms; (iii) create Presentation Attacks from the biometric data, to evaluate Presentation Attack Detection algorithms; (iv) investigate demographic and environmental biases in biometric systems, using the provided metadata. To the best of our knowledge, ours are the largest and most diverse publicly available in-vehicle biometric datasets. Most other datasets contain only one biometric modality (usually face), while our datasets consist of three modalities, all acquired in the same automotive environment. Moreover, iCarB-Fingerprint seems to be the first publicly available in-vehicle fingerprint dataset. Finally, the iCarB datasets boast a rare level of demographic diversity among the 200 data subjects, including a 50/50 gender split, skin colours across the whole Fitzpatrick-scale spectrum, and a wide age range (18-60+). So, these datasets will be valuable for advancing biometrics research., Comment: 8 pages, 13 figures, 4 tables

We introduce a cluster-based technique to automate pixel-wise reconstruction of $\beta$ orientations from parent $\alpha$ orientations over large, indexed regions. This approach provides a valuable tool for analyzing problems that require historical information about current $\alpha$ microstructures, such as investigating variant selection mechanisms during the $\alpha \to \beta \to \alpha$ transformation. Additionally, we present a method for calculating deformation gradient variants associated with phase transformations between hcp ($\alpha$) and bcc ($\beta$) phases based upon a series of frame rotations and shape transformations. This method streamlines the integration of transformation kinematics into continuum-based models by enabling convenient computation of the deformation gradient governing the transformation.

The X-ray polarization observations made possible with the Imaging X-ray Polarimetry Explorer (IXPE) offer new ways of probing high-energy emission processes in astrophysical jets from blazars. Here we report on the first X-ray polarization observation of the blazar S4 0954+65 in a high optical and X-ray state. During our multi-wavelength campaign on the source, we detected an optical flare whose peak coincided with the peak of an X-ray flare. This optical-X-ray flare most likely took place in a feature moving along the parsec-scale jet, imaged at 43 GHz by the Very Long Baseline Array. The 43 GHz polarization angle of the moving component underwent a rotation near the time of the flare. In the optical band, prior to the IXPE observation, we measured the polarization angle to be aligned with the jet axis. In contrast, during the optical flare the optical polarization angle was perpendicular to the jet axis; after the flare, it reverted to being parallel to the jet axis. Due to the smooth behavior of the optical polarization angle during the flare, we favor shocks as the main acceleration mechanism. We also infer that the ambient magnetic field lines in the jet were parallel to the jet position angle. The average degree of optical polarization during the IXPE observation was (14.3$\pm$4.1)%. Despite the flare, we only detected an upper limit of 14% (at 3$\sigma$ level) on the X-ray polarization degree; although a reasonable assumption on the X-ray polarization angle results in an upper limit of 8.8% ($3\sigma$). We model the spectral energy distribution (SED) and spectral polarization distribution (SPD) of S4 0954+65 with leptonic (synchrotron self-Compton) and hadronic (proton and pair synchrotron) models. The constraints we obtain with our combined multi-wavelength polarization observations and SED modeling tentatively disfavor hadronic models for the X-ray emission in S4 0954+65., Comment: Submitted to A&A, 16 pages, 5 figures, and 7 tables

Isolated planetary-mass objects share their mass range with planets but do not orbit a star. They lack the necessary mass to support fusion in their cores and thermally radiate their heat from formation as they cool, primarily at infrared wavelengths. Many isolated planetary-mass objects show variations in their infrared brightness consistent with non-uniform atmospheric features modulated by their rotation. SIMP J013656.5+093347.3 is a rapidly rotating isolated planetary-mass object, and previous infrared monitoring suggests complex atmospheric features rotating in and out of view. The physical nature of these features is not well understood, with clouds, temperature variations, thermochemical instabilities, and infrared-emitting aurora all proposed as contributing mechanisms. Here we report JWST time-resolved low-resolution spectroscopy from 0.8 - 11 micron of SIMP J013656.5+093347.3 which supports the presence of three specific features in the atmosphere: clouds, hot spots, and changing carbon chemistry. We show that no single mechanism can explain the variations in the time-resolved spectra. When combined with previous studies of this object indicating patchy clouds and aurorae, these measurements reveal the rich complexity of the atmosphere of SIMP J013656.5+093347.3. Gas giant planets in the solar system, specifically Jupiter and Saturn, also have multiple cloud layers and high-altitude hot spots, suggesting these phenomena are also present in worlds both within and beyond our solar-system., Comment: Accepted to ApJ Letters

We consider the dynamic range minimum problem on the ultra-wide word RAM model of computation. This model extends the classic $w$-bit word RAM model with special ultrawords of length $w^2$ bits that support standard arithmetic and boolean operation and scattered memory access operations that can access $w$ (non-contiguous) locations in memory. The ultra-wide word RAM model captures (and idealizes) modern vector processor architectures. Our main result is a linear space data structure that supports range minimum queries and updates in $O(\log \log \log n)$ time. This exponentially improves the time of existing techniques. Our result is based on a simple reduction to prefix minimum computations on sequences $O(\log n)$ words combined with a new parallel, recursive implementation of these., Comment: Extended abstract at the 50th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM 2025)

Quantitative and reproducible optical characterization of single quantum emitters is crucial for quantum photonic materials research, yet controlling for experimental conditions remains challenging due to a lack of an established reference standard. We propose nanodiamonds containing single nitrogen vacancy (NV$^{-}$) color centers as reliable, stable and robust sources of single-photon emission. We select 4 potential reference emitter candidates from a study of thousands of NV$^{-}$ centers. Candidates were remeasured at a second laboratory, correlating optical pump power and NV$^{-}$ center emission intensity at saturation in addition to corresponding $g^{(2)}(0)$ values. A reference nanodiamond is demonstrated to control for experimental conditions, with reproducible and reliable single-photon emission, as a model for a new single-photon emitter reference standard.

The recent discovery of the van der Waals (vdW) layered heavy fermion antiferromagnetic metal CeSiI offers promising potential for achieving accessible quantum criticality in the two-dimensional (2D) limit. CeSiI exhibits both heavy fermion behavior and antiferromagnetic (AFM) ordering, while the exact magnetic structure and phase diagram have yet to be determined. Here, we investigate magnetic properties of atomically thin CeSiI devices with thicknesses ranging from 2-15 vdW layers. The thickness-dependent magnetotransport measurement reveals an intrinsic 2D nature of heavy fermion behavior and antiferromagnetism. Notably, we also find an isotropic, time-dependent hysteresis in both magnetoresistance and Hall resistance, showing glassy relaxation dynamics. This glassy behavior in magnetic structures may suggest the presence of spin glass phases or multipolar ordering, further establishing CeSiI as an intriguing material system for investigating the interplay between magnetic orders and the Kondo effect., Comment: 31 pages, 4 main figures, 7 extended data figures

Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into large-scale recommendation models. However, simply increasing the model size in recommendation systems, even with large amounts of data, does not always result in the expected performance improvements. In this paper, we propose a novel framework, Collaborative Ensemble Training Network (CETNet), to leverage multiple distinct models, each with its own embedding table, to capture unique feature interaction patterns. Unlike naive model scaling, our approach emphasizes diversity and collaboration through collaborative learning, where models iteratively refine their predictions. To dynamically balance contributions from each model, we introduce a confidence-based fusion mechanism using general softmax, where model confidence is computed via negation entropy. This design ensures that more confident models have a greater influence on the final prediction while benefiting from the complementary strengths of other models. We validate our framework on three public datasets (AmazonElectronics, TaobaoAds, and KuaiVideo) as well as a large-scale industrial dataset from Meta, demonstrating its superior performance over individual models and state-of-the-art baselines. Additionally, we conduct further experiments on the Criteo and Avazu datasets to compare our method with the multi-embedding paradigm. Our results show that our framework achieves comparable or better performance with smaller embedding sizes, offering a scalable and efficient solution for CTR prediction tasks.

The rapid adoption of electric vehicles (EVs) in modern transport systems has made energy-aware routing a critical task in their successful integration, especially within large-scale networks. In cases where an EV's remaining energy is limited and charging locations are not easily accessible, some destinations may only be reachable through an energy-optimal path: a route that consumes less energy than all other alternatives. The feasibility of such energy-efficient paths depends heavily on the accuracy of the energy model used for planning, and thus failing to account for vehicle dynamics can lead to inaccurate energy estimates, rendering some planned routes infeasible in reality. This paper explores the impact of vehicle dynamics on energy-optimal path planning for EVs. We develop an accurate energy model that incorporates key vehicle dynamics parameters into energy calculations, thereby reducing the risk of planning infeasible paths under battery constraints. The paper also introduces two novel online reweighting functions that allow for a faster, pre-processing free, pathfinding in the presence of negative energy costs resulting from regenerative braking, making them ideal for real-time applications. Through extensive experimentation on real-world transport networks, we demonstrate that our approach considerably enhances energy-optimal pathfinding for EVs in both computational efficiency and energy estimation accuracy., Comment: 12 pages, 7 figures, 5 tables

Single-photon detectors are ``blind" after the detection of a photon, and thereafter display a characteristic recovery in efficiency, during which the number of undetected photons depends on the statistics of the incident light. We show how the efficiency-recovery, photon statistics and intensity have an interdependent relationship which suppresses a detector's ability to count photons and measure correlations. We also demonstrate this effect with an experiment using $n$ such detectors to determine the $n^{\mathrm{th}}$ order correlation function with pseudothermal light., Comment: 8 pages, 6 figures including appendix

Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based stereo matching methods inherently suffer from the loss of geometric structures in certain feature channels, creating a bottleneck in achieving precise detail matching. Additionally, these methods lack interpretability due to the black-box nature of deep learning. In this paper, we propose MoCha-V2, a novel learning-based paradigm for stereo matching. MoCha-V2 introduces the Motif Correlation Graph (MCG) to capture recurring textures, which are referred to as ``motifs" within feature channels. These motifs reconstruct geometric structures and are learned in a more interpretable way. Subsequently, we integrate features from multiple frequency domains through wavelet inverse transformation. The resulting motif features are utilized to restore geometric structures in the stereo matching process. Experimental results demonstrate the effectiveness of MoCha-V2. MoCha-V2 achieved 1st place on the Middlebury benchmark at the time of its release. Code is available at https://github.com/ZYangChen/MoCha-Stereo.

We introduce a new symbolic solver for geometry, called Newclid, which is based on AlphaGeometry. Newclid contains a symbolic solver called DDARN (derived from DDAR-Newclid), which is a significant refactoring and upgrade of AlphaGeometry's DDAR symbolic solver by being more user-friendly - both for the end user as well as for a programmer wishing to extend the codebase. For the programmer, improvements include a modularized codebase and new debugging and visualization tools. For the user, Newclid contains a new command line interface (CLI) that provides interfaces for agents to guide DDARN. DDARN is flexible with respect to its internal reasoning, which can be steered by agents. Further, we support input from GeoGebra to make Newclid accessible for educational contexts. Further, the scope of problems that Newclid can solve has been expanded to include the ability to have an improved understanding of metric geometry concepts (length, angle) and to use theorems such as the Pythagorean theorem in proofs. Bugs have been fixed, and reproducibility has been improved. Lastly, we re-evaluated the five remaining problems from the original AG-30 dataset that AlphaGeometry was not able to solve and contrasted them with the abilities of DDARN, running in breadth-first-search agentic mode (which corresponds to how DDARN runs by default), finding that DDARN solves an additional problem. We have open-sourced our code under: https://github.com/LMCRC/Newclid, Comment: 51 pages

The recently published "MERGE" protocol is designed to be used in the prototype CAC-vote system. The voting kiosk and protocol transmit votes over the internet and then transmit voter-verifiable paper ballots through the mail. In the MERGE protocol, the votes transmitted over the internet are used to tabulate the results and determine the winners, but audits and recounts use the paper ballots that arrive in time. The enunciated motivation for the protocol is to allow (electronic) votes from overseas military voters to be included in preliminary results before a (paper) ballot is received from the voter. MERGE contains interesting ideas that are not inherently unsound; but to make the system trustworthy--to apply the MERGE protocol--would require major changes to the laws, practices, and technical and logistical abilities of U.S. election jurisdictions. The gap between theory and practice is large and unbridgeable for the foreseeable future. Promoters of this research project at DARPA, the agency that sponsored the research, should acknowledge that MERGE is internet voting (election results rely on votes transmitted over the internet except in the event of a full hand count) and refrain from claiming that it could be a component of trustworthy elections without sweeping changes to election law and election administration throughout the U.S.

Growing planets interact with their surrounding protoplanetary disk, generating feedback effects that may promote or suppress nearby planet formation. We study how spiral waves launched by planets affect the motion and collisional evolution of particles in the disk. To this end, we perform local 2D hydrodynamical simulations that include a gap-opening planet and integrate particle trajectories within the gas field. Our results show that particle trajectories bend at the location of the spiral wave, and collisions occurring within the spiral exhibit significantly enhanced collisional velocities compared to elsewhere. To quantify this effect, we ran simulations with varying planetary masses and particle sizes. The resulting collisional velocities within the spiral far exceed the typical fragmentation threshold, even for collisions between particles of relatively similar sizes and for planetary masses below the pebble isolation mass. If collisions within the spiral are frequent, this effect could lead to progressively smaller particle sizes as the radial distance from the planet decreases, impacting processes such as gap filtering, pebble accretion, and planetesimal formation., Comment: Accepted for publication in MNRAS Letters

We present an exact solution to standard model cosmological perturbation theory in a matter-dominated, adiabatic, hydrodynamic era. The solution is in the form of hypergeometric functions. While such functions can oscillate with the sound velocity, they can only do so at high frequency., Comment: 13 pages. Updated treatment of acoustic oscillations, plus change in title of paper

Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper we introduce FoundationShift, a method to apply any AI model from computational pathology without retraining. We show our method is more accurate than state of the art models (SAM, MedSAM, SAM-Med2D, CellProfiler, Hover-Net, PLIP, UNI and ChatGPT), with multiple imaging modalities (OCT and RCM). This is achieved without the need for model retraining or fine-tuning. Applying our method to noninvasive in vivo images could enable physicians to readily incorporate optical imaging modalities into their clinical practice, providing real time tissue analysis and improving patient care.

In two dimensions, $U(N_c)$ gauge theories exhibit a non-trivial topological structure, while $SU(N_c)$ theories are topologically trivial. Hence, for $G = U(N_c)$ the phase space is divided into topological sectors, characterized by a topological index (a.k.a. ``topological charge''). These sectors are separated by action barriers, which diverge if the lattice spacing is taken small, resulting in an algorithmic problem known as ``topological freezing''. We study these theories in various box sizes and at various couplings. With the help of gradient flow we derive instanton-like solutions for 2D $U(N_c)$ theory with a specific focus on the case of $N_c = 2$., Comment: 8 pages, 7 figures, 1 table. Proceedings of the 41st International Symposium on Lattice Field Theory (LATTICE2024), 28 July - 3 August, 2024 Liverpool, UK

There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agents, thereby allowing for a more nuanced study of complex systems. As a result, several LLM-based ABMs have been proposed in the past year. While they hold promise, each simulator is specifically designed to study a particular scenario, making it time-consuming and resource-intensive to explore other phenomena using the same ABM. Additionally, these models simulate only a limited number of agents, whereas real-world social media platforms involve millions of users. To this end, we propose OASIS, a generalizable and scalable social media simulator. OASIS is designed based on real-world social media platforms, incorporating dynamically updated environments (i.e., dynamic social networks and post information), diverse action spaces (i.e., following, commenting), and recommendation systems (i.e., interest-based and hot-score-based). Additionally, OASIS supports large-scale user simulations, capable of modeling up to one million users. With these features, OASIS can be easily extended to different social media platforms to study large-scale group phenomena and behaviors. We replicate various social phenomena, including information spreading, group polarization, and herd effects across X and Reddit platforms. Moreover, we provide observations of social phenomena at different agent group scales. We observe that the larger agent group scale leads to more enhanced group dynamics and more diverse and helpful agents' opinions. These findings demonstrate OASIS's potential as a powerful tool for studying complex systems in digital environments.

Machine learning is an important tool for analyzing high-dimension hyperspectral data; however, existing software solutions are either closed-source or inextensible research products. In this paper, we present cuvis.ai, an open-source and low-code software ecosystem for data acquisition, preprocessing, and model training. The package is written in Python and provides wrappers around common machine learning libraries, allowing both classical and deep learning models to be trained on hyperspectral data. The codebase abstracts processing interconnections and data dependencies between operations to minimize code complexity for users. This software package instantiates nodes in a directed acyclic graph to handle all stages of a machine learning ecosystem, from data acquisition, including live or static data sources, to final class assignment or property prediction. User-created models contain convenient serialization methods to ensure portability and increase sharing within the research community. All code and data are available online: https://github.com/cubert-hyperspectral/cuvis.ai, Comment: 5 pages, 2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)