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In many two-sided labor markets, interviews are conducted before matches are formed. An increase in the number of interviews in the market for medical residencies raised the demand for signaling mechanisms, in which applicants can send a limited number of signals to communicate interest. We study the role of signaling mechanisms in reducing the number of interviews in centralized random matching markets with post-interview shocks. For the market to clear we focus on interim stability, which extends the notion of stability to ensure that agents do not regret not interviewing with each other. A matching is almost interim stable if it is interim stable after removing a vanishingly small fraction of agents. We first study signaling mechanisms in random matching markets when agents on the short side, long side, or both sides signal their top~$d$ preferred partners. Interviews graphs are formed by including all pairs where at least one party has signaled the other. We show that when $d = \omega(1)$, short-side signaling leads to almost interim stable matchings. Long-side signaling is only effective when the market is almost balanced. Conversely, when the interview shocks are negligible and $d = o(\log n)$, both-side signaling fails to achieve almost interim stability. For larger $d \ge \Omega(\log^2 n)$, short-side signaling achieves perfect interim stability, while long-side signaling fails in imbalanced markets. We build on our findings to propose a signaling mechanism for multi-tiered random markets. Our analysis identifies conditions under which signaling mechanisms are incentive compatible. A technical contribution is the analysis of a message-passing algorithm that efficiently determines interim stability and matching outcomes by leveraging local neighborhood structures.

We present results from a high precision experimental study of the nuclear modification of the longitudinal ($F_L$) to transverse ($F_1$) structure function ratio for bound nucleons in the resonance region. The inclusive electron scattering cross sections were measured in Jefferson Lab Experimental Hall C on carbon and deuterium nuclei for a large range of kinematics, allowing for separations of the longitudinal and transverse structure functions to be performed at a range of four-momentum transfer values $0.5 \le Q^2 \le$ 3.75 GeV$^2$. In contrast to the significant body of measurements of the nuclear modification of the $F_2$ structure function in the deep inelastic scattering region, there is very little on $F_L$ and $R = F_L / 2xF_1$ in the region of the nucleon resonances. In this paper we present measurements of the nuclear effect on $R$ for $^{12}$C ($R_C$) relative to deuterium ($R_D$). These results indicate regions in which in $R_C>R_D$, requiring that the nuclear modifications be different in all three structure functions, $F_2$, $F_1$ and $F_L$., Comment: to be published in PRL

The task of policy compliance detection (PCD) is to determine if a scenario is in compliance with respect to a set of written policies. In a conversational setting, the results of PCD can indicate if clarifying questions must be asked to determine compliance status. Existing approaches usually claim to have reasoning capabilities that are latent or require a large amount of annotated data. In this work, we propose logical decomposition for policy compliance (LDPC): a neuro-symbolic framework to detect policy compliance using large language models (LLMs) in a few-shot setting. By selecting only a few exemplars alongside recently developed prompting techniques, we demonstrate that our approach soundly reasons about policy compliance conversations by extracting sub-questions to be answered, assigning truth values from contextual information, and explicitly producing a set of logic statements from the given policies. The formulation of explicit logic graphs can in turn help answer PCDrelated questions with increased transparency and explainability. We apply this approach to the popular PCD and conversational machine reading benchmark, ShARC, and show competitive performance with no task-specific finetuning. We also leverage the inherently interpretable architecture of LDPC to understand where errors occur, revealing ambiguities in the ShARC dataset and highlighting the challenges involved with reasoning for conversational question answering.

Question: What is the performance and reasoning ability of OpenAI o1 compared to other large language models in addressing ophthalmology-specific questions? Findings: This study evaluated OpenAI o1 and five LLMs using 6,990 ophthalmological questions from MedMCQA. O1 achieved the highest accuracy (0.88) and macro-F1 score but ranked third in reasoning capabilities based on text-generation metrics. Across subtopics, o1 ranked first in ``Lens'' and ``Glaucoma'' but second to GPT-4o in ``Corneal and External Diseases'', ``Vitreous and Retina'' and ``Oculoplastic and Orbital Diseases''. Subgroup analyses showed o1 performed better on queries with longer ground truth explanations. Meaning: O1's reasoning enhancements may not fully extend to ophthalmology, underscoring the need for domain-specific refinements to optimize performance in specialized fields like ophthalmology., Comment: 44 pages

We present HyperCam, an energy-efficient image classification pipeline that enables computer vision tasks onboard low-power IoT camera systems. HyperCam leverages hyperdimensional computing to perform training and inference efficiently on low-power microcontrollers. We implement a low-power wireless camera platform using off-the-shelf hardware and demonstrate that HyperCam can achieve an accuracy of 93.60%, 84.06%, 92.98%, and 72.79% for MNIST, Fashion-MNIST, Face Detection, and Face Identification tasks, respectively, while significantly outperforming other classifiers in resource efficiency. Specifically, it delivers inference latency of 0.08-0.27s while using 42.91-63.00KB flash memory and 22.25KB RAM at peak. Among other machine learning classifiers such as SVM, xgBoost, MicroNets, MobileNetV3, and MCUNetV3, HyperCam is the only classifier that achieves competitive accuracy while maintaining competitive memory footprint and inference latency that meets the resource requirements of low-power camera systems.

Chain-of-Thought (CoT) reasoning in smaller language models is a challenging natural language process problem yet highly desirable in many real-life applications. Existing CoT knowledge distillation methods often suffer from overly conservative memorization in smaller LLMs, leading to low generalization confidence. As fully preserving the CoT ability of teacher model is impossible, we hypothesize that adversarial CoT fine-tuning is crucial for developing smaller LLM with robust CoT generalization. To this end, we propose \textit{PRompt-Assisted Domain-Adversarial fine-tuning} (PRADA), a principled fine-tuning framework that integrates diverse CoT domains. Specifically, PRADA pioneers two CoT improvements in smaller LLM: (1) Recovering the domain-invariant feature insight which typically lost during distillation with domain adversarial fine-tuning; (2) Enhancing the domain adaptability of CoT prompt engineering by employing domain-adversarial approaches. We theoretically demonstrate the effectiveness of our approach and empirically show that it significantly outperforms the state of the arts in a wide range of tasks. Moreover, our empirical findings reveal that the smaller LLM, when leveraging PRADA, aligns closely with domain knowledge, thereby improving the explainability of our approach.

We confirm the planetary nature of a pair of transiting sub-Neptune exoplanets orbiting the bright F-type sub-giant star TOI-6054 ($V=8.02$, $K=6.673$) as a part of the OrCAS radial velocity survey using WIYN/NEID observations. We find that TOI-6054b and TOI-6054c have radii of $2.65 \pm 0.15$ $R_{\oplus}$ and $2.81 \pm 0.18$ $R_{\oplus}$, respectively, and masses of $12.4 \pm 1.7$ $M_{\oplus}$ and $9.2 \pm 2.0$ $M_{\oplus}$. The planets have zero-albedo equilibrium temperatures of $1360 \pm 33$ K and $1144 \pm 28$ K. The host star has expanded and will evolve off of the Main Sequence within the next $\sim$500 Myr, and the resulting increase in stellar luminosity has more than doubled the stellar flux the two planets receive compared to the start of the host star's main sequence phase. Consequently, TOI-6054b may be losing some of its primordial H/He atmosphere -- if it has one. Based on dynamical simulations performed using the orbital parameters of the two planets, TOI-6054b, and TOI-6054c are very likely in a 5:3 mean motion resonance. The TOI-6054 system thus has the potential to be an excellent candidate for future atmospheric follow-up observations, with two similarly sized sub-Neptunes around a bright star. We also estimate that if TOI-6054b is currently losing its H/He atmosphere this should be observable from space and from the ground., Comment: 23 pages, 11 figures, submitted to AJ, partially updated with referee's comments

We present a novel finite-volume QED action designed to improve the infinite-volume extrapolation of hadronic observables in precision lattice QCD+QED calculations. The new action, which we call QED$_\text{r}$, is designed to remove kinematics-independent finite-volume corrections that appear at $\mathrm{O}(1/L^3)$ in the commonly used QED$_\text{L}$ formulation, where $L$ is the spatial extent of the physical volume. For a number of key observables, these effects depend on the internal structure of the hadrons and are difficult to evaluate non-perturbatively, making an analytical subtraction of the finite-volume effects impractical. We show that the QED$_\text{r}$ action proposed here corresponds to a particular subset of the infrared-improved QED actions presented by Davoudi et al. in 2019. We explicitly study the QED$_\text{r}$ electromagnetic finite-size effects on hadron masses and leptonic decay rates, relevant for Standard Model precision tests using the Cabibbo-Kobayashi-Maskawa matrix elements. In addition, we propose methods to remove the kinematics-dependent $\mathrm{O}(1/L^3)$ effects in leptonic decays. The removal of such contributions, shifting the leading contamination to $\mathrm{O}(1/L^4)$, will help to reduce the systematic uncertainties associated with finite-volume effects in future lattice QCD+QED calculations.

We report charge to spin conversion in a hybrid heterostructure comprised of atomically thin bismuth (Bi) confined between a silicon carbide (SiC) substrate and epitaxial graphene (EG). We confirm composition, dimensionality, and a 96.5 \% intercalation coverage using X-ray photolectron spectroscopy, scanning transmission microscopy, low energy electron diffraction, and Raman spectroscopy. Electrical transport measurements show signs of weak antilocalization in the heterostructure, consistent with spin-orbit coupling in this hybrid heterostructure. Spin torque ferromagnetic resonance measurements in permalloy/EG/2D-Bi heterostructures probe charge-to-spin conversion and revealing that an in plane polarization of the spin current, perpendicular to the charge current. The ratio of the in-plane to out-of-plane torque is 3.75 times higher than in hydrogenated graphene control samples.

In recent years, AI red teaming has emerged as a practice for probing the safety and security of generative AI systems. Due to the nascency of the field, there are many open questions about how red teaming operations should be conducted. Based on our experience red teaming over 100 generative AI products at Microsoft, we present our internal threat model ontology and eight main lessons we have learned: 1. Understand what the system can do and where it is applied 2. You don't have to compute gradients to break an AI system 3. AI red teaming is not safety benchmarking 4. Automation can help cover more of the risk landscape 5. The human element of AI red teaming is crucial 6. Responsible AI harms are pervasive but difficult to measure 7. LLMs amplify existing security risks and introduce new ones 8. The work of securing AI systems will never be complete By sharing these insights alongside case studies from our operations, we offer practical recommendations aimed at aligning red teaming efforts with real world risks. We also highlight aspects of AI red teaming that we believe are often misunderstood and discuss open questions for the field to consider.

AI Agent, powered by large language models (LLMs) as its cognitive core, is an intelligent agentic system capable of autonomously controlling and determining the execution paths under user's instructions. With the burst of capabilities of LLMs and various plugins, such as RAG, text-to-image/video/3D, etc., the potential of AI Agents has been vastly expanded, with their capabilities growing stronger by the day. However, at the intersection between AI and web3, there is currently no ideal agentic framework that can seamlessly integrate web3 applications into AI agent functionalities. In this paper, we propose Eliza, the first open-source web3-friendly Agentic framework that makes the deployment of web3 applications effortless. We emphasize that every aspect of Eliza is a regular Typescript program under the full control of its user, and it seamlessly integrates with web3 (i.e., reading and writing blockchain data, interacting with smart contracts, etc.). Furthermore, we show how stable performance is achieved through the pragmatic implementation of the key components of Eliza's runtime. Our code is publicly available at https://github.com/ai16z/eliza., Comment: 20 pages, 5 figures

The Inflation Reduction Act subsidizes the deployment of clean electricity, hydrogen production, and carbon capture and storage, which could enable additional actions by other federal, state, and local policymakers to reduce emissions. Power plant rules finalized by the Environmental Protection Agency (EPA) in 2024 are one such example of complementary policies. The rules establish emissions intensity standards, not technology mandates, meaning power plant owners can choose from a range of technologies and control options provided that emissions standards are met. This flexibility makes electricity systems modeling important to understand the potential effects of these regulations. We report below a multi-model analysis of the EPA power plant rules that can provide timely information, including for other countries and states, on emissions impacts, policy design for electricity decarbonization, power sector investments and retirements, cost impacts, and load growth. We also discuss related technical, political, and legal uncertainties.

Evolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation across genomic features (e.g., sites) in sequence alignments. ESL employs sparsity between and within the groups of genomic features (e.g., genomic loci) by using sparse-group LASSO. Although some software packages are available for performing sparse group LASSO, we found them less well-suited for processing and analyzing genome-scale data containing millions of features, such as bases. MyESL software fills the need for open-source software for conducting ESL analyses with facilities to pre-process the input hypotheses and large alignments, make LASSO flexible and computationally efficient, and post-process the output model to produce different metrics useful in functional or evolutionary genomics. MyESL can take phylogenetic trees and sequence alignments as input and transform them into numeric responses and features, respecetively. The model outputs are processed into user-friendly text and graphical files. The computational core of MyESL is written in C++, which offers model building with or without group sparsity, while the pre- and post-processing of inputs and model outputs is performed using customized functions written in Python. One of its applications in phylogenomics showcases the utility of MyESL. Our analysis of empirical genome-scale datasets shows that MyESL can build evolutionary models quickly and efficiently on a personal desktop, while other computational packages were unable due to their prohibitive requirements of computational resources and time. MyESL is available for Python environments on Linux and distributed as a standalone application for Windows and macOS. It is available from https://github.com/kumarlabgit/MyESL.

Current approaches to dichotomous image segmentation (DIS) treat image matting and object segmentation as fundamentally different tasks. As improvements in image segmentation become increasingly challenging to achieve, combining image matting and grayscale segmentation techniques offers promising new directions for architectural innovation. Inspired by the possibility of aligning these two model tasks, we propose a new architectural approach for DIS called Confidence-Guided Matting (CGM). We created the first CGM model called Background Erase Network (BEN). BEN is comprised of two components: BEN Base for initial segmentation and BEN Refiner for confidence refinement. Our approach achieves substantial improvements over current state-of-the-art methods on the DIS5K validation dataset, demonstrating that matting-based refinement can significantly enhance segmentation quality. This work opens new possibilities for cross-pollination between matting and segmentation techniques in computer vision., Comment: 13 pages, 2 figures, 2 tables, and 2 algorithms

Direct exo-Earth imaging is a key science goal for astronomy in the next decade. This ambitious task imposes a target contrast of ~10^-7 at wavelengths from I to J-band. In our prior study, we determined that polarization aberrations can limit the achievable contrast to 10^-5 to 10^-6 in the infrared. However, these results assumed a perfect coronagraph coupled to a telescope with an ideal coating on each of the mirrors. In this study we seek to understand the influence of polarization aberrations from segment-to-segment coating variations on coronagraphy and polarimetry. We use the Poke open-source polarization ray tracing package to compute the Jones pupil of each GSMT with spatially-varying coatings applied to the segments. The influence of the resultant polarization aberrations is simulated by propagating the Jones pupil through physical optics models of coronagraphs using HCIPy. After applying wavefront control from an ideal adaptive optics system, we determine that the segment-to-segment variations applied limit the performance of coronagraphy to a raw contrast of approximately 10^-8 in I-band, which is 2-3 orders of magnitude lower the target performance for high-contrast imaging systems on the ground. This is a negligible addition to the nominal polarization aberrations for ground-based systems. We further observe negligible degradation in polarimetric imaging of debris disks from segment-to-segment aberrations above and beyond the impact of nominal polarization aberration., Comment: 15 pages, 15 figures, Accepted to Astronomy & Astrophysics 13. Astronomical Instrumentation

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

We propose efficient no-regret learning dynamics and ellipsoid-based methods for computing linear correlated equilibria$\unicode{x2014}$a relaxation of correlated equilibria and a strengthening of coarse correlated equilibria$\unicode{x2014}$in general convex games. These are games where the number of pure strategies is potentially exponential in the natural representation of the game, such as extensive-form games. Our work identifies linear correlated equilibria as the tightest known notion of equilibrium that is computable in polynomial time and is efficiently learnable for general convex games. Our results are enabled by a generalization of the seminal framework of of Gordon et al. [2008] for $\Phi$-regret minimization, providing extensions to this framework that can be used even when the set of deviations $\Phi$ is intractable to separate/optimize over. Our polynomial-time algorithms are similarly enabled by extending the Ellipsoid-Against-Hope approach of Papadimitriou and Roughgarden [2008] and its generalization to games of non-polynomial type proposed by Farina and Pipis [2024a]. We provide an extension to these approaches when we do not have access to the separation oracles required by these works for the dual player.

Laser-based displays are highly sought after for their superior brightness and color performance, especially in advanced applications like augmented reality (AR). However, their broader adoption has been hindered by bulky projector designs and complex optical module assemblies. Here, we introduce a new laser display architecture enabled by large-scale visible photonic integrated circuits (PICs) to address these challenges. Unlike previous projector-style laser displays, this architecture features an ultra-thin, flat-panel form factor, replacing bulky free-space illumination modules with a single, high-performance photonic chip. Centimeter-scale PIC devices, which integrate thousands of distinct optical components on-chip, are carefully tailored to achieve high display uniformity, contrast, and efficiency. We demonstrate a 2 mm-thick flat-panel laser display combining the PIC with a liquid-crystal-on-silicon (LCoS) panel, achieving 211% of the color gamut and more than 80% volume reduction compared to traditional LCoS displays. We further showcase its application in a see-through AR system. Our work represents a major advancement in the integration of nanophotonics with display technology, enabling a range of new display concepts, from high-performance immersive displays to slim-panel 3D holography.

In 1987, Albert Schwarz suggested a formula which extends the super Mumford form from the moduli space of super Riemann surfaces into the super Sato Grassmannian. His formula is a remarkably simple combination of super tau functions. We compute the Neveu-Schwarz action on super tau functions, and show that Schwarz's extended Mumford form is invariant under the the super Heisenberg-Neveu-Schwarz action, which strengthens Schwarz's proposal that a locus within the Grassmannian can serve as a universal moduli space with applications to superstring theory. Along the way, we construct the Neveu-Schwarz, super Witt, and super Heisenberg formal groups., Comment: 45 pages

We construct a local universal Mumford form on a product of Sato Grassmannians using the flow of the Virasoro algebra. The existence of this universal Mumford form furthers the proposal that the Sato Grassmannian provides a universal moduli space with applications to string theory. Our approach using the Virasoro flow is an alternative to using the KP flow, which in particular allows for a bosonic universal Mumford form to be constructed. Applying the same method, we construct a local universal super Mumford form on a product of super Sato Grassmannians using the flow of the Neveu-Schwarz algebra., Comment: 19 pages

Purpose: To evaluate various Segmental Anything Model (SAM) prompt strategies across four lesions datasets and to subsequently develop a reinforcement learning (RL) agent to optimize SAM prompt placement. Materials and Methods: This retrospective study included patients with four independent ovarian, lung, renal, and breast tumor datasets. Manual segmentation and SAM-assisted segmentation were performed for all lesions. A RL model was developed to predict and select SAM points to maximize segmentation performance. Statistical analysis of segmentation was conducted using pairwise t-tests. Results: Results show that increasing the number of prompt points significantly improves segmentation accuracy, with Dice coefficients rising from 0.272 for a single point to 0.806 for five or more points in ovarian tumors. The prompt location also influenced performance, with surface and union-based prompts outperforming center-based prompts, achieving mean Dice coefficients of 0.604 and 0.724 for ovarian and breast tumors, respectively. The RL agent achieved a peak Dice coefficient of 0.595 for ovarian tumors, outperforming random and alternative RL strategies. Additionally, it significantly reduced segmentation time, achieving a nearly 10-fold improvement compared to manual methods using SAM. Conclusion: While increased SAM prompts and non-centered prompts generally improved segmentation accuracy, each pathology and modality has specific optimal thresholds and placement strategies. Our RL agent achieved superior performance compared to other agents while achieving a significant reduction in segmentation time.

Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark '24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to text. Here, we summarize the lessons learned from the competition ending on June 1, 2024 (the top 4 entrants also presented their experiences in a recorded webinar). The largest improvements in accuracy were achieved using an ensembling approach, where the output of multiple independent decoders was merged using a fine-tuned large language model (an approach used by all 3 top entrants). Performance gains were also found by improving how the baseline recurrent neural network (RNN) model was trained, including by optimizing learning rate scheduling and by using a diphone training objective. Improving upon the model architecture itself proved more difficult, however, with attempts to use deep state space models or transformers not yet appearing to offer a benefit over the RNN baseline. The benchmark will remain open indefinitely to support further work towards increasing the accuracy of brain-to-text algorithms.

Optical dynamic nuclear polarization (DNP) offers an attractive approach to enhancing the sensitivity of nuclear magnetic resonance (NMR) spectroscopy. Efficient, optically-generated electron polarization can be leveraged to operate across a broad range of temperatures and magnetic fields, making it particularly appealing for applications requiring high DNP efficiency or spatial resolution. While a large class of systems hold promise for optical DNP, many candidates display both variable electron polarizability and electron and nuclear T1 relaxation times as functions of magnetic field and temperature. This necessitates tools capable of studying DNP under diverse experimental conditions. To address this, we introduce a cryogenic field cycling instrument that facilitates optical DNP studies across a wide range of magnetic fields (10mT to 9.4T) and temperatures (10K to 300K). Continuous cryogen replenishment enables sustained, long-term operation. Additionally, the system supports the ability to manipulate and probe hyperpolarized nuclear spins via pulse sequences involving millions of RF pulses. We describe innovations in the device design and demonstrate its operation on a model system of 13C nuclear spins in diamond polarized through optically pumped nitrogen vacancy (NV) centers. We anticipate the use of the instrument for a broad range of optical DNP systems and studies., Comment: 10 figures, 11 pages (including references)

Cummings, Foreman, and Magidor investigated the extent to which square principles are compact at singular cardinals. The first author proved that if $\kappa$ is a singular strong limit of uncountable cofinality, all scales on $\kappa$ are good, and $\square^*_\delta$ holds for all $\delta<\kappa$, then $\square_\kappa^*$ holds. In this paper we will present a strongly contrasting result for $\aleph_\omega$. We construct a model in which $\square_{\aleph_n}$ holds for all $n<\omega$, all scales on $\aleph_\omega$ are good, but in which $\square_{\aleph_\omega}^*$ fails and some weak forms of internal approachability for $[H(\aleph_{\omega+1})]^{\aleph_1}$ fail. This requires an extensive analysis of the dominating and approximation properties of a version of Namba forcing. We also prove some supporting results.

E-learning platforms that personalise content selection with AI are often criticised for lacking transparency and controllability. Researchers have therefore proposed solutions such as open learner models and letting learners select from ranked recommendations, which engage learners before or after the AI-supported selection process. However, little research has explored how learners - especially adolescents - could engage during such AI-supported decision-making. To address this open challenge, we iteratively designed and implemented a control mechanism that enables learners to steer the difficulty of AI-compiled exercise series before practice, while interactively analysing their control's impact in a 'what-if' visualisation. We evaluated our prototypes through four qualitative studies involving adolescents, teachers, EdTech professionals, and pedagogical experts, focusing on different types of visual explanations for recommendations. Our findings suggest that 'why' explanations do not always meet the explainability needs of young learners but can benefit teachers. Additionally, 'what-if' explanations were well-received for their potential to boost motivation. Overall, our work illustrates how combining learner control and visual explanations can be operationalised on e-learning platforms for adolescents. Future research can build upon our designs for 'why' and 'what-if' explanations and verify our preliminary findings., Comment: 16 pages, accepted as a full paper for LAK25: The 15th International Learning Analytics and Knowledge Conference (LAK 2025), March 03-07, 2025, Dublin, Ireland

The free graviton theory given by linearising Einstein's theory has a dual formulation in terms of a dual graviton field. The dual graviton theory has two gauge invariances giving rise to two conserved charges, while the ADM charges of the graviton theory become magnetic charges for the dual graviton theory. These charges can be ill-defined in topologically non-trivial settings and we find improvement terms that can be added to these to give gauge-invariant conserved charges. These gauge-invariant charges, which have local expressions in both the graviton and dual graviton formulation, give topological operators of the theory that should be considered as the generators of the genuine symmetries of the theory., Comment: 27 pages + appendix

We present OpenNER 1.0, a standardized collection of openly available named entity recognition (NER) datasets. OpenNER contains 34 datasets spanning 51 languages, annotated in varying named entity ontologies. We correct annotation format issues, standardize the original datasets into a uniform representation, map entity type names to be more consistent across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER. We provide baseline models using three pretrained multilingual language models to compare the performance of recent models and facilitate future research in NER.

The global food system provides the energy that supports human metabolism, with complex spatial interdependencies between food production, transformation, and consumption. Empirical food system data for these global processes are often fragmented and inconsistent, with only certain components captured in spatially resolved formats. Here we propose a flexible approach to allocating country-level food system data subnationally. We estimate the spatial patterns of food energy production and supply, which we compare to estimates of human metabolism based on average body size. We downscale these rates onto a one-degree resolution grid with 95 corresponding food types to derive an internally consistent, energy-conserving, and spatially resolved dataset. We show that national food supply varies linearly with metabolism per capita, with about half the variation in food supply explained by metabolic rates. Our data processing pipeline is openly available and can readily incorporate new inputs in order to advance trans-disciplinary food system modeling efforts., Comment: 19 pages, 4 figures

We introduce PyPulse, a Python package for imputation of biosignals in both clinical and wearable sensor settings. Missingness is commonplace in these settings and can arise from multiple causes, such as insecure sensor attachment or data transmission loss. PyPulse's framework provides a modular and extendable framework with high ease-of-use for a broad userbase, including non-machine-learning bioresearchers. Specifically, its new capabilities include using pre-trained imputation methods out-of-the-box on custom datasets, running the full workflow of training or testing a baseline method with a single line of code, and comparing baseline methods in an interactive visualization tool. We released PyPulse under the MIT License on Github and PyPI. The source code can be found at: https://github.com/rehg-lab/pulseimpute., Comment: 7 pages, 3 figures. Implementation and documentation are available at https://github.com/rehg-lab/pulseimpute

We present a numerical investigation of the relativistic-field-theoretic (RFT) formalism, used to predict the discrete energy spectrum of three pions in a finite volume. Applying the previously derived generalization, we extract results for all non-maximal isospin values ($I_{\pi\pi\pi} = 2,1,$ and $0$), for different total momenta $\boldsymbol{P}$, and for various irreducible representations of the finite-volume symmetry group. We restrict attention to the unphysical scenario in which the three-particle interactions are set to zero. This set-up thus serves as a baseline for future lattice QCD calculations that will aim to extract such three-body interactions., Comment: 12 pages, 4 figures, Contribution to Lattice 2024

The synthesis of ternary nitrides is uniquely difficult, in large part because elemental N$_2$ is relatively inert. However, lithium reacts readily with other metals and N$_2$, making Li-M-N the most numerous sub-set of ternary nitrides. Here, we use Li$_2$ZrN$_2$, a ternary with a simple synthesis recipe, as a precursor for ion exchange reactions towards AZrN$_2$ (A = Mg, Fe, Cu, Zn). In situ synchrotron powder X-ray diffraction studies show that Li$^+$ and Mg$^{2+}$ undergo ion exchange topochemically, preserving the layers of octahedral [ZrN$_6$] to yield a metastable layered polymorph of MgZrN$_2$ (spacegroup $R\overline{3}m$) rather than the calculated ground state structure ($I41/amd$). UV-vis measurements show an optical absorption onset near 2.0 eV, consistent with the calculated bandgap for this polymorph. Our experimental attempts to extend this ion exchange method towards FeZrN$_2$, CuZrN$_2$, and ZnZrN$_2$ resulted in decomposition products (A + ZrN + 1/6 N$_2$), an outcome that our computational results explain via the higher metastability of these phases. We successfully extended this ion exchange method to other Li-M-N precursors by synthesizing MgHfN$_2$ from Li$_2$HfN$_2$. In addition to the discovery of metastable $R\overline{3}m$ MgZrN$_2$ and MgHfN$_2$, this work highlights the potential of the 63 unique Li-M-N phases as precursors to synthesize new ternary nitrides.

What use can there be for a function from the $p$-adic numbers to the $q$-adic numbers, where $p$ and $q$ are distinct primes? The traditional answer, courtesy of the half-century old theory of non-archimedean functional analysis: not much. It turns out this judgment was premature. '$\left(p,q\right)$-adic analysis' of this sort appears to be naturally suited for studying the infamous Collatz map and similar arithmetical dynamical systems. Given such a map $H:\mathbb{Z}\rightarrow\mathbb{Z}$, one can construct a function $\chi_{H}:\mathbb{Z}_{p}\rightarrow\mathbb{Z}_{q}$ for an appropriate choice of distinct primes $p,q$ with the property that $x\in\mathbb{Z}\backslash\left\{ 0\right\} $ is a periodic point of $H$ if and only if there is a $p$-adic integer $\mathfrak{z}\in\left(\mathbb{Q}\cap\mathbb{Z}_{p}\right)\backslash\left\{ 0,1,2,\ldots\right\} $ so that $\chi_{H}\left(\mathfrak{z}\right)=x$. By generalizing Monna-Springer integration theory and establishing a $\left(p,q\right)$-adic analogue of the Wiener Tauberian Theorem, one can show that the question 'is $x\in\mathbb{Z}\backslash\left\{ 0\right\} $ a periodic point of $H$?' is essentially equivalent to 'is the span of the translates of the Fourier transform of $\chi_{H}\left(\mathfrak{z}\right)-x$ dense in an appropriate non-archimedean function space?' This presents an exciting new frontier in Collatz research, and these methods can be used to study Collatz-type dynamical systems on the lattice $\mathbb{Z}^{d}$ for any $d\geq1$., Comment: This is the author's PhD dissertation. 467 pages. 1 table

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics or centers with varying selection mechanisms that may depend on the disease or outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to five times lower relative bias and three times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates more closely aligned with national estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure is not available in the external probability sample.

Achieving high-throughput, comprehensive analysis of single nanoparticles to determine their size, shape, and composition is essential for understanding particle heterogeneity with applications ranging from drug delivery to environmental monitoring. Existing techniques are hindered by low throughput, lengthy trapping times, irreversible particle adsorption, or limited characterization capabilities. Here, we introduce Interferometric Electrohydrodynamic Tweezers (IET), an integrated platform that rapidly traps single nanoparticles in parallel within three seconds. IET enables label-free characterization of particle size and shape via interferometric imaging and identifies molecular composition through Raman spectroscopy, all without the need for fluorescent labeling. We demonstrate the platform's capabilities by trapping and imaging colloidal polymer beads, nanoscale extracellular vesicles (EVs), and newly discovered extracellular nanoparticles known as supermeres. By monitoring their interferometric contrast images while trapped, we accurately determine the sizes of EVs and supermeres. Our IET represents a powerful optofluidics platform for comprehensive characterization of nanoscale objects, opening new avenues in nanomedicine, environmental monitoring, and beyond.

We present RelCon, a novel self-supervised *Rel*ative *Con*trastive learning approach that uses a learnable distance measure in combination with a softened contrastive loss for training an motion foundation model from wearable sensors. The learnable distance measure captures motif similarity and domain-specific semantic information such as rotation invariance. The learned distance provides a measurement of semantic similarity between a pair of accelerometer time-series segments, which is used to measure the distance between an anchor and various other sampled candidate segments. The self-supervised model is trained on 1 billion segments from 87,376 participants from a large wearables dataset. The model achieves strong performance across multiple downstream tasks, encompassing both classification and regression. To our knowledge, we are the first to show the generalizability of a self-supervised learning model with motion data from wearables across distinct evaluation tasks., Comment: Accepted to ICLR 2025

We consider the jacobian of a random transverse polarisation field, from the transverse plane to the Poincar\'e sphere, as a Skyrme density partially covering the sphere. Connected domains of the plane where the jacobian has the same sign -- patches -- map to facets subtending some general solid angle on the Poincar\'e sphere. As a generic continuous mapping between surfaces, we interpret the polarisation pattern on the sphere in terms of fold lines (corresponding to the crease lines between neighbouring patches) and cusp points (where fold lines meet). We perform a basic statistical analysis of the properties of the patches and facets, including a brief discussion of the percolation properties of the jacobian domains. Connections with abstract origami manifolds are briefly considered. This analysis combines previous studies of structured skyrmionic polarisation patterns with random polarisation patterns, suggesting a particle-like interpretation of random patches as polarisation skyrmionic anyons., Comment: 14 pages, 7 figures. Submitted to IOP Journal of Optics Focus Issue on Optical Skyrmions

The Coronagraphic Instrument onboard the Nancy Grace Roman Space Telescope is an important stepping stone towards the characterization of habitable, rocky exoplanets. In a technology demonstration phase conducted during the first 18 months of the mission (expected to launch in late 2026), novel starlight suppression technology may enable direct imaging of a Jupiter analog in reflected light. Here we summarize the current activities of the Observation Planning working group formed as part of the Community Participation Program. This working group is responsible for target selection and observation planning of both science and calibration targets in the technology demonstration phase of the Roman Coronagraph. We will discuss the ongoing efforts to expand target and reference catalogs, and to model astrophysical targets (exoplanets and circumstellar disks) within the Coronagraph's expected sensitivity. We will also present preparatory observations of high priority targets., Comment: Proceedings Volume 13092, Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave; 1309255 (2024)

Sub-Neptunes - volatile-rich exoplanets smaller than Neptune - are intrinsically the most common type of planet known. However, the formation and nature of these objects, as well as the distinctions between sub-classes (if any), remain unclear. Two powerful tools to tease out the secrets of these worlds are measurements of (i) atmospheric composition and structure revealed by transit and/or eclipse spectroscopy, and (ii) mass, radius, and density revealed by transit photometry and Doppler spectroscopy. Here we present OrCAS, a survey to better elucidate the origins, compositions, and atmospheres of sub-Neptunes. This radial velocity survey uses a repeatable, quantifiable metric to select targets suitable for subsequent transmission spectroscopy and address key science themes about the atmospheric & internal compositions and architectures of these systems. Our survey targets 26 systems with transiting sub-Neptune planet candidates, with the overarching goal of increasing the sample of such planets suitable for subsequent atmospheric characterization. This paper lays out our survey's science goals, defines our target prioritization metric, and performs light-curve fits and statistical validation using existing TESS photometry and ground-based follow-up observations. Our survey serves to continue expanding the sample of small exoplanets with well-measured properties orbiting nearby bright stars, ensuring fruitful studies of these systems for many years to come., Comment: 20 pages, 4 figures, 4 tables, 26 sub-Neptunes, 31 TOIs. Accepted to AJ

Micro and nanoscale particles are crucial in various fields, from biomedical imaging to environmental processes. While conventional spectroscopy and microscopy methods for characterizing these particles often involve bulky equipment and complex sample preparation, optical micro-sensors have emerged as a promising alternative. However, their broad applicability is limited by the need for surface binding and difficulty in differentiating between sensing targets. This study introduces an optofluidic, high-throughput optical microresonator sensor that captures subtle acoustic signals generated by particles absorbing pulsed light energy. This novel approach enables real-time, label-free detection and interrogation of particles and cells in their native environments across an extended sensing volume. By leveraging unique optical absorption properties, our technique selectively detects and classifies flowing particles without surface binding, even in complex matrices like whole blood samples. We demonstrate the measurement of gold nanoparticles with diverse geometries and different species of red blood cells amidst other cellular elements and proteins. These particles are identified and classified based on their photoacoustic fingerprint, which captures shape, composition, and morphology features. This work opens new avenues for rapid, reliable, and high-throughput particle and cell identification in clinical and industrial applications, offering a valuable tool for understanding complex biological and environmental systems., Comment: 14 pages, 4 figures

On May 24th, 2023, the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO), joined by the Advanced Virgo and KAGRA detectors, began the fourth observing run for a two-year-long dedicated search for gravitational waves. The LIGO Hanford and Livingston detectors have achieved an unprecedented sensitivity to gravitational waves, with an angle-averaged median range to binary neutron star mergers of 152 Mpc and 160 Mpc, and duty cycles of 65.0% and 71.2%, respectively, with a coincident duty cycle of 52.6%. The maximum range achieved by the LIGO Hanford detector is 165 Mpc and the LIGO Livingston detector 177 Mpc, both achieved during the second part of the fourth observing run. For the fourth run, the quantum-limited sensitivity of the detectors was increased significantly due to the higher intracavity power from laser system upgrades and replacement of core optics, and from the addition of a 300 m filter cavity to provide the squeezed light with a frequency-dependent squeezing angle, part of the A+ upgrade program. Altogether, the A+ upgrades led to reduced detector-wide losses for the squeezed vacuum states of light which, alongside the filter cavity, enabled broadband quantum noise reduction of up to 5.2 dB at the Hanford observatory and 6.1 dB at the Livingston observatory. Improvements to sensors and actuators as well as significant controls commissioning increased low frequency sensitivity. This paper details these instrumental upgrades, analyzes the noise sources that limit detector sensitivity, and describes the commissioning challenges of the fourth observing run., Comment: 26 pages, 18 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

Thermodynamic computing has emerged as a promising paradigm for accelerating computation by harnessing the thermalization properties of physical systems. This work introduces a novel approach to solving quadratic programming problems using thermodynamic hardware. By incorporating a thermodynamic subroutine for solving linear systems into the interior-point method, we present a hybrid digital-analog algorithm that outperforms traditional digital algorithms in terms of speed. Notably, we achieve a polynomial asymptotic speedup compared to conventional digital approaches. Additionally, we simulate the algorithm for a support vector machine and predict substantial practical speedups with only minimal degradation in solution quality. Finally, we detail how our method can be applied to portfolio optimization and the simulation of nonlinear resistive networks., Comment: 13 pages, 4 figures

We introduce a novel protocol, which enables Heisenberg-limited quantum-enhanced sensing using the dynamics of any interacting many-body Hamiltonian. Our approach - dubbed butterfly metrology - utilizes a single application of forward and reverse time evolution to produce a coherent superposition of a "scrambled" and "unscrambled" quantum state. In this way, we create metrologically-useful long-range entanglement from generic local quantum interactions. The sensitivity of butterfly metrology is given by a sum of local out-of-time-order correlators (OTOCs) - the prototypical diagnostic of quantum information scrambling. Our approach broadens the landscape of platforms capable of performing quantum-enhanced metrology; as an example, we provide detailed blueprints and numerical studies demonstrating a route to scalable quantum-enhanced sensing in ensembles of solid-state spin defects.

The goal of this paper is to construct the Hilbert scheme of complete intersections in the biprojective space $X=\mathbb{P}^m\times\mathbb{P}^n$ and for this, we define a partial order on the bidegrees of the bihomogeneous forms. As a consequence of this construction, we computer explicitly the Hilbert scheme for curves of genus 7 and 8 listed in \cite{MUK95} and \cite{MUKIDE03} that are complete intersections. Finally, we construct the coarse moduli space of complete intersections in $\mathbb{P}^1\times\mathbb{P}^1$., Comment: 19 pages

This work augments the recently introduced Stabilizer Tensor Network (STN) protocol with magic state injection, reporting a new framework with significantly enhanced ability to simulate circuits with an extensive number of non-Clifford operations. Specifically, for random $T$-doped $N$-qubit Clifford circuits the computational cost of circuits prepared with magic state injection scales as $\mathcal{O}(\text{poly}(N))$ when the circuit has $t \lesssim N$ $T$-gates compared to an exponential scaling for the STN approach, which is demonstrated in systems of up to $200$ qubits. In the case of the Hidden Bit Shift circuit, a paradigmatic benchmarking system for extended stabilizer methods with a tunable amount of magic, we report that our magic state injected STN framework can efficiently simulate $4000$ qubits and $320$ $T$-gates. These findings provide a promising outlook for the use of this protocol in the classical modelling of quantum circuits that are conventionally difficult to simulate efficiently., Comment: 5+5 pages, 3+2 figures

There is a tension in dynamic language runtime design between speed and correctness: state-of-the-art JIT compilation, the result of enormous industrial investment and significant research, achieves heroic speedups at the cost of complexity that can result in serious correctness bugs. Much of this complexity comes from the existence of multiple tiers and the need to maintain correspondence between these separate definitions of the language's semantics; also, from the indirect nature of the semantics implicitly encoded in a compiler backend. One way to address this complexity is to automatically derive, as much as possible, the compiled code from a single source-of-truth; for example, the interpreter tier. In this work, we introduce a partial evaluator that can derive compiled code ``for free'' by specializing an interpreter with its bytecode. This transform operates on the interpreter body at a basic-block IR level and is applicable to almost unmodified existing interpreters in systems languages such as C or C++. We show the effectiveness of this new tool by applying it to the interpreter tier of an existing industrial JavaScript engine, SpiderMonkey, yielding $2.17\times$ speedups, and the PUC-Rio Lua interpreter, yielding $1.84\times$ speedups with only three hours' effort. Finally, we outline an approach to carry this work further, deriving more of the capabilities of a JIT backend from first principles while retaining semantics-preserving correctness.

In this paper we study a variant of the solitaire game Lights-Out, where the player's goal is to turn off a grid of lights. This variant is a two-player impartial game where the goal is to make the final valid move. This version is playable on any simple graph where each node is given an assignment of either a 0 (representing a light that is off) or 1 (representing a light that is on). We focus on finding the Nimbers of this game on grid graphs and generalized Petersen graphs. We utilize a recursive algorithm to compute the Nimbers for 2 x n grid graphs and for some generalized Petersen graphs.

In this work, we shall investigate existence and multiplicity of solutions for a nonlocal elliptic systems driven by the fractional Laplacian. Specifically, we establish the existence of two positive solutions for following class of nonlocal elliptic systems: \begin{equation*} \left\{\begin{array}{lll} (-\Delta)^su +V_1(x)u = \lambda|u|^{p - 2}u+ \frac{\alpha}{\alpha+\beta}\theta |u|^{\alpha - 2}u|v|^{\beta}, \;\;\; \mbox{in}\;\;\; \mathbb{R}^N, (-\Delta)^sv +V_2(x)v= \lambda|v|^{q - 2}v+ \frac{\beta}{\alpha+\beta}\theta |u|^{\alpha}|v|^{\beta-2}v, \;\;\; \mbox{in}\;\;\; \mathbb{R}^N, (u, v) \in H^s(\mathbb{R}^N) \times H^s(\mathbb{R}^N). \end{array}\right. \end{equation*} Here we mention that $\alpha > 1, \beta > 1, 1 \leq p \leq q < 2 < \alpha + \beta < 2^*_s$, $\theta > 0, \lambda > 0, N > 2s$, and $s \in (0,1)$. Notice also that continuous potentials $V_1, V_2: \mathbb{R}^N \to \mathbb{R}$ satisfy some extra assumptions. Furthermore, we find the largest positive number $\lambda^* > 0$ such that our main problem admits at least two positive solutions for each $ \lambda \in (0, \lambda^*)$. This can be done by using the nonlinear Rayleigh quotient together with the Nehari method. The main feature here is to minimize the energy functional in Nehari manifold which allows us to prove our main results without any restriction on size of parameter $\theta > 0$., Comment: In this work, we shall investigate existence and multiplicity of solutions for a nonlocal elliptic systems driven by the fractional Laplacian. Specifically, we establish the existence of two positive solutions for following class of nonlocal elliptic systems

In Large Language Model (LLM) development, Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning models with human values and preferences. RLHF traditionally relies on the Kullback-Leibler (KL) divergence between the current policy and a frozen initial policy as a reference, which is added as a penalty in policy optimization algorithms like Proximal Policy Optimization (PPO). While this constraint prevents models from deviating too far from the initial checkpoint, it limits exploration of the reward landscape, reducing the model's ability to discover higher-quality solutions. As a result, policy optimization is often trapped in a narrow region of the parameter space, leading to suboptimal alignment and performance. This paper presents SALSA (Soup-based Alignment Learning for Stronger Adaptation), a novel approach designed to overcome these limitations by creating a more flexible and better located reference model through weight-space averaging of two independent supervised fine-tuned (SFT) models. This model soup allows for larger deviation in KL divergence and exploring a promising region of the solution space without sacrificing stability. By leveraging this more robust reference model, SALSA fosters better exploration, achieving higher rewards and improving model robustness, out-of-distribution generalization, and performance. We validate the effectiveness of SALSA through extensive experiments on popular open models (Llama2-7B, Mistral-7B, and Gemma-2B) across various benchmarks (MT-Bench, Arena-Hard, UltraFeedback), where it consistently surpasses PPO by fostering deeper exploration and achieving superior alignment in LLMs.