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Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

We report the emergence of a polar metal phase in layered van der Waals compound FePSe$_3$. This Mott insulator with antiferromagnetic order offers a unique opportunity to fully tune an insulator into a polar metal state with pressure, without doping-induced disorder or impurities. Our synchrotron and neutron diffraction data unambiguously show a structural transition and loss of the inversion symmetry. We also observed the suppression of magnetic ordering and an insulator-to-metal transition correspondent with this structural transformation. The loss of the inversion symmetry combined with the pressure-induced metallicity in FePSe$_3$ offers a new platform to investigate polar metallicity at accessible pressures. Moreover, the high-pressure metallic phase shows unconventional resistivity deviating from the Fermi-liquid description, close to the magnetic critical transition pressure at sufficiently low temperatures, which strongly suggests underlying quantum criticality. Our work not only explores the comprehensive temperature-pressure phase diagram of FePSe$_3$ but also provides insights for further investigation of van der Waals strongly correlated magnetic compounds., Comment: 13 pages, 5 figures

This paper establishes a mathematically precise definition of dataset poisoning attack and proves that the very act of effectively poisoning a dataset ensures that the attack can be effectively detected. On top of a mathematical guarantee that dataset poisoning is identifiable by a new statistical test that we call the Conformal Separability Test, we provide experimental evidence that we can adequately detect poisoning attempts in the real world.

The fastrerandomize R package provides hardware-accelerated tools for performing rerandomization and randomization testing in experimental research. Using a JAX backend, the package enables exact rerandomization inference even for large experiments with hundreds of billions of possible randomizations. Key functionalities include generating pools of acceptable rerandomizations based on covariate balance, conducting exact randomization tests, and performing pre-analysis evaluations to determine optimal rerandomization acceptance thresholds. Through batched processing and GPU acceleration, fastrerandomize achieves substantial performance gains compared to existing implementations, making previously intractable designs computationally feasible. The package therefore extends the randomization-based inference toolkit in R, allowing researchers to efficiently implement more stringent rerandomization designs and conduct valid inference even with large sample sizes or in high-dimensional settings., Comment: 35 pages, 9 figures

We present PDLP, a practical first-order method for linear programming (LP) designed to solve large-scale LP problems. PDLP is based on the primal-dual hybrid gradient (PDHG) method applied to the minimax formulation of LP. PDLP incorporates several enhancements to PDHG, including diagonal preconditioning, presolving, adaptive step sizes, adaptive restarting, and feasibility polishing. Our algorithm is implemented in C++, available in Google's open-source OR-Tools library, and supports multithreading. To evaluate our method, we introduce a new collection of eleven large-scale LP problems with sizes ranging from 125 million to 6.3 billion nonzeros. PDLP solves eight of these instances to optimality gaps of 1\% (with primal and dual feasibility errors of less than $10^{-8}$) within six days on a single machine. We also compare PDLP with Gurobi barrier, primal simplex, and dual simplex implementations. Gurobi barrier solves only three instances, exceeding our 1TB RAM limit on the other eight. While primal and dual simplex are more memory-efficient than the barrier method, they are slower and solve only three instances within six days. Compared with the conference version of this work (in: Advances in Neural Information Processing Systems 34 (NeurIPS 2021)), the key new contributions are: (i) feasibility polishing, a technique that quickly finds solutions that are approximately optimal but almost exactly feasible (without which only three of the eleven problems can be solved); (ii) a multithreaded C++ implementation available in Google OR-Tools; and (iii) a new collection of large-scale LP problems. Note that the conference version should be referred to for comparisons with SCS and ablation studies, which we do not repeat in this paper.

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

Our study has investigated the effect of music on the experience of viewing art, investigating the factors which create a sense of connectivity between the two forms. We worked with 138 participants, and included multiple choice and open-ended questions. For the latter, we performed both a qualitative analysis and also sentiment analysis using text-mining. We investigated the relationship between the user experience and the emotions in the artwork and music. We found that, besides emotion, theme, story, and to a lesser extent music tempo were factors which helped form connections between artwork and music. Overall, participants rated the music as being helpful in developing an appreciation of the art. We propose guidelines for using music to enhance the experience of viewing art, and we propose directions for future research.

We report our findings on a spectroscopic survey of seven unresolved DA+DB binary white dwarf candidates. We have discovered extreme spectroscopic variations in one of these candidates, SDSS J084716.21+484220.40. Previous analysis failed to reproduce the optical spectrum using a single object with a homogeneous atmosphere. Our time-resolved spectroscopy reveals a double-faced white dwarf that switches between a DBA and DA spectral type over 6.5 or 8.9 hours due to varying surface abundances. We also provide time-series spectroscopy of the magnetic DBA, SDSS J085618.94+161103.6 (LB 8915), and confirm an inhomogeneous atmosphere. We employ an atmosphere model with hydrogen caps and a helium belt that yields excellent fits to our time-resolved spectra. We use the oblique rotator model to derive the system geometry for both targets. With the addition of these two objects, the emerging class of double-faced white dwarfs now consists of seven members. We summarize the properties of this new class of objects, and discuss how magnetism impacts the convective processes and leads to the formation of double-faced white dwarfs. We identify cooler versions of white dwarfs with inhomogeneous atmospheres among the cool magnetic DA white dwarf sample, where the H$\alpha$ line is shallower than expected based on pure hydrogen atmosphere models., Comment: 14 pages, 9 figures, Accepted for publication in ApJ

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

This article is a continuation of [6] where a classification of when the space of minimal prime subgroups of a given lattice-ordered group equipped with the inverse topology has a clopen $\pi$-base. For nice $\ell$-groups, (e.g. W-objects) this occurs precisely when the space of maximal $d$-subgroups (qua the hull kernel topology) has a clopen $\pi$-base. It occurred to us that presently there is no classification of when the space of maximal $d$-subgroups of a W-object is zero-dimensional, except for the case of the $C(X)$, the real-valued continuous functions on a topological space $X$, considered in [5]., Comment: 9

Multi-agent systems are increasingly applied in space missions, including distributed space systems, resilient constellations, and autonomous rendezvous and docking operations. A critical emerging application is collaborative spacecraft servicing, which encompasses on-orbit maintenance, space debris removal, and swarm-based satellite repositioning. These missions involve servicing spacecraft interacting with malfunctioning or defunct spacecraft under challenging conditions, such as limited state information, measurement inaccuracies, and erratic target behaviors. Existing approaches often rely on assumptions of full state knowledge or single-integrator dynamics, which are impractical for real-world applications involving second-order spacecraft dynamics. This work addresses these challenges by developing a distributed state estimation and tracking framework that requires only relative position measurements and operates under partial state information. A novel $\rho$-filter is introduced to reconstruct unknown states using locally available information, and a Lyapunov-based deep neural network adaptive controller is developed that adaptively compensates for uncertainties stemming from unknown spacecraft dynamics. To ensure the collaborative spacecraft regulation problem is well-posed, a trackability condition is defined. A Lyapunov-based stability analysis is provided to ensure exponential convergence of errors in state estimation and spacecraft regulation to a neighborhood of the origin under the trackability condition. The developed method eliminates the need for expensive velocity sensors or extensive pre-training, offering a practical and robust solution for spacecraft servicing in complex, dynamic environments., Comment: 24 pages, 4 Figures, Journal

In this paper, we have introduced and studied the feasibility of a hybrid solid liquid vibration energy harvester. The energy harvester consists of a ferrofluid partially filled in a tank and a piezoelectric beam fixed at one of the tank walls. The tank is assumed to be placed in a nonuniform magnetic field created by placing two powerful magnets symmetrically external to the tank walls. This magnetic field was then implemented by a Magnetic field function modeling the magnetic field produced by the two magnets. We used piezoelectric beam configurations and oscillation loads to study and characterize this 2D multiscale, multiphysics, and multiphase fluid structure interaction. The tank is subjected to an external oscillatory motion, which sloshes the ferrofluid and oscillates the beam utilizing two modes, one due to the beams inertia and the second due to the impact of the ferrofluid on the beam. The parameters varied in piezo materials, ferrofluid fill height, piezoelectric beam length, and oscillation frequency. The natural frequency modes for the piezoelectric beam are very important since the beam harvests the highest power in those modes. We have observed that when the fluid motion vibrates the beam, in some instances, the voltage output from piezoelectric material peaks to a high value, which is indicative of material nearing resonance frequencies at the fluid loading realized in those instances. However, in other cases, the voltage output varies with the loading, and hence, the response of the piezoelectric beam is broadband, very similar to the sloshing, which is inherently broadband.

We study ``nonlocal diffusion equations'' of the form \[ \partial_{t}\frac{d\rho_{t}}{d\pi}(x)+\int_{X}\left(\frac{d\rho_{t}}{d\pi}(x)-\frac{d\rho_{t}}{d\pi}(y)\right)\eta(x,y)d\pi(y)=0\qquad(\dagger) \] where $X$ is either $\mathbb{R}^{d}$ or $\mathbb{T}^{d}$, $\pi$ is a probability distribution on $X$, and $\eta(x,y)$ is a ``transition kernel'' which may be singular as $x\rightarrow y$. For a suitable notion of weak solutions which we discuss below, we show that solutions to these nonlocal diffusion equations can be interpreted as gradient flows of the relative entropy with respect to a certain nonlocal Wasserstein-type metric defined in terms of $\eta$ and $\pi$. These ``nonlocal Wasserstein metrics'' endow the space of probability measures on $X$ with a formal Riemannian structure, thereby providing for us a nonlocal analogue of the \emph{Otto calculus} originally developed in the context of the 2-Wasserstein metric. The class of equations $(\dagger)$ includes a family of ``nonlocal Fokker-Planck equations'', which are thus identified as nonlocal Wasserstein gradient flows of the relative entropy, analogously with the usual Fokker-Planck equation and the $W_{2}$ metric. The gradient flow structure we provide allows us to deduce: existence and uniqueness of solutions to ($\dagger$) in a suitable class of weak solutions; stability of solutions in the sense of evolutionary $\Gamma$-convergence, with respect to perturbations of initial condition, reference measure $\pi$, and transition kernel $\eta$; sufficient conditions for exponential convergence to equilibrium, in terms of a nonlocal analogue of the log-Sobolev inequality; as well as the consistency of a finite-volume-type spatial discretization scheme in the $\mathbb{T}^{d}$ case., Comment: 59 pages. Draft, comments welcome

Controlling nonlinear stochastic dynamical systems involves substantial challenges when the dynamics contain unknown and unstructured nonlinear state-dependent terms. For such complex systems, deep neural networks can serve as powerful black box approximators for the unknown drift and diffusion processes. Recent developments construct Lyapunov-based deep neural network (Lb-DNN) controllers to compensate for deterministic uncertainties using adaptive weight update laws derived from a Lyapunov-based analysis based on insights from the compositional structure of the DNN architecture. However, these Lb-DNN controllers do not account for non-deterministic uncertainties. This paper develops Lb-DNNs to adaptively compensate for both the drift and diffusion uncertainties of nonlinear stochastic dynamic systems. Through a Lyapunov-based stability analysis, a DNN-based approximation and corresponding DNN weight adaptation laws are constructed to eliminate the unknown state-dependent terms resulting from the nonlinear diffusion and drift processes. The tracking error is shown to be uniformly ultimately bounded in probability. Simulations are performed on a nonlinear stochastic dynamical system to show efficacy of the proposed method.

The titular ordinary differential equation (ODE) is encountered in the theory of on-axis inertial particle capture by a blunt stationary collector at a viscous-flow stagnation point. Phase space for the ODE divides into two attractor basins, representing particle trajectories which do or do not collide with the collector in a finite time. Written as $\ddot{x} + \dot{x} + \epsilon x^2 = 0$, we formulate the renormalisation group (RG) amplitude equations for this problem and argue that the critical trajectory which separates the attractor basins corresponds to a trivial but exact solution of these, and can therefore be extracted as a power series in $\epsilon$. We show how this can be used to find the cross-over between capture and non-capture as a function of distance from the stagnation point, for a particle released into the flow with no initial acceleration. This cross-over, previously only computable by numerical integration of the ODE, can therefore be expressed as a (numerically) convergent series with rational terms., Comment: 8 pages (7 + references), 4 figures

The Mekong Delta Region of Vietnam faces increasing dengue risks driven by urbanization, globalization, and climate change. This study introduces a probabilistic forecasting model for predicting dengue incidence and outbreaks with one to three month lead times, integrating meteorological, sociodemographic, preventive, and epidemiological data. Seventy-two models were evaluated, and an ensemble combining top-performing spatiotemporal, supervised PCA, and semi-mechanistic hhh4 frameworks was developed. Using data from 2004-2022 for training, validation, and evaluation, the ensemble model demonstrated 69% accuracy at a 3-month horizon, outperforming a baseline model. While effective, its performance declined in years with atypical seasonality, such as 2019 and 2022. The model provides critical lead time for targeted dengue prevention and control measures, addressing a growing public health need in the region., Comment: 34 pages, 6 figures

We show the existence of a properly immersed translating solution to curve diffusion flow in the plane. Curve diffusion flow is a higher order version of curve shortening flow, namely \[ \left( \frac{dX}{dt}\right) ^{\perp}=-\kappa_{ss}N. \]

It is advantageous for any quantum processor to support different classes of two-qubit quantum logic gates when compiling quantum circuits, a property that is typically not seen with existing platforms. In particular, access to a gate set that includes support for the CZ-type, the iSWAP-type, and the SWAP-type families of gates, renders conversions between these gate families unnecessary during compilation as any two-qubit Clifford gate can be executed using at most one two-qubit gate from this set, plus additional single-qubit gates. We experimentally demonstrate that a SWAP gate can be decomposed into one iSWAP gate followed by one CZ gate, affirming a more efficient compilation strategy over the conventional approach that relies on three iSWAP or three CZ gates to replace a SWAP gate. Our implementation makes use of a superconducting quantum processor design based on fixed-frequency transmon qubits coupled together by a parametrically modulated tunable transmon coupler, extending this platform's native gate set so that any two-qubit Clifford unitary matrix can be realized using no more than two two-qubit gates and single-qubit gates., Comment: 29 pages, 13 figures

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

We present a scalable and agile approach for ads image content moderation at Google, addressing the challenges of moderating massive volumes of ads with diverse content and evolving policies. The proposed method utilizes human-curated textual descriptions and cross-modal text-image co-embeddings to enable zero-shot classification of policy violating ads images, bypassing the need for extensive supervised training data and human labeling. By leveraging large language models (LLMs) and user expertise, the system generates and refines a comprehensive set of textual descriptions representing policy guidelines. During inference, co-embedding similarity between incoming images and the textual descriptions serves as a reliable signal for policy violation detection, enabling efficient and adaptable ads content moderation. Evaluation results demonstrate the efficacy of this framework in significantly boosting the detection of policy violating content.

The growing number of satellite constellations in low Earth orbit (LEO) enhances global communications and Earth observation, and support of space commerce is a high priority of many governments. At the same time, the proliferation of satellites in LEO has negative effects on astronomical observations and research, and the preservation of the dark and quiet sky. These satellite constellations reflect sunlight onto optical telescopes, and their radio emission impacts radio observatories, jeopardising our access to essential scientific discoveries through astronomy. The changing visual appearance of the sky also impacts our cultural heritage and environment. Both ground-based observatories and space-based telescopes in LEO are affected, and there are no places on Earth that can escape the effects of satellite constellations given their global nature. The minimally disturbed dark and radio-quiet sky is crucial for conducting fundamental research in astronomy and important public services such as planetary defence, technology development, and high-precision geolocation. Some aspects of satellite deployment and operation are regulated by States and intergovernmental organisations. While regulatory agencies in some States have started to require operators to coordinate with their national astronomy agencies over impacts, mitigation of the impact of space objects on astronomical activities is not sufficiently regulated. To address this issue, the CPS urges States and the international community to take steps to protect the dark and quiet sky as specified in this paper., Comment: This position paper was developed by the IAU Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (CPS). It can also be downloaded at the CPS website at https://cps.iau.org/news/cps-urges-action-in-first-recommendations-paper/

Zero-shot capabilities of large language models make them powerful tools for solving a range of tasks without explicit training. It remains unclear, however, how these models achieve such performance, or why they can zero-shot some tasks but not others. In this paper, we shed some light on this phenomenon by defining and investigating algorithmic stability in language models -- changes in problem-solving strategy employed by the model as a result of changes in task specification. We focus on a task where algorithmic stability is needed for generalization: two-operand arithmetic. Surprisingly, we find that Gemma-2-2b employs substantially different computational models on closely related subtasks, i.e. four-digit versus eight-digit addition. Our findings suggest that algorithmic instability may be a contributing factor to language models' poor zero-shot performance across certain logical reasoning tasks, as they struggle to abstract different problem-solving strategies and smoothly transition between them., Comment: 10 pages, 5 figures

We increase the spectroscopic completeness of the 100 pc white dwarf sample in the SDSS footprint with 840 additional spectra. Our spectroscopy is 86% complete for white dwarfs hotter than $T_{\rm eff}= 5000$ K, where H$\alpha$ remains visible and provides reliable constraints on the atmospheric composition. We identify 2108 DA white dwarfs with pure hydrogen atmospheres, and show that ultramassive DA white dwarfs with $M\geq1.1~M_{\odot}$ are an order of magnitude less common below 10,000 K. This is consistent with a fraction of them getting stuck on the crystallization sequence due to $^{22}$Ne distillation. In addition, there are no ultramassive DA white dwarfs with $M\geq1.1~M_{\odot}$ and $T_{\rm eff}\leq6000$ K in our sample, likely because Debye cooling makes them rapidly fade away. We detect a significant trend in the fraction of He-atmosphere white dwarfs as a function of temperature; the fraction increases from 9% at 20,000 K to 32% at 6000 K. This provides direct evidence of convective mixing in cool DA white dwarfs. Finally, we detect a relatively tight sequence of low-mass DQ white dwarfs in color-magnitude diagrams for the first time. We discuss the implications of this tight DQ sequence, and conclude with a discussion of the future prospects from the upcoming ULTRASAT mission and the large-scale multi-fiber spectroscopic surveys., Comment: ApJ, in press

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings., Comment: Presented at the precision medicine workshop at the AI in Medicine conference (2024) in Salt Lake City

Hands are the primary means through which humans interact with the world. Reliable and always-available hand pose inference could yield new and intuitive control schemes for human-computer interactions, particularly in virtual and augmented reality. Computer vision is effective but requires one or multiple cameras and can struggle with occlusions, limited field of view, and poor lighting. Wearable wrist-based surface electromyography (sEMG) presents a promising alternative as an always-available modality sensing muscle activities that drive hand motion. However, sEMG signals are strongly dependent on user anatomy and sensor placement, and existing sEMG models have required hundreds of users and device placements to effectively generalize. To facilitate progress on sEMG pose inference, we introduce the emg2pose benchmark, the largest publicly available dataset of high-quality hand pose labels and wrist sEMG recordings. emg2pose contains 2kHz, 16 channel sEMG and pose labels from a 26-camera motion capture rig for 193 users, 370 hours, and 29 stages with diverse gestures - a scale comparable to vision-based hand pose datasets. We provide competitive baselines and challenging tasks evaluating real-world generalization scenarios: held-out users, sensor placements, and stages. emg2pose provides the machine learning community a platform for exploring complex generalization problems, holding potential to significantly enhance the development of sEMG-based human-computer interactions., Comment: Published at NeurIPS 2024 Datasets and Benchmarks Track

The design of fast reactors burning MOX fuels requires accurate capture and fission cross sections. For the particular case of neutron capture on 242Pu, the NEA recommends that an accuracy of 8-12% should be achieved in the fast energy region (2 keV-500 keV) compared to their estimation of 35% for the current uncertainty. Integral irradiation experiments suggest that the evaluated cross section of the JEFF-3.1 library overestimates the 242Pu(n,{\gamma}) cross section by 14% in the range between 1 keV and 1 MeV. In addition, the last measurement at LANSCE reported a systematic reduction of 20-30% in the 1-40 keV range relative to the evaluated libraries and previous data sets. In the present work this cross section has been determined up to 600 keV in order to solve the mentioned discrepancies. A 242Pu target of 95(4) mg enriched to 99.959% was irradiated at the n TOF-EAR1 facility at CERN. The capture cross section of 242Pu has been obtained between 1 and 600 keV with a systematic uncertainty (dominated by background subtraction) between 8 and 12%, reducing the current uncertainties of 35% and achieving the accuracy requested by the NEA in a large energy range. The shape of the cross section has been analyzed in terms of average resonance parameters using the FITACS code as implemented in SAMMY, yielding results compatible with our recent analysis of the resolved resonance region.The results are in good agreement with the data of Wisshak and K\"appeler and on average 10-14% below JEFF-3.2 from 1 to 250 keV, which helps to achieve consistency between integral experiments and cross section data. At higher energies our results show a reasonable agreement within uncertainties with both ENDF/B-VII.1 and JEFF-3.2. Our results indicate that the last experiment from DANCE underestimates the capture cross section of 242Pu by as much as 40% above a few keV., Comment: 20 pages, 19 figures, submitted to Phys. Rev. C

We present the discovery and analysis of a nearby eclipsing ultra-compact accreting binary at coordinates 11:38:10.91 $-$51:39:49.15 (SMSS J1138$-$5139), the first well-constrained LISA-detectable Type Ia supernova progenitor. Our time series optical spectroscopy identifies its orbital period through radial velocity monitoring at $P_{\rm orb,RV}=27.69\pm0.03~{\rm min}$; twice the photometric period seen in 2-minute cadence data from TESS Sector 37. We model our optical spectroscopy together with new simultaneous multi-band time series photometry from Gemini to place constraints on the binary parameters. Our light curve modeling finds that SMSS J1138$-$5139 contains an $M_2=0.24~{\rm M_\odot}$ pre-white dwarf donor with a massive $M_1=0.99~{\rm M_\odot}$ white dwarf accretor at orbital inclination $i=88.7~{\rm deg}$. Based on our photometrically derived system parameters, we expect that gravitational wave radiation will drive SMSS J1138$-$5139 to a merger within $\tau=5.7\pm0.3~{\rm Myr}$ and result in a Type Ia supernova. Even without a direct merger event, the component masses of SMSS J1138$-$5139 and active hydrogen accretion suggest that eventual helium accretion will likely also trigger a Type Ia supernova explosion through the dynamically-driven double-degenerate double-detonation (D6) channel. We expect LISA to detect the gravitational wave emission from SMSS J1138$-$5139 with signal-to-noise $7-10$ after a 48-month mission., Comment: 15 pages, 10 figures, 1 table. Submitted to ApJ

This research explores the application of large language models (LLMs) to generate synthetic datasets for Product Desirability Toolkit (PDT) testing, a key component in evaluating user sentiment and product experience. Utilizing gpt-4o-mini, a cost-effective alternative to larger commercial LLMs, three methods, Word+Review, Review+Word, and Supply-Word, were each used to synthesize 1000 product reviews. The generated datasets were assessed for sentiment alignment, textual diversity, and data generation cost. Results demonstrated high sentiment alignment across all methods, with Pearson correlations ranging from 0.93 to 0.97. Supply-Word exhibited the highest diversity and coverage of PDT terms, although with increased generation costs. Despite minor biases toward positive sentiments, in situations with limited test data, LLM-generated synthetic data offers significant advantages, including scalability, cost savings, and flexibility in dataset production., Comment: 9 pages, 2 figures, 6 tables, updated author list

DNSSEC, a DNS security extension, is essential to accurately translating domain names to IP addresses. Digital signatures provide the foundation for this reliable translation, however, the evolution of 'Quantum Computers' has made traditional digital signatures vulnerable. In light of this, NIST has recently selected potential post-quantum digital signatures that can operate on conventional computers and resist attacks made with Quantum Computers. Since these post-quantum digital signatures are still in their early stages of development, replacing pre-quantum digital signature schemes in DNSSEC with post-quantum candidates is risky until the post-quantum candidates have undergone a thorough security analysis. Given this, herein, we investigate the viability of employing 'Double-Signatures' in DNSSEC, combining a post-quantum digital signature and a classic one. The rationale is that double-signatures will offer protection against quantum threats on conventional signature schemes as well as unknown non-quantum attacks on post-quantum signature schemes, hence even if one fails the other provides security guarantees. However, the inclusion of two signatures in the DNSSEC response message doesn't bode well with the maximum allowed size of DNSSEC responses (i.e., 1232B, a limitation enforced by MTU of physical links). To counter this issue, we leverage a way to do application-layer fragmentation of DNSSEC responses with two signatures. We implement our solution on top of OQS-BIND and through experiments show that the addition of two signatures in DNSSEC and application-layer fragmentation of all relevant resource records and their reassembly does not have any substantial impact on the efficiency of the resolution process and thus is suitable for the interim period at least until the quantum computers are fully realized.

Extreme ultraviolet (XUV) light sources allow for the probing of bound electron dynamics on attosecond scales, interrogation of high-energy-density matter, and access to novel regimes of strong-field quantum electrodynamics. Despite the importance of these applications, coherent XUV sources remain relatively rare, and those that do exist are limited in their peak intensity and spatio-polarization structure. Here, we demonstrate that photon acceleration of an optical vector vortex pulse in the moving density gradient of an electron beam-driven plasma wave can produce a high-intensity, tunable-wavelength XUV pulse with the same vector vortex structure as the original pulse. Quasi-3D, boosted-frame particle-in-cell simulations show the transition of optical vector vortex pulses with 800-nm wavelengths and intensities below $10^{18}$ W/cm$^2$ to XUV vector vortex pulses with 36-nm wavelengths and intensities exceeding $10^{20}$ W/cm$^2$ over a distance of 1.2 cm. The XUV pulses have sub-femtosecond durations and nearly flat phase fronts. The production of such high-quality, high-intensity XUV vector vortex pulses could expand the utility of XUV light as a diagnostic and driver of novel light-matter interactions., Comment: 25 pages, 5 figures, planned submission to scientific journal

We introduce PropNEAT, a fast backpropagation implementation of NEAT that uses a bidirectional mapping of the genome graph to a layer-based architecture that preserves the NEAT genomes whilst enabling efficient GPU backpropagation. We test PropNEAT on 58 binary classification datasets from the Penn Machine Learning Benchmarks database, comparing the performance against logistic regression, dense neural networks and random forests, as well as a densely retrained variant of the final PropNEAT model. PropNEAT had the second best overall performance, behind Random Forest, though the difference between the models was not statistically significant apart from between Random Forest in comparison with logistic regression and the PropNEAT retrain models. PropNEAT was substantially faster than a naive backpropagation method, and both were substantially faster and had better performance than the original NEAT implementation. We demonstrate that the per-epoch training time for PropNEAT scales linearly with network depth, and is efficient on GPU implementations for backpropagation. This implementation could be extended to support reinforcement learning or convolutional networks, and is able to find sparser and smaller networks with potential for applications in low-power contexts.

An accurate description of elastic effects of coherent microstructures is necessary for the predictive modeling of microstructural evolution in many structural materials. To date, there has not been a demonstration on how continuum elasticity models are able to reproduce finite-temperature stress-fields and elastic energy estimates of coherent precipitates from atomistic simulations. We present a comparison of stress-fields of coherent precipitates in the body-centered cubic (BCC) Fe-Cr system obtained from atomistic simulation data and from continuum elasticity modeling. The magnitude and topology of the stress-fields show good agreement between the two approaches, and we show the importance of elastic effects on the Gibbs-Thompson effect for this small lattice misfit system. We conclude with a discussion of potential complications of continuum modeling for systems with larger misfit., Comment: 19 pages, 5 figures, 4 tables, supplemental material in anc directory in source

Earth Observation (EO) data are increasingly used in policy analysis by enabling granular estimation of treatment effects. However, a challenge in EO-based causal inference lies in balancing the trade-off between capturing fine-grained individual heterogeneity and broader contextual information. This paper introduces Multi-scale Concatenation, a family of composable procedures that transform arbitrary single-scale CATE estimation algorithms into multi-scale algorithms. We benchmark the performance of Multi-scale Concatenation on a CATE estimation pipeline combining Vision Transformer (ViT) models fine-tuned on satellite images to encode images of different scales with Causal Forests to obtain the final CATE estimate. We first perform simulation studies, showing how a multi-scale approach captures multi-level dynamics that single-scale ViT models fail to capture. We then apply the multi-scale method to two randomized controlled trials (RCTs) conducted in Peru and Uganda using Landsat satellite imagery. In the RCT analysis, the Rank Average Treatment Effect Ratio (RATE Ratio) measure is employed to assess performance without ground truth individual treatment effects. Results indicate that Multi-scale Concatenation improves the performance of deep learning models in EO-based CATE estimation without the complexity of designing new multi-scale architectures for a specific use case., Comment: 27 pages, 13 figures

Crop biomass offers crucial insights into plant health and yield, making it essential for crop science, farming systems, and agricultural research. However, current measurement methods, which are labor-intensive, destructive, and imprecise, hinder large-scale quantification of this trait. To address this limitation, we present a biomass prediction network (BioNet), designed for adaptation across different data modalities, including point clouds and drone imagery. Our BioNet, utilizing a sparse 3D convolutional neural network (CNN) and a transformer-based prediction module, processes point clouds and other 3D data representations to predict biomass. To further extend BioNet for drone imagery, we integrate a neural feature field (NeFF) module, enabling 3D structure reconstruction and the transformation of 2D semantic features from vision foundation models into the corresponding 3D surfaces. For the point cloud modality, BioNet demonstrates superior performance on two public datasets, with an approximate 6.1% relative improvement (RI) over the state-of-the-art. In the RGB image modality, the combination of BioNet and NeFF achieves a 7.9% RI. Additionally, the NeFF-based approach utilizes inexpensive, portable drone-mounted cameras, providing a scalable solution for large field applications.

Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets, and image generation. However, learning the underlying SDE from observational data is a challenging task, especially when individual trajectories are not observable. Motivated by burgeoning research in single-cell datasets, we present the first comprehensive approach for jointly estimating the drift and diffusion of an SDE from its temporal marginals. Assuming linear drift and additive diffusion, we prove that these parameters are identifiable from marginals if and only if the initial distribution is not invariant to a class of generalized rotations, a condition that is satisfied by most distributions. We further prove that the causal graph of any SDE with additive diffusion can be recovered from the SDE parameters. To complement this theory, we adapt entropy-regularized optimal transport to handle anisotropic diffusion, and introduce APPEX (Alternating Projection Parameter Estimation from $X_0$), an iterative algorithm designed to estimate the drift, diffusion, and causal graph of an additive noise SDE, solely from temporal marginals. We show that each of these steps are asymptotically optimal with respect to the Kullback-Leibler divergence, and demonstrate APPEX's effectiveness on simulated data from linear additive noise SDEs.

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that minimizes the model's use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information minimized, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two state-of-the-art T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD). Approximately 4 years before MCI diagnosis, dMRI-based brain age yields better performance than T1w MRI-based brain ages in predicting transition from CN to MCI.

Hybrid quantum-classical computing algorithms offer significant potential for accelerating the calculation of the electronic structure of strongly correlated molecules. In this work, we present the first quantum simulation of conical intersections (CIs) in a biomolecule, cytosine, using a superconducting quantum computer. We apply the Contracted Quantum Eigensolver (CQE) -- with comparisons to conventional Variational Quantum Deflation (VQD) -- to compute the near-degenerate ground and excited states associated with the conical intersection, a key feature governing the photostability of DNA and RNA. The CQE is based on an exact ansatz for many-electron molecules in the absence of noise -- a critically important property for resolving strongly correlated states at CIs. Both methods demonstrate promising accuracy when compared with exact diagonalization, even on noisy intermediate-scale quantum computers, highlighting their potential for advancing the understanding of photochemical and photobiological processes. The ability to simulate these intersections is critical for advancing our knowledge of biological processes like DNA repair and mutation, with potential implications for molecular biology and medical research.

A gradient of a single salt in a solution generates an electric field, but not a current. Recent theoretical work by one of us [Phys. Rev. Lett. 24, 248004 (2020)] showed that the Nernst-Planck equations imply that crossed gradients of two or more different salts generate ion currents. These currents in solution have associated non-local electric fields. Particle motion driven by these non-local fields has recently been observed in experiment by Williams et al. [Phys. Rev. Fluids 9, 014201 (2024)]; a phenomenon which was dubbed action-at-a-distance diffusiophoresis. Here we use a magnetostatic analogy to show that in the far-field limit, these non-local currents and electric fields both have the functional form of the magnetic field of a magnetic dipole, decaying as r^(-d) in d = 2 and d = 3 dimensions. These long-ranged electric fields are generated entirely within solutions and have potential practical applications since they can drive both electrophoretic motion of particles, and electro-osmotic flows. The magnetostatic analogy also allows us to import tools and ideas from classical electromagnetism, into the study of multicomponent salt solutions., Comment: 14 pages, 7 figures, RevTeX 4.2, jupyter notebook included as ancillary file

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. This paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

Plasma composition measurements are a vital tool for the success of current and future solar missions, but density and temperature insensitive spectroscopic diagnostic ratios are sparse, and their underlying accuracy in determining the magnitude of the First Ionization Potential (FIP) effect in the solar atmosphere remains an open question. Here we assess the Fe VIII 185.213A/Ne VIII 770.428A intensity ratio that can be observed as a multi-spacecraft combination between Solar Orbiter/SPICE and Hinode/EIS. We find that it is fairly insensitive to temperature and density in the range of log (T/K) = 5.65-6.05 and is therefore useful, in principle, for analyzing on-orbit EUV spectra. We also perform an empirical experiment, using Hinode/EIS measurements of coronal fan loop temperature distributions weighted by randomnly generated FIP bias values, to show that our diagnostic method can provide accurate results as it recovers the input FIP bias to within 10--14%. This is encouraging since it is smaller than the magnitude of variations seen throughout the solar corona. We apply the diagnostic to coordinated observations from 2023 March, and show that the combination of SPICE and EIS allows measurements of the Fe/Ne FIP bias in the regions where the footpoints of the magnetic field connected to Solar Orbiter are predicted to be located. The results show an increase in FIP bias between the main leading polarity and the trailing decayed polarity that broadly agrees with Fe/O in-situ measurements from Solar Orbiter/SWA. Multi-spacecraft coordinated observations are complex, but this diagnostic also falls within the planned wavebands for Solar-C/EUVST., Comment: To be published in The Astrophysical Journal

The boolean satisfiability (SAT) problem asks whether there exists an assignment of boolean values to the variables of an arbitrary boolean formula making the formula evaluate to True. It is well-known that all NP-problems can be coded as SAT problems and therefore SAT is important both practically and theoretically. From both of these perspectives, better understanding the patterns and structure implicit in SAT data is of significant value. In this paper, we describe several advances that we believe will help open the door to such understanding: we introduce hardware accelerated algorithms for fast SAT problem generation, a geometric SAT encoding that enables the use of transformer architectures typically applied to vision tasks, and a simple yet effective technique we term head slicing for reducing sequence length representation inside transformer architectures. These advances allow us to scale our approach to SAT problems with thousands of variables and tens of thousands of clauses. We validate our architecture, termed Satisfiability Transformer (SaT), on the SAT prediction task with data from the SAT Competition (SATComp) 2022 problem sets. Prior related work either leveraged a pure machine learning approach, but could not handle SATComp-sized problems, or was hybrid in the sense of integrating a machine learning component in a standard SAT solving tool. Our pure machine learning approach achieves prediction accuracies comparable to recent work, but on problems that are an order of magnitude larger than previously demonstrated. A fundamental aspect of our work concerns the very nature of SAT data and its suitability for training machine learning models. We both describe experimental results that probe the landscape of where SAT data can be successfully used for learning and position these results within the broader context of complexity and learning., Comment: 22 pages

Ground-penetrating radar (GPR) is a mature geophysical method that has gained increasing popularity in planetary science over the past decade. GPR has been utilised both for Lunar and Martian missions providing pivotal information regarding the near surface geology of Terrestrial planets. Within that context, numerous processing pipelines have been suggested to address the unique challenges present in planetary setups. These processing pipelines often require manual tuning resulting to ambiguous outputs open to non-unique interpretations. These pitfalls combined with the large number of planetary GPR data (kilometers in magnitude), highlight the necessity for automatic, objective and advanced processing and interpretation schemes. The current paper investigates the potential of deep learning for interpreting and processing GPR data. The one-shot multi-offset configuration is investigated via a coherent numerical case study, showcasing the potential of deep learning for A) reconstructing the dielectric distribution of the the near surface of Terrestrial planets, and B) filling missing or bad-quality traces. Special care was taken for the numerical data to be both realistic and challenging. Moreover, the generated synthetic data are properly labelled and made publicly available for training future data-driven pipelines and contributing towards developing pre-trained foundation models for GPR.

Understanding the exact fault location in the post-event analysis is the key to improving the accuracy of outage management. Unfortunately, the fault location is not generally well documented during the restoration process, creating a big challenge for post-event analysis. By utilizing various data source systems, including outage management system (OMS) data, asset geospatial information system (GIS) data, and vehicle location data, this paper creates a novel method to pinpoint the outage location accurately to create additional insights for distribution operations and performance teams during the post-event analysis.

A $d$-subgroup of a lattice-ordered group ($\ell$-group) $G$ is a subgroup that contains the principal polars generated by each of its elements. We call $G$ a Martinez $\ell$-group if every convex $\ell$-subgroup of $G$ is a $d$-subgroup (equivalently: $G(a)=a^{\perp \perp}$ for every $a \in G$); known examples include all hyperarchimedean $\ell$-groups and all existentially closed abelian $\ell$-groups. This paper gives new characterizations of the Martinez $\ell$-groups, shows that the abelian Martinez $\ell$-groups form a radical class, investigates a related type of archimedean $\ell$-group called a Yosida $\ell$-group, and uses an analogue of a construction from ring theory, and other methods, to produce new examples of Martinez $\ell$-groups with special properties.

In this work, we investigate the properties of a population of cool clouds in simulated galaxy outflows. Using data from the CGOLS isolated galaxy simulations, we generate catalogues of $\sim 10^5$ clouds. We describe the impact of two different supernova feedback models -- a centrally concentrated starburst and disk-wide distributed star formation -- on the resulting cloud population. In both cases we find that the mass distribution function $dN/dM \propto M^{-2}$, in good agreement with model predictions of turbulent fragmentation. We explore how cloud properties change with distance from the galaxy and find no qualitative distinction between the two feedback modes, although significant quantitative differences exist in attributes such as the total number of clouds, their densities, etc. We further show that both internal cloud velocities and cloud-cloud relative velocities are described well by properties of turbulent motion, despite significant bulk radial velocities. Finally, we investigate the distribution of cloud sizes in the context of recent theoretical arguments about cloud survival in winds. We find that proposed cloud survival criteria are a good predictor of cloud survival, in both the case where clouds are primarily destroyed and the case where cloud growth occurs in the outflow., Comment: 24 pages, 18 figures, submitted to The Astrophysical Journal

The $\mathcal{A}$-theory takes U-duality symmetry as a guiding principle, with the SL(5) U-duality symmetry being described as the world-volume theory of a 5-brane. Furthermore, by unifying the 6-dimensional world-volume Lorentz symmetry with the SL(5) spacetime symmetry, it extends to SL(6) U-duality symmetry. The SL(5) spacetime vielbein fields and the 5-brane world-volume vielbein fields are mixed under the SL(6) U-duality transformation. We demonstrate that consistent sectionings of the SL(6) $\mathcal{A}$5-brane world-volume Lagrangian yield Lagrangians of the ${\cal T}$-string with O(D,D) T-duality symmetry, the conventional string, the $\mathcal{M}$5-brane with GL(4) duality symmetry, and the non-perturbative M2-brane in supergravity theory. The GL(4) covariant Lagrangian of the $\mathcal{M}$5-brane derived in this manner is a new, perturbatively quantizable theory., Comment: 43 pages, revised introduction