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For any given submersion $\pi:X\to B$ with closed, oriented and spin$^c$ fibers of even dimension, equipped with a Riemannian and differential spin$^c$ structure $\boldsymbol{\pi}$, we construct an analytic index $\textrm{ind}^a_k$ in odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory on the cocycle level by associating to every cocycle $(\mathbf{E}, \mathbf{F}, \alpha)$ of the odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory group of $X$ a cocycle $\textrm{ind}^a_k(\mathbf{E}, \mathbf{F}, \alpha)$ of the odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory group of $B$. The cocycle $\textrm{ind}^a_k(\mathbf{E}, \mathbf{F}, \alpha)$ is defined in terms of the twisted spin$^c$ Dirac operators associated to $(\mathbf{E}, \boldsymbol{\pi})$ and $(\mathbf{F}, \boldsymbol{\pi})$, which are not assumed to satisfy the kernel bundle assumption. We prove a Riemann-Roch-Grothendieck type formula in odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory, which expresses the Cheeger-Chern-Simons form of $\textrm{ind}^a_k(\mathbf{E}, \mathbf{F}, \alpha)$ in terms of that of $(\mathbf{E}, \mathbf{F}, \alpha)$. Furthermore, we show that the analytic index $\textrm{ind}^a_k$ and the Riemann-Roch-Grothendieck type formula in odd $\mathbb{Z}/k\mathbb{Z}$ $K$-theory refine the geometric bundle part of the analytic index and the Riemann-Roch-Grothendieck theorem in $\mathbb{R}/\mathbb{Z}$ $K$-theory, respectively. An as intermediate result, we give a proof that the analytic indexes in differential $K$-theory defined without the kernel bundle assumption via the Atiyah-Singer-Gorokhovsky-Lott approach and the Miscenko-Fomenko-Freed-Lott approach, respectively, are equal., Comment: 60 pages. Submitted for publication. Comments are very welcome

Magnetic fields (B-fields) are ubiquitous in the interstellar medium (ISM), and they play an essential role in the formation of molecular clouds and subsequent star formation. However, B-fields in interstellar environments remain challenging to measure, and their properties typically need to be inferred from dust polarization observations over multiple physical scales. In this work, we seek to use a recently proposed approach called the Velocity Gradient Technique (VGT) to study B-fields in star-forming regions and compare the results with dust polarization observations in different wavelengths. The VGT is based on the anisotropic properties of eddies in magnetized turbulence to derive B-field properties in the ISM. We investigate that this technique is synergistic with dust polarimetry when applied to a turbulent diffused medium for the purpose of measuring its magnetization. Specifically, we use the VGT on molecular line data toward the NGC~1333 star-forming region ($\rm ^{12}CO$, $\rm ^{13}CO$, $\rm C^{18}O$, and $\rm N_{2}H^{+}$), and we compare the derived B-field properties with those inferred from 214 and 850~$\mu$m dust polarization observations of the region using SOFIA/HAWC+ and JCMT/POL-2, respectively. We estimate both the inclination angle and the 3D Alfv\'enic Mach Number $M_A$ from the molecular line gradients. Crucially, testing this technique on gravitationally bound, dynamic, and turbulent regions, and comparing the results with those obtained from polarization observations at different wavelength, such as the plane-of-the-sky field orientation, is an important test on the applicability of the VGT in various density regimes of the ISM., Comment: 16 pages, 6 figures, Accepted for publication in ApJ

We present results from a high-resolution interstellar turbulence simulation and show that it closely reproduces recent $Planck$ measurements. Our model captures the scaling of $EE$ and $BB$ spectra, and the $EE/BB$ ratio in the inertial range. The PDF of the dust polarization fraction is also consistent with observations. The $TE$ cross-correlation is in broad agreement with observations. This simulation provides new insights into the physical origins of the observed $E/B$ asymmetry and positive $TE$ signal, facilitating the development of advanced Galactic dust emission models for current and future CMB experiments., Comment: 6 pages, 3 figures, 1 table

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

Time series data from various domains is continuously growing, and extracting and analyzing temporal patterns within these series can provide valuable insights. Temporal pattern mining (TPM) extends traditional pattern mining by incorporating event time intervals into patterns, making them more expressive but also increasing the computational complexity in terms of time and space. One important type of temporal pattern is the rare temporal pattern (RTP), which occurs infrequently but with high confidence. Mining these rare patterns poses several challenges, for example, the low support threshold can lead to a combinatorial explosion and the generation of many irrelevant patterns. To address this, an efficient approach to mine rare temporal patterns is essential. This paper introduces the Rare Temporal Pattern Mining from Time Series (RTPMfTS) method, designed to discover rare temporal patterns. The key contributions of this work are as follows: (1) An end-to-end RTPMfTS process that takes time series data as input and outputs rare temporal patterns. (2) A highly efficient Rare Temporal Pattern Mining (RTPM) algorithm, which leverages optimized data structures for fast event and pattern retrieval, as well as effective pruning techniques to accelerate the mining process. (3) A comprehensive experimental evaluation of RTPM, demonstrating that it outperforms the baseline in both runtime and memory efficiency., Comment: arXiv admin note: substantial text overlap with arXiv:2306.10994

We revisit the classic Joule-expansion experiments, now with a quantum-degenerate atomic Bose gas. In contrast to the classical-gas experiments, where no temperature change was measured, here we observe both cooling and heating effects, which arise, respectively, due to quantum statistics and inter-particle interactions. Our observations are reproduced in numerical calculations within the Hartree-Fock approximation., Comment: Main text: 3 pages, 3 figures. Supplemental Information: 2 pages, 2 figures

Owing to the ability of nonlinear domain decomposition methods to improve the nonlinear convergence behavior of Newton's method, they have experienced a rise in popularity recently in the context of problems for which Newton's method converges slowly or not at all. This article introduces a novel parallel implementation of a two-level nonlinear Schwarz solver based on the FROSch (Fast and Robust Overlapping Schwarz) solver framework, part of Sandia's Trilinos library. First, an introduction to the key concepts underlying two-level nonlinear Schwarz methods is given, including a brief overview of the coarse space used to build the second level. Next, the parallel implementation is discussed, followed by preliminary parallel results for a scalar nonlinear diffusion problem and a 2D nonlinear plane-stress Neo-Hooke elasticity problem with large deformations.

In this paper, we study a free scalar field in a specific (1+1)-dimensional curved spacetime. By introducing an algebraic state that is locally Hadamard, we derive the renormalized Wightman function and explicitly calculate the covariantly conserved quantum energy-momentum tensor up to a relevant order. From this result, we show that the Hadamard renormalization scheme, which has been effective in traditional quantum field theory in curved spacetime, is also applicable in the quantum inhomogeneous field theory. As applications of this framework, we show the existence of an Unruh-like effect for an observer slightly out of the right asymptotic region, as well as a quantum frictional effect on the bubble wall expansion during the electroweak phase transition in the early universe. Consequently, this study validates the consistency of our method for constructing meaningful physical quantities in quantum inhomogeneous field theory., Comment: 31 pages, 1 figure

Recent advances in quantum information processing with superconducting qubits have fueled a growing demand for scaling and miniaturizing circuit layouts. Despite significant progress, accurately predicting the Hamiltonian of complex circuits remains a challenging task. In this work, we propose an improved method for quantizing superconducting circuits that incorporates material- and geometry-dependent kinetic inductance. Our approach models thin superconducting films as equivalent reactive boundary elements, seamlessly integrating into the conventional circuit quantization framework without adding computational complexity. As a proof of concept, we experimentally verify our method using planar superconducting quantum devices made of 35 nm-thick disordered niobium films, known to exhibit large kinetic inductance values. We demonstrate significantly improved accuracy in predicting the Hamiltonian based solely on the chip layout and material properties of the superconducting films and Josephson junctions, with discrepancies in mode frequencies remaining below 2%. Our method enables systematic studies of superconducting devices based on disordered thin films or compact, fine-pitched elements and, more broadly, facilitates the precise engineering of superconducting quantum circuits at scale., Comment: 12 pages, 4 figures, 1 table

As the scale of data and models for video understanding rapidly expand, handling long-form video input in transformer-based models presents a practical challenge. Rather than resorting to input sampling or token dropping, which may result in information loss, token merging shows promising results when used in collaboration with transformers. However, the application of token merging for long-form video processing is not trivial. We begin with the premise that token merging should not rely solely on the similarity of video tokens; the saliency of tokens should also be considered. To address this, we explore various video token merging strategies for long-form video classification, starting with a simple extension of image token merging, moving to region-concentrated merging, and finally proposing a learnable video token merging (VTM) algorithm that dynamically merges tokens based on their saliency. Extensive experimental results show that we achieve better or comparable performances on the LVU, COIN, and Breakfast datasets. Moreover, our approach significantly reduces memory costs by 84% and boosts throughput by approximately 6.89 times compared to baseline algorithms., Comment: 21 pages, NeurIPS 2024

We present PiMForce, a novel framework that enhances hand pressure estimation by leveraging 3D hand posture information to augment forearm surface electromyography (sEMG) signals. Our approach utilizes detailed spatial information from 3D hand poses in conjunction with dynamic muscle activity from sEMG to enable accurate and robust whole-hand pressure measurements under diverse hand-object interactions. We also developed a multimodal data collection system that combines a pressure glove, an sEMG armband, and a markerless finger-tracking module. We created a comprehensive dataset from 21 participants, capturing synchronized data of hand posture, sEMG signals, and exerted hand pressure across various hand postures and hand-object interaction scenarios using our collection system. Our framework enables precise hand pressure estimation in complex and natural interaction scenarios. Our approach substantially mitigates the limitations of traditional sEMG-based or vision-based methods by integrating 3D hand posture information with sEMG signals. Video demos, data, and code are available online., Comment: Accepted to NeurIPS 2024. Project Page Link: https://pimforce.hcitech.org/

The superionic state represents a fundamental phase of matter where liquid-like mobility emerges within a solid crystalline lattice. This peculiar state has recently been discovered in the Earth's inner core. Despite extensive research on the kinetics of the superionic state and its geophysical impact on the Earth's core, the equilibration of the superionic phase with the liquid solution under core conditions has yet to be discovered. In this study, we develop a method to calculate the ab initio Gibbs free energy and the superionic-liquid phase diagram for the Fe1-xOx system under conditions near the Earth's inner core boundary. Our findings indicate oxygen can form superionic states in hcp and bcc iron. We elucidate the stability fields of these superionic phases, which vary significantly with oxygen stoichiometry. Furthermore, we show that the oxygen concentration in the Earth's inner core is likely higher than previously estimated due to the superionic state. This work offers a quantitative approach to studying the equilibrium between liquid and superionic solutions in Fe-light element systems under the extreme conditions of the Earth's core.

We introduce new high-resolution galaxy simulations accelerated by a surrogate model that reduces the computation cost by approximately 75 percent. Massive stars with a Zero Age Main Sequence mass of about 8 solar masses and above explode as core-collapse supernovae (CCSNe), which play a critical role in galaxy formation. The energy released by CCSNe is essential for regulating star formation and driving feedback processes in the interstellar medium (ISM). However, the short integration timesteps required for SNe feedback present significant bottlenecks in star-by-star galaxy simulations that aim to capture individual stellar dynamics and the inhomogeneous shell expansion of SNe within the turbulent ISM. Our new framework combines direct numerical simulations and surrogate modeling, including machine learning and Gibbs sampling. The star formation history and the time evolution of outflow rates in the galaxy match those obtained from resolved direct numerical simulations. Our new approach achieves high-resolution fidelity while reducing computational costs, effectively bridging the physical scale gap and enabling multi-scale simulations., Comment: 20 pages, 14 figures, 3 tables, submitted to ApJ

We analyse the entanglement structure of states generated by random constant-depth two-dimensional quantum circuits, followed by projective measurements of a subset of sites. By deriving a rigorous lower bound on the average entanglement entropy of such post-measurement states, we prove that macroscopic long-ranged entanglement is generated above some constant critical depth in several natural classes of circuit architectures, which include brickwork circuits and random holographic tensor networks. This behaviour had been conjectured based on previous works, which utilize non-rigorous methods such as replica theory calculations, or work in regimes where the local Hilbert space dimension grows with system size. To establish our lower bound, we develop new replica-free theoretical techniques that leverage tools from multi-user quantum information theory, which are of independent interest, allowing us to map the problem onto a statistical mechanics model of self-avoiding walks without requiring large local Hilbert space dimension. Our findings have consequences for the complexity of classically simulating sampling from random shallow circuits, and of contracting tensor networks: First, we show that standard algorithms based on matrix product states which are used for both these tasks will fail above some constant depth and bond dimension, respectively. In addition, we also prove that these random constant-depth quantum circuits cannot be simulated by any classical circuit of sublogarithmic depth., Comment: 22 + 12 pages, 11 figures

3D models have recently been popularized by the potentiality of end-to-end training offered first by Neural Radiance Fields and most recently by 3D Gaussian Splatting models. The latter has the big advantage of naturally providing fast training convergence and high editability. However, as the research around these is still in its infancy, there is still a gap in the literature regarding the model's scalability. In this work, we propose an approach enabling both memory and computation scalability of such models. More specifically, we propose an iterative pruning strategy that removes redundant information encoded in the model. We also enhance compressibility for the model by including in the optimization strategy a differentiable quantization and entropy coding estimator. Our results on popular benchmarks showcase the effectiveness of the proposed approach and open the road to the broad deployability of such a solution even on resource-constrained devices.

The simultaneous presence of polarity and metallicity in a material signifies an exotic polar metal state, but such materials are extremely rare, especially in bulk form, due to mutually exclusive nature of the fundamental defining properties. Here, we report experimental findings that HgPSe3 is a robust bulk polar metal at room temperature with a chiral structure stabilized by pressure and, remarkably, this polar metal hosts superconductivity with critical temperature Tc up to 11 K. Theoretical analysis reveals a two-step interlayer sliding-then-compressing mechanism for coexistence of polarity and metallicity in HgPSe3. This work unveils a new paradigm for creating the bulk polar metal state and simultaneous presence of coexisting quantum orders, raising the prospect of discovering novel emergent physics using pressure as a tuning knob., Comment: 19 pages, 4 main figures + 6 extented figures

Urban traffic congestion, worsened by the rapid urbanization and the increasing prevalence of private vehicles, has significantly increased commuting time for everyone. In this paper, we used a dataset with over 400,000 real mobility trajectories of individuals spanning 9 days in a major Chinese city to investigate an innovative approach to swap homes between households in addressing the challenge of peak-hour traffic congestion. We observed that, empirically, households choose their home location strategically such that the average commuting distance is roughly 3 times less than that when their home is randomly located, showing features of self-organization. Remarkably, we found that the average commuting distance can be further reduced by 50% through home swapping at the city-level, leading to a large reduction in traffic congestion. To make home swapping more realistic, we swap homes only if the following socio-demographic factors including the distance from the city center, housing price and amenity accessibility are preserved for both households, such that the average commuting distance can still be reduced by 13%. As both home-workplace distance and traffic congestion are reduced, as a side benefit, carbon emissions from vehicles are also greatly reduced by almost 80%, and 40% when socio-demographic factors are considered. The distance from the city center is shown to be the most influential factor affecting the benefit brought by home swapping, and further analysis indicates that developing a polycentric city layout could significantly enhance such benefit. This study suggests that mitigating traffic congestion requires a long-term, holistic and strategic approach to urban planning, suggesting a need for coordinating individual residence locations and a polycentric city layout., Comment: 56 pages, 6+23 figures

Most learned B-frame codecs with hierarchical temporal prediction suffer from the domain shift issue caused by the discrepancy in the Group-of-Pictures (GOP) size used for training and test. As such, the motion estimation network may fail to predict large motion properly. One effective strategy to mitigate this domain shift issue is to downsample video frames for motion estimation. However, finding the optimal downsampling factor involves a time-consuming rate-distortion optimization process. This work introduces lightweight classifiers to determine the downsampling factor. To strike a good rate-distortion-complexity trade-off, our classifiers observe simple state signals, including only the coding and reference frames, to predict the best downsampling factor. We present two variants that adopt binary and multi-class classifiers, respectively. The binary classifier adopts the Focal Loss for training, classifying between motion estimation at high and low resolutions. Our multi-class classifier is trained with novel soft labels incorporating the knowledge of the rate-distortion costs of different downsampling factors. Both variants operate as add-on modules without the need to re-train the B-frame codec. Experimental results confirm that they achieve comparable coding performance to the brute-force search methods while greatly reducing computational complexity.

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.

Single photon detectors (SPDs) are essential technology in quantum science, quantum network, biology, and advanced imaging. To detect the small quantum of energy carried in a photon, conventional SPDs rely on energy excitation across either a semiconductor bandgap or superconducting gap. While the energy gap suppresses the false-positive error, it also sets an energy scale that can limit the detection efficiency of lower energy photons and spectral bandwidth of the SPD. Here, we demonstrate an orthogonal approach to detect single near-infrared photons using graphene calorimeters. By exploiting the extremely low heat capacity of the pseudo-relativistic electrons in graphene near its charge neutrality point, we observe an electron temperature rise up to ~2 K using a hybrid Josephson junction. In this proof-of-principle experiment, we achieve an intrinsic quantum efficiency of 87% (75%) with dark count < 1 per second (per hour) at operation temperatures as high as 1.2 K. Our results highlight the potential of electron calorimetric SPDs for detecting lower-energy photons from the mid-IR to microwave regimes, opening pathways to study space science in far-infrared regime, to search for dark matter axions, and to advance quantum technologies across a broader electromagnetic spectrum.

We develop a generic policy gradient method with the global optimality guarantee for robust Markov Decision Processes (MDPs). While policy gradient methods are widely used for solving dynamic decision problems due to their scalable and efficient nature, adapting these methods to account for model ambiguity has been challenging, often making it impractical to learn robust policies. This paper introduces a novel policy gradient method, Double-Loop Robust Policy Mirror Descent (DRPMD), for solving robust MDPs. DRPMD employs a general mirror descent update rule for the policy optimization with adaptive tolerance per iteration, guaranteeing convergence to a globally optimal policy. We provide a comprehensive analysis of DRPMD, including new convergence results under both direct and softmax parameterizations, and provide novel insights into the inner problem solution through Transition Mirror Ascent (TMA). Additionally, we propose innovative parametric transition kernels for both discrete and continuous state-action spaces, broadening the applicability of our approach. Empirical results validate the robustness and global convergence of DRPMD across various challenging robust MDP settings.

We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of activation functions in the ReLU family, leapfrog HMC for networks with these activation functions has a large local error rate of $\Omega(\epsilon)$ rather than the classical error rate of $O(\epsilon^3)$. This leads to a higher rejection rate of the proposals, making the method inefficient. We then verify our theoretical findings through empirical simulations as well as experiments on a real-world dataset that highlight the inefficiency of HMC inference on ReLU-based neural networks compared to analytical networks., Comment: Paper published at NeurIPS 2024

Quantum coherence, fundamental to the distinct behavior of quantum systems, serves as a static resource in both quantum states and measurements, the latter termed measurement-coherence. Previous studies have established the conversion of static quantum coherence into entanglement for states and measurements, and the conversion from coherence generating power to entanglement-generating power in quantum dynamics. In this work, we extend this framework by demonstrating that a quantum channel's measurement-coherence generating power can also be converted to measurement-entanglement generating power without requiring additional measurement-coherence. This result completes the coherence-to-entanglement conversion picture at the dynamical level, incorporating the static cases. We formulate resource theories for both measurement-coherence and measurement-entanglement generating powers, and elucidate the structure of incoherent measurements to facilitate our investigation into measurement-coherence generating powers. Furthermore, we establish that a quantum channel's coherence generating power is equivalent to the measurement-coherence generating power of its adjoint map, with a corresponding, though weaker, relation for quantum entanglement., Comment: 9+13 pages, 8 figures

We study noncooperative games, in which each agent's objective is composed of a sequence of ordered-and potentially conflicting-preferences. Problems of this type naturally model a wide variety of scenarios: for example, drivers at a busy intersection must balance the desire to make forward progress with the risk of collision. Mathematically, these problems possess a nested structure, and to behave properly agents must prioritize their most important preference, and only consider less important preferences to the extent that they do not compromise performance on more important ones. We consider multi-agent, noncooperative variants of these problems, and seek generalized Nash equilibria in which each agent's decision reflects both its hierarchy of preferences and other agents' actions. We make two key contributions. First, we develop a recursive approach for deriving the first-order optimality conditions of each agent's nested problem. Second, we propose a sequence of increasingly tight relaxations, each of which can be transcribed as a mixed complementarity problem and solved via existing methods. Experimental results demonstrate that our approach reliably converges to equilibrium solutions that strictly reflect agents' individual ordered preferences.

The goal of this project is to construct an estimator for the masses of supermassive black holes in active galactic nuclei (AGNs) based on the broad Halpha emission line. We make use of published reverberation mapping data. We remeasure all Halpha time lags from the original data as we find that often the reverberation measurements are improved by detrending the light curves. We produce mass estimators that require only the Halpha luminosity and the width of the Halpha emission line as characterized by either the FWHM or the line dispersion. It is possible, on the basis of a single spectrum covering the Halpha emission line, to estimate the mass of the central supermassive black hole in AGNs, taking into account all three parameters believed to affect mass measurement: luminosity, line width, and Eddington ratio. The typical formal accuracy in such estimates is of order 0.2-0.3 dex., Comment: 15 pages, 9 figures, submitted to A&A

As language models (LMs) become integral to fields like healthcare, law, and journalism, their ability to differentiate between fact, belief, and knowledge is essential for reliable decision-making. Failure to grasp these distinctions can lead to significant consequences in areas such as medical diagnosis, legal judgments, and dissemination of fake news. Despite this, current literature has largely focused on more complex issues such as theory of mind, overlooking more fundamental epistemic challenges. This study systematically evaluates the epistemic reasoning capabilities of modern LMs, including GPT-4, Claude-3, and Llama-3, using a new dataset, KaBLE, consisting of 13,000 questions across 13 tasks. Our results reveal key limitations. First, while LMs achieve 86% accuracy on factual scenarios, their performance drops significantly with false scenarios, particularly in belief-related tasks. Second, LMs struggle with recognizing and affirming personal beliefs, especially when those beliefs contradict factual data, which raises concerns for applications in healthcare and counseling, where engaging with a person's beliefs is critical. Third, we identify a salient bias in how LMs process first-person versus third-person beliefs, performing better on third-person tasks (80.7%) compared to first-person tasks (54.4%). Fourth, LMs lack a robust understanding of the factive nature of knowledge, namely, that knowledge inherently requires truth. Fifth, LMs rely on linguistic cues for fact-checking and sometimes bypass the deeper reasoning. These findings highlight significant concerns about current LMs' ability to reason about truth, belief, and knowledge while emphasizing the need for advancements in these areas before broad deployment in critical sectors., Comment: https://github.com/suzgunmirac/belief-in-the-machine

Protecting intellectual property (IP) in federated learning (FL) is increasingly important as clients contribute proprietary data to collaboratively train models. Model watermarking, particularly through backdoor-based methods, has emerged as a popular approach for verifying ownership and contributions in deep neural networks trained via FL. By manipulating their datasets, clients can embed a secret pattern, resulting in non-intuitive predictions that serve as proof of participation, useful for claiming incentives or IP co-ownership. However, this technique faces practical challenges: client watermarks can collide, leading to ambiguous ownership claims, and malicious clients may exploit watermarks to inject harmful backdoors, jeopardizing model integrity. To address these issues, we propose Sanitizer, a server-side method that ensures client-embedded backdoors cannot be triggered on natural queries in harmful ways. It identifies subnets within client-submitted models, extracts backdoors throughout the FL process, and confines them to harmless, client-specific input subspaces. This approach not only enhances Sanitizer's efficiency but also resolves conflicts when clients use similar triggers with different target labels. Our empirical results demonstrate that Sanitizer achieves near-perfect success in verifying client contributions while mitigating the risks of malicious watermark use. Additionally, it reduces GPU memory consumption by 85% and cuts processing time by at least 5 times compared to the baseline.

In semiconducting monolayer transition metal dichalcogenides (ML-TMDs), broken inversion symmetry and strong spin-orbit coupling result in spin-valley lock-in effects so that the valley degeneracy may be lifted by external magnetic fields, potentially leading to real-space structural transformation. Here, we report magnetic field (B)-induced giant electric hysteretic responses to back-gate voltages in ML-MoS2 field-effect transistors (FETs) on SiO2/Si at temperatures < 20 K. The observed hysteresis increases with |B| up to 12 T and is tunable by varying the temperature. Raman spectroscopic and scanning tunneling microscopic studies reveal significant lattice expansion with increasing |B| at 4.2 K, and this lattice expansion becomes asymmetric in ML-MoS2 FETs on rigid SiO2/Si substrates, leading to out-of-plane mirror symmetry breaking and the emergence of a tunable out-of-plane ferroelectric-like polar order. This broken symmetry-induced polarization in ML-MoS2 shows typical ferroelectric butterfly hysteresis in piezo-response force microscopy, adding ML-MoS2 to the single-layer material family that exhibit out-of-plane polar order-induced ferroelectricity, which is promising for such technological applications as cryo-temperature ultracompact non-volatile memories, memtransistors, and ultrasensitive magnetic field sensors. Moreover, the polar effect induced by asymmetric lattice expansion may be further generalized to other ML-TMDs and achieved by nanoscale strain engineering of the substrate without magnetic fields.

We investigate the influence of inelastic neutrino microphysics in general-relativistic magnetohydrodynamics simulations of a hypermassive neutron star. In particular, we include species/energy groups coupled neutrino-matter interactions, such as inelastic neutrino-electron scattering and electron-positron annihilation kernels, into simulations up to 50 ms. Neutrino-electron inelastic scattering is known to have effective neutrino-matter energy exchange. We show that, with neutrino-electron inelastic scattering, simulations predict 75% higher disc mass with slightly different mass-averaged compositions, and 18% more ejected mass with similar distributions. The enhancement of the mass of the disc and the ejecta results in stronger baryon pollution, leading to less favourable jet launching environments. Furthermore, neutrino luminosities are about 50, 40, and 30% higher for electron neutrino, electron anti-neutrino, and heavy-lepton neutrinos. In contrast, we do not see any significant impacts due to electron-positron annihilation.

In this study, we employ the combined charged particle measurements from Integrated Science Investigation of the Sun (\ISOIS) onboard the Parker Solar Probe (PSP) and Energetic Particle Detector (EPD) onboard the Solar Orbiter (SolO) to study the composition variation of the solar energetic particle (SEP) event occurring on May 16, 2023. During the event, SolO and PSP were located at a similar radial distance of ~0.7 au and were separated by $\sim$60$^\circ$ in longitude. The footpoints of both PSP and SolO were west of the flare region but the former was much closer (18$^\circ$ vs 80$^\circ$). Such a distribution of observers is ideal for studying the longitudinal dependence of the ion composition with the minimum transport effects of particles along the radial direction. We focus on H, He, O, and Fe measured by both spacecraft in sunward and anti-sunward directions. Their spectra are in a double power-law shape, which is fitted best by the Band function. Notably, the event was Fe-rich at PSP, where the mean Fe/O ratio at energies of 0.1 - 10 Mev/nuc was 0.48, higher than the average Fe/O ratio in previous large SEP events. In contrast, the mean Fe/O ratio at SolO over the same energy range was considerable lower at 0.08. The Fe/O ratio between 0.5 and 10 MeV/nuc at both spacecraft is nearly constant. Although the He/H ratio shows energy dependence, decreasing with increasing energy, the He/H ratio at PSP is still about twice as high as that at SolO. Such a strong longitudinal dependence of element abundances and the Fe-rich component in the PSP data could be attributed to the direct flare contribution. Moreover, the temporal profiles indicate that differences in the Fe/O and He/H ratios between PSP and SolO persisted throughout the entire event rather than only at the start., Comment: 11 pages, 5 figures

The design and control of atomic-scale spin structures constitute major challenges for spin-based quantum technology platforms, including quantum dots, color centers, and molecular spins. Here, we showcase a strategy for designing the quantum properties of molecular spin qubits by combining tip-assisted on-surface assembly with electron spin resonance scanning tunneling microscopy (ESR-STM): We fabricate magnetic dimer complexes that consist of an iron phthalocyanine (FePc) molecule and an organometallic half-sandwich complex formed by the FePc ligand and an attached iron atom, Fe(C6H6). The total complex forms a mixed-spin (1/2,1) quantum ferrimagnet with a well-separated correlated ground state doublet, which we utilize for coherent control. As a result of the correlation, the quantum ferrimagnet shows an improved spin lifetime (>1.5 ${\mu}$s) as it is partially protected against inelastic electron scattering. Lastly, the ferrimagnet units also enable intermolecular coupling, that can be used to realize both ferromagnetic or antiferromagnetic structures. Thus, quantum ferrimagnets provide a versatile platform to improve coherent control in general and to study complex magnetic interactions., Comment: 17 pages, 4 figures

Thermally driven transitions between ferromagnetic and paramagnetic phases are characterized by critical behavior with divergent susceptibilities, long-range correlations, and spin dynamics that can span kHz to GHz scales as the material approaches the critical temperature $\mathrm{T_c}$, but it has proven technically challenging to probe the relevant length and time scales with most conventional measurement techniques. In this study, we employ scanning nitrogen-vacancy center based magnetometry and relaxometry to reveal the critical behavior of a high-$\mathrm{T_c}$ ferromagnetic oxide near its Curie temperature. Cluster analysis of the measured temperature-dependent nanoscale magnetic textures points to a 3D universality class with a correlation length that diverges near $\mathrm{T_c}$. Meanwhile, the temperature-dependent spin dynamics, measured through all optical relaxometry suggest that the phase transition is in the XY universality class. Our results capture both static and dynamic aspects of critical behavior, providing insights into universal properties that govern phase transitions in magnetic materials.

Statistical hypothesis testing is the central method to demarcate scientific theories in both exploratory and inferential analyses. However, whether this method befits such purpose remains a matter of debate. Established approaches to hypothesis testing make several assumptions on the data generation process beyond the scientific theory. Most of these assumptions not only remain unmet in realistic datasets, but often introduce unwarranted bias in the analysis. Here, we depart from such restrictive assumptions to propose an alternative framework of total empiricism. We derive the Information-test ($I$-test) which allows for testing versatile hypotheses including non-null effects. To exemplify the adaptability of the $I$-test to application and study design, we revisit the hypothesis of interspecific metabolic scaling in mammals, ultimately rejecting both competing theories of pure allometry., Comment: Main Article and Supplementary Material, 1 Table, 2 Figures

With the explosion in popularity of the Rust programming language, a wealth of tools have recently been developed to analyze, verify, and test Rust programs. Alas, the Rust ecosystem remains relatively young, meaning that every one of these tools has had to re-implement difficult, time-consuming machinery to interface with the Rust compiler and its cargo build system, to hook into the Rust compiler's internal representation, and to expose an abstract syntax tree (AST) that is suitable for analysis rather than optimized for efficiency. We address this missing building block of the Rust ecosystem, and propose Charon, an analysis framework for Rust. Charon acts as a swiss-army knife for analyzing Rust programs, and deals with all of the tedium above, providing clients with a clean, stable AST that can serve as the foundation of many analyses. We demonstrate the usefulness of Charon through a series of case studies, ranging from a Rust verification framework (Aeneas), a compiler from Rust to C (Eurydice), and a novel taint-checker for cryptographic code. To drive the point home, we also re-implement a popular existing analysis (Rudra), and show that it can be replicated by leveraging the Charon framework.

We present numerical simulation results for the 7-to-1 and 15-to-1 state distillation circuits, constructed using transversal CNOTs acting on multiple surface code patches. The distillation circuits are decoded iteratively using the method outlined in [arXiv:2407.20976]. We show that, with a re-configurable qubit architecture, we can perform fast, $\mathcal{O}(1)$ code cycles magic state distillation. We confirm the error suppression ability of both distillation circuits, from input error rate $p\rightarrow \mathcal{O}(p^{3})$ under circuit-level noise.

Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge. However, adapting general-purpose RAG systems to specialized fields such as science and medicine poses unique challenges due to distribution shifts and limited access to domain-specific data. To tackle this, we propose SimRAG, a self-training approach that equips the LLM with joint capabilities of question answering and question generation for domain adaptation. Our method first fine-tunes the LLM on instruction-following, question-answering, and search-related data. Then, it prompts the same LLM to generate diverse domain-relevant questions from unlabeled corpora, with an additional filtering strategy to retain high-quality synthetic examples. By leveraging these synthetic examples, the LLM can improve their performance on domain-specific RAG tasks. Experiments on 11 datasets, spanning two backbone sizes and three domains, demonstrate that SimRAG outperforms baselines by 1.2\%--8.6\%., Comment: Work in Progress

II Zw 40 is a starburst dwarf and merger product, and holds a radio-infrared supernebula excited by thousands of embedded OB stars. We present here observations of three aspects of the supernebula: maps of the K and KU radio continuum that trace dense ionized gas with spatial resolution $\sim0.1^{\prime\prime}$, a spectral data cube of the [S IV]$10.5\mu$m emission line that measures the kinematics of the ionized gas with velocity resolution $4.5$ km s$^{-1}$, and an ALMA spectral cube of the CO(3-2) line that probes the dense warm molecular gas with spatial and velocity resolution comparable to the ionized gas. The observations suggest that the supernebula is the overlap,collision or merger of two star clusters, each associated with an elongated molecular cloud. We accordingly modelled the supernebula with simulations of colliding clusters. The model that best agrees with the data is a grazing collision that has distorted the gas and stars to create the distinctive structures observed. These models may have wide applicability in the cluster-rich regions of young starbursts., Comment: Accepted for publication at MNRAS

Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, these methods depend heavily on the existence and quality of reasoning paths, which limits their general applicability in different scenarios. In addition, we observe that latent type constraints and neighboring facts inherent in KGs are also vital in inferring missing triples. To effectively utilize all useful information in KGs, we introduce CATS, a novel context-aware inductive KGC solution. With sufficient guidance from proper prompts and supervised fine-tuning, CATS activates the strong semantic understanding and reasoning capabilities of large language models to assess the existence of query triples, which consist of two modules. First, the type-aware reasoning module evaluates whether the candidate entity matches the latent entity type as required by the query relation. Then, the subgraph reasoning module selects relevant reasoning paths and neighboring facts, and evaluates their correlation to the query triple. Experiment results on three widely used datasets demonstrate that CATS significantly outperforms state-of-the-art methods in 16 out of 18 transductive, inductive, and few-shot settings with an average absolute MRR improvement of 7.2%.

The use of foundation models in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs. To address this issue, we develop ClimaGen (Climate QA Generator), an automated algorithmic framework that generates question-answer pairs from graduate textbooks with climate scientists in the loop. As a result, we present ClimaQA-Gold, an expert-annotated benchmark dataset alongside ClimaQA-Silver, a large-scale, comprehensive synthetic QA dataset for climate science. Finally, we develop evaluation strategies and compare different Large Language Models (LLMs) on our benchmarks. Our results offer novel insights into various approaches used to enhance climate foundation models.