Search

For over 25 years, the DAMA/LIBRA collaboration has claimed to observe an annual modulation signal, suggesting the existence of dark matter interactions. However, no other experiments have replicated their result using different detector materials. To address this puzzle, the COSINE-100 collaboration conducted a model-independent test using 106 kg of sodium iodide as detectors, the same target material as DAMA/LIBRA. Analyzing data collected over 6.4 years, with improved energy calibration and time-dependent background description, we found no evidence of an annual modulation signal, challenging the DAMA/LIBRA result with a confidence level greater than 3$\sigma$. This finding represents a significant step toward resolving the long-standing debate surrounding DAMA/LIBRA's dark matter claim, indicating that the observed modulation is unlikely to be caused by dark matter interactions.

High-cadence surveys of the sky are revealing that a large fraction of red-supergiant (RSG) stars, which are progenitors of Type II-Plateau (II-P) supernovae (SNe), explode within circumstellar material (CSM). Such SNe II-P/CSM exhibit considerable diversity, with interaction signatures lasting from hours to days, potentially merging with the Type IIn subclass for which longer-duration interaction typically occurs. To tackle this growing sample of transients and to understand the pre-SN mass loss histories of RSGs, we train on the highest quality, spectropolarimetric observations of a young Type IIn SN taken to date: Those of SN1998S at ~5d after explosion. We design an approach based on a combination of radiation hydrodynamics with HERACLES and polarized radiative transfer with CMFGEN and LONG_POL. The adopted asymmetries are based on a latitudinal, depth- and time-independent, scaling of the density of 1D models of SNe II-P/CSM (e.g., model r1w6b with a `wind' mass-loss rate of 0.01Msun/yr used for SN2023ixf). For a pole-to-equator density ratio of five, we find that the polarization reaches, and then remains for days, at a maximum value of 1.0, 1.4, and 1.8% as the CSM extent is changed from 6, to 8 and 10x10^14cm. The polarization is independent of wavelength away from funnel-shaped depolarizations within emission lines. Our models implicate a significant depolarization at line cores, which we use to constrain the interstellar polarization of SN1998S. Our 2D, prolate ejecta models with moderate asymmetry match well the spectropolarimetric observations of SN1998S at 5d, supporting a polarization level of about ~2%. This study provides a framework for interpreting future spectropolarimetric observations of SNe II-P/CSM and SNe IIn and fostering a better understanding of the origin of their pre-SN mass loss., Comment: Submitted to A&A Letters

We propose a nonlinear opinion dynamics model for an agent making decisions about a continuous distribution of options in the presence of distributed input. Inspired by perceptual decision-making, we develop the theory for options distributed on the circle, representing, e.g., the set of possible heading directions in planar robotic navigation problems. Interactions among options are encoded through a spatially invariant kernel. We design the kernel to ensure that decision-making is robust, in the sense that only a finite subset of options can be favored over the continuum. We prove the spatial invariance of the model linearization and use this result to prove an opinion-forming bifurcation in the model with zero input. We then use space and time frequency domain analysis of the model linearization to infer the ultra-sensitive input-output behavior of the nonlinear dynamics with input. We illustrate our model's versatility with a robotic navigation example in crowded spaces.

We present Excitable Nonlinear Opinion Dynamics (E-NOD), which describe opinion-forming and decision-making behavior with superior "agility" in responding and adapting to fast and unpredictable changes in context, environment, or information about available options. E-NOD is derived by introducing a single extra term to the previously presented Nonlinear Opinion Dynamics (NOD), which have been shown to enable fast and flexible multi-agent behavior. This extra term is inspired by the fast-positive, slow-negative mixed-feedback structure of excitable systems. The agile behaviors resulting from the excitable nature of decision-making driven by E-NOD are analyzed in a general setting and illustrated through an application to robot navigation around human movers., Comment: 6 pages, 5 figures

We prove that, unlike in the case of paths, the signature matrix of a membrane does not satisfy any algebraic relations. We derive novel closed-form expressions for the signatures of polynomial membranes and piecewise bilinear interpolations for arbitrary $2$-parameter data in $d$-dimensional space. We show that these two families of membranes admit the same set of signature matrices and scrutinize the corresponding affine variety., Comment: 24 pages, comments are welcome!

Underwater object-level mapping requires incorporating visual foundation models to handle the uncommon and often previously unseen object classes encountered in marine scenarios. In this work, a metric of semantic uncertainty for open-set object detections produced by visual foundation models is calculated and then incorporated into an object-level uncertainty tracking framework. Object-level uncertainties and geometric relationships between objects are used to enable robust object-level loop closure detection for unknown object classes. The above loop closure detection problem is formulated as a graph-matching problem. While graph matching, in general, is NP-Complete, a solver for an equivalent formulation of the proposed graph matching problem as a graph editing problem is tested on multiple challenging underwater scenes. Results for this solver as well as three other solvers demonstrate that the proposed methods are feasible for real-time use in marine environments for the robust, open-set, multi-object, semantic-uncertainty-aware loop closure detection. Further experimental results on the KITTI dataset demonstrate that the method generalizes to large-scale terrestrial scenes.

Macroscopic properties of equity markets affect the performance of active equity strategies but many are not adequately captured by conventional models of financial mathematics and econometrics. Using the CRSP Database of the US equity market, we study empirically several macroscopic properties defined in terms of market capitalizations and returns, and highlight a list of stylized facts and open questions motivated in part by stochastic portfolio theory. Additionally, we present a systematic backtest of the diversity-weighted portfolio under various configurations and study its performance in relation to macroscopic quantities. All of our results can be replicated using codes made available on our GitHub repository., Comment: 32 Pages, 15 Figures

Let $G$ be a discrete, countably infinite group and $H$ a subgroup of $G$. If $H$ acts continuously on a compact metric space $X$, then we can induce a continuous action of $G$ on $\prod_{H\backslash G}X$ where $H\backslash G$ is the collection of right-cosets of $H$ in $G$. This process is known as the co-induction. In this article, we will calculate the maximal pattern entropy of the co-induction. If $[G:H] < +\infty$ we will show that the $H$ action is null if and only if the co-induced action of $G$ is null. Also, we will discuss an example where $H$ is a proper subgroup of $G$ with finite index where the maximal pattern entropy of the $H$ action is equal to the co-induced action of $G$. If $[G:H] = +\infty$ we will show that the maximal pattern entropy of the co-induction is always $+\infty$ given the $H$-system is not trivial., Comment: 41 pages

Anomalous metallic behavior is ubiquitously observed near superconductor-insulator quantum critical points and, if persistent to zero temperature, poses a challenge to current theories of metals. One explanation for this behavior could be incomplete thermal equilibrium between the sample and the cryostat. However, despite decades of study, the actual temperature of an anomalous metallic sample has not been measured. We therefore introduce a new experimental probe by measuring microwave radiation emitted from the anomalous metal, using a two-dimensional array of superconductor-semiconductor hybrid Josephson junctions as a model system. The total emitted radiation exceeds the limits of thermodynamic equilibrium, but is well described by an elevated sample temperature. This extracted sample temperature matches the onset of anomalous metallic behavior. Additionally, we discover scaling behavior of radiative noise in the presence of an applied bias. Elements of our noise-scaling observations were predicted based on nonlinear critical field theories and gauge-gravity duality. This work shows that, in a prominent model system, anomalous metallic behavior is a non-equilibrium effect, and opens a new frontier in the study of universal, non-equilibrium phenomena near quantum criticality., Comment: 6 pages, 4 figures

This paper presents a possible link between Cages and Expander Graphs by introducing three interconnected variants of the Bermond and Bollob\'as Conjecture, originally formulated in 1981 within the context of the Degree/Diameter Problem. We adapt these conjectures to cages, with the most robust variant posed as follows: Does there exist a constant $c$ such that for every pair of parameters $(k,g)$ there exists a $k$-regular graph of girth $g$ and order not exceeding $ M(k,g) + c $?; where $M(k,g)$ denotes the value of the so-called Moore bound for cages. We show that a positive answer to any of the three variants of the Bermond and Bollob\'as Conjecture for cages considered in our paper would yield expander graphs (expander families); thereby establishing a connection between Cages and Expander Graphs.

Sparse Mixture of Expert (SMoE) models have emerged as a scalable alternative to dense models in language modeling. These models use conditionally activated feedforward subnetworks in transformer blocks, allowing for a separation between total model parameters and per-example computation. However, large token-routed SMoE models face a significant challenge: during inference, the entire model must be used for a sequence or a batch, resulting in high latencies in a distributed setting that offsets the advantages of per-token sparse activation. Our research explores task-specific model pruning to inform decisions about designing SMoE architectures, mainly modulating the choice of expert counts in pretraining. We investigate whether such pruned models offer advantages over smaller SMoE models trained from scratch, when evaluating and comparing them individually on tasks. To that end, we introduce an adaptive task-aware pruning technique UNCURL to reduce the number of experts per MoE layer in an offline manner post-training. Our findings reveal a threshold pruning factor for the reduction that depends on the number of experts used in pretraining, above which, the reduction starts to degrade model performance. These insights contribute to our understanding of model design choices when pretraining with SMoE architectures, particularly useful when considering task-specific inference optimization for later stages.

The broad physics reach of the LUX-ZEPLIN (LZ) experiment covers rare phenomena beyond the direct detection of dark matter. We report precise measurements of the extremely rare decay of $^{124}$Xe through the process of two-neutrino double electron capture (2$\nu$2EC), utilizing a $1.39\,\mathrm{kg} \times \mathrm{yr}$ isotopic exposure from the first LZ science run. A half-life of $T_{1/2}^{2\nu2\mathrm{EC}} = (1.09 \pm 0.14_{\text{stat}} \pm 0.05_{\text{sys}}) \times 10^{22}\,\mathrm{yr}$ is observed with a statistical significance of $8.3\,\sigma$, in agreement with literature. First empirical measurements of the KK capture fraction relative to other K-shell modes were conducted, and demonstrate consistency with respect to recent signal models at the $1.4\,\sigma$ level., Comment: 15 pages, 3 figures

COSINE-100 is a direct dark matter search experiment, with the primary goal of testing the annual modulation signal observed by DAMA/LIBRA, using the same target material, NaI(Tl). In previous analyses, we achieved the same 1 keV energy threshold used in the DAMA/LIBRA's analysis that reported an annual modulation signal with 11.6$\sigma$ significance. In this article, we report an improved analysis that lowered the threshold to 0.7 keV, thanks to the application of Multi-Layer Perception network and a new likelihood parameter with waveforms in the frequency domain. The lower threshold would enable a better comparison of COSINE-100 with new DAMA results with a 0.75 keV threshold and account for differences in quenching factors. Furthermore the lower threshold can enhance COSINE-100's sensitivity to sub-GeV dark matter searches.

Category-level object pose and shape estimation from a single depth image has recently drawn research attention due to its wide applications in robotics and self-driving. The task is particularly challenging because the three unknowns, object pose, object shape, and model-to-measurement correspondences, are compounded together but only a single view of depth measurements is provided. The vast majority of the prior work heavily relies on data-driven approaches to obtain solutions to at least one of the unknowns and typically two, running with the risk of failing to generalize to unseen domains. The shape representations used in the prior work also mainly focus on point cloud and signed distance field (SDF). In stark contrast to the prior work, we approach the problem using an iterative estimation method that does not require learning from any pose-annotated data. In addition, we adopt a novel mesh-based object active shape model that has not been explored by the previous literature. Our algorithm, named ShapeICP, has its foundation in the iterative closest point (ICP) algorithm but is equipped with additional features for the category-level pose and shape estimation task. The results show that even without using any pose-annotated data, ShapeICP surpasses many data-driven approaches that rely on the pose data for training, opening up new solution space for researchers to consider.

COSINE-100 aims to conclusively test the claimed dark matter annual modulation signal detected by DAMA/LIBRA collaboration. DAMA/LIBRA has released updated analysis results by lowering the energy threshold to 0.75 keV through various upgrades. They have consistently claimed to have observed the annual modulation. In COSINE-100, it is crucial to lower the energy threshold for a direct comparison with DAMA/LIBRA, which also enhances the sensitivity of the search for low-mass dark matter, enabling COSINE-100 to explore this area. Therefore, it is essential to have a precise and quantitative understanding of the background spectrum across all energy ranges. This study expands the background modeling from 0.7 to 4000 keV using 2.82 years of COSINE-100 data. The modeling has been improved to describe the background spectrum across all energy ranges accurately. Assessments of the background spectrum are presented, considering the nonproportionality of NaI(Tl) crystals at both low and high energies and the characteristic X-rays produced by the interaction of external backgrounds with materials such as copper. Additionally, constraints on the fit parameters obtained from the alpha spectrum modeling fit are integrated into this model. These improvements are detailed in the paper.

We have experimentally studied dissociative recombination (DR) of electronically and vibrationally relaxed ArH$^+$ in its lowest rotational levels, using an electron--ion merged-beams setup at the Cryogenic Storage Ring. We report measurements for the merged-beams rate coefficient of ArH$^+$ and compare it to published experimental and theoretical results. In addition, by measuring the kinetic energy released to the DR fragments, we have determined the internal state of the DR products after dissociation. At low collision energies, we find that the atomic products are in their respective ground states, which are only accessible via non-adiabatic couplings to neutral Rydberg states. Published theoretical results for ArH$^+$ have not included this DR pathway. From our measurements, we have also derived a kinetic temperature rate coefficient for use in astrochemical models., Comment: Main paper: PDFLaTeX with 12 pages, 7 figures, 1 table. Appendix starting on page 12: PDFLaTeX with 10 pages, 4 figures, 2 tables. This is the Accepted Manuscript version of an article accepted for publication in the Physical Review A

The sheer scale and diversity of transportation make it a formidable sector to decarbonize. Here, we consider an emerging opportunity to reduce carbon emissions: the growing adoption of semi-autonomous vehicles, which can be programmed to mitigate stop-and-go traffic through intelligent speed commands and, thus, reduce emissions. But would such dynamic eco-driving move the needle on climate change? A comprehensive impact analysis has been out of reach due to the vast array of traffic scenarios and the complexity of vehicle emissions. We address this challenge with large-scale scenario modeling efforts and by using multi-task deep reinforcement learning with a carefully designed network decomposition strategy. We perform an in-depth prospective impact assessment of dynamic eco-driving at 6,011 signalized intersections across three major US metropolitan cities, simulating a million traffic scenarios. Overall, we find that vehicle trajectories optimized for emissions can cut city-wide intersection carbon emissions by 11-22%, without harming throughput or safety, and with reasonable assumptions, equivalent to the national emissions of Israel and Nigeria, respectively. We find that 10% eco-driving adoption yields 25%-50% of the total reduction, and nearly 70% of the benefits come from 20% of intersections, suggesting near-term implementation pathways. However, the composition of this high-impact subset of intersections varies considerably across different adoption levels, with minimal overlap, calling for careful strategic planning for eco-driving deployments. Moreover, the impact of eco-driving, when considered jointly with projections of vehicle electrification and hybrid vehicle adoption remains significant. More broadly, this work paves the way for large-scale analysis of traffic externalities, such as time, safety, and air quality, and the potential impact of solution strategies., Comment: In review

This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations, e.g. multiple deployments of payloads, unfolding of appendages and booms, propellant consumption as well as during in-orbit servicing and active debris removal operations. The machine learning approach uses time series clustering together with an optimised actuation sequence generated by reinforcement learning to facilitate distinguishing among different inertial parameter sets. The performance of the proposed strategy is assessed against the case of a multi-satellite deployment system showing that the algorithm is resilient towards common disturbances in such kinds of operations., Comment: 6 pages, 3 figures, 1 table. To be presented in ESA - AI for Space (SPAICE)

Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramatically with access to user information including emails and documents, this raises privacy concerns about assistants sharing inappropriate information with third parties without user supervision. To steer information-sharing assistants to behave in accordance with privacy expectations, we propose to operationalize contextual integrity (CI), a framework that equates privacy with the appropriate flow of information in a given context. In particular, we design and evaluate a number of strategies to steer assistants' information-sharing actions to be CI compliant. Our evaluation is based on a novel form filling benchmark composed of human annotations of common webform applications, and it reveals that prompting frontier LLMs to perform CI-based reasoning yields strong results.

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

We introduce Diffusion Augmented Agents (DAAG), a novel framework that leverages large language models, vision language models, and diffusion models to improve sample efficiency and transfer learning in reinforcement learning for embodied agents. DAAG hindsight relabels the agent's past experience by using diffusion models to transform videos in a temporally and geometrically consistent way to align with target instructions with a technique we call Hindsight Experience Augmentation. A large language model orchestrates this autonomous process without requiring human supervision, making it well-suited for lifelong learning scenarios. The framework reduces the amount of reward-labeled data needed to 1) finetune a vision language model that acts as a reward detector, and 2) train RL agents on new tasks. We demonstrate the sample efficiency gains of DAAG in simulated robotics environments involving manipulation and navigation. Our results show that DAAG improves learning of reward detectors, transferring past experience, and acquiring new tasks - key abilities for developing efficient lifelong learning agents. Supplementary material and visualizations are available on our website https://sites.google.com/view/diffusion-augmented-agents/, Comment: Published at 3rd Conference on Lifelong Learning Agents (CoLLAs), 2024

Understanding the dynamics of the land-atmosphere exchange of CO$_2$ is key to advance our predictive capacities of the coupled climate-carbon feedback system. In essence, the net vegetation flux is the difference of the uptake of CO$_2$ via photosynthesis and the release of CO$_2$ via respiration, while the system is driven by periodic processes at different time-scales. The complexity of the underlying dynamics poses challenges to classical decomposition methods focused on maximizing data variance, such as singular spectrum analysis. Here, we explore whether nonlinear data-driven methods can better separate periodic patterns and their harmonics from noise and stochastic variability. We find that Nonlinear Laplacian Spectral Analysis (NLSA) outperforms the linear method and detects multiple relevant harmonics. However, these harmonics are not detected in the presence of substantial measurement irregularities. In summary, the NLSA approach can be used to both extract the seasonal cycle more accurately than linear methods, but likewise detect irregular signals resulting from irregular land-atmosphere interactions or measurement failures. Improving the detection capabilities of time-series decomposition is essential for improving land-atmosphere interactions models that should operate accurately on any time scale., Comment: 18 pages, 11 figures, research article

Root Cause Analysis (RCA) is a quality management method that aims to systematically investigate and identify the cause-and-effect relationships of problems and their underlying causes. Traditional methods are based on the analysis of problems by subject matter experts. In modern production processes, large amounts of data are collected. For this reason, increasingly computer-aided and data-driven methods are used for RCA. One of these methods are Causal Discovery Algorithms (CDA). This publication demonstrates the application of CDA on data from the assembly of a leading automotive manufacturer. The algorithms used learn the causal structure between the characteristics of the manufactured vehicles, the ergonomics and the temporal scope of the involved assembly processes, and quality-relevant product features based on representative data. This publication compares various CDAs in terms of their suitability in the context of quality management. For this purpose, the causal structures learned by the algorithms as well as their runtime are compared. This publication provides a contribution to quality management and demonstrates how CDAs can be used for RCA in assembly processes., Comment: in German language

Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge here is to develop steerable language models that trade-off multiple (conflicting) objectives in a flexible and efficient manner. This paper presents Conditioned Language Policy (CLP), a general framework for finetuning language models on multiple objectives. Building on techniques from multi-task training and parameter-efficient finetuning, CLP can learn steerable models that effectively trade-off conflicting objectives at inference time. Notably, this does not require training or maintaining multiple models to achieve different trade-offs between the objectives. Through an extensive set of experiments and ablations, we show that the CLP framework learns steerable models that outperform and Pareto-dominate the current state-of-the-art approaches for multi-objective finetuning., Comment: 40 pages

Non-equilibrium electronic quantum transport is crucial for the operation of existing and envisioned electronic, optoelectronic, and spintronic devices. The ultimate goal of encompassing atomistic to mesoscopic length scales in the same nonequilibrium device simulation approach has traditionally been challenging due to the computational cost of high-fidelity coupled multiphysics and multiscale requirements. In this work, we present ELEQTRONeX (ELEctrostatic Quantum TRansport modeling Of Nanomaterials at eXascale), a massively-parallel GPU-accelerated framework for self-consistently solving the nonequilibrium Green's function formalism and electrostatics in complex device geometries. By customizing algorithms for GPU multithreading, we achieve orders of magnitude improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. We validate our code by computing band structures, current-voltage characteristics, conductance, and drain-induced barrier lowering for various 3D configurations of carbon nanotube field-effect transistors. We also demonstrate that ELEQTRONeX is suitable for complex device/material geometries where periodic approaches are not feasible, such as modeling of arrays of misaligned carbon nanotubes requiring fully 3D simulations., Comment: 29 pages, 6 figures, 3 tables

Reinforcement learning from human feedback (RLHF) is a key driver of quality and safety in state-of-the-art large language models. Yet, a surprisingly simple and strong inference-time strategy is Best-of-N sampling that selects the best generation among N candidates. In this paper, we propose Best-of-N Distillation (BOND), a novel RLHF algorithm that seeks to emulate Best-of-N but without its significant computational overhead at inference time. Specifically, BOND is a distribution matching algorithm that forces the distribution of generations from the policy to get closer to the Best-of-N distribution. We use the Jeffreys divergence (a linear combination of forward and backward KL) to balance between mode-covering and mode-seeking behavior, and derive an iterative formulation that utilizes a moving anchor for efficiency. We demonstrate the effectiveness of our approach and several design choices through experiments on abstractive summarization and Gemma models. Aligning Gemma policies with BOND outperforms other RLHF algorithms by improving results on several benchmarks.

One of us (L.S.) and H. Verlinde independently conjectured a holographic duality between the double-scaled SYK model at infinite temperature and dimensionally reduced $(2+1)$-dimensional de Sitter space [1]-[8]. Beyond the statement that such a duality exists there was deep disagreement between the two proposals [9]. In this note, we trace the origin of the disagreement to a superficial similarity between two q-deformed algebraic structures: the algebra of "chords" in DSSYK, and the algebra of line operators in the Chern-Simons formulation of 3D de Sitter gravity. Assuming that these two structures are the same requires an identification of parameters [7][10] which leads to a collapse of the separation of scales [9] -- the separation being required by the semiclassical limit [3][9]. Dropping that assumption restores the separation of scales but leaves unexplained the relation between chords and Chern-Simons line operators. In this note we point out the existence of a third q-deformed algebra that appears in DSSYK: the algebra of ``wee-chords." Identifying the Chern-Simons line operators with wee-chords removes the discrepancy and leads to a satisfying relation between the two sides of the duality., Comment: 30 + 15 pages, 3 figures

The AMoRE collaboration searches for neutrinoless double beta decay of $^{100}$Mo using molybdate scintillating crystals via low temperature thermal calorimetric detection. The early phases of the experiment, AMoRE-pilot and AMoRE-I, have demonstrated competitive discovery potential. Presently, the AMoRE-II experiment, featuring a large detector array with about 90 kg of $^{100}$Mo isotope, is under construction.This paper discusses the baseline design and characterization of the lithium molybdate cryogenic calorimeters to be used in the AMoRE-II detector modules. The results from prototype setups that incorporate new housing structures and two different crystal masses (316 g and 517 - 521 g), operated at 10 mK temperature, show energy resolutions (FWHM) of 7.55 - 8.82 keV at the 2.615 MeV $^{208}$Tl $\gamma$ line, and effective light detection of 0.79 - 0.96 keV/MeV. The simultaneous heat and light detection enables clear separation of alpha particles with a discrimination power of 12.37 - 19.50 at the energy region around $^6$Li(n, $\alpha$)$^3$H with Q-value = 4.785 MeV. Promising detector performances were demonstrated at temperatures as high as 30 mK, which relaxes the temperature constraints for operating the large AMoRE-II array.

Deformable image registration is fundamental to many medical imaging applications. Registration is an inherently ambiguous task often admitting many viable solutions. While neural network-based registration techniques enable fast and accurate registration, the majority of existing approaches are not able to estimate uncertainty. Here, we present PULPo, a method for probabilistic deformable registration capable of uncertainty quantification. PULPo probabilistically models the distribution of deformation fields on different hierarchical levels combining them using Laplacian pyramids. This allows our method to model global as well as local aspects of the deformation field. We evaluate our method on two widely used neuroimaging datasets and find that it achieves high registration performance as well as substantially better calibrated uncertainty quantification compared to the current state-of-the-art., Comment: Accepted as full paper to MICCAI 2024

AMoRE searches for the signature of neutrinoless double beta decay of $^{100}$Mo with a 100 kg sample of enriched $^{100}$Mo. Scintillating molybdate crystals coupled with a metallic magnetic calorimeter operate at milli-Kelvin temperatures to measure the energy of electrons emitted in the decay. As a demonstration of the full-scale AMoRE, we conducted AMoRE-I, a pre-experiment with 18 molybdate crystals, at the Yangyang Underground Laboratory for over two years. The exposure was 8.02 kg$\cdot$year (or 3.89 kg$_{\mathrm{^{100}Mo}}\cdot$year) and the total background rate near the Q-value was 0.025 $\pm$ 0.002 counts/keV/kg/year. We observed no indication of $0\nu\beta\beta$ decay and report a new lower limit of the half-life of $^{100}$Mo $0\nu\beta\beta$ decay as $ T^{0\nu}_{1/2}>3.0\times10^{24}~\mathrm{years}$ at 90\% confidence level. The effective Majorana mass limit range is $m_{\beta\beta}<$(210--610) meV using nuclear matrix elements estimated in the framework of different models, including the recent shell model calculations., Comment: 7 pages, 4 figures

This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image analysis. The reBEN dataset consists of 549,488 pairs of Sentinel-1 and Sentinel-2 image patches. To construct reBEN, we initially consider the Sentinel-1 and Sentinel-2 tiles used to construct the BigEarthNet dataset and then divide them into patches of size 1200 m x 1200 m. We apply atmospheric correction to the Sentinel-2 patches using the latest version of the sen2cor tool, resulting in higher-quality patches compared to those present in BigEarthNet. Each patch is then associated with a pixel-level reference map and scene-level multi-labels. This makes reBEN suitable for pixel- and scene-based learning tasks. The labels are derived from the most recent CORINE Land Cover (CLC) map of 2018 by utilizing the 19-class nomenclature as in BigEarthNet. The use of the most recent CLC map results in overcoming the label noise present in BigEarthNet. Furthermore, we introduce a new geographical-based split assignment algorithm that significantly reduces the spatial correlation among the train, validation, and test sets with respect to those present in BigEarthNet. This increases the reliability of the evaluation of DL models. To minimize the DL model training time, we introduce software tools that convert the reBEN dataset into a DL-optimized data format. In our experiments, we show the potential of reBEN for multi-modal multi-label image classification problems by considering several state-of-the-art DL models. The pre-trained model weights, associated code, and complete dataset are available at https://bigearth.net.

Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. Version accepted by Physical Review D for publication

We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide two sizes of models, containing 2B and 9B parameters, and provide pre-trained and instruction tuned variants for both. Our models achieve comparable performance to similarly-sized Gemma baselines despite being trained on fewer tokens.

Two players play a game by alternately splitting a surface of a compact $2$-manifold along a simple closed curve that is not null-homotopic and attaching disks to the resulting boundary; the last player who can move wins. Starting from an orientable surface, the $G$-series is $01\dot{2}\dot{0}$ according to increasing genus. Starting from a nonorientable surface, the $G$-series is $012\dot{4}60\dot{3}$ according to increasing genus. Nim addition determines the $G$-values of the remaining compact $2$-manifolds., Comment: 12 pages. To be submitted to the Bulletin of The Institute for Combinatorics and its Applications This version corrects the email: lleonardjr@icloud.com

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is $^{136}$Xe, which would double beta decay into $^{136}$Ba. Detecting the single $^{136}$Ba daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon., Comment: 9 pages, 8 figures

Reinforcement learning from human feedback (RLHF) aligns large language models (LLMs) by encouraging their generations to have high rewards, using a reward model trained on human preferences. To prevent the forgetting of pre-trained knowledge, RLHF usually incorporates a KL regularization; this forces the policy to remain close to its supervised fine-tuned initialization, though it hinders the reward optimization. To tackle the trade-off between KL and reward, in this paper we introduce a novel alignment strategy named Weight Averaged Rewarded Policies (WARP). WARP merges policies in the weight space at three distinct stages. First, it uses the exponential moving average of the policy as a dynamic anchor in the KL regularization. Second, it applies spherical interpolation to merge independently fine-tuned policies into a new enhanced one. Third, it linearly interpolates between this merged model and the initialization, to recover features from pre-training. This procedure is then applied iteratively, with each iteration's final model used as an advanced initialization for the next, progressively refining the KL-reward Pareto front, achieving superior rewards at fixed KL. Experiments with GEMMA policies validate that WARP improves their quality and alignment, outperforming other open-source LLMs., Comment: 11 main pages (34 pages with Appendix)

From pedestrians to Kuramoto oscillators, interactions between agents govern how a multitude of dynamical systems evolve in space and time. Discovering how these agents relate to each other can improve our understanding of the often complex dynamics that underlie these systems. Recent works learn to categorize relationships between agents based on observations of their physical behavior. These approaches are limited in that the relationship categories are modelled as independent and mutually exclusive, when in real world systems categories are often interacting. In this work, we introduce a level of abstraction between the physical behavior of agents and the categories that define their behavior. To do this, we learn a mapping from the agents' states to their affinities for each category in a graph neural network. We integrate the physical proximity of agents and their affinities in a nonlinear opinion dynamics model which provides a mechanism to identify mutually exclusive categories, predict an agent's evolution in time, and control an agent's behavior. We demonstrate the utility of our model for learning interpretable categories for mechanical systems, and demonstrate its efficacy on several long-horizon trajectory prediction benchmarks where we consistently out perform existing methods., Comment: 14 pages, 7 figures

Small Angle-X-ray Scattering Tensor Tomography (SAS-TT) is a relatively new, but powerful technique for studying the multiscale architecture of hierarchical structures, which is of particular interest for life science applications. Currently, the technique is very demanding on synchrotron beamtime, which limits its applications, especially for cases requiring a statistically relevant amount of sample. This study reports the first SAS-TT measurement at a macromolecular X-ray crystallography beamline, PX-I at the SLS, with an improvement in data acquisition time from 96 h/Mvoxel in the pilot experiments to 6 h/Mvoxel, defining a new standard for fast SAS-TT and allowing the measurement of a full tomogram in 1.2 hours. Measurements were performed on the long and lenticular process of the incus bone, one of the three human auditory ossicles. The main orientation and degree of alignment of the mineralised collagen fibrils are characterised, as well as the size and shape of the mineral particles which show relevant variations in different tissue locations. The study reveals three distinct regions of high fibril alignment, most likely important pathways of sound throughout the ossicular chain, and highlights the potential of the technique to aid in future developments in middle ear reconstructive surgery.

Quadrotors are among the most agile flying robots. Despite recent advances in learning-based control and computer vision, autonomous drones still rely on explicit state estimation. On the other hand, human pilots only rely on a first-person-view video stream from the drone onboard camera to push the platform to its limits and fly robustly in unseen environments. To the best of our knowledge, we present the first vision-based quadrotor system that autonomously navigates through a sequence of gates at high speeds while directly mapping pixels to control commands. Like professional drone-racing pilots, our system does not use explicit state estimation and leverages the same control commands humans use (collective thrust and body rates). We demonstrate agile flight at speeds up to 40km/h with accelerations up to 2g. This is achieved by training vision-based policies with reinforcement learning (RL). The training is facilitated using an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use the inner edges of the gates as a sensor abstraction. This simple yet robust, task-relevant representation can be simulated during training without rendering images. During deployment, a Swin-transformer-based gate detector is used. Our approach enables autonomous agile flight with standard, off-the-shelf hardware. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments.

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups. Code and datasets are made publicly available.

Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosystem services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.

With the rapid development of Large Language Models (LLMs), it is crucial to have benchmarks which can evaluate the ability of LLMs on different domains. One common use of LLMs is performing tasks on scientific topics, such as writing algorithms, querying databases or giving mathematical proofs. Inspired by the way university students are evaluated on such tasks, in this paper, we propose SciEx - a benchmark consisting of university computer science exam questions, to evaluate LLMs ability on solving scientific tasks. SciEx is (1) multilingual, containing both English and German exams, and (2) multi-modal, containing questions that involve images, and (3) contains various types of freeform questions with different difficulty levels, due to the nature of university exams. We evaluate the performance of various state-of-the-art LLMs on our new benchmark. Since SciEx questions are freeform, it is not straightforward to evaluate LLM performance. Therefore, we provide human expert grading of the LLM outputs on SciEx. We show that the free-form exams in SciEx remain challenging for the current LLMs, where the best LLM only achieves 59.4\% exam grade on average. We also provide detailed comparisons between LLM performance and student performance on SciEx. To enable future evaluation of new LLMs, we propose using LLM-as-a-judge to grade the LLM answers on SciEx. Our experiments show that, although they do not perform perfectly on solving the exams, LLMs are decent as graders, achieving 0.948 Pearson correlation with expert grading.

Non-cooperative interactions commonly occur in multi-agent scenarios such as car racing, where an ego vehicle can choose to overtake the rival, or stay behind it until a safe overtaking "corridor" opens. While an expert human can do well at making such time-sensitive decisions, the development of safe and efficient game-theoretic trajectory planners capable of rapidly reasoning discrete options is yet to be fully addressed. The recently developed nonlinear opinion dynamics (NOD) show promise in enabling fast opinion formation and avoiding safety-critical deadlocks. However, it remains an open challenge to determine the model parameters of NOD automatically and adaptively, accounting for the ever-changing environment of interaction. In this work, we propose for the first time a learning-based, game-theoretic approach to synthesize a Neural NOD model from expert demonstrations, given as a dataset containing (possibly incomplete) state and action trajectories of interacting agents. The learned NOD can be used by existing dynamic game solvers to plan decisively while accounting for the predicted change of other agents' intents, thus enabling situational awareness in planning. We demonstrate Neural NOD's ability to make fast and robust decisions in a simulated autonomous racing example, leading to tangible improvements in safety and overtaking performance over state-of-the-art data-driven game-theoretic planning methods.

AMoRE-II aims to search for neutrinoless double beta decay with an array of 423 Li$_2$$^{100}$MoO$_4$ crystals operating in the cryogenic system as the main phase of the Advanced Molybdenum-based Rare process Experiment (AMoRE). AMoRE has been planned to operate in three phases: AMoRE-pilot, AMoRE-I, and AMoRE-II. AMoRE-II is currently being installed at the Yemi Underground Laboratory, located approximately 1000 meters deep in Jeongseon, Korea. The goal of AMoRE-II is to reach up to $T^{0\nu\beta\beta}_{1/2}$ $\sim$ 6 $\times$ 10$^{26}$ years, corresponding to an effective Majorana mass of 15 - 29 meV, covering all the inverted mass hierarchy regions. To achieve this, the background level of the experimental configurations and possible background sources of gamma and beta events should be well understood. We have intensively performed Monte Carlo simulations using the GEANT4 toolkit in all the experimental configurations with potential sources. We report the estimated background level that meets the 10$^{-4}$counts/(keV$\cdot$kg$\cdot$yr) requirement for AMoRE-II in the region of interest (ROI) and show the projected half-life sensitivity based on the simulation study.

Generative artificial intelligence (GenAI) and large language models (LLMs) have simultaneously opened new avenues for enhancing human learning and increased the prevalence of poor-quality information in student response - termed Botpoop. This study introduces Professor Leodar, a custom-built, Singlish-speaking Retrieval Augmented Generation (RAG) chatbot designed to enhance educational while reducing Botpoop. Deployed at Nanyang Technological University, Singapore, Professor Leodar offers a glimpse into the future of AI-assisted learning, offering personalized guidance, 24/7 availability, and contextually relevant information. Through a mixed-methods approach, we examine the impact of Professor Leodar on learning, engagement, and exam preparedness, with 97.1% of participants reporting positive experiences. These findings help define possible roles of AI in education and highlight the potential of custom GenAI chatbots. Our combination of chatbot development, in-class deployment and outcomes study offers a benchmark for GenAI educational tools and is a stepping stone for redefining the interplay between AI and human learning., Comment: 13 pages, 5 figures, SI with Annexes A, B and C upon request

The recent IceCube detection of TeV neutrino emission from the nearby active galaxy NGC 1068 suggests that active galactic nuclei (AGN) could make a sizable contribution to the diffuse flux of astrophysical neutrinos. The absence of TeV $\gamma$-rays from NGC 1068 indicates neutrino production in the vicinity of the supermassive black hole, where the high radiation density leads to $\gamma$-ray attenuation. Therefore, any potential neutrino emission from similar sources is not expected to correlate with high-energy $\gamma$-rays. Disk-corona models predict neutrino emission from Seyfert galaxies to correlate with keV X-rays, as they are tracers of coronal activity. Using through-going track events from the Northern Sky recorded by IceCube between 2011 and 2021, we report results from a search for individual and aggregated neutrino signals from 27 additional Seyfert galaxies that are contained in the BAT AGN Spectroscopic Survey (BASS). Besides the generic single power-law, we evaluate the spectra predicted by the disk-corona model. Assuming all sources to be intrinsically similar to NGC 1068, our findings constrain the collective neutrino emission from X-ray bright Seyfert galaxies in the Northern Hemisphere, but, at the same time, show excesses of neutrinos that could be associated with the objects NGC 4151 and CGCG 420-015. These excesses result in a 2.7$\sigma$ significance with respect to background expectations., Comment: 17 pages, 9 figures

Active Galactic Nuclei (AGN) are promising candidate sources of high-energy astrophysical neutrinos since they provide environments rich in matter and photon targets where cosmic ray interactions may lead to the production of gamma rays and neutrinos. We searched for high-energy neutrino emission from AGN using the $\textit{Swift}$-BAT Spectroscopic Survey (BASS) catalog of hard X-ray sources and 12 years of IceCube muon track data. First, upon performing a stacked search, no significant emission was found. Second, we searched for neutrinos from a list of 43 candidate sources and found an excess from the direction of two sources, Seyfert galaxies NGC 1068 and NGC 4151. We observed NGC 1068 at flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $4.02_{-1.52}^{+1.58} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV, with power-law spectral index, $\gamma$ = 3.10$^{+0.26}_{-0.22}$, consistent with previous IceCube results. The observation of a neutrino excess from the direction of NGC 4151 is at a post-trial significance of 2.9$\sigma$. If interpreted as an astrophysical signal, the excess observed from NGC 4151 corresponds to a flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $1.51_{-0.81}^{+0.99} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV and $\gamma$ = 2.83$^{+0.35}_{-0.28}$.

This paper presents a trajectory optimization and control approach for the guidance of an orbital four-arm robot in extravehicular activities. The robot operates near the target spacecraft, enabling its arm's end-effectors to reach the spacecraft's surface. Connections to the target spacecraft can be established by the arms through specific footholds (docking devices). The trajectory optimization allows the robot path planning by computing the docking positions on the target spacecraft surface, along with their timing, the arm trajectories, the six degrees of freedom body motion, and the necessary contact forces during docking. In addition, the paper introduces a controller designed to track the planned trajectories derived from the solution of the nonlinear programming problem. A weighted controller formulated as a convex optimization problem is proposed. The controller is defined as the optimization of an objective function that allows the system to perform a set of tasks simultaneously. Simulation results show the application of the trajectory optimization and control approaches to an on-orbit servicing scenario., Comment: 8 pages. To be presented at the 15th IEEE International Conference on Mechanical and Aerospace Engineering (ICMAE)

Recent hydrodynamical simulations of the late stages of supernova remnant (SNR) evolution have revealed that as they merge with the ambient medium, SNRs implode, leading to the formation of dense clouds in their center. While being highly chemically enriched by their host SNR, these clouds appear to have similar properties as giant molecular clouds, which are believed to be the main site of star formation. Here, we develop a simple model, in order to estimate the efficiency of the star formation that might be triggered by the implosion of SNRs. We separately consider two cases, cyclic star formation, maintained by the episodic driving of feedback from new generations of stars; and a single burst of star formation, triggered by a single explosion. We find that in the cyclic case, star formation is inefficient, with implosion-triggered star-formation contributing a few percent of the observed star-formation efficiency per free-fall timescale. In the single-burst case, higher star-formation efficiencies can be obtained. However, while the implosion-triggered process might not contribute much to the overall star-formation, due to the high chemical enrichment of the birth clouds, it can explain the formation of a significant fraction of metal-rich stars., Comment: 9 pages, 3 figures, accepted for publication in ApJL