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We prove that the functor taking an animated ring $R$ to its mod $(p,v_1^{p^n})$ derived syntomic cohomology factors through the functor $R \mapsto R/p^{n+2}$. We then use this to completely and explicitly compute the mod $(p,v_1 ^{p^{n}})$ syntomic cohomology of $\mathbb{Z}/p^{k}$ whenever $k \geq n+2$., Comment: 45 pages

This paper presents a novel approach to evaluating blue-chip art as a viable asset class for portfolio diversification. We present the Arte-Blue Chip Index, an index that tracks 100 top-performing artists based on 81,891 public transactions from 157 artists across 584 auction houses over the period 1990 to 2024. By comparing blue-chip art price trends with stock market fluctuations, our index provides insights into the risk and return profile of blue-chip art investments. Our analysis demonstrates that a 20% allocation of blue-chip art in a diversified portfolio enhances risk-adjusted returns by around 20%, while maintaining volatility levels similar to the S&P 500., Comment: 15 pages, 1798 words, 3 figures, 3 tables

Many of the recent breakthroughs in language modeling have resulted from scaling effectively the same model architecture to larger datasets. In this vein, recent work has highlighted performance gains from increasing training dataset size and quality, suggesting a need for novel sources of large-scale datasets. In this work, we introduce BeanCounter, a public dataset consisting of more than 159B tokens extracted from businesses' disclosures. We show that this data is indeed novel: less than 0.1% of BeanCounter appears in Common Crawl-based datasets and it is an order of magnitude larger than datasets relying on similar sources. Given the data's provenance, we hypothesize that BeanCounter is comparatively more factual and less toxic than web-based datasets. Exploring this hypothesis, we find that many demographic identities occur with similar prevalence in BeanCounter but with significantly less toxic context relative to other datasets. To demonstrate the utility of BeanCounter, we evaluate and compare two LLMs continually pre-trained on BeanCounter with their base models. We find an 18-33% reduction in toxic generation and improved performance within the finance domain for the continually pretrained models. Collectively, our work suggests that BeanCounter is a novel source of low-toxicity and high-quality domain-specific data with sufficient scale to train multi-billion parameter LLMs.

Semiconductor crosshatch patterns in thin film heterostructures form as a result of strain relaxation processes and dislocation pile-ups during growth of lattice mismatched materials. Due to their connection with the internal misfit dislocation network, these crosshatch patterns are a complex fingerprint of internal strain relaxation and growth anisotropy. Therefore, this mesoscopic fingerprint not only describes the residual strain state of a near-surface quantum well, but also could provide an indicator of the quality of electron transport through the material. Here, we present a method utilizing computer vision and machine learning to analyze AFM crosshatch patterns that exhibits this correlation. Our analysis reveals optimized electron transport for moderate values of $\lambda$ (crosshatch wavelength) and $\epsilon$ (crosshatch height), roughly 1 $\mu$m and 4 nm, respectively, that define the average waveform of the pattern. Simulated 2D AFM crosshatch patterns are used to train a machine learning model to correlate the crosshatch patterns to dislocation density. Furthermore, this model is used to evaluate the experimental AFM images and predict a dislocation density based on the crosshatch waveform. Predicted dislocation density, experimental AFM crosshatch data, and experimental transport characterization are used to train a final model to predict 2D electron gas mean free path. This model shows electron scattering is strongly correlated with elastic effects (e.g. dislocation scattering) below 200 nm $\lambda_{MFP}$.

Physics-Informed Neural Networks (PINNs) are fast becoming an important tool to solve differential equations rapidly and accurately, and to identify the systems parameters that best agree with a given set of measurements. PINNs have been used for cardiac electrophysiology (EP), but only in simple 1D and 2D geometries and for sinus rhythm or single rotor dynamics. Here, we demonstrate how PINNs can be used to accurately reconstruct the propagation of cardiac action potential in more complex geometries and dynamical regimes. These include 3D spherical geometries and spiral break-up conditions that model cardiac fibrillation, with a mean RMSE $< 5.1\times 10^{-2}$ overall. We also demonstrate that PINNs can be used to reliably parameterise cardiac EP models with some biological detail. We estimate the diffusion coefficient and parameters related to ion channel conductances in the Fenton-Karma model in a 2D setup, achieving a mean relative error of $-0.09\pm 0.33$. Our results are an important step towards the deployment of PINNs to realistic cardiac geometries and arrhythmic conditions., Comment: Accepted for publication in the 15th Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop 2024; 12 pages

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using virtual instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

Efficiently trading off exploration and exploitation is one of the key challenges in online Reinforcement Learning (RL). Most works achieve this by carefully estimating the model uncertainty and following the so-called optimistic model. Inspired by practical ensemble methods, in this work we propose a simple and novel batch ensemble scheme that provably achieves near-optimal regret for stochastic Multi-Armed Bandits (MAB). Crucially, our algorithm has just a single parameter, namely the number of batches, and its value does not depend on distributional properties such as the scale and variance of the losses. We complement our theoretical results by demonstrating the effectiveness of our algorithm on synthetic benchmarks.

This article revolves around shape and topology optimization, in the applicative context where the objective and constraint functionals depend on the solution to a physical boundary value problem posed on the optimized domain. We introduce a novel framework based on modern concepts from computational geometry, optimal transport and numerical analysis. Its pivotal feature is a representation of the optimized shape by the cells of an adapted version of a Laguerre diagram. Although such objects are originally described by a collection of seed points and weights, recent results from optimal transport theory suggest a more intuitive parametrization in terms of the seed points and measures of the associated cells. The polygonal mesh of the shape induced by this diagram serves as support for the deployment of the Virtual Element Method for the numerical solution of the physical boundary value problem at play and the calculation of the objective and constraint functionals. The sensitivities of the latter are derived next; at first, we calculate their derivatives with respect to the positions of the vertices of the Laguerre diagram by shape calculus techniques; a suitable adjoint methodology is then developed to express them in terms of the seed points and cell measures of the diagram. The evolution of the shape is realized by first updating the design variables according to these sensitivities and then reconstructing the diagram with efficient algorithms from computational geometry. Our shape optimization strategy is versatile: it can be applied to a wide gammut of physical situations. It is Lagrangian by essence, and it thereby benefits from all the assets of a consistently meshed representation of the shape. Yet, it naturally handles dramatic motions, including topological changes, in a very robust fashion. These features, among others, are illustrated by a series of 2d numerical examples.

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

We tune the onset of optical response in aluminium kinetic inductance detectors from a natural cutoff frequency of 90 GHz to 60 GHz by applying an external magnetic field. The change in spectral response is due to the decrease of the superconducting gap, from 90 GHz at zero magnetic field to 60 GHz at a magnetic field of around 3 mT. We characterize the variation of the superconducting gap, the detector frequency shift and the internal quality factor as a function of the applied field. In principle, the magnetic field tunable response could be used to make spectroscopic measurements. In practice, the internal quality factor behaves hysteretically with the magnetic field due to the presence of vortices in the thin superconducting film. We conclude by discussing possible solutions to achieve spectroscopy measurements using kinetic inductance detectors and magnetic field.

Neutrinos at ultrahigh energies can originate both from interactions of cosmic rays at their acceleration sites and through cosmic-ray interactions as they propagate through the universe. These neutrinos are expected to have a low flux which drives the need for instruments with large effective areas. Radio observations of the inclined air showers induced by tau neutrino interactions in rock can achieve this, because radio waves can propagate essentially unattenuated through the hundreds of kilometers of atmosphere. Proposed arrays for radio detection of tau neutrinos focus on either arrays of inexpensive receivers distributed over a large area, the GRAND concept, or compact phased arrays on elevated mountains, the BEACON concept, to build up a large detector area with a low trigger threshold. We present a concept that combines the advantages of these two approaches with a trigger driven by phased arrays at a moderate altitude (1 km) and sparse, high-gain outrigger receivers for reconstruction and background rejection. We show that this design has enhanced sensitivity at 100 PeV over the two prior designs with fewer required antennas and discuss the need for optimized antenna designs., Comment: ARENA2024 Conference Proceeding PoS(ARENA2024)058

General web-based agents are increasingly essential for interacting with complex web environments, yet their performance in real-world web applications remains poor, yielding extremely low accuracy even with state-of-the-art frontier models. We observe that these agents can be decomposed into two primary components: Planning and Grounding. Yet, most existing research treats these agents as black boxes, focusing on end-to-end evaluations which hinder meaningful improvements. We sharpen the distinction between the planning and grounding components and conduct a novel analysis by refining experiments on the Mind2Web dataset. Our work proposes a new benchmark for each of the components separately, identifying the bottlenecks and pain points that limit agent performance. Contrary to prevalent assumptions, our findings suggest that grounding is not a significant bottleneck and can be effectively addressed with current techniques. Instead, the primary challenge lies in the planning component, which is the main source of performance degradation. Through this analysis, we offer new insights and demonstrate practical suggestions for improving the capabilities of web agents, paving the way for more reliable agents.

Random networks offer fertile ground for achieving complexity and criticality, both crucial for an unconventional computing paradigm inspired by biological brains' features. In this work, we focus on characterizing and modeling different electrical transport regimes of self-assemblies of silver nanowires (AgNWs). As percolation plays an essential role in such a scenario, we explore a broad range of areal density coverage. Close-to-percolation realizations (usually used to demonstrate neuromorphic computing capabilities) have large pristine resistance and require an electrical activation. Up to now, highly conductive over-percolated systems (commonly used in electrode fabrication technology) have not been thoroughly considered for hardware-based neuromorphic applications, though biological systems exhibit such an extremely high degree of interconnections. Here, we show that high current densities in over-percolated low-resistance AgNW networks induce a fuse-type process, allowing a switching operation. Such electro-fusing discriminates between weak and robust NW-to-NW links and enhances the role of filamentary junctions. Their reversible resistive switching enable different conductive paths exhibiting linear I-V features. We experimentally study both percolation regimes and propose a model comprising two types of junctions that can describe, through numerical simulations, the overall behavior and observed phenomenology. These findings unveil a potential interplay of functionalities of neuromorphic systems and transparent electrodes.

In a recent paper, we studied a modified version of the Einstein-Rosen bridge. This modified bridge is traversable and works as a one-way membrane: a particle on the first sheet falling toward the throat will reach it in finite time (in Eddington coordinates), and will continue its trajectory on the second sheet. In this paper, we show that the particle undergoes a PT-symmetry as it crosses the throat. This could lead to observable effects thanks to an additional ingredient proposed by Einstein and Rosen: congruent points on the two sheets are identified. We propose a bimetric model to realize this identification for our modified bridge.

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

The end of Moore's Law is driving cloud providers to offload virtualization and the network data plane to SmartNICs to improve compute efficiency. Even though individual OS control plane tasks consume up to 5% of cycles across the fleet, they remain on the host CPU because they are tightly intertwined with OS mechanisms. Moreover, offloading puts the slow PCIe interconnect in the critical path of OS decisions. We propose Tide, a new split OS architecture that separates OS control plane policies from mechanisms and offloads the control plane policies onto a SmartNIC. Tide has a new host-SmartNIC communication API, state synchronization mechanism, and communication mechanisms that overcome the PCIe bottleneck, even for $\mu$s-scale workloads. Tide frees up host compute for applications and unlocks new optimization opportunities, including machine learning-driven policies, scheduling on the network I/O path, and reducing on-host interference. We demonstrate that Tide enables OS control planes that are competitive with on-host performance for the most difficult $\mu$s-scale workloads. Tide outperforms on-host control planes for memory management (saving 16 host cores), Stubby network RPCs (saving 8 cores), and GCE virtual machine management (11.2% performance improvement)., Comment: About 11 pages

Large-scale cosmic-ray detectors like the Giant Radio Array for Neutrino Detection (GRAND) are pushing the boundaries of our ability to identify air shower events. Existing trigger schemes rely solely on the timing of signals detected by individual antennas, which brings many challenges in distinguishing true air shower signals from background. This work explores novel event-level radio trigger methods specifically designed for GRAND, but also applicable to other systems, such as the Radio Detector (RD) of the Pierre Auger Observatory. In addition to an upgraded plane wave front reconstruction technique, we introduce orthogonal and complementary approaches that analyze the radio-emission footprint, the spatial distribution of signal strength across triggered antennas, to refine event selection. We test our methods on mock data sets constructed with simulated showers and real background noise measured with the GRAND prototype, to assess the performance potential in terms of sensitivity and background rejection in GRAND. Our preliminary results are a first step to identifying the most discriminating radio signal features at event-level, and optimizing the techniques for future implementation on experimental data., Comment: 8 pages, 5 figures, 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities - ARENA2024

When performing radio detection of ultra-high energy astroparticles, the planar wavefront model is often used as a first step to evaluate the arrival direction of primary particles, by adjusting the wavefront orientation based on the peak of signal traces of individual antennas. The benefits of this approach are however limited by the lack of a good evaluation of its robustness. In order to mitigate this limitation, we derive in this work an analytical solution for the arrival direction as well as the corresponding analytical reconstruction uncertainty. Because it is fast and robust, this reconstruction method can be used as a proxy for more complex estimators, or implemented online for low-level triggering., Comment: Submitted to NIMA

We consider a Gatenby--Gawlinski-type model of invasive tumors in the presence of an Allee effect. We describe the construction of bistable one-dimensional traveling fronts using singular perturbation techniques in different parameter regimes corresponding to tumor interfaces with, or without, acellular gap. By extending the front as a planar interface, we perform a stability analysis to long wavelength perturbations transverse to the direction of front propagation and derive a simple stability criterion for the front in two spatial dimensions. In particular we find that in general the presence of the acellular gap indicates transversal instability of the associated planar front, which can lead to complex interfacial dynamics such as the development of finger-like protrusions and/or different invasion speeds., Comment: 24 pages, 12 figures

The Giant Radio Array for Neutrino Detection (GRAND) aims to detect highly inclined extensive air showers (EAS) with down-going and up-going trajectories. Several working groups in the GRAND collaboration are developing methods to reconstruct the incoming direction, core position, primary energy, and composition of the showers. The reconstruction pipeline -- currently under development in the France/IAP working group -- relies on a model of spherical wavefront emission for arrival times, which is possible because the radio signals are generated far away from the antenna stations. The amplitude distribution of the signals at the antenna level is described by an Angular Distribution Function that considers various asymmetries in the data, including geomagnetic effects. In this contribution, we present preliminary results from testing our EAS reconstruction procedure using realistic mock observations., Comment: V2: Fixed typo in axis labels of Fig.5 and collaboration author list updated

The exponential growth of scientific literature necessitates advanced tools for effective knowledge exploration. We present Knowledge Navigator, a system designed to enhance exploratory search abilities by organizing and structuring the retrieved documents from broad topical queries into a navigable, two-level hierarchy of named and descriptive scientific topics and subtopics. This structured organization provides an overall view of the research themes in a domain, while also enabling iterative search and deeper knowledge discovery within specific subtopics by allowing users to refine their focus and retrieve additional relevant documents. Knowledge Navigator combines LLM capabilities with cluster-based methods to enable an effective browsing method. We demonstrate our approach's effectiveness through automatic and manual evaluations on two novel benchmarks, CLUSTREC-COVID and SCITOC. Our code, prompts, and benchmarks are made publicly available.

We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retaining the same or better quality as Transformer models. We release two model sizes: Jamba-1.5-Large, with 94B active parameters, and Jamba-1.5-Mini, with 12B active parameters. Both models are fine-tuned for a variety of conversational and instruction-following capabilties, and have an effective context length of 256K tokens, the largest amongst open-weight models. To support cost-effective inference, we introduce ExpertsInt8, a novel quantization technique that allows fitting Jamba-1.5-Large on a machine with 8 80GB GPUs when processing 256K-token contexts without loss of quality. When evaluated on a battery of academic and chatbot benchmarks, Jamba-1.5 models achieve excellent results while providing high throughput and outperforming other open-weight models on long-context benchmarks. The model weights for both sizes are publicly available under the Jamba Open Model License and we release ExpertsInt8 as open source., Comment: Webpage: https://www.ai21.com/jamba

We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality sequences. We pretrain multiple Transfusion models up to 7B parameters from scratch on a mixture of text and image data, establishing scaling laws with respect to a variety of uni- and cross-modal benchmarks. Our experiments show that Transfusion scales significantly better than quantizing images and training a language model over discrete image tokens. By introducing modality-specific encoding and decoding layers, we can further improve the performance of Transfusion models, and even compress each image to just 16 patches. We further demonstrate that scaling our Transfusion recipe to 7B parameters and 2T multi-modal tokens produces a model that can generate images and text on a par with similar scale diffusion models and language models, reaping the benefits of both worlds., Comment: 23 pages

We study active steering protocols for weakly measured qubits in the presence of error channels due to amplitude and phase noise. If the error rate is sufficiently small, the protocol approaches and stabilizes a predesignated pure target state with high fidelity and high purity, and thus implements autonomous state stabilization. We present numerical simulation results for one and two qubits, taking Andreev qubit circuits as example. As function of the error rate, a sharp threshold separates an error-correcting weak-damping regime from a strong-damping regime where the target state cannot be reached anymore. At the threshold, the purity gap closes., Comment: 15 pages, 8 figures

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses., Comment: 11 pages, 9 figures, plus appendices

In this paper, we endeavour to build up a non-Abelian formulation to describe the self-interactions of massless vector bosons in the context of Loop Quantum Gravity (LQG). To accomplish this task, we start off from the modified Maxwell equations with the inclusion of LQG corrections and its corresponding local $U(1)$ gauge invariance. LQG effects in the electromagnetic interactions have significant importance, as they might be adopted to describe the flight time of cosmic photons coming from very high-energy explosions in the Universe, such as events of Gamma-Ray Bursts (GRBs). These photons have energy-dependent speeds, indicating that the velocity of light in the vacuum is not constant. To carry out the extension from the Abelian to the non-Abelian scenario, we shall follow the so-called Noether current procedure, which consists in recurrently introducing self-interactions into an initially free action for vector bosons by coupling the latter to the conserved currents of a global symmetry present in the action of departure. In the end of the non-Abelianization process, the initial global symmetry naturally becomes local. Once the Yang-Mills system includes LQG correction terms, it becomes possible to analyze how quantum-gravity induced contributions show up in both the electroweak and the QCD sectors of the Standard Model, providing a set-up for phenomenological investigations that may bring about new elements to discuss Physics beyond the Standard-Model., Comment: 7 pages

By coupling a semiconductor-based planar Josephson junction to a superconducting resonator, we investigate the Andreev bound states in the junction using dispersive readout techniques. Using electrostatic gating to create a narrow constriction in the junction, our measurements unveil a strong coupling interaction between the resonator and the Andreev bound states. This enables the mapping of isolated tunable Andreev bound states, with an observed transparency of up to 99.94\% along with an average induced superconducting gap of $\sim 150 \mu$eV. Exploring the gate parameter space further elucidates a non-monotonic evolution of multiple Andreev bound states with varying gate voltage. Complimentary tight-binding calculations of an Al-InAs planar Josephson junction with strong Rashba spin-orbit coupling provide insight into possible mechanisms responsible for such behavior. Our findings highlight the subtleties of the Andreev spectrum of Josephson junctions fabricated on superconductor-semiconductor heterostructures and offering potential applications in probing topological states in these hybrid platforms.

We present new JWST observations of the nearby, prototypical edge-on, spiral galaxy NGC 891. The northern half of the disk was observed with NIRCam in its F150W and F277W filters. Absorption is clearly visible in the mid-plane of the F150W image, along with vertical dusty plumes that closely resemble the ones seen in the optical. A $\sim 10 \times 3~{\rm kpc}^2$ area of the lower circumgalactic medium (CGM) was mapped with MIRI F770W at 12 pc scales. Thanks to the sensitivity and resolution of JWST, we detect dust emission out to $\sim 4$ kpc from the disk, in the form of filaments, arcs, and super-bubbles. Some of these filaments can be traced back to regions with recent star formation activity, suggesting that feedback-driven galactic winds play an important role in regulating baryonic cycling. The presence of dust at these altitudes raises questions about the transport mechanisms at play and suggests that small dust grains are able to survive for several tens of million years after having been ejected by galactic winds in the disk-halo interface. We lay out several scenarios that could explain this emission: dust grains may be shielded in the outer layers of cool dense clouds expelled from the galaxy disk, and/or the emission comes from the mixing layers around these cool clumps where material from the hot gas is able to cool down and mix with these cool cloudlets. This first set of data and upcoming spectroscopy will be very helpful to understand the survival of dust grains in energetic environments, and their contribution to recycling baryonic material in the mid-plane of galaxies., Comment: Accepted for publication in Astronomy & Astrophysics; 16 pages, 8 figures

Humans continuously perceive and process visual signals. However, current video models typically either sample key frames sparsely or divide videos into chunks and densely sample within each chunk. This approach stems from the fact that most existing video benchmarks can be addressed by analyzing key frames or aggregating information from separate chunks. We anticipate that the next generation of vision models will emulate human perception by processing visual input continuously and holistically. To facilitate the development of such models, we propose the Continuous Perception Benchmark, a video question answering task that cannot be solved by focusing solely on a few frames or by captioning small chunks and then summarizing using language models. Extensive experiments demonstrate that existing models, whether commercial or open-source, struggle with these tasks, indicating the need for new technical advancements in this direction.

Objective. Magnetomotive ultrasound (MMUS) using magnetic nanoparticle contrast agents has shown promise for thrombosis imaging and quantitative elastometry via magnetomotive resonant acoustic spectroscopy (MRAS). Young's modulus measurements of smaller, stiffer thrombi require an MRAS system capable of generating forces at higher temporal frequencies. Solenoids with fewer turns, and thus less inductance, could improve high frequency performance, but the reduced force may compromise results. In this work, a computational model capable of predicting improved MRAS magnet configurations optimized for elastometry is presented and validated. Approach. Finite element analysis (FEA) was used to model the force and inductance of MRAS systems. The simulations incorporated both solenoid electromagnets and permanent magnets in three-dimensional steady-state, frequency domain, and time domain studies. Main results. The model successfully predicted a configuration in which permanent magnets could be used to increase the force supplied by an existing MRAS system. Accordingly, the displacement measured in a magnetically labeled validation phantom increased by a factor of $2.2 \pm 0.3$ when the force was predicted to increase by a factor of $2.2 \pm 0.2$. The model additionally identified a new solenoid configuration consisting of four smaller coils capable of providing sufficient force at higher driving frequencies. Significance. These results indicate two methods by which MRAS systems could be designed to deliver higher frequency magnetic forces without the need for experimental trial and error. Either the number of turns within each solenoid could be reduced while permanent magnets are added at precise locations, or a larger number of smaller solenoids could be used. These findings overcome a key challenge toward the goal of thrombosis elastometry via MMUS., Comment: 12 pages, 8 figures

Similarity measures play a central role in various data science application domains for a wide assortment of tasks. This guide describes a comprehensive set of prevalent similarity measures to serve both non-experts and professional. Non-experts that wish to understand the motivation for a measure as well as how to use it may find a friendly and detailed exposition of the formulas of the measures, whereas experts may find a glance to the principles of designing similarity measures and ideas for a better way to measure similarity for their desired task in a given application domain., Comment: 27 pages

We present a near infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses $>10^4$ M$_\odot$. Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of $\approx$1 pc and have stellar masses up to $10^6$ M$_\odot$. By comparing the color-color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A$_{\rm V}\sim3-24$ mag, corresponding to A$_{\rm 2.5\mu m}\sim0.3-2.1$ mag. There is still appreciable dust extinction towards these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is $\beta=1.9\pm0.2$, in excellent agreement with studies of star clusters in other galaxies., Comment: Resubmitted to ApJL

The gas-phase velocity dispersions in disk galaxies, which trace turbulence in the interstellar medium, are observed to increase with lookback time. However, the mechanisms that set this rise in turbulence are observationally poorly constrained. To address this, we combine kiloparsec-scale ALMA observations of CO(3-2) and CO(4-3) with HST observations of H$\alpha$ to characterize the molecular gas and star formation properties of seven local analogues of main sequence galaxies at $z \sim 1-2$, drawn from the DYNAMO sample. Investigating the ''molecular gas main sequence'' on kpc-scales, we find that galaxies in our sample are more gas-rich than local star-forming galaxies at all disk positions. We measure beam smearing corrected molecular gas velocity dispersions and relate them to the molecular gas and star formation rate surface densities. Despite being relatively nearby ($z \sim 0.1$), DYNAMO galaxies exhibit high velocity dispersions and gas and star formation rate surface densities throughout their disks, when compared to local star forming samples. Comparing these measurements to predictions from star formation theory, we find very good agreements with the latest feedback-regulated star formation models. However, we find that theories which combine gravitational energy dissipation from radial gas transport with feedback over-estimate the observed molecular gas velocity dispersions., Comment: 24 pages, 12 figures, 2 tables, accepted for publication in The Astrophysical Journal

Multiple-input multiple-output (MIMO) systems play a key role in wireless communication technologies. A widely considered approach to realize scalable MIMO systems involves architectures comprised of multiple separate modules, each with its own beamforming capability. Such models accommodate cell-free massive MIMO and partially connected hybrid MIMO architectures. A core issue with the implementation of modular MIMO arises from the need to rapidly set the beampatterns of the modules, while maintaining their power efficiency. This leads to challenging constrained optimization that should be repeatedly solved on each coherence duration. In this work, we propose a power-oriented optimization algorithm for beamforming in uplink modular hybrid MIMO systems, which learns from data to operate rapidly. We derive our learned optimizer by tackling the rate maximization objective using projected gradient ascent steps with momentum. We then leverage data to tune the hyperparameters of the optimizer, allowing it to operate reliably in a fixed and small number of iterations while completely preserving its interpretable operation. We show how power efficient beamforming can be encouraged by the learned optimizer, via boosting architectures with low-resolution phase shifts and with deactivated analog components. Numerical results show that our learn-to-optimize method notably reduces the number of iterations and computation latency required to reliably tune modular MIMO receivers, and that it allows obtaining desirable balances between power efficient designs and throughput., Comment: Under review for possible publication in the IEEE

We present the detection and characterization of fluctuations in linearly polarized emission from the atmosphere above the South Pole. These measurements make use of Austral winter survey data from the SPT-3G receiver on the South Pole Telescope in three frequency bands centered at 95, 150, and 220 GHz. We use the cross-correlation between detectors to produce an unbiased estimate of the power in Stokes I, Q, and U parameters on large angular scales. Our results are consistent with the polarized signal being produced by the combination of Rayleigh scattering of thermal radiation from the ground and thermal emission from a population of horizontally aligned ice crystals with an anisotropic distribution described by Kolmogorov turbulence. The signal is most significant at large angular scales, high observing frequency, and low elevation angle. Polarized atmospheric emission has the potential to significantly impact observations on the large angular scales being targeted by searches for inflationary B-mode CMB polarization. We present the distribution of measured angular power spectrum amplitudes in Stokes Q and I for 4 years of winter observations, which can be used to simulate the impact of atmospheric polarization and intensity fluctuations at the South Pole on a specified experiment and observation strategy. For the SPT-3G data, downweighting the small fraction of significantly contaminated observations is an effective mitigation strategy. In addition, we present a strategy for further improving sensitivity on large angular scales where maps made in the 220 GHz band are used to measure and subtract the polarized atmosphere signal from the 150 GHz band maps. In observations with the SPT-3G instrument at the South Pole, the polarized atmospheric signal is a well-understood and sub-dominant contribution to the measured noise after implementing the mitigation strategies described here., Comment: 32 pages, 28 figures

One of the many tasks facing the typically-developing child language learner is learning to discriminate between the distinctive sounds that make up words in their native language. Here we investigate whether multimodal information--specifically adult speech coupled with video frames of speakers' faces--benefits a computational model of phonetic learning. We introduce a method for creating high-quality synthetic videos of speakers' faces for an existing audio corpus. Our learning model, when both trained and tested on audiovisual inputs, achieves up to a 8.1% relative improvement on a phoneme discrimination battery compared to a model trained and tested on audio-only input. It also outperforms the audio model by up to 3.9% when both are tested on audio-only data, suggesting that visual information facilitates the acquisition of acoustic distinctions. Visual information is especially beneficial in noisy audio environments, where an audiovisual model closes 67% of the loss in discrimination performance of the audio model in noise relative to a non-noisy environment. These results demonstrate that visual information benefits an ideal learner and illustrate some of the ways that children might be able to leverage visual cues when learning to discriminate speech sounds., Comment: 12 pages, 5 figures

We propose WayEx, a new method for learning complex goal-conditioned robotics tasks from a single demonstration. Our approach distinguishes itself from existing imitation learning methods by demanding fewer expert examples and eliminating the need for information about the actions taken during the demonstration. This is accomplished by introducing a new reward function and employing a knowledge expansion technique. We demonstrate the effectiveness of WayEx, our waypoint exploration strategy, across six diverse tasks, showcasing its applicability in various environments. Notably, our method significantly reduces training time by 50% as compared to traditional reinforcement learning methods. WayEx obtains a higher reward than existing imitation learning methods given only a single demonstration. Furthermore, we demonstrate its success in tackling complex environments where standard approaches fall short. More information is available at: https://waypoint-ex.github.io., Comment: ICRA 2024