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We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures. For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achieving optimal conformations with 25% fewer training episodes. For longer sequences, we employ a long short-term memory network with multi-headed attention, matching best-known energy values. Both architectures leverage a stabilized Deep Q-Learning framework with experience replay and target networks, demonstrating consistent achievement of optimal conformations while significantly improving training efficiency compared to existing methods., Comment: 15 pages, 9 figures

The availability of pre-trained models (PTMs) has enabled faster deployment of machine learning across applications by reducing the need for extensive training. Techniques like quantization and distillation have further expanded PTM applicability to resource-constrained IoT hardware. Given the many PTM options for any given task, engineers often find it too costly to evaluate each model's suitability. Approaches such as LogME, LEEP, and ModelSpider help streamline model selection by estimating task relevance without exhaustive tuning. However, these methods largely leave hardware constraints as future work-a significant limitation in IoT settings. In this paper, we identify the limitations of current model recommendation approaches regarding hardware constraints and introduce a novel, hardware-aware method for PTM selection. We also propose a research agenda to guide the development of effective, hardware-conscious model recommendation systems for IoT applications., Comment: Accepted at SERP4IOT'25

This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of electron-neutrino-like events observed by the MiniBooNE experiment. This is the first measurement to use data from all five years of operation of the MicroBooNE experiment, corresponding to an exposure of $1.11\times 10^{21}$ protons on target, a $70\%$ increase on past results. Two samples of electron neutrino interactions without visible pions are used, one with visible protons and one without any visible protons. MicroBooNE data is compared to two empirical models that modify the predicted rate of electron-neutrino interactions in different variables in the simulation to match the unfolded MiniBooNE low energy excess. In the first model, this unfolding is performed as a function of electron neutrino energy, while the second model aims to match the observed shower energy and angle distributions of the MiniBooNE excess. This measurement excludes an electron-like interpretation of the MiniBooNE excess based on these models at $> 99\%$ CL$_\mathrm{s}$ in all kinematic variables., Comment: 8 pages, 5 figures, 1 table

The problem of decentralized multi-robot patrol has previously been approached primarily with hand-designed strategies for minimization of 'idlenes' over the vertices of a graph-structured environment. Here we present two lightweight neural network-based strategies to tackle this problem, and show that they significantly outperform existing strategies in both idleness minimization and against an intelligent intruder model, as well as presenting an examination of robustness to communication failure. Our results also indicate important considerations for future strategy design.

The Centaurus A radio galaxy, due to its proximity, presents itself as one of the few systems that allow the study of relativistic jet outflows at sub-parsec distances from the central supermassive black holes, with high signal to noise. We present the results from the first multi-epoch spectropolarimetric observations of Centaurus A at milliarcsecond resolution, with a continuous frequency coverage of $4.59-7.78$\,GHz. Using a Bayesian framework, we perform a comprehensive study of the jet kinematics, and discuss aspects of the jet geometry including the jet inclination angle, jet opening angle, and the jet expansion profile. We calculate an upper limit on the jet's inclination to the line of sight to be $<25^{\circ}$, implying the lower limit on the intrinsic jet speed to be $0.2$\,c. On the observed VLBA scales we detect new jet components launched by the central engine since our previous study. Using the observed frequency-dependent core shift in Centaurus A, we find the jet to have reached constant bulk speed and conical outflow at the regions probed by the base of the jet at $7.78- 4.59$\,GHz, and we also estimate the location of the central black hole further upstream. Through polarimetric analysis (by applying RM synthesis for the first time on VLBI data), we find evidence to suggest the possible onset of acceleration towards the leading edge of Centaurus A's subparsec-scale jet studied here., Comment: Accepted in The Astronomical Journal. 34 pages, 21 figures, and 3 tables

The river Ganga is one of the Earth's most critically important river basins, yet it faces significant pollution challenges, making it crucial to evaluate its vulnerability for effective and targeted remediation efforts. While the Analytic Hierarchy Process (AHP) is widely regarded as the standard in decision making methodologies, uncertainties arise from its dependence on expert judgments, which can introduce subjectivity, especially when applied to remote sensing data, where expert knowledge might not fully capture spatial and spectral complexities inherent in such data. To address that, in this paper, we applied AHP alongside a suite of alternative existing and novel variants of AHP-based decision analysis on remote sensing data to assess the vulnerability of the river Ganga to pollution. We then compared the areas where the outputs of each variant may provide additional insights over AHP. Lastly, we utilized our learnings to design a composite variable to robustly define the vulnerability of the river Ganga to pollution. This approach contributes to a more comprehensive understanding of remote sensing data applications in environmental assessment, and these decision making variants can also have broader applications in other areas of environment management and sustainability, facilitating more precise and adaptable decision support frameworks.

Overlay is an effective approach for creating FPGA-based AI accelerators, enabling software-programmable specialized hardware datapaths to flexibly support various DNN operations. Traditional DNN overlays typically base their instruction set design on the von Neumann model but adapt them to be more coarse-grained. These instruction sets control execution at the layer granularity and impose restricted patterns for mapping computation and bandwidth resources. Such constraints cause inefficiencies from the imperfect match between supported execution patterns and diverse DNN layer shapes and types. This work proposes a Reconfigurable Stream Network architecture, a unique ISA abstraction tailored for flexible FPGA overlay execution at low cost, marking it as the first known FPGA design to support dynamic sequential linear layer pipelining. This novel architecture presents a datapath abstraction modeled after a specialized circuit-switched network with stateful functional units (FUs) as nodes and data streaming on edges. Programming a computation corresponds to triggering a network path in this stream-connected datapath. The program can individually control FUs to form paths that exploit both spatial and pipeline parallelism between independent and dependent concurrent computations. We present a proof-of-concept design RSN-XNN on the Versal VCK190. Evaluations show a 22x latency reduction for BERT compared to the state of the art, along with throughput improvements of 3.2x, 2.4x, 2.5x, and 2.8x for BERT, VIT, NCF, and MLP, respectively. RSN-XNN matches the latency of the T4 GPU with the same FP32 performance but only 18% of the memory bandwidth. Compared to the A100 GPU under the same 7nm process node, it achieves 2.1x/4.5x better operating/dynamic energy efficiency in FP32.

We report the successful growth of CeCuSi single crystals using a metallic flux method and the physical properties using structural, magnetic, electrical transport, optical, and heat capacity measurements. CeCuSi crystallizes in a hexagonal-bar shape, and single crystal x-ray diffraction confirms the ZrBeSi-type structure (space group $P6_{3}/mmc$). CeCuSi orders ferromagnetically below $T_\textrm{C}=15.5$ K with easy magnetization direction within the basal plane. The Ce$^{3+}$ ions are situated within a triangular lattice with a point group of $D_{3d}$. We perform a detailed crystalline electric field (CEF) analysis of the anisotropic magnetic susceptibility, the Schottky anomaly in heat capacity, and the Raman-active excitations. The results indicate a ground state doublet with magnetic moment primarily in the basal plane, and a ferromagnetic interaction along both directions. The exponential behavior in resistivity and in heat capacity below $T_\textrm{C}$ can also be well explained by the ferromagnetic magnon model. We found that CeCuSi does not exhibit the CEF hard axis ordering observed in many ferromagnetic Kondo lattice (FM-KL) compounds. Our CEF analysis suggests that the exchange interactions along both axes are ferromagnetic, potentially explaining the absence of hard-axis ordering., Comment: 11 pages, 6 figures, Accepted by Physics Review B

Strongly magnetized plasmas, characterized by having a gyrofrequency larger than the plasma frequency ($\beta = \omega_c/\omega_p \gg 1$), are known to exhibit novel transport properties. Previous works studying pure electron plasmas have shown that strong magnetization significantly inhibits energy exchange between parallel and perpendicular directions, leading to a prolonged time for relaxation of a temperature anisotropy. Recent work studying repulsive electron-ion interactions showed that strong magnetization increases both the parallel and perpendicular temperature relaxation rates of ions, but in differing magnitudes, resulting in the formation of temperature anisotropy during equilibration. This previous study treated electrons as a heat bath and assumed weak magnetization of ions. Here, we broaden this analysis and compute the full temperature and temperature anisotropy evolution over a broad range of magnetic field strengths. It is found that when electrons are strongly magnetized ($\beta_e \gg 1$) and ions are weakly magnetized ($\beta_i \ll 1$), the magnetic field strongly suppresses the perpendicular energy exchange rate of electrons, whereas the parallel exchange rate slightly increases in magnitude compared to the value at weak magnetization. In contrast, the ion perpendicular and parallel energy exchange rates both increase in magnitude compared to the values at weak magnetization. Consequently, equilibration causes the electron parallel temperature to rapidly align with the ion temperature, while the electron perpendicular temperature changes much more slowly. It is also shown that when both ions and electrons are strongly magnetized ($\beta_i, \beta_e \gg 1$) the ion-electron perpendicular relaxation rate dramatically decreases with magnetization strength as well.

Transitional millisecond pulsars (tMSPs) are neutron-star systems that alternate between a rotation-powered radio millisecond pulsar state and an accretion-disk-dominated low-mass X-ray binary (LMXB)-like state on multi-year timescales. During the LMXB-like state, the X-ray emission from tMSPs switches between "low" and "high" X-ray brightness modes on a timescale of seconds to minutes (or longer), while the radio emission shows variability on timescales of roughly minutes. Coordinated VLA and Chandra observations of the nearby tMSP PSR J1023+0038 uncovered a clear anti-correlation between radio and X-ray luminosities such that the radio emission consistently peaks during the X-ray low modes. In addition, there are sometimes also radio/X-ray flares that show no obvious correlation. In this paper, we present simultaneous radio and X-ray observations of a promising tMSP candidate system, 3FGL J1544.6-1125, which shows optical, gamma-ray, and X-ray phenomena similar to PSR J1023+0038, but which is challenging to study because of its greater distance. Using simultaneous VLA and Chandra observations we find that the radio and X-ray emission are consistent with being anti-correlated in a manner similar to PSR J1023+0038. We discuss how our results help in understanding the origin of bright radio emission from tMSPs. The greater sensitivity of upcoming telescopes like the Square Kilometre Array will be crucial for studying the correlated radio/X-ray phenomena of tMSP systems., Comment: 21 pages, 14 figures, 2 tables. Submitted to MNRAS

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

We present a search for long-lived particles (LLPs) produced from kaon decay that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data collected in the Neutrinos at the Main Injector (NuMI) beam at Fermilab corresponding to $2.41\times 10^{20}$ protons-on-target. No new physics signal is observed, and we set world-leading limits on heavy QCD axions, as well as for the Higgs portal scalar among dedicated searches. Limits are also presented in a model-independent way applicable to any new physics model predicting the process $K\to \pi+S(\to\mu\mu)$, for a long-lived particle S. This result is the first search for new physics performed with the ICARUS detector at Fermilab. It paves the way for the future program of long-lived particle searches at ICARUS.

Intense electromagnetic fields can result in dramatic changes in the electronic properties of solids. These changes are commonly studied using optical probes of the modified electronic structure. Here we use optical-scanning tunneling microscopy (optical-STM) equipped with near-field continuous wave (CW) laser excitation to directly measure the electronic structure of light-dressed states in a monolayer transition metal dichalcogenide (TMD) semiconductor, WSe2. We find that the effective tunneling gap and tunneling density of states are strongly influenced by the intensity of the electromagnetic field when the applied field is resonant with the bandgap of the semiconductor. Our findings indicate that CW laser excitation can be used to generate light-dressed electronic states of quantum materials when confined strongly to the nanoscale.

Ultraluminous X-ray sources (ULXs) were once largely believed to be powered by super-Eddington accretion onto stellar-mass black holes, although in some rare cases, ULXs also serve as potential candidates for (sub-Eddington) intermediate mass black holes. However, a total of eight ULXs have now been confirmed to be powered by neutron stars, thanks to observed pulsations, and may act as contaminants for radio/X-ray selection of intermediate mass black holes. Here we present the first comprehensive radio study of seven known neutron star ULXs using new and archival data from the Karl G. Jansky Very Large Array and the Australia Telescope Compact Array, combined with the literature. Across this sample there is only one confident radio detection, from the Galactic neutron star ULX Swift J0243.6+6124. The other six objects in our sample are extragalactic, and only one has coincident radio emission, which we conclude is most likely contamination from a background HII region. We conclude that with current facilities, neutron star ULXs do not produce significant enough radio emission to cause them to be misidentified as radio/X-ray selected intermediate mass black hole candidates. Thus, if background star formation has been properly considered, the current study indicates that a ULX with a compact radio counterpart is not likely to be a neutron star., Comment: 14 pages, 2 figures, accepted to ApJ

Computing systems are consuming an increasing and unsustainable fraction of society's energy footprint, notably in data centers. Meanwhile, energy-efficient software engineering techniques are often absent from undergraduate curricula. We propose to develop a learning module for energy-efficient software, suitable for incorporation into an undergraduate software engineering class. There is one major problem with such an endeavor: undergraduate curricula have limited space for mastering energy-related systems programming aspects. To address this problem, we propose to leverage the domain expertise afforded by large language models (LLMs). In our preliminary studies, we observe that LLMs can generate energy-efficient variations of basic linear algebra codes tailored to both ARM64 and AMD64 architectures, as well as unit tests and energy measurement harnesses. On toy examples suitable for classroom use, this approach reduces energy expenditure by 30-90%. These initial experiences give rise to our vision of LLM-based meta-compilers as a tool for students to transform high-level algorithms into efficient, hardware-specific implementations. Complementing this tooling, we will incorporate systems thinking concepts into the learning module so that students can reason both locally and globally about the effects of energy optimizations.

This study introduces a compositional autoencoder (CAE) framework designed to disentangle the complex interplay between genotypic and environmental factors in high-dimensional phenotype data to improve trait prediction in plant breeding and genetics programs. Traditional predictive methods, which use compact representations of high-dimensional data through handcrafted features or latent features like PCA or more recently autoencoders, do not separate genotype-specific and environment-specific factors. We hypothesize that disentangling these features into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features. Our CAE framework employs a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrates superior modeling of environmental influences and 5-10 times improved predictive performance for key traits like Days to Pollen and Yield, compared to the traditional methods, including standard autoencoders, PCA with regression, and Partial Least Squares Regression (PLSR). By disentangling latent features, the CAE provides powerful tool for precision breeding and genetic research. This work significantly enhances trait prediction models, advancing agricultural and biological sciences.

Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips, present in their data. We demonstrate new calorimetric and particle discrimination capabilities at the MeV energy scale using reconstructed blips in data from the MicroBooNE LArTPC at Fermilab. We observe a concentration of low energy ($<$3 MeV) blips around fiberglass mechanical support struts along the TPC edges with energy spectrum features consistent with the Compton edge of 2.614 MeV $^{208}$Tl decay $\gamma$ rays. These features are used to verify proper calibration of electron energy scales in MicroBooNE's data to few percent precision and to measure the specific activity of $^{208}$Tl in the fiberglass composing these struts, $(11.7 \pm 0.2 ~\text{(stat)} \pm 2.8~\text{(syst)})~\text{Bq/kg}$. Cosmogenically-produced blips above 3 MeV in reconstructed energy are used to showcase the ability of large LArTPCs to distinguish between low-energy proton and electron energy depositions. An enriched sample of low-energy protons selected using this new particle discrimination technique is found to be smaller in data than in dedicated CORSIKA cosmic ray simulations, suggesting either incorrect CORSIKA modeling of incident cosmic fluxes or particle transport modeling issues in Geant4., Comment: 19 pages, 15 figures total including the supplementary material section, 1 table. CC BY license

Formal verification provides mathematical guarantees that a software is correct. Design-level verification tools ensure software specifications are correct, but they do not expose defects in actual implementations. For this purpose, engineers use code-level tools. However, such tools struggle to scale to large software. The process of "Unit Proofing" mitigates this by decomposing the software and verifying each unit independently. We examined AWS's use of unit proofing and observed that current approaches are manual and prone to faults that mask severe defects. We propose a research agenda for a unit proofing framework, both methods and tools, to support software engineers in applying unit proofing effectively and efficiently. This will enable engineers to discover code-level defects early., Comment: 6 pages, 2 figures

Energy-efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for energy efficiency. We describe and evaluate a prototype, finding that over 6 small programs our system can improve energy efficiency in 3 of them, up to 2x better than compiler optimizations alone. From our experience, we identify some of the challenges of energy-efficient LLM code optimization and propose a research agenda.

For humans and robots to form an effective human-robot team (HRT) there must be sufficient trust between team members throughout a mission. We analyze data from an HRT experiment focused on trust dynamics in teams of one human and two robots, where trust was manipulated by robots becoming temporarily unresponsive. Whole-body movement tracking was achieved using ultrasound beacons, alongside communications and performance logs from a human-robot interface. We find evidence that synchronization between time series of human-robot movement, within a certain spatial proximity, is correlated with changes in self-reported trust. This suggests that the interplay of proxemics and kinesics, i.e. moving together through space, where implicit communication via coordination can occur, could play a role in building and maintaining trust in human-robot teams. Thus, quantitative indicators of coordination dynamics between team members could be used to predict trust over time and also provide early warning signals of the need for timely trust repair if trust is damaged. Hence, we aim to develop the metrology of trust in mobile human-robot teams.

Recently we find in Einstein Gravity a single-metric solution for a point mass residing in an expanding universe \cite{wong}, which apart from the Newtonian acceleration, gives rise to an additional MOND-like acceleration in which the MOND acceleration $a_0$ is replaced by the cosmological acceleration $\frac{1}{2}H^2(z)r$. We study a protogalactic cloud in this acceleration such that an overdensity mass shell growth stops at its turnaround point and and every point on the shell picks up maximum but non systematic angular momentum. Assuming an initial power law matter density distribution, the central region of the virialised sphere is Newtoinan acceleration dominant but in its outer-most region the dominant acceleration is MOND-like. We evaluate the effective MOND acceleration $a_0^{VM}$ at the redshift where the early virialisation occurs. We find that $a_0^{VM}\sim a_0$. Working with a realistic overdensity at recombination, the central core can form a Quasi-Stationary-State (QSS) by $z> 7$, which could explain the galaxy morphology stability observations for $z<6.5$ in \cite{ferreira2}., Comment: 12 pages, 0 figure

Diagnostic imaging studies are an increasingly important component of the workup and management of acutely presenting patients. However, ordering appropriate imaging studies according to evidence-based medical guidelines is a challenging task with a high degree of variability between healthcare providers. To address this issue, recent work has investigated if generative AI and large language models can be leveraged to help clinicians order relevant imaging studies for patients. However, it is challenging to ensure that these tools are correctly aligned with medical guidelines, such as the American College of Radiology's Appropriateness Criteria (ACR AC). In this study, we introduce a framework to intelligently leverage language models by recommending imaging studies for patient cases that are aligned with evidence-based guidelines. We make available a novel dataset of patient "one-liner" scenarios to power our experiments, and optimize state-of-the-art language models to achieve an accuracy on par with clinicians in image ordering. Finally, we demonstrate that our language model-based pipeline can be used as intelligent assistants by clinicians to support image ordering workflows and improve the accuracy of imaging study ordering according to the ACR AC. Our work demonstrates and validates a strategy to leverage AI-based software to improve trustworthy clinical decision making in alignment with expert evidence-based guidelines., Comment: 15 pages main text, 4 figures, 1 table

Intracellular processes triggered by neural activity include changes in ionic concentrations, protein release, and synaptic vesicle cycling. These processes play significant roles in neurological disorders. The beneficial effects of brain stimulation may also be mediated through intracellular changes. There is a lack of label-free techniques for monitoring activity-dependent intracellular changes. Electromagnetic (EM) waves at frequencies larger than 1x10^6 Hz (1 MHz) were previously used to probe intracellular contents of cells, as cell membrane becomes transparent at this frequency range. EM waves interact with membranes of intracellular organelles, proteins, and water in the MHz-GHz range. In this work, we developed a device for probing the interaction between intracellular contents of active neurons and EM waves. The device used an array of grounded coplanar waveguides (GCPWs) to deliver EM waves to a three-dimensional (3D) spheroid of rat cortical neurons. Neural activity was evoked using optogenetics, with synchronous detection of propagation of EM waves. Broadband measurements were conducted in the MHz-GHz range to track changes in transmission coefficients. Neuronal activity was found to reversibly alter EM wave transmission. Pharmacological suppression of neuronal activity abolished changes in transmission. Time constants of changes in transmission were in the range of seconds to tens of seconds, suggesting the presence of relatively slow, activity-dependent intracellular processes. This study provides the first evidence that EM transmission through neuronal tissue is activity-dependent in MHz-GHz range. Device developed in this work may find future applications in studies of the mechanisms of neurological disorders and the development of new therapies.

The spontaneous emission of internal waves (IWs) from balanced mesoscale eddies has been previously proposed to provide a source of oceanic IW kinetic energy (KE). This study examines the mechanisms leading to the spontaneous emission of spiral-shaped IWs from an anticyclonic eddy with an order-one Rossby number, using a high-resolution numerical simulation of a flat-bottomed, wind-forced, reentrant channel flow configured to resemble the Antarctic Circumpolar Current. It is demonstrated that IWs are spontaneously generated as a result of a loss of balance process that is concentrated at the eddy edge, and then radiate radially outward. A 2D linear stability analysis of the eddy shows that the spontaneous emission arises from a radiative instability which involves an interaction between a vortex Rossby wave supported by the radial gradient of potential vorticity and an outgoing IWs. This particular instability occurs when the perturbation frequency is superinertial. This finding is supported by a KE analysis of the unstable modes and the numerical solution, where it is shown that the horizontal shear production provides the source of perturbation KE. Furthermore, the horizontal length scale and frequency of the most unstable mode from the stability analysis agree well with those of the spontaneously emitted IWs in the numerical solution.

We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839-10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less, and can detect 10x more FRBs than the current CRAFT incoherent sum system (i.e., 0.5-2 localised FRBs per day), enabling us to better constrain he models for FRBs and use them as cosmological probes., Comment: 26 pages, 19 figures, 9 tables, Accepted for publication in PASA

We propose Vision Token Turing Machines (ViTTM), an efficient, low-latency, memory-augmented Vision Transformer (ViT). Our approach builds on Neural Turing Machines and Token Turing Machines, which were applied to NLP and sequential visual understanding tasks. ViTTMs are designed for non-sequential computer vision tasks such as image classification and segmentation. Our model creates two sets of tokens: process tokens and memory tokens; process tokens pass through encoder blocks and read-write from memory tokens at each encoder block in the network, allowing them to store and retrieve information from memory. By ensuring that there are fewer process tokens than memory tokens, we are able to reduce the inference time of the network while maintaining its accuracy. On ImageNet-1K, the state-of-the-art ViT-B has median latency of 529.5ms and 81.0% accuracy, while our ViTTM-B is 56% faster (234.1ms), with 2.4 times fewer FLOPs, with an accuracy of 82.9%. On ADE20K semantic segmentation, ViT-B achieves 45.65mIoU at 13.8 frame-per-second (FPS) whereas our ViTTM-B model acheives a 45.17 mIoU with 26.8 FPS (+94%).

Many stochastic optimization problems in multi-agent systems can be decomposed into smaller subproblems or reduced decision subspaces. The cyclic and distributed approaches are two widely used strategies for solving such problems. In this manuscript, we review four existing methods for addressing these problems and compare them based on their suitable problem frameworks and update rules.

This paper derives and demonstrates a new, purely density-based ab initio approach for calculation of the energies and properties of many-electron systems. It is based upon the discovery of relationships that govern the "mechanics" of the electron density -- i.e., relations that connect its behaviors at different points in space. Unlike wave mechanics or prior electron-density-based implementations, such as DFT, this density-mechanical implementation of quantum mechanics involves no many-electron or one-electron wave functions (i.e., orbitals). Thus, there is no need to calculate exchange energies, because there are no orbitals to permute or "exchange" within two-electron integrals used to calculate electron-electron repulsion energies. In practice, exchange does not exist within quantum density mechanics. In fact, no two-electron integrals need be calculated at all, beyond a single coulomb integral for the 2-electron system. Instead, a "radius expansion method" is introduced that permits determination of the two-electron interaction for an N-electron system from one with (N-1)-electrons. Also, the method does not rely upon a Schrodinger-like equation or the variational method for determination of accurate energies and densities. Rather, the above-described results follow from the derivation and solution of a "governing equation" for each number of electrons to obtain a screening relation that connects the behavior at the "tail" of a one-electron density, to that at the Bohr radius. Solution of these equations produces simple expressions that deliver a total energy for a 2-electron atom that is nearly identical to the experimental value, plus accurate energies for neutral 3, 4, and 5-electron atoms, along with accurate one-electron densities of these atoms. Further, these methods scale in complexity only as N, not as a power of N, as do most other accurate many-electron methods., Comment: 29 pages, 2 figures, 11 tables

Ramsey et al. report on the characteristics and temporal distribution of an interesting vibrational signal that they term the whooping signal, primarily based upon a long-term study of vibrations recorded by accelerometers placed inside two honey bee (Apis mellifera) colonies, one in France and one in the United Kingdom. The goal of the study, the long-term automated recording and analysis of honey bee vibrational signaling, is worthwhile. However, we believe that some of the conclusions drawn by the authors are not well supported, given the evidence.

This work proposes a general framework for capturing noise-driven transitions in spatially extended non-equilibrium systems and explains the emergence of coherent patterns beyond the instability onset. The framework relies on stochastic parameterizations to reduce the original equations' complexity while capturing the key effects of unresolved scales. It works for both Gaussian and Levy-type noise. Our parameterizations offer two key advantages. First, they approximate stochastic invariant manifolds when the latter exist. Second, even when such manifolds break down, our formulas can be adapted by a simple optimization of its constitutive parameters. This allows us to handle scenarios with weak time-scale separation where the system has undergone multiple transitions, resulting in large-amplitude solutions not captured by invariant manifold or other time-scale separation methods. The optimized stochastic parameterizations capture how small-scale noise impacts larger scales through the system's nonlinear interactions. This effect is achieved by the very fabric of our parameterizations incorporating non-Markovian coefficients into the reduced equation. Such coefficients account for the noise's past influence using a finite memory length, selected for optimal performance. The specific "memory" function, which determines how this past influence is weighted, depends on the noise's strength and how it interacts with the system's nonlinearities. Remarkably, training our theory-guided reduced models on a single noise path effectively learns the optimal memory length for out-of-sample predictions, including rare events. This success stems from our "hybrid" approach, which combines analytical understanding with data-driven learning. This combination avoids a key limitation of purely data-driven methods: their struggle to generalize to unseen scenarios, also known as the "extrapolation problem."

We examine the energy distribution of the fast radio burst (FRB) population using a well-defined sample of 63 FRBs from the ASKAP radio telescope, 28 of which are localised to a host galaxy. We apply the luminosity-volume ($V/V_{\mathrm{max}}$) test to examine the distribution of these transient sources, accounting for cosmological and instrumental effects, and determine the energy distribution for the sampled population over the redshift range $0.01 \lesssim z \lesssim 1.02$. We find the distribution between $10^{23}$ and $10^{26}$J Hz$^{-1}$ to be consistent with both a pure power-law with differential slope $\gamma=-1.96 \pm 0.15$, and a Schechter function with $\gamma = -1.82 \pm 0.12$ and downturn energy $E_{\rm max} \sim 6.3 \cdot 10^{25}$J Hz$^{-1}$. We identify systematic effects which currently limit our ability to probe the luminosity function outside this range and give a prescription for their treatment. Finally, we find that with the current dataset, we are unable to distinguish between the evolutionary and spectral models considered in this work., Comment: 23 pages, 10 figures, submitted to PASA

In order to understand the nature of super-Eddington accretion we must explore both the emission emerging directly from the inflow and its impact on the surroundings. In this paper we test whether we can use the optical line emission of spatially resolved, ionized nebulae around ultraluminous X-ray sources (ULXs) as a proxy for their X-ray luminosity. We choose the ULX NGC 6946 X-1 and its nebula, MF16, as a test case. By studying how the nebular optical line emission responds to assumed irradiation, we can infer the degree to which we require the UV or X-ray emission from the inflow to be collimated by optically thick winds seemingly ubiquitously associated with ULXs. We find that the nebula is highly sensitive to compact UV emission but mostly insensitive to hard X-rays. Our attempts to quantify the beaming of the soft and hard X-rays therefore strongly depends on the UV luminosity of the ULX in the center of the nebula. We find that it is not possible to conclude a lack of geometrical beaming of hard X-rays from such sources via nebula feedback., Comment: 10 pages, 6 figures, 4 tables

Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. Finally, we propose a general recipe to choose the best paradigm for a given registration problem, based on these observations., Comment: 16 pages; Accepted to NeurIPS 2024

The design and optimization of laser-Compton x-ray systems based on compact distributed charge accelerator structures can enable micron-scale imaging of disease and the concomitant production of beams of Very High Energy Electrons (VHEEs) capable of producing FLASH-relevant dose rates. The physics of laser-Compton x-ray scattering ensures that the scattered x-rays follow exactly the trajectory of the incident electrons, thus providing a route to image-guided, VHEE FLASH radiotherapy. The keys to a compact architecture capable of producing both laser-Compton x-rays and VHEEs are the use of X-band RF accelerator structures which have been demonstrated to operate with over 100 MeV/m acceleration gradients. The operation of these structures in a distributed charge mode in which each radiofrequency (RF) cycle of the drive RF pulse is filled with a low-charge, high-brightness electron bunch is enabled by the illumination of a high-brightness photogun with a train of UV laser pulses synchronized to the frequency of the underlying accelerator system. The UV pulse trains are created by a patented pulse synthesis approach which utilizes the RF clock of the accelerator to phase and amplitude modulate a narrow band continuous wave (CW) seed laser. In this way it is possible to produce up to 10 $\mu$A of average beam current from the accelerator. Such high current from a compact accelerator enables production of sufficient x-rays via laser-Compton scattering for clinical imaging and does so from a machine of "clinical" footprint. At the same time, the production of 1000 or greater individual micro-bunches per RF pulse enables > 10 nC of charge to be produced in a macrobunch of < 100 ns. The design, construction, and test of the 100-MeV class prototype system in Irvine, CA is also presented., Comment: Accepted by Frontiers in Physics; Changes: expanded section 6, added numerical spectrum plot to Figure 10, expanded table 2 info, added citations; 29 pages, 13 figures, 2 tables

With wide-field phased array feed technology, the Australian Square Kilometre Array Pathfinder (ASKAP) is ideally suited to search for seemingly rare radio transient sources. The Commensal Real-time ASKAP Fast Transient (CRAFT) Survey Science Project has developed instrumentation to continuously search for fast radio transients (duration $\lesssim$ 1 second) with ASKAP, with a particular focus on finding and localising Fast Radio Bursts (FRBs). Of particular interest are Fast Radio Bursts (FRBs). Since 2018, the CRAFT survey has been searching for FRBs and other fast transients by incoherently adding the intensities received by individual ASKAP antennas, and then correcting for the impact of frequency dispersion on these short-duration signals in the resultant incoherent sum (ICS) in real-time. This low-latency detection enables the triggering of voltage buffers, which facilitates the localisation of the transient source and the study spectro-polarimetric properties at high time resolution. Here we report the sample of 43 FRBs discovered in this CRAFT/ICS survey to date. This includes 22 FRBs that had not previously been reported: 16 FRBs localised by ASKAP to $\lesssim$ 1 arcsec and 6 FRBs localised to approximately 10 arcmin. Of the new arcsecond-localised FRBs, we have identified and characterised host galaxies (and measured redshifts) for 11. The median of all 30 measured host redshifts from the survey to date is z = 0.23. We summarise results from the searches, in particular those contributing to our understanding of the burst progenitors and emission mechanisms, and on the use of bursts as probes of intervening media. We conclude by foreshadowing future FRB surveys with ASKAP using a coherent detection system that is currently being commissioned., Comment: 37 pages, 23 Figures, 7 Tables. Submitted for publication in PASA

This paper reports on a measurement of electron-ion recombination in liquid argon in the ICARUS liquid argon time projection chamber (LArTPC). A clear dependence of recombination on the angle of the ionizing particle track relative to the drift electric field is observed. An ellipsoid modified box (EMB) model of recombination describes the data across all measured angles. These measurements are used for the calorimetric energy scale calibration of the ICARUS TPC, which is also presented. The impact of the EMB model is studied on calorimetric particle identification, as well as muon and proton energy measurements. Accounting for the angular dependence in EMB recombination improves the accuracy and precision of these measurements.

The ICARUS liquid argon time projection chamber (LArTPC) neutrino detector has been taking physics data since 2022 as part of the Short-Baseline Neutrino (SBN) Program. This paper details the equalization of the response to charge in the ICARUS time projection chamber (TPC), as well as data-driven tuning of the simulation of ionization charge signals and electronics noise. The equalization procedure removes non-uniformities in the ICARUS TPC response to charge in space and time. This work leverages the copious number of cosmic ray muons available to ICARUS at the surface. The ionization signal shape simulation applies a novel procedure that tunes the simulation to match what is measured in data. The end result of the equalization procedure and simulation tuning allows for a comparison of charge measurements in ICARUS between Monte Carlo simulation and data, showing good performance with minimal residual bias between the two.

The explosion in known microbial diversity in the last two decades has made it abundantly clear that microbes in the environment do not exist in isolation; they are members of communities. Accordingly, omics approaches such as metagenomics have revealed that interactions between diverse groups of community members such as archaea, bacteria, and viruses (bacteriophage) are common and have significant impacts on entire microbiomes. Thus, to have a well-developed understanding of microbes as they naturally exist in the environment, biological entities of all kinds must be studied together. While numerous protocols for metagenome analysis exist, comprehensive published protocols for the simultaneous analysis of viruses and prokaryotes together are scarce. Further, as bioinformatic methods for microbiology rapidly advance, existing metagenomic tools and pipelines require frequent reevaluation. This ensures the adherence of best practices for microbiome and metagenomic data analysis. Here, we offer an expansive approach for the joint analysis of bulk sequence data from a mixed microbial community (metagenomes) and viral-sized fraction communities (viromes). This chapter serves as a beginner's-level guide for researchers with limited bioinformatics expertise who wish to engage in multi-scale metagenome and virome analyses. We cover steps from initial study design to sequence read processing, metagenome assembly, quality control, virus identification, microbial and viral genome binning, taxonomic characterization, species-level clustering, and host-virus predictions. We also provide the bioinformatic scripts used in our workflow for reuse in one's own computational methods. Lastly, we discuss additional approaches a researcher can take after processing data with this workflow.

This study explores how different weather conditions influence public sentiment on social media, focusing on Twitter data from the UK. By considering climate and linguistic baselines, we improve the accuracy of weather-related sentiment analysis. Our findings show that emotional responses to weather are complex, influenced by combinations of weather variables and regional language differences. The results highlight the importance of context-sensitive methods for better understanding public mood in response to weather, which can enhance impact-based forecasting and risk communication in the context of climate change., Comment: 12 pages, 5 figures