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Since dark matter particles have never been directly detected, we do not know how they move, and in particular we do not know how they fall inside gravitational potential wells. Usually it is assumed that dark matter only interacts gravitationally with itself and with particles of the standard model, and therefore that its motion is governed by Euler's equation. In this paper, we test this assumption for the first time at cosmological scales, by combining measurements of galaxy velocities with measurements of gravitational potential wells, encoded in the Weyl potential. We find that current data are consistent with Euler's equation at redshifts $z\in [0.3,0.8]$, and we place constraints on the strength of a potential fifth force, which would alter the way dark matter particles fall. We find that a positive fifth force cannot exceed 7% of the gravitational interaction strength, while a negative fifth force is limited to 21%. The coming generation of surveys, including the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory and the Dark Energy Spectroscopic Instrument (DESI) will drastically improve the constraints, allowing to constrain a departure from pure gravitational interaction at the level of 2%., Comment: 11 pages, 4 figures

TOI-2015 is a known exoplanetary system around an M4 dwarf star, consisting of a transiting sub-Neptune planet in a 3.35-day orbital period, TOI-2015b, accompanied by a non-transiting companion, TOI-2015c. High-precision RV measurements were taken with the MAROON-X spectrograph, and high-precision photometric data were collected several networks. We re-characterize the target star by combining optical spectr, Bayesian Model Averaging (BMA) and Spectral Energy Distribution (SED) analysis. The TOI-2015 host star is a K=10.3mag M4-type dwarf with a sub-solar metallicity of [Fe/H]=-0.31+/-0.16, and a Teff=3200K. Our photodynamical analysis of the system strongly favors the 5:3 mean motion resonance and in this scenario the planet b has an orbital period of 3.34days, a mass of Mp=9.02+/-0.34Me, a radius of Rp=3.309+/-0.012Re, resulting in a density of rhop= 1.40+/-0.06g/cm3, indicative of a Neptune like composition. Its transits exhibit large (>1hr) timing variations indicative of an outer perturber in the system. We performed a global analysis of the high-resolution RV measurements, the photometric data, and the TTVs, and inferred that TOI-2015 hosts a second planet, TOI-2015c, in a non-transiting configuration. TOI-2015c has an orbital period of Pc=5.583days and a mass of Mp=8.91+0.38-0.40Me. The dynamical configuration of TOI-2015b and TOI-2015c can be used to constrain the system's planetary formation and migration history. Based on the mass-radius composition models, TOI-2015b is a water-rich or rocky planet with a hydrogen-helium envelope. Moreover, TOI-2015b has a high transmission spectroscopic metric (TSM=149), making it a favorable target for future transmission spectroscopic observations with JWST to constrain the atmospheric composition of the planet. Such observations would also help to break the degeneracies in theoretical models of the planet's interior structure., Comment: The paper has been accepted for publication in Astronomy and Astrophysics

Ultracold quantum-gas mixtures of fermionic atoms with resonant control of interactions offer a unique test-bed to explore few- and many-body quantum states with unconventional properties. The emergence of such strongly correlated systems, as for instance symmetry-broken superfluids, is usually accompanied by hydrodynamic collective behavior. Thus, experimental progress in this field naturally requires a deep understanding of hydrodynamic regimes. Here, we report on experiments employing a tunable Fermi-Fermi mixture of $^{161}$Dy and $^{40}$K near quantum degeneracy. We investigate the full spectrum of dipole modes across a Feshbach resonance and characterize the crossover from collisionless to deep hydrodynamic behavior in measurements of frequencies and damping rates. We compare our results with a theoretical model that considers the motion of the mass centers of the two species and we identify the contributions of friction and mean-field interaction. We show that one oscillating mode exists over the whole range of interactions, exhibiting striking changes of frequency and damping in the deep hydrodynamic regime. We observe the second oscillating mode to split into two purely exponential damping modes. One of these exponential modes shows very fast damping, faster than any other relevant timescale, and is largely insensitive against experimental imperfections. It provides an accurate measure for the interspecies drag effect, which generalizes the concept of spin drag explored in other experiments. We characterize the interspecies drag locally in terms of a microscopic friction coefficient and we discuss its unitarity-limited universal behavior on top of the resonance., Comment: 20 pages, 11 figures

The objective of this paper is to provide a baseline for performing multi-modal data classification on a novel open multimodal dataset of hepatocellular carcinoma (HCC), which includes both image data (contrast-enhanced CT and MRI images) and tabular data (the clinical laboratory test data as well as case report forms). TNM staging is the classification task. Features from the vectorized preprocessed tabular data and radiomics features from contrast-enhanced CT and MRI images are collected. Feature selection is performed based on mutual information. An XGBoost classifier predicts the TNM staging and it shows a prediction accuracy of $0.89 \pm 0.05$ and an AUC of $0.93 \pm 0.03$. The classifier shows that this high level of prediction accuracy can only be obtained by combining image and clinical laboratory data and therefore is a good example case where multi-model classification is mandatory to achieve accurate results.

The audio visual benefit in speech perception, where congruent visual input enhances auditory processing, is well documented across age groups, particularly in challenging listening conditions and among individuals with varying hearing abilities. However, most studies rely on highly controlled laboratory environments with scripted stimuli. Here, we examine the audio visual benefit using unscripted, natural speech from untrained speakers within a virtual acoustic environment. Using electroencephalography (EEG) and cortical speech tracking, we assessed neural responses across audio visual, audio only, visual only, and masked lip conditions to isolate the role of lip movements. Additionally, we analysed individual differences in acoustic and visual features of the speakers, including pitch, jitter, and lip openness, to explore their influence on the audio visual speech tracking benefit. Results showed a significant audio visual enhancement in speech tracking with background noise, with the masked lip condition performing similarly to the audio-only condition, emphasizing the importance of lip movements in adverse listening situations. Our findings reveal the feasibility of cortical speech tracking with naturalistic stimuli and underscore the impact of individual speaker characteristics on audio-visual integration in real world listening contexts.

We show variants of the genus inequality for the irreducible components of the special fiber of an arithmetic curve over a henselian discrete valuation ring of residue characteristic zero that take into account the non-existence of rational, respectively real points on the the components. We then apply this inequality to obtain the bound $2^{ng}$ (respectively $2^{n(g+1)}$) on the totally positive sum-of-two-squares index in the function field of a curve of genus $g$ over the field of $n$-fold iterated real Laurent series with (respectively without) real points. The bound $2^{n(g+1)}$ had been previously known only for hyperelliptic curves.

The search for highly coherent degrees of freedom in noisy solid-state environments is a major challenge in condensed matter. In disordered dipolar systems, such as magnetically doped insulators, compact clusters of two-level systems (TLS) have recently been shown to have significantly longer coherence times than typical single TLS. Coupling weakly to their environment, they sense and probe its many-body dynamics through the induced dephasing. However, it has remained an open question whether further mechanisms exist that protect the coherence of such solid-state qubits. Here we show that symmetric clusters of few TLS couple even more weakly to their surroundings, making them highly sensitive quantum sensors of slow many-body dynamics. Furthermore, we explore their use as qubits for quantum information storage, detailing the techniques required for their preparation and manipulation. Our findings elucidate the role of symmetry in enhancing quantum coherence in disordered and noisy systems, opening a route toward a sensitive experimental probe of many-body quasi-localization dynamics as well as the development of quantum technologies in solid-state systems.

As part of the PIP-II project at Fermilab, a pre-production cryomodule featuring 325 MHz Single Spoke Resonator type 2 (SSR2) superconducting RF cavities is under construction. These SSR2 cavities are fabricated by industry partners and undergo initial cold testing at our collaborating institution, IJCLab in France, utilizing low-power coupler. Subsequently, the cavities are subjected to final qualification at Fermilab, complete with tuner and high-power coupler assemblies. This paper provides an overview of the ongoing efforts dedicated to high-power testing of jacketed SSR2 cavities in the Spoke Test Cryostat (STC) at Fermilab. Performance parameters obtained from these tests are presented, offering valuable insights into the cavities operational characteristics and readiness for integration into the PIP-II cryomodule., Comment: 32nd Linear Accelerator Conference (LINAC 2024)

The nature of sub-Neptunes remains unknown due to degeneracies in interior structure solutions. However, a statistical set of small planets with measured masses and radii has been compiled. It can be used to test the prediction of large water reservoirs on sub-Neptunes by planet formation theory. We want to find out whether this water reservoir is included in photoevaporative winds and how much of it can partition into the rocky and metallic interior. We couple the result of a planetary formation model to evolution models which assume perfect mixing of water with H/He in the envelope or complete segregation. For the mixed envelopes, we also include fractionation during photoevaporative mass-loss. Further, the effect of equilibrium dissolution of water into an assumed magma ocean and into the metallic core is studied for the first time in coupled formation-evolution models. Out of the tested scenarios, the mass-radius relation of exoplanets is best matched under the mixed assumption without water sequestration to the interior. We quantify the radius valley location and scaling with stellar mass. Fractionation is not found to significantly alter the composition of the planets for our initial conditions due to initially massive envelopes on all planets. In contrast, water sequestration has a profound effect on the radius evolution and compositional budget of the planets. The model predicts the preservation of large quantities of water even if the gaseous envelope is lost. Planets with corresponding bulk densities are not observed in comparably large numbers. By combining formation and evolution model, we probe a parameter space favored by core accretion theory. We conclude that the dissolution of different volatiles into the planetary interior and solidification of the magma ocean are natural next steps for comprehensive treatment of atmosphere-interior interaction. (abridged), Comment: 25 pages, 17 figures, submitted to A&A

Pulsar timing array experiments have recently found evidence for a stochastic gravitational wave (GW) background, which induces correlations among pulsar timing residuals described by the Hellings and Downs (HD) curve. Standard calculations of the HD correlation and its variance assume an isotropic background. However, for a background of astrophysical origin, we expect a higher GW spectral density in directions with higher galaxy number densities. In a companion paper, we have developed a theoretical formalism to account for the anisotropies arising from large-scale galaxy clustering, leading to a new contribution to the variance of the HD correlation. In this subsequent work, we provide numerical results for this novel effect. We consider a GW background resulting from mergers of supermassive black hole binaries, and relate the merger number density to the overdensity of galaxies. We find that anisotropies due to large-scale galaxy clustering lead to a standard deviation of the HD correlation at most at percent level, remaining well below the standard contributions to the HD variance. Hence, this kind of anisotropies in the GW source distribution does not represent a substantial contamination to the correlations of timing residuals in present and future PTA surveys. Suitable statistical methods to extract the galaxy clustering signal from PTA data will be investigated in the future., Comment: 20 pages, 6 figures

Generative deep learning has become pivotal in molecular design for drug discovery and materials science. A widely used paradigm is to pretrain neural networks on string representations of molecules and fine-tune them using reinforcement learning on specific objectives. However, string-based models face challenges in ensuring chemical validity and enforcing structural constraints like the presence of specific substructures. We propose to instead combine graph-based molecular representations, which can naturally ensure chemical validity, with transformer architectures, which are highly expressive and capable of modeling long-range dependencies between atoms. Our approach iteratively modifies a molecular graph by adding atoms and bonds, which ensures chemical validity and facilitates the incorporation of structural constraints. We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned using a new training algorithm that combines elements of the deep cross-entropy method with self-improvement learning from language modeling, allowing stable fine-tuning of deep transformers with many layers. We evaluate GraphXForm on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques, while it can flexibly enforce structural constraints or initiate the design from existing molecular structures.

We initiate a study of the complexity of quantum field theories (QFTs) by proposing a measure of information contained in a QFT and its observables. We show that from minimal assertions, one is naturally led to measure complexity by two integers, called format and degree, which characterize the information content of the functions and domains required to specify a theory or an observable. The strength of this proposal is that it applies to any physical quantity, and can therefore be used for analyzing complexities within an individual QFT, as well as studying the entire space of QFTs. We discuss the physical interpretation of our approach in the context of perturbation theory, symmetries, and the renormalization group. Key applications include the detection of complexity reductions in observables, for example due to algebraic relations, and understanding the emergence of simplicity when considering limits. The mathematical foundations of our constructions lie in the framework of sharp o-minimality, which ensures that the proposed complexity measure exhibits general properties inferred from consistency and universality., Comment: 33 pages

In this paper we propose a novel approach based on knowledge graphs to provide timely access to structured information, to enable actionable technology intelligence, and improve cyber-physical systems planning. Our framework encompasses a text mining process, which includes information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. Following this data exploration process, we employ a selective knowledge graph construction (KGC) approach supported by an electronics and innovation ontology-backed pipeline for multi-objective decision-making with a focus on cyber-physical systems. We apply our methodology to the domain of automotive electrical systems to demonstrate the approach, which is scalable. Our results demonstrate that our construction process outperforms GraphGPT as well as our bi-LSTM and transformer REBEL with a pre-defined dataset by several times in terms of class recognition, relationship construction and correct "sublass of" categorization. Additionally, we outline reasoning applications and provide a comparison with Wikidata to show the differences and advantages of the approach., Comment: Presented at CAIPI Workshop at AAAI 2024

A powerful approach to computing Feynman integrals or cosmological correlators is to consider them as solution to systems of differential equations. Often these can be chosen to be Gelfand-Kapranov-Zelevinsky (GKZ) systems. However, their naive construction introduces a significant amount of unnecessary complexity. In this paper we present an algorithm which allows for reducing these GKZ systems to smaller subsystems if a parameter associated to the GKZ systems is resonant. These simpler subsystems can then be solved separately resulting in solutions for the full system. The algorithm makes it possible to check when reductions happen and allows for finding the associated simpler solutions. While originating in the mathematical theory of D-modules analyzed via exact sequences of Euler-Koszul homologies, the algorithm can be used without knowledge of this framework. We motivate the need for such reduction techniques by considering cosmological correlators on an FRW space-time and solve the tree-level single-exchange correlator in this way. It turns out that this integral exemplifies an interesting relation between locality and the reduction of the differential equations., Comment: 45 pages plus appendices (16 pages), 4 figures, v2: Fixed typos, including an important one in the abstract, added references

This project explores methods to enhance sign language translation of German sign language, specifically focusing on disambiguation of homonyms. Sign language is ambiguous and understudied which is the basis for our experiments. We approach the improvement by training transformer-based models on various bodypart representations to shift the focus on said bodypart. To determine the impact of, e.g., the hand or mouth representations, we experiment with different combinations. The results show that focusing on the mouth increases the performance in small dataset settings while shifting the focus on the hands retrieves better results in larger dataset settings. Our results contribute to better accessibility for non-hearing persons by improving the systems powering digital assistants, enabling a more accurate interaction. The code for this project can be found on GitHub., Comment: LIMO2024 @ KONVENS 2024, 8 pages, 3 figures

The ability to process and store information on surrounding nuclear spins is a major requirement for group-IV color center-based repeater nodes. We demonstrate coherent control of a ${}^{13}$C nuclear spin strongly coupled to a negatively charged germanium-vacancy center in diamond with coherence times beyond 2.5s at mK temperatures, which is the longest reported for group-IV defects. Detailed analysis allows us to model the system's dynamics, extract the coupling parameters, and characterize noise. We estimate an achievable memory time of 18.1s with heating limitations considered, paving the way to successful applications as a quantum repeater node.

In practice, we are often faced with small-sized tabular data. However, current tabular benchmarks are not geared towards data-scarce applications, making it very difficult to derive meaningful conclusions from empirical comparisons. We introduce PMLBmini, a tabular benchmark suite of 44 binary classification datasets with sample sizes $\leq$ 500. We use our suite to thoroughly evaluate current automated machine learning (AutoML) frameworks, off-the-shelf tabular deep neural networks, as well as classical linear models in the low-data regime. Our analysis reveals that state-of-the-art AutoML and deep learning approaches often fail to appreciably outperform even a simple logistic regression baseline, but we also identify scenarios where AutoML and deep learning methods are indeed reasonable to apply. Our benchmark suite, available on https://github.com/RicardoKnauer/TabMini , allows researchers and practitioners to analyze their own methods and challenge their data efficiency., Comment: AutoML 2024 Workshop Track

Atmospheres above lava-ocean planets (LOPs) hold clues as to the properties of their interiors, owing to the expectation that the two reservoirs are in chemical equilibrium. Here we consider `mineral' atmospheres produced in equilibrium with silicate liquids. We treat oxygen fugacity ($f$O$_2$) as an independent variable, together with temperature ($T$) and composition ($X$), to compute equilibrium partial pressures ($p$) of stable gas species at the liquid-gas interface. Above this boundary, the atmospheric speciation and the pressure-temperature structure are computed self-consistently to yield emission spectra. We explore a wide array of plausible compositions, oxygen fugacities (between 6 log$_{10}$ units below- and above the iron-w\"ustite buffer, IW) and irradiation temperatures (2000, 2500, 3000 and 3500 K) relevant to LOPs. We find that SiO(g), Fe(g) and Mg(g) are the major species below $\sim$IW, ceding to O$_2$(g) and O(g) in more oxidised atmospheres. The transition between the two regimes demarcates a minimum in total pressure ($P$). Because $p$ scales linearly with $X$, emission spectra are only modest functions of composition. By contrast, $f$O$_2$ can vary over orders of magnitude, thus causing commensurate changes in $p$. Reducing atmospheres show intense SiO emission, creating a temperature inversion in the upper atmosphere. Conversely, oxidised atmospheres have lower $p$SiO and lack thermal inversions, with resulting emission spectra that mimic that of a black body. Consequently, the intensity of SiO emission relative to the background, generated by MgO(g), can be used to quantify the $f$O$_2$ of the atmosphere. Depending on the emission spectroscopy metric of the target, deriving the $f$O$_2$ of known nearby LOPs is possible with a few secondary occultations observed by JWST.

Selective fruit harvesting is a challenging manipulation problem due to occlusions and clutter arising from plant foliage. A harvesting gripper should i) have a small cross-section, to avoid collisions while approaching the fruit; ii) have a soft and compliant grasp to adapt to different fruit geometry and avoid bruising it; and iii) be capable of rigidly holding the fruit tightly enough to counteract detachment forces. Previous work on fruit harvesting has primarily focused on using grippers with a single actuation mode, either suction or fingers. In this paper we present a compact robotic gripper that combines the benefits of both. The gripper first uses an array of compliant suction cups to gently attach to the fruit. After attachment, telescoping cam-driven fingers deploy, sweeping obstacles away before pivoting inwards to provide a secure grip on the fruit for picking. We present and analyze the finger design for both ability to sweep clutter and maintain a tight grasp. Specifically, we use a motorized test bed to measure grasp strength for each actuation mode (suction, fingers, or both). We apply a tensile force at different angles (0{\deg}, 15{\deg}, 30{\deg} and 45{\deg}), and vary the point of contact between the fingers and the fruit. We observed that with both modes the grasp strength is approximately 40 N. We use an apple proxy to test the gripper's ability to obtain a grasp in the presence of occluding apples and leaves, achieving a grasp success rate over 96% (with an ideal controller). Finally, we validate our gripper in a commercial apple orchard., Comment: 8 pages, 9 figures

Our hypothesis is that UDA using diffusion-weighted images, generated with a unified model, offers a promising and reliable strategy for enhancing the performance of supervised learning models in multi-site prostate lesion detection, especially when various b-values are present. This retrospective study included data from 5,150 patients (14,191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for multi-site PCa detection. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual DW images acquired using various b-values, to align with the style of images acquired using b-values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1,692 test cases (2,393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (p<.001), respectively, for PI-RADS>=3, and 0.77 and 0.80 (p<.001) for PI-RADS>=4 PCa lesions. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (p<.001) for PI-RADS>=3, and 0.50 and 0.77 (p<.001) for PI-RADS>=4 PCa lesions. The results indicate the proposed UDA with generated images improved the performance of SL methods in multi-site PCa lesion detection across datasets with various b values, especially for images acquired with significant deviations from the PI-RADS recommended DWI protocol (e.g. with an extremely high b-value)., Comment: Accept at Radiology: Artificial Intelligence. Journal reference and external DOI will be added once published

The optimization of yields in multi-reactor systems, which are advanced tools in heterogeneous catalysis research, presents a significant challenge due to hierarchical technical constraints. To this respect, this work introduces a novel approach called process-constrained batch Bayesian optimization via Thompson sampling (pc-BO-TS) and its generalized hierarchical extension (hpc-BO-TS). This method, tailored for the efficiency demands in multi-reactor systems, integrates experimental constraints and balances between exploration and exploitation in a sequential batch optimization strategy. It offers an improvement over other Bayesian optimization methods. The performance of pc-BO-TS and hpc-BO-TS is validated in synthetic cases as well as in a realistic scenario based on data obtained from high-throughput experiments done on a multi-reactor system available in the REALCAT platform. The proposed methods often outperform other sequential Bayesian optimizations and existing process-constrained batch Bayesian optimization methods. This work proposes a novel approach to optimize the yield of a reaction in a multi-reactor system, marking a significant step forward in digital catalysis and generally in optimization methods for chemical engineering.

The constructive approach within Neural Combinatorial Optimization (NCO) treats a combinatorial optimization problem as a finite Markov decision process, where solutions are built incrementally through a sequence of decisions guided by a neural policy network. To train the policy, recent research is shifting toward a 'self-improved' learning methodology that addresses the limitations of reinforcement learning and supervised approaches. Here, the policy is iteratively trained in a supervised manner, with solutions derived from the current policy serving as pseudo-labels. The way these solutions are obtained from the policy determines the quality of the pseudo-labels. In this paper, we present a simple and problem-independent sequence decoding method for self-improved learning based on sampling sequences without replacement. We incrementally follow the best solution found and repeat the sampling process from intermediate partial solutions. By modifying the policy to ignore previously sampled sequences, we force it to consider only unseen alternatives, thereby increasing solution diversity. Experimental results for the Traveling Salesman and Capacitated Vehicle Routing Problem demonstrate its strong performance. Furthermore, our method outperforms previous NCO approaches on the Job Shop Scheduling Problem., Comment: Accepted at ECAI-2024

Decoherence between qubits is a major bottleneck in quantum computations. Decoherence results from intrinsic quantum and thermal fluctuations as well as noise in the external fields that perform the measurement and preparation processes. With prescribed colored noise spectra for intrinsic and extrinsic noise, we present a numerical method, Quantum Accelerated Stochastic Propagator Evaluation (Q-ASPEN), to solve the time-dependent noise-averaged reduced density matrix in the presence of intrinsic and extrinsic noise. Q-ASPEN is arbitrarily accurate and can be applied to provide estimates for the resources needed to error-correct quantum computations. We employ spectral tensor trains, which combine the advantages of tensor networks and pseudospectral methods, as a variational ansatz to the quantum relaxation problem and optimize the ansatz using methods typically used to train neural networks. The spectral tensor trains in Q-ASPEN make accurate calculations with tens of quantum levels feasible. We present benchmarks for Q-ASPEN on the spin-boson model in the presence of intrinsic noise and on a quantum chain of up to 32 sites in the presence of extrinsic noise. In our benchmark, the memory cost of Q-ASPEN scales linearly with the system size once the number of states is larger than the number of basis functions., Comment: 6 figures. 13 Pages

Many observables in quantum field theories are involved non-analytic functions of the parameters of the theory. However, it is expected that they are not arbitrarily wild, but rather have only a finite amount of geometric complexity. This expectation has been recently formalized by a tameness principle: physical observables should be definable in o-minimal structures and their sharp refinements. In this work, we show that a broad class of non-analytic partition and correlation functions are tame functions in the o-minimal structure known as $\mathbb{R}_{\mathscr{G}}$ - the structure defining Gevrey functions. Using a perturbative approach, we expand the observables in asymptotic series in powers of a small coupling constant. Although these series are often divergent, they can be Borel-resummed in the absence of Stokes phenomena to yield the full partition and correlation functions. We show that this makes them definable in $\mathbb{R}_{\mathscr{G}}$ and provide a number of motivating examples. These include certain 0-dimensional quantum field theories and a set of higher-dimensional quantum field theories that can be analyzed using constructive field theory. Finally, we discuss how the eigenvalues of certain Hamiltonians in quantum mechanics are also definable in $\mathbb{R}_{\mathscr{G}}$., Comment: 27 pages

Dormant pruning, or the removal of unproductive portions of a tree while a tree is not actively growing, is an important orchard task to help maintain yield, requiring years to build expertise. Because of long training periods and an increasing labor shortage in agricultural jobs, pruning could benefit from robotic automation. However, to program robots to prune branches, we first need to understand how pruning decisions are made, and what variables in the environment (e.g., branch size and thickness) we need to capture. Working directly with three pruning stakeholders -- horticulturists, growers, and pruners -- we find that each group of human experts approaches pruning decision-making differently. To capture this knowledge, we present three studies and two extracted pruning protocols from field work conducted in Prosser, Washington in January 2022 and 2023. We interviewed six stakeholders (two in each group) and observed pruning across three cultivars -- Bing Cherries, Envy Apples, and Jazz Apples -- and two tree architectures -- Upright Fruiting Offshoot and V-Trellis. Leveraging participant interviews and video data, this analysis uses grounded coding to extract pruning terminology, discover horticultural contexts that influence pruning decisions, and find implementable pruning heuristics for autonomous systems. The results include a validated terminology set, which we offer for use by both pruning stakeholders and roboticists, to communicate general pruning concepts and heuristics. The results also highlight seven pruning heuristics utilizing this terminology set that would be relevant for use by future autonomous robot pruning systems, and characterize three discovered horticultural contexts (i.e., environmental management, crop-load management, and replacement wood) across all three cultivars., Comment: 36 pages; 21 figures

The smooth emergence of shear elasticity is an hallmark of the liquid to glass transition. In a liquid, viscous stresses arise from local structural rearrangements. In the solid, Eshelby has shown that stresses around an inclusion decay as a power law $r^{-D}$, where $D$ is the dimension of the system. We study glass-forming hard sphere fluids by simulation and observe the emergence of the unscreened power-law Eshelby pattern in the stress correlations of the isotropic liquid state. By a detailed tensorial analysis, we show that the fluctuating force field, viz.~the divergence of the stress field, relaxes to zero with time in all states, while the shear stress correlations develop spatial power-law structures inside regions that grow with longitudinal and transverse sound speeds; we observe the predicted exponents $r^{-D}$ and $r^{-D-2}$. In Brownian systems, shear stresses relax diffusively within these regions, with the diffusion coefficient determined by the shear modulus and the friction coefficient., Comment: 15 pages, 18 figures

Apple(\textit{Malus domestica} Borkh.) trees are deciduous, shedding leaves each year. This process is preceded by a gradual change in leaf color from green to yellow as chlorophyll is degraded prior to abscission. The initiation and rate of this color change are affected by many factors including leaf nitrogen (N) concentration. We predict that leaf color during this transition may be indicative of the nitrogen status of apple trees. This study assesses a machine vision-based system for quantifying the change in leaf color and its correlation with leaf nitrogen content. An image dataset was collected in color and 3D over five weeks in the fall of 2021 and 2023 at a commercial orchard using a ground vehicle-based stereovision sensor. Trees in the foreground were segmented from the point cloud using color and depth thresholding methods. Then, to estimate the proportion of yellow leaves per canopy, the color information of the segmented canopy area was quantified using a custom-defined metric, \textit{yellowness index} (a normalized ratio of yellow to green foliage in the tree) that varied from -1 to +1 (-1 being completely green and +1 being completely yellow). Both K-means-based methods and gradient boosting methods were used to estimate the \textit{yellowness index}. The gradient boosting based method proposed in this study was better than the K-means-based method (both in terms of computational time and accuracy), achieving an $R^2$ of 0.72 in estimating the \textit{yellowness index}. The metric was able to capture the gradual color transition from green to yellow over the study duration. Trees with lower leaf nitrogen showed the color transition to yellow earlier than the trees with higher nitrogen. Keywords: Fruit Tree Nitrogen Management, Machine Vision, Point Cloud Segmentation, Precision Nitrogen Management