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In the relentless pursuit of expanding the periodic table, the discovery of element 119 remains elusive, despite two decades of dedicated research efforts. The traditional fusion-evaporation approach, although fruitful in the past, now appears to be approaching its operational limits. This scenario sets the stage for considering innovative methodologies essential for further advancements in the field of superheavy elements. Here, we introduce a pioneering strategy aimed at synthesizing element 119 by adapting and extending the nuclear reaction processes previously successful in producing element $^{294}$Og. This involved the fusion of $^{48}$Ca and $^{249}$Cf. Building on this, our novel approach incorporates an additional reactive target -- specifically, hydrogen -- positioned strategically behind the $^{249}$Cf. This configuration is designed to facilitate an immediate secondary reaction of the nascent $^{294}$Og with hydrogen, potentially forging new pathways to element 119. Preliminary insights also suggest that employing isotopes like deuterium or helium-3 as targets may not only enhance the production rates of element 119 but might also pave the way for the synthesis of even heavier elements, extending up to elements 120 and 121. We delve into the technicalities and feasibility of employing a dual-target method using a $^{48}$Ca beam, exploring new horizons in the quest for the superheavy unknown., Comment: 5 pages, 4 figures, submitted

The dispersive approach to the hadronic light-by-light contribution to the muon $g-2$ involves an integral over three virtual photon momenta appearing in the light-by-light tensor. Building upon previous works, we systematically derive short-distance constraints in the region where two momenta are large compared to the third, the so-called Melnikov-Vainshtein or corner region. We include gluonic corrections for the different scalar functions appearing in a Lorentz decomposition of the underlying tensor, and explicitly check analytic agreement with alternative operator product expansions in overlapping regimes of validity, and observe a very strong pattern of cancellations for the final $g-2$ integrand. The last observation suggests that a very compact expression only containing the axial current form factors can give a good approximation of the corner region of the hadronic light-by-light.

Within the Standard Model Effective Field Theory framework, we set indirect constraints on top quark operators that violate baryon number by one unit above the TeV scale. We find that these constraints are typically many orders of magnitude more stringent than the recently derived direct bounds from collider experiments. Therefore, direct observation of baryon number violation in top-quark observables at the TeV scale would imply a large fine-tuning among operators across different energy scales. This possibility is not protected under universal radiative corrections or any known symmetry principles., Comment: 8 pages, 2 tables, 2 figures

General-purpose agents require fine-grained controls and rich sensory inputs to perform a wide range of tasks. However, this complexity often leads to intractable decision-making. Traditionally, agents are provided with task-specific action and observation spaces to mitigate this challenge, but this reduces autonomy. Instead, agents must be capable of building state-action spaces at the correct abstraction level from their sensorimotor experiences. We leverage the structure of a given set of temporally-extended actions to learn abstract Markov decision processes (MDPs) that operate at a higher level of temporal and state granularity. We characterize state abstractions necessary to ensure that planning with these skills, by simulating trajectories in the abstract MDP, results in policies with bounded value loss in the original MDP. We evaluate our approach in goal-based navigation environments that require continuous abstract states to plan successfully and show that abstract model learning improves the sample efficiency of planning and learning., Comment: Published in the Proceedings of Reinforcement Learning Conference

The literature shows the possible existence of a problem called collinearity in both Nelson-Siegel and Nelson-Siegel-Svensson models due to the relationship between the slope and curvature components. The presence of this problem and the estimation of both models by Ordinary Least Squares would lead to coefficients estimates that may be unstable among other consequences. However, these estimates are used to make monetary policy decisions. For this reason, it is important to try mitigating this collinearity problem. Consequently, some authors propose traditional procedures for the treatment of collinearity such as: non-linear optimisation, to fix the shape parameter or ridge regression. Nevertheless, all these processes have their disadvantages. Alternatively, a new method with good properties called raise regression is proposed in this paper. Finally, the methodologies are illustrated with an empirical comparison on Euribor Overnight Index Swap and Euribor Interest Rates Swap data between 2011 and 2021., Comment: Working paper with 11 pages, 6 tables, 4 figures

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.

Traditional field phenotyping methods are often manual, time-consuming, and destructive, posing a challenge for breeding progress. To address this bottleneck, robotics and automation technologies offer efficient sensing tools to monitor field evolution and crop development throughout the season. This study aimed to develop an autonomous ground robotic system for LiDAR-based field phenotyping in plant breeding trials. A Husky platform was equipped with a high-resolution three-dimensional (3D) laser scanner to collect in-field terrestrial laser scanning (TLS) data without human intervention. To automate the TLS process, a 3D ray casting analysis was implemented for optimal TLS site planning, and a route optimization algorithm was utilized to minimize travel distance during data collection. The platform was deployed in two cotton breeding fields for evaluation, where it autonomously collected TLS data. The system provided accurate pose information through RTK-GNSS positioning and sensor fusion techniques, with average errors of less than 0.6 cm for location and 0.38$^{\circ}$ for heading. The achieved localization accuracy allowed point cloud registration with mean point errors of approximately 2 cm, comparable to traditional TLS methods that rely on artificial targets and manual sensor deployment. This work presents an autonomous phenotyping platform that facilitates the quantitative assessment of plant traits under field conditions of both large agricultural fields and small breeding trials to contribute to the advancement of plant phenomics and breeding programs., Comment: Submitted to Journal of Field Robotics

Production yields of single-$\Lambda$ hypernuclei from simulated peripheral annihilations of antiprotons after capture on various target nuclei are reported. The initial annihilation process and the production of excited hypernuclei are estimated within the GiBUU transport framework, while their deexcitation process is treated with the ABLA++ code. The yield of excited hypernuclei range from $\sim$ 0.3% for $^{16}$O to $\sim$ 1.2% for $^{132}$Xe per annihilation, consistent with previous measurements at LEAR, CERN. The yield of specific ground state hypernuclei after deexcitation reaches up to a few 10$^{-4}$ per annihilation. The hypernuclei are produced in strangeness exchange reactions occuring between a nucleon of the target and the kaons originating from the annihilation in $\sim$ 80% of the cases, while the strangeness pair production in secondary pion-nucleon collision contributes to the remaining $\sim$ 20%. The simulations indicate that antiproton annihilations at rest on different nuclei could populate a wide range of so-far unexplored hypernuclei.

The accuracy and precision of high-energy spallation models are key issues for the design and development of new applications and experiments. We present a method to estimate model parameters and associated uncertainties by leveraging the Bayesian version of the Generalised Least Squares method, which enables us to incorporate prior knowledge on the parameter values. This approach is designed to adjust parameters based on experimental data, accounting for experimental uncertainty information, and providing uncertainties for all adjusted parameters. This approach is designed in order both to improve the accuracy of models through the modification of free parameters of these models, which results in a better reproduction of experimental data, and to estimate the uncertainties of these parameters and, by extension, their impacts on the model output. We aim at demonstrating the Generalised Least Square method can be applied in the case of Monte Carlo models. We present a proof-of-concept for Monte Carlo models in the specific case of nuclear physics with the model combination INCL/ABLA. We discuss the challenges in the application of this method to high-energy spallation models, notably the large runtime and the stochasticity of the models. Our results indicate this framework can also be applied to analogous situations where parameters of a computationally expensive Monte Carlo code should be inferred/improved.

Learning from demonstration (LfD) provides an efficient way to train robots. The learned motions should be convergent and stable, but to be truly effective in the real world, LfD-capable robots should also be able to remember multiple motion skills. Existing stable-LfD approaches lack the capability of multi-skill retention. Although recent work on continual-LfD has shown that hypernetwork-generated neural ordinary differential equation solvers (NODE) can learn multiple LfD tasks sequentially, this approach lacks stability guarantees. We propose an approach for stable continual-LfD in which a hypernetwork generates two networks: a trajectory learning dynamics model, and a trajectory stabilizing Lyapunov function. The introduction of stability generates convergent trajectories, but more importantly it also greatly improves continual learning performance, especially in the size-efficient chunked hypernetworks. With our approach, a single hypernetwork learns stable trajectories of the robot's end-effector position and orientation simultaneously, and does so continually for a sequence of real-world LfD tasks without retraining on past demonstrations. We also propose stochastic hypernetwork regularization with a single randomly sampled regularization term, which reduces the cumulative training time cost for N tasks from O$(N^2)$ to O$(N)$ without any loss in performance on real-world tasks. We empirically evaluate our approach on the popular LASA dataset, on high-dimensional extensions of LASA (including up to 32 dimensions) to assess scalability, and on a novel extended robotic task dataset (RoboTasks9) to assess real-world performance. In trajectory error metrics, stability metrics and continual learning metrics our approach performs favorably, compared to other baselines. Our open-source code and datasets are available at https://github.com/sayantanauddy/clfd-snode., Comment: This paper is currently under peer review

The computational requirements of future large scale radio telescopes are expected to scale well beyond the capabilities of conventional digital resources. Current and planned telescopes are generally limited in their scientific potential by their ability to efficiently process the vast volumes of generated data. To mitigate this problem, we investigate the viability of emerging quantum computers for radio astronomy applications. In this a paper we demonstrate the potential use of variational quantum linear solvers in Noisy Intermediate Scale Quantum (NISQ) computers and combinatorial solvers in quantum annealers for a radio astronomy calibration pipeline. While we demonstrate that these approaches can lead to satisfying results when integrated in calibration pipelines, we show that current restrictions of quantum hardware limit their applicability and performance.

Present and next generation of long-baseline accelerator experiments are bringing the measurement of neutrino oscillations into the precision era with ever-increasing statistics. One of the most challenging aspects of achieving such measurements is developing relevant systematic uncertainties in the modeling of nuclear effects in neutrino-nucleus interactions. To address this problem, state-of-the-art detectors are being developed to extract detailed information about all particles produced in neutrino interactions. To fully profit from these experimental advancements, it is essential to have reliable models of propagation of the outgoing hadrons through nuclear matter able to predict how the energy is distributed between all the final-state observed particles. In this article, we investigate the role of nuclear de-excitation in neutrino-nucleus scattering using two Monte Carlo cascade models: NuWro and INCL coupled with the de-excitation code ABLA. The ablation model ABLA is used here for the first time to model de-excitation in neutrino interactions. As input to ABLA, we develop a consistent simulation of nuclear excitation energy tuned to electron-scattering data. The paper includes the characterization of the leading proton kinematics and of the nuclear cluster production during cascade and de-excitation. The observability of nuclear clusters as vertex activity and their role in a precise neutrino energy reconstruction is quantified., Comment: 14 pages, 13 figures

This paper advocates for an intertwined design of the dense linear algebra software stack that breaks down the strict barriers between the high-level, blocked algorithms in LAPACK (Linear Algebra PACKage) and the low-level, architecture-dependent kernels in BLAS (Basic Linear Algebra Subprograms). Specifically, we propose customizing the GEMM (general matrix multiplication) kernel, which is invoked from the blocked algorithms for relevant matrix factorizations in LAPACK, to improve performance on modern multicore processors with hierarchical cache memories. To achieve this, we leverage an analytical model to dynamically adapt the cache configuration parameters of the GEMM to the shape of the matrix operands. Additionally, we accommodate a flexible development of architecture-specific micro-kernels that allow us to further improve the utilization of the cache hierarchy. Our experiments on two platforms, equipped with ARM (NVIDIA Carmel, Neon) and x86 (AMD EPYC, AVX2) multi-core processors, demonstrate the benefits of this approach in terms of better cache utilization and, in general, higher performance. However, they also reveal the delicate balance between optimizing for multi-threaded parallelism versus cache usage.

Experimental studies of nuclear fission induced by fusion, transfer, spallation, fragmentation, and electromagnetic reactions in combination with state-of-the-art calculations are successful to investigate the nuclear dissipation mechanism in normal nuclear matter, containing only nucleons. The dissipation mechanism has been widely studied by the use of many different fission observables and nowadays the dissipation coefficients involved in transport theories are well constrained. However, the existence of hypernuclei and the possible presence of hyperons in neutron stars make it necessary to extend the investigation of the nuclear dissipation coefficient to the strangeness sector. In this Letter, we use fission reactions of hypernuclei to constrain for the first time the dissipation coefficient in hypernuclear matter, observing that this coefficient increases a factor of 6 in presence of a single $\Lambda$-hyperon with respect to normal nuclear matter., Comment: 6 pages, 2 figures

Three different approaches to precisely describe the Adler function in the Euclidean regime at around $2\, \mathrm{GeVs}$ are available: dispersion relations based on the hadronic production data in $e^+e^-$ annihilation, lattice simulations and perturbative QCD (pQCD). We make a comprehensive study of the perturbative approach, supplemented with the leading power corrections in the operator product expansion. All known contributions are included, with a careful assessment of uncertainties. The pQCD predictions are compared with the Adler functions extracted from $\Delta\alpha^{\mathrm{had}}_{\mathrm{QED}}(Q^2)$, using both the DHMZ compilation of $e^+e^-$ data and published lattice results. Taking as input the FLAG value of $\alpha_s$, the pQCD Adler function turns out to be in good agreement with the lattice data, while the dispersive results lie systematically below them. Finally, we explore the sensitivity to $\alpha_s$ of the direct comparison between the data-driven, lattice and QCD Euclidean Adler functions. The precision with which the renormalisation group equation can be tested is also evaluated., Comment: 56 pages, 22 figures, 14 tables. Published version

The muon anomalous magnetic moment continues to attract attention due to the possible tension between the experimentally measured value and the theoretical Standard Model prediction. With the aim to reduce the uncertainty on the hadronic light-by-light contribution to the magnetic moment, we derive short-distance constraints in the Melnikov-Vainshtein regime which are useful for data-driven determinations. In this kinematical region, two of the four electromagnetic currents are close in the four-point function defining the hadronic light-by-light tensor. To obtain the constraints, we develop a systematic operator product expansion of the tensor in question to next-to-leading order in the expansion in operators. We evaluate the leading in $\alpha_s$ contributions and derive constraints for the next-to-leading operators that are also valid nonperturbatively., Comment: 35 pages, 4 figures. Published version

Affordance detection from visual input is a fundamental step in autonomous robotic manipulation. Existing solutions to the problem of affordance detection rely on convolutional neural networks. However, these networks do not consider the spatial arrangement of the input data and miss parts-to-whole relationships. Therefore, they fall short when confronted with novel, previously unseen object instances or new viewpoints. One solution to overcome such limitations can be to resort to capsule networks. In this paper, we introduce the first affordance detection network based on dynamic tree-structured capsules for sparse 3D point clouds. We show that our capsule-based network outperforms current state-of-the-art models on viewpoint invariance and parts-segmentation of new object instances through a novel dataset we only used for evaluation and it is publicly available from github.com/gipfelen/DTCG-Net. In the experimental evaluation we will show that our algorithm is superior to current affordance detection methods when faced with grasping previously unseen objects thanks to our Capsule Network enforcing a parts-to-whole representation., Comment: IEEE-RAS International Conference on Humanoid Robots (Humanoids 2022)