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This paper presents an advanced method for addressing the inverse kinematics and optimal path planning challenges in robot manipulators. The inverse kinematics problem involves determining the joint angles for a given position and orientation of the end-effector. Furthermore, the path planning problem seeks a trajectory between two points. Traditional approaches in computer algebra have utilized Gr\"obner basis computations to solve these problems, offering a global solution but at a high computational cost. To overcome the issue, the present authors have proposed a novel approach that employs the Comprehensive Gr\"obner System (CGS) and CGS-based quantifier elimination (CGS-QE) methods to efficiently solve the inverse kinematics problem and certify the existence of solutions for trajectory planning. This paper extends these methods by incorporating smooth curves via cubic spline interpolation for path planning and optimizing joint configurations using shortest path algorithms to minimize the sum of joint configurations along a trajectory. This approach significantly enhances the manipulator's ability to navigate complex paths and optimize movement sequences., Comment: 16 pages

We introduce a combinatorial topological framework for characterizing the global dynamics of ordinary differential equations (ODEs). The approach is motivated by the study of gene regulatory networks, which are often modeled by ODEs that are not explicitly derived from first principles. The proposed method involves constructing a combinatorial model from a set of parameters and then embedding the model into a continuous setting in such a way that the algebraic topological invariants are preserved. In this manuscript, we build upon the software Dynamic Signatures Generated by Regulatory Networks (DSGRN), a software package that is used to explore the dynamics generated by a regulatory network. By extending its functionalities, we deduce the global dynamical information of the ODE and extract information regarding equilibria, periodic orbits, connecting orbits and bifurcations. We validate our results through algebraic topological tools and analytical bounds, and the effectiveness of this framework is demonstrated through several examples and possible future directions., Comment: Appendix by Daniel Gameiro

Let $f$ be a (possibly Newton degenerate) weighted homogeneous polynomial defining an isolated surface singularity at the origin of $\mathbb{C}^3$, and let $\{f_s\}$ be a generic deformation of its coefficients such that $f_s$ is Newton non-degenerate for $s\not=0$. We show that there exists an ''admissible'' simultaneous good resolution of the family of functions $f_s$ for all small $s$, including $s=0$ which corresponds to the (possibly Newton degenerate) function $f$. As an application, we give a new geometrical proof of a weak version of the Milnor-Orlik theorem that asserts that the monodromy zeta-function of $f$ (and hence its Milnor number) is completely determined by its weight, its weighted degree and its Newton boundary.

Electrons are accelerated to high, non-thermal energies during explosive energy-release events in space, such as magnetic reconnection. However, the properties and acceleration mechanisms of relativistic electrons directly associated with reconnection X-line are not well understood. This study utilizes Magnetospheric Multiscale (MMS) measurements to analyze the flux and spectral features of sub-relativistic to relativistic (~ 80 to 560 keV) electrons during a magnetic reconnection event in Earth's magnetotail. This event provided a unique opportunity to measure the electrons directly energized by X-line as MMS stayed in the separatrix layer, where the magnetic field directly connects to the X-line, for approximately half of the observation period. Our analysis revealed that the fluxes of relativistic electrons were clearly enhanced within the separatrix layer, and the highest flux was directed away from the X-line, which suggested that these electrons originated directly from the X-line. Spectral analysis showed that these relativistic electrons deviated from the main plasma sheet population and exhibited an "ankle" feature similar to that observed in galactic cosmic rays. The contribution of "ankle" electrons to the total electron energy density increased from 0.1% to 1% in the separatrix layer, though the spectral slopes did not exhibit clear variations. Further analysis indicated that while these relativistic electrons originated from the X-line, they experienced a non-negligible degree of scattering during transport. These findings provide clear evidence that magnetic reconnection in Earth's magnetotail can efficiently energize relativistic electrons directly at the X-line, providing new insights into the complex processes governing electron dynamics during magnetic reconnection., Comment: 23 pages, 5 figures

Particles are accelerated to very high, non-thermal energies in space, solar, and astrophysical plasma environments. In cosmic ray physics, the "Hillas limit" is often used as a rough estimate (or the necessary condition) of the maximum energy of particles. This limit is based on the concepts of one-shot direct acceleration by a system-wide motional electric field, as well as stochastic and diffusive acceleration in strongly turbulent environments. However, it remains unclear how well this limit explains the actual observed maximum energies of particles. Here we show, based on a systematic review, that the observed maximum energy of particles -- those in space, solar, astrophysical, and laboratory environments -- often reach the energy predicted by the Hillas limit. We also found several exceptions, such as electrons in solar flares and jet-terminal lobes of radio galaxies, as well as protons in planetary radiation belts, where deviations from this limit occur. We discuss possible causes of such deviations, and we argue in particular that there is a good chance of detecting ultra-high-energy ($\sim$100 GeV) solar flare electrons that have not yet been detected. We anticipate that this study will facilitate further interdisciplinary discussions on the maximum energy of particles and the underlying mechanisms of particle acceleration in diverse plasma environments., Comment: 18 pages, 4 figures; accepted for publication in Astrophysical Journal

Chiral effective theory of light diquarks is revisited. We construct an effective Lagrangian based on the linear representation of three-flavor chiral symmetry. Here, we focus on the effect of a chiral and $U(1)_A$ symmetric term originated from an eight-point quark interaction. From this model, we obtain the mass formulas of scalar, pseudoscalar, vector, and axial vector diquarks, which also describe the dependence of diquark masses on the spontaneous chiral symmetry breaking and the $U(1)_A$ anomaly. We regard singly heavy baryons as two-body systems composed of one heavy quark and one diquark and then predict the fate of the mass spectrum and the strong decay widths under chiral symmetry restoration., Comment: 14 pages, 8 figures, 2 tables

Masses of the singly heavy baryons (SHBs), composed of a heavy quark and a light diquark, are studied from the viewpoints of heavy-quark spin symmetry (HQSS) and chiral-symmetry restoration at finite temperature. We consider the light diquarks with spin-parity $J^P=0^\pm$ and $1^\pm$. Medium corrections to the SHBs are provided through the diquarks whereas the heavy quark is simply regarded as a spectator. The chiral dynamics of the diquark are described by the Nambu-Jona-Lasinio (NJL) model having (pseudo)scalar-type and (axial)vector-type four-point interactions and the six-point ones responsible for the $U(1)_A$ axial anomaly. The divergences are handled by means of the three-dimensional proper-time regularization with both ultraviolet and infrared cutoffs included, in order to eliminate unphysical imaginary parts. As a result, the mass degeneracies between the parity partners of all the SHBs are predicted in accordance with the chiral restoration. In particular, the HQS-doublet SHBs exhibit clear mass degeneracies due to the absence of the direct anomaly effects. We also predict a mass degeneracy of $\Sigma_c$ and $\Omega_c$ above the pseudocritical temperature $T_{\rm pc}$ of chiral restoration, which results in a peculiar mass hierarchy for positive-parity HQS-doublet SHBs where $\Xi_c'$ becomes heavier than $\Omega_c$ Besides, it is found that the decay width of $\Sigma_c\to\Lambda_c\pi$ vanishes above $T_{\rm pc}$ reflecting a closing of the threshold. The predicted modifications of masses and decay widths of the SHBs are expected to provide future heavy-ion collision experiments and lattice simulations with useful information on chiral dynamics of the diquarks., Comment: 21 pages, and 6 figures

This study presents a scalable Bayesian estimation algorithm for sparse estimation in exploratory item factor analysis based on a classical Bayesian estimation method, namely Bayesian joint modal estimation (BJME). BJME estimates the model parameters and factor scores that maximize the complete-data joint posterior density. Simulation studies show that the proposed algorithm has high computational efficiency and accuracy in variable selection over latent factors and the recovery of the model parameters. Moreover, we conducted a real data analysis using large-scale data from a psychological assessment that targeted the Big Five personality traits. This result indicates that the proposed algorithm achieves computationally efficient parameter estimation and extracts the interpretable factor loading structure.

Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A

Pauli spin blockade (PSB) has been used in fundamental studies on spins in quantum dots (QDs) and qubit readouts. The operating temperature of PSB is limited by that of QDs and remains below 10 K, limiting wide application development. Herein, we confirm that a single deep dopant in the channel of a field effect transistor functions as a room-temperature QD; consequently, transport through two different deep dopants exhibits PSB up to room temperature. The characteristic magnetoconductance provides a means to identify PSB and enables the PSB device to function as a magnetic sensor with a sensitivity of <20 uT. Liftings of PSB by magnetic resonance are also observed at low temperatures. This unique system is expected to realize room-temperature quantum technologies based on silicon technology., Comment: 29 pages, 11 figures, 1 table

We theoretically study nonlinear processes driven by a DC spin-electric field in the antiferromagnetic spin-1 Heisenberg model starting from the ground state in the Haldane phase. The DC spin-electric field generates finite spin current and accumulation since the symmetry protected topological order is destroyed by the field. We find two microscopic mechanisms responsible for the breakdown and a crossover between them: In weak fields, tripron-antitripron pair creation occurs through the tunneling mechanism, and in strong fields, the system is described by an effective Hamiltonian breaking the protecting symmetries. We analyze the numerically obtained results in terms of the Dykhne--Davis--Pechukas theory and Floquet theory, verifying the universal picture of the many-body Keldysh crossover in the DC-driven quantum spin systems., Comment: 8pages, 5figures

We study Dirac fermions in the presence of a space-dependent chiral gauge field and thermodynamic gradients, establishing a connection to the inverse spin Hall effect. The chiral gauge field induces a chiral magnetic field, resulting in a surface Fermi arc state and a chiral Landau level state which, although is delocalized in the bulk, we show to be more robust against impurities. By applying chemical potential and temperature gradients, we achieve nonzero charge currents, with each gradient leading to distinct Fermi level dependencies, both of which have been observed in a recent experiment. Unlike the conventional mixed axial-gravitational anomaly, our currents require a noncollinear chiral magnetic field and thermodynamic gradient. We further derive low-energy transport formulas and demonstrate the importance of carefully treating the ultraviolet cutoff for understanding our lattice calculations., Comment: 7 pages, 3 figures

We report the discovery of a characteristic trend in the intensity ratios of SiO emissions across the Central Molecular Zone (CMZ) of our Galaxy. Using the Nobeyama Radio Observatory 45-m telescope, we conducted large-scale, high-sensitivity imaging observations in molecular lines including SiO $J$=2$-$1 and CS $J$=2$-$1. By identifying SiO-emitting clouds and examining their intensity ratios relative to the other molecular lines, we unveiled a parabolic-like trend showing lower ratios near the Galactic nucleus, Sgr A$^*$, with gradual increases toward the edges of the CMZ. This pattern suggests a possible outburst of the nucleus within the last $\sim 10^5$ yr, which may have propagated through the entire CMZ with strong shocks. Alternatively, the observed trend may also be attributed to the destruction of small dust grains by high-energy photons. Our results can potentially lead to a new perspective on the history of nuclear activity and its impact on the surrounding molecular environment., Comment: 9 pages, 4 figures, accepted for publication in the Astrophysical Journal Letters

This study examines the electronic states of quantum materials influenced by waves V(Qx-omega t) propagating through them. These waves can include laser light, polaritons, acoustic waves, and sliding density waves, whose speed may vary widely. Since these waves are periodic in both space and time, the electrons in the materials form Floquet-Bloch states when influenced by the waves. We investigate band engineering by these waves, using three-dimensional (3D) Dirac electrons in circularly polarized laser wave as a case study. We classify the fields by their speed into time, light and space-like waves, and find that the electronic states are distinctive in the three cases. We demonstrate phase transitions of the electronic bands and discover emergent Floquet Weyl bands, which potentially become Type-II in the vicinity of the transition. The effect of Floquet band engineering is enhanced by Lorentz contraction, which is maximized when the wave speed approaches the Fermi velocity. Additionally, we discuss the geometric nature of the response currents., Comment: 6 pages, 3 figures

This paper is concerned with configurations of two-material thermal conductors that minimize the Dirichlet energy for steady-state diffusion equations with nonlinear boundary conditions described mainly by maximal monotone operators. To find such configurations, a homogenization theorem will be proved and applied to an existence theorem for minimizers of a relaxation problem whose minimum value is equivalent to an original design problem. As a typical example of nonlinear boundary conditions, thermal radiation boundary conditions will be the focus, and then the Fr\'echet derivative of the Dirichlet energy will be derived, which is used to estimate the minimum value. Since optimal configurations of the relaxation problem involve the so-called grayscale domains that do not make sense in general, a perimeter constraint problem via the positive part of the level set function will be introduced as an approximation problem to avoid such domains, and moreover, the existence theorem for minimizers of the perimeter constraint problem will be proved. In particular, it will also be proved that the limit of minimizers for the approximation problem becomes that of the relaxation problem in a specific case, and then candidates for minimizers of the approximation problem will be constructed by employing time-discrete versions of nonlinear diffusion equations. In this paper, it will be shown that optimized configurations deeply depend on force terms as a characteristic of nonlinear problems and will also be applied to real physical problems.

We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been extensively used to estimate treatment effects in various scientific fields. However, to gain deeper insights, it is essential to estimate distributional treatment effects rather than relying solely on average effects. Our approach incorporates pre-treatment covariates into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators. The proposed approach can be readily implemented with off-the-shelf machine learning methods and remains valid as long as the nuisance components are reasonably well estimated. Also, we establish the asymptotic properties of the proposed estimator and present a uniformly valid inference method. Through simulation results and real data analysis, we demonstrate the effectiveness of integrating machine learning techniques in reducing the variance of distributional treatment effect estimators in finite samples.

In this paper, we address the issue of estimating and inferring the distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comprehensive understanding of treatment effects by characterizing heterogeneous effects across individual units, as opposed to relying solely on the average treatment effect. To enhance the precision of distributional treatment effect estimation, we propose a regression adjustment method that utilizes the distributional regression and pre-treatment information. Our method is designed to be free from restrictive distributional assumptions. We establish theoretical efficiency gains and develop a practical, statistically sound inferential framework. Through extensive simulation studies and empirical applications, we illustrate the substantial advantages of our method, equipping researchers with a powerful tool for capturing the full spectrum of treatment effects in experimental research.

This short article highlights the unsolved problems of magnetic reconnection in collisionless plasma. The advanced in-situ plasma measurements and simulations enabled scientists to gain a novel understanding of magnetic reconnection. Still, outstanding questions remain on the complex dynamics and structures in the diffusion region, on the cross-scale and regional couplings, on the onset of magnetic reconnection, and on the details of energetics. Future directions of the magnetic reconnection research in terms of new observations, new simulations and interdisciplinary approaches are discussed., Comment: Submitted to Space Science Reviews. This is a review paper and is an outcome of the International Space Science Institute (ISSI) Workshop on Magnetic Reconnection: Explosive Energy Conversion in Space Plasmas held at June 27 - July 1, 2022

Floquet engineering, which involves controlling systems through time-periodic driving, is a method for coherently manipulating quantum materials and realizing dynamical states with novel functionalities. Most research in solid-state systems has focused on the use of AC-electric fields as the controlling drive. In this study, we investigate the effects of AC-magnetic fields on two-dimensional (2D) Dirac electrons and report the emergence of new states and new transport phenomena. In a magnetic field that temporarily changes its direction, an electron nor a hole can sorely complete a cyclotron orbit. However, their resonant state alternating between electron and hole can do so; This leads to a new localized state that forms a flat band dubbed as a $\pi$-Landau level. Then, what would be the counterpart of the Hall effect in AC-magnetic fields? We find that a DC-current in the transverse direction, i.e. a homodyne Hall current, is generated when an additional AC-electric field is applied. In the case of Dirac electrons, several electronic states contribute to this phenomenon including the $\pi$-Landau level. However, when the chemical potential $\mu$ is near the Dirac point, the dominant contribution comes from the low-energy electrons and we numerically find the homodyne Hall current to behave as $I_y=-\frac{e}{h}\mu$ per valley and spin. We explain this phenomenon through the high-frequency effective Floquet Hamiltonian which resembles the chiral Landau level Hamiltonian of three-dimensional Weyl Hamiltonian exhibiting chiral anomaly. We discuss the experimental feasibility and conclude that it is possible to realize this new exotic state using techniques such as THz metamaterial enhancement of magnetic fields., Comment: 15 pages, 6 figures

Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A

To improve the reasoning and question-answering capabilities of Large Language Models (LLMs), several multi-agent approaches have been introduced. While these methods enhance performance, the application of collective intelligence-based approaches to complex network structures and the dynamics of agent interactions remain underexplored. This work extends the concept of multi-agent debate to more general network topologies, measuring the question-answering accuracy, influence, consensus, and the effects of bias on the collective. The results show that random networks perform similarly to fully connected networks despite using significantly fewer tokens. Furthermore, a strong consensus among agents correlates with correct answers, whereas divided responses typically indicate incorrect answers. Analysing the influence of the agents reveals a balance between self-reflection and interconnectedness; self-reflection aids when local interactions are incorrect, and local interactions aid when the agent itself is incorrect. Additionally, bias plays a strong role in system performance with correctly biased hub nodes boosting performance. These insights suggest that using random networks or scale-free networks with knowledgeable agents placed in central positions can enhance the overall question-answering performance of multi-agent systems., Comment: 8 pages, 2024 Conference on Artificial Life

Recent models for natural language understanding are inclined to exploit simple patterns in datasets, commonly known as shortcuts. These shortcuts hinge on spurious correlations between labels and latent features existing in the training data. At inference time, shortcut-dependent models are likely to generate erroneous predictions under distribution shifts, particularly when some latent features are no longer correlated with the labels. To avoid this, previous studies have trained models to eliminate the reliance on shortcuts. In this study, we explore a different direction: pessimistically aggregating the predictions of a mixture-of-experts, assuming each expert captures relatively different latent features. The experimental results demonstrate that our post-hoc control over the experts significantly enhances the model's robustness to the distribution shift in shortcuts. Besides, we show that our approach has some practical advantages. We also analyze our model and provide results to support the assumption., Comment: 21 pages, 5 figures (the layout differs from the MIT Press publication version)

Latent variable models are widely used in social and behavioural sciences, such as education, psychology, and political science. In recent years, high-dimensional latent variable models have become increasingly common for analysing large and complex data. Estimating high-dimensional latent variable models using marginal maximum likelihood is computationally demanding due to the complexity of integrals involved. To address this challenge, stochastic optimisation, which combines stochastic approximation and sampling techniques, has been shown to be effective. This method iterates between two steps -- (1) sampling the latent variables from their posterior distribution based on the current parameter estimate, and (2) updating the fixed parameters using an approximate stochastic gradient constructed from the latent variable samples. In this paper, we propose a computationally more efficient stochastic optimisation algorithm. This improvement is achieved through the use of a minibatch of observations when sampling latent variables and constructing stochastic gradients, and an unadjusted Langevin sampler that utilises the gradient of the negative complete-data log-likelihood to sample latent variables. Theoretical results are established for the proposed algorithm, showing that the iterative parameter update converges to the marginal maximum likelihood estimate as the number of iterations goes to infinity. Furthermore, the proposed algorithm is shown to scale well to high-dimensional settings through simulation studies and a personality test application with 30,000 respondents, 300 items, and 30 latent dimensions.

High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 535 authors from 84 institutions, 12 pages, 8 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Simulations have played a critical role in the advancement of our knowledge of magnetic reconnection. However, due to the inherently multiscale nature of reconnection, it is impossible to simulate all physics at all scales. For this reason, a wide range of simulation methods have been crafted to study particular aspects and consequences of magnetic reconnection. This chapter reviews many of these methods, laying out critical assumptions, numerical techniques, and giving examples of scientific results. Plasma models described include magnetohydrodynamics (MHD), Hall MHD, Hybrid, kinetic particle-in-cell (PIC), kinetic Vlasov, Fluid models with embedded PIC, Fluid models with direct feedback from energetic populations, and the Rice Convection Model (RCM)., Comment: Chapter 5.2 of ISSI Book on Magnetic Reconnection, submitted to Space Science Reviews

Creating systems capable of generating virtually infinite variations of complex and novel behaviour without predetermined goals or limits is a major challenge in the field of AI. This challenge has been addressed through the development of several open-ended algorithms that can continuously generate new and diverse behaviours, such as the POET and Enhanced-POET algorithms for co-evolving environments and agent behaviour. One of the challenges with existing methods however, is that they struggle to continuously generate complex environments. In this work, we propose LLM-POET, a modification of the POET algorithm where the environment is both created and mutated using a Large Language Model (LLM). By fine-tuning a LLM with text representations of Evolution Gym environments and captions that describe the environment, we were able to generate complex and diverse environments using natural language. We found that not only could the LLM produce a diverse range of environments, but compared to the CPPNs used in Enhanced-POET for environment generation, the LLM allowed for a 34% increase in the performance gain of co-evolution. This increased performance suggests that the agents were able to learn a more diverse set of skills by training on more complex environments.

We report the results of the CO $\textit J$=1-0 and SiO $\textit J$=2-1 mapping observations towards the broad-velocity-width molecular feature CO 16.134-0.553 with the Nobeyama Radio Observatory 45 m telescope. The high quality CO map shows that the 5-pc size broad-velocity-width feature bridges two separate velocity components at $\textit V_{\rm{LSR}}$$\quad$$\simeq$ 40 km s$^{-1}$ and 65 km s$^{-1}$ in the position-velocity space. The kinetic power of CO 16.134-0.553 amounts to $7.8\times10^2$ $\textit L$$_\odot$ whereas no apparent driving sources were identified. Prominent SiO emission was detected from the broad-velocity-width feature and its root in the $\textit V_{\rm{LSR}}$$\quad$$\simeq$ 40 km s$^{-1}$ component. In the CO Galactic plane survey data, CO 16.134-0.553 appears to correspond to the Galactic eastern rim of a 15-pc diameter expanding CO shell. An $1\deg$-diameter H I emission void and $4\deg$-long vertical H I filament were also found above and below the CO shell, respectively. We propose that the high-velocity plunge of a dark matter subhalo with a clump of baryonic matter was responsible for the formation of the H I void, CO 16.134-0.553/CO shell, and the H I filament., Comment: 7 pages, 6 figures, 2 table, accepted for publication in ApJ

A compact source, G0.02467-0.0727, was detected in ALMA \threemm observations in continuum and very broad line emission. The continuum emission has a spectral index $\alpha\approx3.3$, suggesting that the emission is from dust. The line emission is detected in several transitions of CS, SO, and SO$_2$ and exhibits a line width FWHM $\approx160$ \kms. The line profile appears Gaussian. The emission is weakly spatially resolved, coming from an area on the sky $\lesssim1"$ in diameter ($\lesssim10^4$ AU at the distance of the Galactic Center; GC). The centroid velocity is $v_{LSR}\approx40$-$50$ \kms, which is consistent with a location in the Galactic Center. With multiple SO lines detected, and assuming local thermodynamic equilibrium (LTE) conditions, $T_\mathrm{LTE} = 13$ K, which is colder than seen in typical GC clouds, though we cannot rule out low-density, subthermally excited, warmer gas. Despite the high velocity dispersion, no emission is observed from SiO, suggesting that there are no strong ($\gtrsim10~\mathrm{km~s}^{-1}$) shocks in the molecular gas. There are no detections at other wavelengths, including X-ray, infrared, and radio. We consider several explanations for the Millimeter Ultra-Broad Line Object (MUBLO), including protostellar outflow, explosive outflow, collapsing cloud, evolved star, stellar merger, high-velocity compact cloud, intermediate mass black hole, and background galaxy. Most of these conceptual models are either inconsistent with the data or do not fully explain it. The MUBLO is, at present, an observationally unique object., Comment: Accepted to ApJL

We statistically investigate high-frequency whistler waves (with frequencies higher than $\sim 10$ % of the local elect ron cyclotron frequency) at Earth's bow shock using Magnetospheric Multi-Scale (MMS) spacecraft observations. We focus specifically on the wave power within the shock transition layer, where we expect electron acceleration via stochastic sh ock drift acceleration (SSDA) to occur associated with efficient pitch-angle scattering by whistler waves. We find that the wave power is positively correlated with both the Alfv\'en Mach number in the normal incidence frame $M_{\rm A}$ and in the de Hoffmann-Teller frame $M_{\rm A}/\cos \theta_{Bn}$. The empirical relation with $M_{\rm A}/\cos \theta_{Bn}$ is compared with the theory of SSDA that predicts a threshold wave power proportional to $(M_{\rm A}/\cos \theta_{Bn})^{-2}$. The result suggests that the wave power exceeds the theoretical threshold for $M_{\rm A} / \cos \theta_{Bn} \gtrsim 30-60$, beyond which efficient electron acceleration is expected. This aligns very well with previous statistical analysis of electron acceleration at Earth's bow shock (M. Oka, G eophys.~Res.~Lett., 33, 5, 2006). Therefore, we consider that this study provides further support for SSDA as the mechanism of electron acceleration at Earth's bow shock. At higher-Mach-number astrophysical shocks, SSDA will be able to inject electrons into the diffusive shock acceleration process for subsequent acceleration to cosmic-ray energies., Comment: 15 pages, 12 figures. Accepted for publication in Physics of Plasmas

The aim of this paper is to give existence and uniqueness results for solutions of the Cauchy problem for semilinear heat equations on stratified Lie groups $\mathbb{G}$ with the homogeneous dimension $N$. We consider the nonlinear function behaves like $|u|^{\alpha}$ or $|u|^{\alpha-1}u$ $(\alpha>1)$ and the initial data $u_0$ belongs to the Sobolev spaces $L^p_s(\mathbb{G})$ for $1

A comprehensive study of the $S$-wave heavy tetraquark states with identical quarks and antiquarks, specifically $QQ{\bar Q'}\bar Q'$ ($Q, Q'=c,b$), $QQ\bar s\bar s$/$\bar Q\bar Q ss$, and $QQ\bar q\bar q$/$\bar Q\bar Q qq$ ($q=u,d$), are studied in a unified constituent quark model. This model contains the one-gluon exchange and confinement potentials. The latter is modeled as the sum of all two-body linear potentials. We employ the Gaussian expansion method to solve the full four-body Schr\"{o}dinger equations, and search bound and resonant states using the complex-scaling method. We then identify $3$ bound and $62$ resonant states. The bound states are all $QQ\bar q\bar q$ states with the isospin and spin-parity quantum numbers $I(J^P)=0(1^+)$: two bound $bb\bar{q}\bar{q}$ states with the binding energies, 153 MeV and 4 MeV below the $BB^*$ threshold, and a shallow $cc\bar{q}\bar{q}$ state at $-15$ MeV from the $DD^*$ threshold. The deeper $bb\bar q \bar q$ bound state aligns with the lattice QCD predictions, while $cc\bar q\bar q$ bound state, still has a much larger binding energy than the recently observed $T^+_{cc}$ by LHCb collaboration. No bound states are identified for the $QQ\bar Q'\bar Q'$, $QQ\bar s\bar s$ and $QQ\bar q\bar q$ with $I=1$. Our analysis shows that the bound $QQ\bar Q'\bar Q'$ states are more probable with a larger mass ratio, $m_Q/m_{Q'}$. Experimental investigation for these states is desired, which will enrich our understanding of hadron spectroscopy and probe insights into the confinement mechanisms within tetraquarks., Comment: 38 pages, 18 figures

Macro variables frequently display time-varying distributions, driven by the dynamic and evolving characteristics of economic, social, and environmental factors that consistently reshape the fundamental patterns and relationships governing these variables. To better understand the distributional dynamics beyond the central tendency, this paper introduces a novel semi-parametric approach for constructing time-varying conditional distributions, relying on the recent advances in distributional regression. We present an efficient precision-based Markov Chain Monte Carlo algorithm that simultaneously estimates all model parameters while explicitly enforcing the monotonicity condition on the conditional distribution function. Our model is applied to construct the forecasting distribution of inflation for the U.S., conditional on a set of macroeconomic and financial indicators. The risks of future inflation deviating excessively high or low from the desired range are carefully evaluated. Moreover, we provide a thorough discussion about the interplay between inflation and unemployment rates during the Global Financial Crisis, COVID, and the third quarter of 2023.

Although the MeV gamma-ray band is a promising energy-band window in astrophysics, the current situation of MeV gamma-ray astronomy significantly lags behind those of the other energy bands in angular resolution and sensitivity. An electron-tracking Compton camera (ETCC), a next-generation MeV detector, is expected to revolutionize the situation. An ETCC tracks each Compton-recoil electron with a gaseous electron tracker and determines the incoming direction of each gamma-ray photon; thus, it has a strong background rejection power and yields a better angular resolution than classical Compton cameras. Here, we study ETCC events in which the Compton-recoil electrons do not deposit all energies to the electron tracker but escape and hit the surrounding pixel scintillator array (PSA). We developed an analysis method for this untapped class of events and applied it to laboratory and simulation data. We found that the energy spectrum obtained from the simulation agreed with that of the actual data within a factor of 1.2. We then evaluated the detector performance using the simulation data. The angular resolution for the new-class events was found to be twice as good as in the previous study at the energy range 1.0--2.0~MeV, where both analyses overlap. We also found that the total effective area is dominated by the contribution of the double-hit events above an energy of 1.5~MeV. Notably, applying this new method extends the sensitive energy range with the ETCC from 0.2--2.1 MeV in the previous studies to up to 3.5~MeV. Adjusting the PSA dynamic range should improve the sensitivity in even higher energy gamma-rays. The development of this new analysis method would pave the way for future observations by ETCC to fill the MeV-band sensitivity gap in astronomy., Comment: 11 pages, 8 figures, Accepted for publication in NIM A

Large Language Models (LLMs) have demonstrated a number of human-like abilities, however the empathic understanding and emotional state of LLMs is yet to be aligned to that of humans. In this work, we investigate how the emotional state of generative LLM agents evolves as they perceive new events, introducing a novel architecture in which new experiences are compared to past memories. Through this comparison, the agent gains the ability to understand new experiences in context, which according to the appraisal theory of emotion is vital in emotion creation. First, the agent perceives new experiences as time series text data. After perceiving each new input, the agent generates a summary of past relevant memories, referred to as the norm, and compares the new experience to this norm. Through this comparison we can analyse how the agent reacts to the new experience in context. The PANAS, a test of affect, is administered to the agent, capturing the emotional state of the agent after the perception of the new event. Finally, the new experience is then added to the agents memory to be used in the creation of future norms. By creating multiple experiences in natural language from emotionally charged situations, we test the proposed architecture on a wide range of scenarios. The mixed results suggests that introducing context can occasionally improve the emotional alignment of the agent, but further study and comparison with human evaluators is necessary. We hope that this paper is another step towards the alignment of generative agents., Comment: 14 pages, 6 figures

We introduce a class of complex surface singularities - the blow-$ADE$ singularities - which are likely to be stable with respect to $\mu^*$-constant deformations. We prove such a stability property in several special cases. Here, we emphasize that we are not just considering deformation families for small values of the deformation parameter but families connecting any two elements in the $\mu^*$-constant stratum.

Motivated by recent progress of structural optimization problems, the paper presents a new method for constructing the distance function to the boundary of given sets of interest, which simplifies the optimization procedure. We extend the celebrated Varadhan's elliptic equation theory in 1967 by adding the source term to the equation, in which we encode the information about the set. We will also establish the rate of convergence in this new framework, which is sharp at least in the one-dimensional case.

While generative AI is now widespread and useful in society, there are potential risks of misuse, e.g., unconsciously influencing cognitive processes or decision-making. Although this causes a security problem in the cognitive domain, there has been no research about neural and computational mechanisms counteracting the impact of malicious generative AI in humans. We propose DecNefGAN, a novel framework that combines a generative adversarial system and a neural reinforcement model. More specifically, DecNefGAN bridges human and generative AI in a closed-loop system, with the AI creating stimuli that induce specific mental states, thus exerting external control over neural activity. The objective of the human is the opposite, to compete and reach an orthogonal mental state. This framework can contribute to elucidating how the human brain responds to and counteracts the potential influence of generative AI.

Uranus and Neptune have intrinsic magnetic fields generated via convection in a molten H2O layer, where the field strength is determined by its electrical conductivity (EC) along with convection size and velocity. Previous shock experiments reported that the EC of molten H2O is high enough to generate magnetic fields of these ice giant planets with adiabatic thermal structures. Here we measured the EC of ionic H2O fluid for the first time by static compression experiments up to 45 GPa and 2,750 K. The EC determined is lower by a few orders of magnitude than earlier data by shock compression measurements and not capable of generating a magnetic field with the conventional interior thermal structures. Our results necessitate recently-suggested fewfold hotter interiors of Uranus and Neptune to explain their magnetic fields.

We investigate the structure of ground-state heavy mesons within the light-front quark model, utilizing wave functions derived from the Single Gaussian Ansatz (SGA) and the Gaussian Expansion Method (GEM). By performing a $\chi^2$ fit to static properties such as mass spectra and decay constants, we determine the model parameters for each approach. We then compare the impacts of both methods on the light-front wave functions and structural observables. Our analysis reveals significant differences in the distribution amplitudes (DAs) $\phi_{2;M}(x)$ near the endpoints, with GEM showing enhanced amplitudes and correct asymptotic behavior $\phi_{2;M}(x \to 1) \propto (1-x)$, consistent with perturbative QCD. This endpoint behavior is linked to the short-range (high-momentum) wave function governed by color Coulomb interaction and relativistic kinematics. GEM accurately reproduces a power-law damping $\psi_0(k \to \infty) \propto 1/k_\perp^2$, aligning with perturbative QCD predictions. Furthermore, the electromagnetic form factors of pseudoscalar mesons in the low-$Q^2$ region fall off faster with GEM than with SGA. Overall, while both methods adequately describe static properties, GEM provides a more accurate description of structural properties, being more sensitive to details and asymptotic behaviors., Comment: 17 pages, 11 figures, and 6 tables. We add more results and discussion

Here, we study the motion of axisymmetric hypersurfaces $\{\Gamma_t\}_{t\ge0}$ evolved by forced mean curvature flows in the periodic setting. We establish conditions that quenching occurs or does not occur in terms of the initial data and forcing term. We also study the locations where the quenching happens in some special cases., Comment: 16 pages

Perovskites with Bi or Pb on the A-site host a number of interesting and yet to be understood phenomena such as negative thermal expansion in BiNiO$_3$. We employ hard x-ray photoemission spectroscopy of Ni 2$p$ core-level as well as valence band to probe the electronic structure of BiNiO$_3$ and PbNiO$_3$. The experimental results supported by theoretical calculations using dynamical mean-field theory reveal essentially identical electronic structure of the Ni-O subsystem typical of Ni$^{2+}$ charge-transfer insulators. The two materials are distinguished by filling of the Bi(Pb)-O antibonding states in the vicinity of the Fermi level, which is responsible for the Bi disproportionation in BiNiO$_3$ at ambient pressure and absence of similar behavior in PbNiO$_3$. The present experiments provide evidence for this conclusion by revealing the presence/absence of Bi/Pb $6s$ states at the top of the valence band in the two materials.