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We analyze the latest LHCb data on the $\pi^+\pi^-$ spectrum in the isospin-violating $X(3872)~\to~J/\psi \pi^+\pi^-$ decay, employing a model-independent approach based on dispersion theory to deal with the $\pi\pi$ final state interactions. Additionally, the isospin breaking effects are properly introduced, allowing for reliable and accurate extraction of the ratio, $R_X$, between the $X(3872)$ couplings to the $J/\psi \rho$ and $J/\psi \omega$ channels from the data. We find very good agreement with the LHCb data for the whole range of the $\pi^+\pi^-$ invariant mass, and $R_X$ is determined to be $0.19\pm 0.02$, significantly smaller than what was determined earlier. Using this value, we make predictions for the $\pi^+\pi^0\pi^-$ mass distribution in the $X(3872)~\to~J/\psi \pi^+\pi^0\pi^-$ process, which is currently accessible by the BESIII Collaboration, and update a prediction for the pole positions of the isovector partner states of the $X(3872)$, $W_{c1}$, with $I(J^{PC})=1(1^{++})$., Comment: 9 pages, 1 table and 2 figures

Here we propose a class of frameworks in the plane, braced polygons, that may be globally rigid and are analogous to convex polyopes in 3 space that are rigid by Cauchy's rigidity Theorem in 1813., Comment: 35 pages, 25 figures

The brightest-of-all-time gamma-ray burst (GRB), GRB 221009A, is the first GRB observed to have emission line (up to 37 MeV) in its prompt emission spectra. It is naturally explained as \pair annihilation line that was Doppler boosted in the relativistic jet of the GRB. In this work, we repeatedly apply the simple optical depth argument to different physical processes necessary to produce an observable \pair annihilation line. This approach results in robust constraints on the physics of the line: We conclude that in GRB 221009A, the \pair pairs were produced at a radius greater than $4.3\times 10^{15}$\,cm from the central engine, and annihilated in a region between $1.4\times 10^{16}$\,cm and $4.3\times 10^{16}$\,cm. From these constraints, we established a self-consistent picture of \pair production, cooling, and annihilation. We also derived a criterion for pair production in the GRB prompt emission: $E_{\rm{iso}} \gtrsim3.3\times 10^{53} E_{\rm{peak},100} (1+z) R^2_{\rm{prod},16}~\text{erg}$. Using this criterion, we find tens of candidate GRBs that could have produced \pair in prompt emissions to annihilate. GRB 221009A is with the highest likelihood according to this criterion. We also predict the presence of a thermal radiation, with a time-evolving black body temperature, sweeping through soft X-ray during the prompt emission phase., Comment: 10 pages, 4 figures, submitted to MNRAS

We integrate neural operators with diffusion models to address the spectral limitations of neural operators in surrogate modeling of turbulent flows. While neural operators offer computational efficiency, they exhibit deficiencies in capturing high-frequency flow dynamics, resulting in overly smooth approximations. To overcome this, we condition diffusion models on neural operators to enhance the resolution of turbulent structures. Our approach is validated for different neural operators on diverse datasets, including a high Reynolds number jet flow simulation and experimental Schlieren velocimetry. The proposed method significantly improves the alignment of predicted energy spectra with true distributions compared to neural operators alone. Additionally, proper orthogonal decomposition analysis demonstrates enhanced spectral fidelity in space-time. This work establishes a new paradigm for combining generative models with neural operators to advance surrogate modeling of turbulent systems, and it can be used in other scientific applications that involve microstructure and high-frequency content. See our project page: vivekoommen.github.io/NO_DM

Simulation results are presented to demonstrate electron temperature and electrical potential development in dilute and cold plasma development. The simulation method is a hybrid method which adopted fluid model for electrons due to their high mobility, while heavy ions and neutrals are modelled with the direct simulation Monte Carlo and Particle-In-Cell methods. The flows include steady, starting-up and shutting-down scenarios. The goal is to illustrate the exponential behaviors which were predicted in several recently developed formulas. Those formulas include many coefficients related with local properties, and they are difficult to determine. Hence, those trends can only efficiently demonstrate by numerical simulations which are more convenient than experimental measurements. The results confirm several facts. For steady plasma flows, the electron temperature and potential profiles are smooth, very likely, they can be approximated with exponential functions. For unsteady flows, the property developing trends in the shutting down or starting-up processes change monotonically. Further, at locations with large gradients, the property change trends are less ideal than those formulas. This is consistent with the assumptions with which those formulas were developed., Comment: 8 pages. arXiv admin note: text overlap with arXiv:2311.08423

In this work, we systematically study the heavy baryonium and heavy dibaryon systems using the Bethe-Salpeter equation in the ladder and instantaneous approximations for the kernel. Our results indicate that all the heavy baryonium systems, specifically $\Lambda_Q\bar{\Lambda}_Q$, $\Xi_Q\bar{\Xi}_Q$, $\Sigma_Q\bar{\Sigma}_Q$, $\Xi'_Q\bar{\Xi}'_Q$, and $\Omega_Q\bar{\Omega}_Q$ ($Q=c, b$), can form bound states. Among the heavy dibaryon systems, only the $\Xi_Q\Xi_Q$ system with $I=0$ and the $\Sigma_Q\Sigma_Q$ systems with $I=0$ and $I=1$ can exist as bound states. Additionally, the $\Sigma_Q\bar{\Sigma}_Q$ system with $I=2$ and the $\Sigma_Q\Sigma_Q$ system with $I=1$ are not deeply bound.

This paper introduces a novel incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting (MCS) problems, enabling decision makers to progressively provide assignment example preference information. Specifically, we first construct a max-margin optimization-based model to model potentially non-monotonic preferences and inconsistent assignment example preference information in each iteration of the incremental preference elicitation process. Using the optimal objective function value of the max-margin optimization-based model, we devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration within the framework of uncertainty sampling in active learning. Once the termination criterion is satisfied, the sorting result for non-reference alternatives can be determined through the use of two optimization models, i.e., the max-margin optimization-based model and the complexity controlling optimization model. Subsequently, two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences, considering different termination criteria. Ultimately, we apply the proposed approach to a credit rating problem to elucidate the detailed implementation steps, and perform computational experiments on both artificial and real-world data sets to compare the proposed question selection strategies with several benchmark strategies., Comment: 37 pages, 22 figures

Deriving a representative model using value function-based methods from the perspective of preference disaggregation has emerged as a prominent and growing topic in multi-criteria sorting (MCS) problems. A noteworthy observation is that many existing approaches to learning a representative model for MCS problems traditionally assume the monotonicity of criteria, which may not always align with the complexities found in real-world MCS scenarios. Consequently, this paper proposes some approaches to learning a representative model for MCS problems with non-monotonic criteria through the integration of the threshold-based value-driven sorting procedure. To do so, we first define some transformation functions to map the marginal values and category thresholds into a UTA-like functional space. Subsequently, we construct constraint sets to model non-monotonic criteria in MCS problems and develop optimization models to check and rectify the inconsistency of the decision maker's assignment example preference information. By simultaneously considering the complexity and discriminative power of the models, two distinct lexicographic optimization-based approaches are developed to derive a representative model for MCS problems with non-monotonic criteria. Eventually, we offer an illustrative example and conduct comprehensive simulation experiments to elaborate the feasibility and validity of the proposed approaches., Comment: 45 pages, 12 figures

Accurately predicting stock returns is crucial for effective portfolio management. However, existing methods often overlook a fundamental issue in the market, namely, distribution shifts, making them less practical for predicting future markets or newly listed stocks. This study introduces a novel approach to address this challenge by focusing on the acquisition of invariant features across various environments, thereby enhancing robustness against distribution shifts. Specifically, we present InvariantStock, a designed learning framework comprising two key modules: an environment-aware prediction module and an environment-agnostic module. Through the designed learning of these two modules, the proposed method can learn invariant features across different environments in a straightforward manner, significantly improving its ability to handle distribution shifts in diverse market settings. Our results demonstrate that the proposed InvariantStock not only delivers robust and accurate predictions but also outperforms existing baseline methods in both prediction tasks and backtesting within the dynamically changing markets of China and the United States.

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combines symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: i) G-SympGNN and ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. We also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks., Comment: 17 pages, 10 figures

Whether the $N\bar{N}$ interaction could form a state or not is a long standing question, even before the observation of the $p\bar{p}$ threshold enhancement in 2003. The recent high statistic measurement in the $J/\psi \to \gamma 3(\pi^+\pi^-)$ channel would provide a good opportunity to probe the nature of the peak structures around the $p\bar{p}$ threshold in various processes. By constructing the $N\bar{N}$ interaction respecting chiral symmetry, we extract the pole positions by fitting the $p\bar{p}$ and $3(\pi^+\pi^-)$ invariant mass distributions of the $J/\psi \to \gamma p \bar p$ and $J/\psi \to \gamma 3(\pi^+\pi^-)$ processes. The threshold enhancement in the $p\bar{p}$ invariant mass distribution is from the pole on the third Riemann sheet, which more couples to the isospin triplet channel. The broader structure in the $3(\pi^+\pi^-)$ invariant mass comes from the pole on the physical Riemann sheet, which more couples to the isospin singlet channel. Furthermore, the large compositeness indicates that there should exit $p\bar{p}$ resonance based on the current experimental data. In addition, we also see a clear threshold enhancement in the $n\bar{n}$ channel, but not as significant as that in $p\bar{p}$ channel, which is useful and compared with further experimental measurement., Comment: 16 pages, 8 figures

One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical support for ensuring that the underlying states can be uniquely recovered through identifiability. Consequently, some existing work focuses on establishing identifiability from a causal perspective to aid in the design of algorithms. However, these results are often derived from a purely causal viewpoint, which may overlook the specific RL context. We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states. More importantly, removing these assumptions allows algorithm design to go beyond the earlier boundaries constrained by them. Leveraging these insights, we propose a novel approach for general partially observable Markov Decision Processes (POMDPs) by replacing the complicated structural constraints in previous methods with two simple constraints for transition and reward preservation. With the two constraints, the proposed algorithm is guaranteed to disentangle state and noise that is faithful to the underlying dynamics. Empirical evidence from extensive benchmark control tasks demonstrates the superiority of our approach over existing counterparts in effectively disentangling state belief from noise.

Online advertising is a primary source of income for e-commerce platforms. In the current advertising pattern, the oriented targets are the online store owners who are willing to pay extra fees to enhance the position of their stores. On the other hand, brand suppliers are also desirable to advertise their products in stores to boost brand sales. However, the currently used advertising mode cannot satisfy the demand of both stores and brand suppliers simultaneously. To address this, we innovatively propose a joint advertising model termed Joint Auction, allowing brand suppliers and stores to collaboratively bid for advertising slots, catering to both their needs. However, conventional advertising auction mechanisms are not suitable for this novel scenario. In this paper, we propose JRegNet, a neural network architecture for the optimal joint auction design, to generate mechanisms that can achieve the optimal revenue and guarantee near dominant strategy incentive compatibility and individual rationality. Finally, multiple experiments are conducted on synthetic and real data to demonstrate that our proposed joint auction significantly improves platform revenue compared to the known baselines.

Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the deep probabilistic subsampling (DPS) network acquires an optimal fixed pilot pattern. Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI. Simulation results show that the WEI-CSIP can reduce pilot overhead from 1/5 to 1/8, while improving prediction accuracy with normalized mean squared error reduced to 0.0113, an improvement of 83.2% compared to traditional channel prediction methods.

We propose a novel neural network architecture (TSympOCNet) to address high--dimensional optimal control problems with linear and nonlinear dynamics. An important application of this method is to solve the path planning problem of multi-agent vehicles in real time. The new method extends our previous SympOCNet framework by introducing a time-dependent symplectic network into the architecture. In addition, we propose a more general latent representation, which greatly improves model expressivity based on the universal approximation theorem. We demonstrate the efficacy of TSympOCNet in path planning problems with obstacle and collision avoidance, including systems with Newtonian dynamics and non-convex environments, up to dimension 512. Our method shows significant promise in handling efficiently both complex dynamics and constraints.

A mechanism of generating CP violation through the imaginary part of the interference of two amplitudes is proposed. This mechanism has shown clear evidence in decays such as $B^\pm\to \pi^\pm\pi^+\pi^-$. The proposed mechanism is helpful in searching for CP violation in bottom and charmed baryon decay processes., Comment: 9 pages

Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement for representation learning in CTDGs is the appropriate estimation and preservation of proximity. However, due to the sparse and evolving characteristics of CTDGs, the spatial-temporal properties inherent in high-order proximity remain largely unexplored. Despite its importance, this property presents significant challenges due to the computationally intensive nature of personalized interaction intensity estimation and the dynamic attributes of CTDGs. To this end, we propose a novel Correlated Spatial-Temporal Positional encoding that incorporates a parameter-free personalized interaction intensity estimation under the weak assumption of the Poisson Point Process. Building on this, we introduce the Dynamic Graph Transformer with \Correlated Spatial-Temporal Positional Encoding (CorDGT), which efficiently retains the evolving spatial-temporal high-order proximity for effective node representation learning in CTDGs. Extensive experiments on seven small and two large-scale datasets demonstrate the superior performance and scalability of the proposed CorDGT.

X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on systematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reliable classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed Random Forest classifier, built on selected features derived from light curves, energy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classification during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

Since 2003, plenty of resonant structures have been observed in the heavy quarkonium regime. Many of them are close to the thresholds of a few pairs of heavy hadrons. They are candidates of exotic hadrons and have attracted immense attentions. Based on a coupled-channel nonrelativistic effective field theory, we classify the near-threshold structures of a symmetry-related two-channel system according to the scattering length and channel coupling strength. We show that the evolution of the scattering amplitude line shapes can be understood from the pole trajectories in the complex energy plane, and the pole evolution can be traced back to the renormalization group fixed points. We provide a dictionary of correspondence between the evolution of line shapes and pole trajectories, which can be used to understand the experimental observations of the near-threshold structures., Comment: 8 pages, 2 figures, 2 tables. Comments are welcome

Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies. Despite the proliferation of methods designed for this emerging task, the lack of standard experimental settings and fair performance comparisons makes it challenging to understand which and when models perform well across different scenarios. To fill this gap, we present the first comprehensive benchmark for unsupervised graph domain adaptation named GDABench, which encompasses 16 algorithms across 5 datasets with 74 adaptation tasks. Through extensive experiments, we observe that the performance of current UGDA models varies significantly across different datasets and adaptation scenarios. Specifically, we recognize that when the source and target graphs face significant distribution shifts, it is imperative to formulate strategies to effectively address and mitigate graph structural shifts. We also find that with appropriate neighbourhood aggregation mechanisms, simple GNN variants can even surpass state-of-the-art UGDA baselines. To facilitate reproducibility, we have developed an easy-to-use library PyGDA for training and evaluating existing UGDA methods, providing a standardized platform in this community. Our source codes and datasets can be found at: https://github.com/pygda-team/pygda.

Type I gamma-ray bursts (GRBs) are believed to originate from compact binary merger usually with duration less than 2 seconds for the main emission. However, recent observations of GRB 211211A and GRB 230307A indicate that some merger-origin GRBs could last much longer. Since they show strikingly similar properties (indicating a common mechanism) which are different from the classic "long"-short burst (e.g. GRB 060614), forming an interesting subclass of type I GRBs, we suggest to name them as type IL GRBs. By identifying the first peak of GRB 230307A as a quasi-thermal precursor, we find that the prompt emission of type IL GRB is composed of three episodes: (1) a precursor followed by a short quiescent (or weak emission) period, (2) a long-duration main emission, and (3) an extended emission. With this burst pattern, a good candidate, GRB 170228A, was found in the Fermi/GBM archive data, and subsequent temporal and spectral analyses indeed show that GRB 170228A falls in the same cluster with GRB 211211A and GRB 230307A in many diagnostic figures. Thus this burst pattern could be a good reference for rapidly identifying type IL GRB and conducting low-latency follow-up observation. We estimated the occurrence rate and discussed the physical origins and implications for the three emission episodes of type IL GRBs. Our analysis suggests the pre-merger precursor model, especially the super flare model, is more favored for type IL GRBs.

The Goos-H{\"a}nchen (GH) shift is a specifical optical phenomenon that describes a shift parallel to the reflected light inside the plane of incidence, when a finite-width light undergoes total internal reflection at the interface of medium. Although the GH shift in optics has been widely observed experimentally, its generalization remains uncovered completely in relativistic quantum mechanics for the existence of Klein's paradox. Recently, Wang has solved Klein's paradox based on the different solutions adpoted for Dirac's equation with step potential in corresponding energy regions \href{https://dx.doi.org/10.1088/2399-6528/abd340}{[J. Phys. Commun. {\bf 4}, 125010 (2020)]}. In the light of Wang's method, we calculate the GH shift for Dirac fermions under relativistic conditions when they are incident obliquely on a three-dimensional infinite potential barrier. Furthermore, we find that the relativistic quantum GH shift can be negative, which is different from the non-relativistic case., Comment: 9 pages, 2 figures

The medium range order (MRO) in amorphous systems has been linked to complex features such as the dynamic heterogeneity of supercooled liquids or the plastic deformation of glasses. However, the nature of the MRO in these materials has remained elusive, primarily due to the lack of methods capable of characterizing this order. Here, we leverage standard two-body structural correlators and advanced many-body correlation functions to probe numerically the MRO in prototypical network glassformers, i.e., silica and sodium silicates, systems that are of importance in natural as well as industrial settings. With increasing Na concentration, one finds that the local environment of Na becomes more structured and the spatial distribution of Na on intermediate length scales changes from blob-like to channel-like, indicating a growing inhomogeneity in the spatial Na arrangement. In parallel, we find that the Si-O network becomes increasingly depolymerized, resulting in a ring size distribution that broadens. The radius of gyration of the rings is well described by a power-law with an exponent around 0.75, indicating that the rings are progressively more crumbled with increasing size. Using a recently proposed four-point correlation function, we reveal that the relative orientation of the tetrahedra shows a transition at a distance around 4 Angstroms, a structural modification that is not seen in standard two-point correlation functions. Furthermore, we find that the length scale characterizing the MRO is non-monotonic as a function of temperature, caused by the competition between energetic and entropic terms. Finally, we demonstrate that the structural correlation lengths as obtained from the correlation functions that quantify the MRO are correlated with macroscopic observables such as the kinetic fragility of the liquids and the elastic properties of the glasses.

With rapidly increasing distributed deep learning workloads in large-scale data centers, efficient distributed deep learning framework strategies for resource allocation and workload scheduling have become the key to high-performance deep learning. The large-scale environment with large volumes of datasets, models, and computational and communication resources raises various unique challenges for resource allocation and workload scheduling in distributed deep learning, such as scheduling complexity, resource and workload heterogeneity, and fault tolerance. To uncover these challenges and corresponding solutions, this survey reviews the literature, mainly from 2019 to 2024, on efficient resource allocation and workload scheduling strategies for large-scale distributed DL. We explore these strategies by focusing on various resource types, scheduling granularity levels, and performance goals during distributed training and inference processes. We highlight critical challenges for each topic and discuss key insights of existing technologies. To illustrate practical large-scale resource allocation and workload scheduling in real distributed deep learning scenarios, we use a case study of training large language models. This survey aims to encourage computer science, artificial intelligence, and communications researchers to understand recent advances and explore future research directions for efficient framework strategies for large-scale distributed deep learning.

The recent PREX-2 and CREX data on the model-independent extraction of the charge-weak form factor difference $\Delta F_{\rm CW}$ in $^{208}$Pb and $^{48}$Ca challenge modern nuclear energy density functionals (EDFs) as well as our present understanding on the neutron skin and nuclear symmetry energy. Within the Skyrme-like EDFs, we demonstrate that the isovector spin-orbit interaction can strongly change the $\Delta F_{\rm CW}$ in $^{48}$Ca while it has essentially no influence on the $\Delta F_{\rm CW}$ in $^{208}$Pb, mainly due to the eight spin-orbit unpaired $1f_{7/2}$ neutrons in $^{48}$Ca. To simultaneously describe PREX-2 and CREX data in $1\sigma$ error, we find the strength of isovector spin-orbit interaction should be larger than about four times of that in the conventional Skyrme-like EDFs, implying the neutrons and protons have significantly different spin-orbit interaction. To further reconcile the data on electric dipole polarizability in $^{208}$Pb and $^{48}$Ca, we obtain $L \approx 55$ MeV for the slope parameter of the symmetry energy, $\Delta r_{\rm np}(^{208}\rm{Pb}) \approx 0.19$ fm and $\Delta r_{\rm np}(^{48}\rm{Ca}) \approx 0.12$ fm for the neutron skin thickness. The implications of the strong isovector spin-orbit interaction are discussed., Comment: 14 pages, 6 figures (including Supplemental Material)

Dimerized quantum magnets are exotic crystalline materials where Bose-Einstein condensation of magnetic excitations can happen. However, known dimerized quantum magnets are limited to only a few oxides and halides. Here, we unveil 9 dimerized quantum magnets and 11 conventional antiferromagnets in ternary metal borides MTB$_4$ (M = Sc, Y, La, Ce, Lu, Mg, Ca, Al; T = V, Cr, Mn, Fe, Co, Ni). In this type of structure, 3d transition-metal atoms T are arranged in dimers. Quantum magnetism in these compounds is dominated by strong antiferromagnetic interactions between Cr (both Cr and Mn for M = Mg and Ca) atoms within the structural dimers, with much weaker interactions between the dimers. These systems are proposed to be close to a quantum critical point between a disordered singlet spin-dimer phase, with a spin gap, and the ordered conventional N\'eel antiferromagnetic phase. This new family of dimerized quantum magnets greatly enriches the materials inventory that allows investigations of the spin-gap phase. All the quantum-, conventionally-, and non-magnetic systems identified, together with experimental synthesis methods of a phase suitable for characterization, provide a platform with abundant possibilities to tune the magnetic exchange coupling by doping and study this unconventional type of quantum phase transition. This work opens up new avenues for studying the quantum magnetism of spin dimers in borides and establishes a theoretical workflow for future searches for dimerized quantum magnets in other families or types of materials.

The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool support is available for computing basic properties such as expected rewards on basic models such as Markov chains. Previous editions of QComp, the comparison of tools for the analysis of quantitative formal models, focused on this setting. Many application scenarios, however, require more advanced property types such as LTL and parameter synthesis queries as well as advanced models like stochastic games and partially observable MDPs. For these, tool support is in its infancy today. This paper presents the outcomes of QComp 2023: a survey of the state of the art in quantitative verification tool support for advanced property types and models. With tools ranging from first research prototypes to well-supported integrations into established toolsets, this report highlights today's active areas and tomorrow's challenges in tool-focused research for quantitative verification.

Ultra-relativistic jets are believed to play important role in producing prompt emission and afterglow of gamma-ray burst (GRB), but the nature of the jet is poorly known owing to the lack of decisive features observed in the prompt emission. The discovery of emission line evolving from about 37 MeV to 6 MeV in the brightest-of-all-time GRB 221009A provides an unprecedented opportunity to probe GRB-jet physics. The time evolution of the central energy of the line with power-law index $-1$ is naturally explained by high-latitude curvature effect. Under the assumption that the line emission is generated in the prompt emission by $e^\pm$ pair production, cooling and annihilation in the jet, we can strictly constrain jet physics with observed line-emission properties. We find that the radius of the emission region is $r\gtrsim10^{16}$ cm. The narrow line width of $\sim10\%$ requires that the line emission occurs within 10\% of the dynamical time, which further implies short timescales of pair cooling to non-relativistic state and pair annihilation, and a slightly clumpy emission region. If the jet's Lorentz factor is $\Gamma\gtrsim400$, the fast cooling requirement needs an energy density of magnetic field in the jet much larger than that of prompt gamma-rays, i.e., a magnetically dominated jet. The temporal behavior of line flux suggests some angle dependence of line emission. We also discuss the difficulties of other scenarios for the observed emission line., Comment: 8 pages, 3 figures, ApJL accepted; pair density and optical depth modified, main conclusions unchanged