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We have computed exactly the energy spectrum and corresponding wave functions of two bosonic particles, which are confined in a spherically symmetric shell-shaped trap and interact with each other via a three-dimensional zero-range potential. Confinement induced resonances (CIRs), originating entirely from the strong coupling of the relative and center-of-mass motions of the two particles, are identified as avoided crossings at certain values of the shell radius. By working close to the found CIRs, these results offer a new way to enhance the atom-atom interaction in the atomic gas by tuning only the geometrical parameters of the shell.

Understanding the late-time acceleration of the universe and its subtleties is one of the biggest mysteries in cosmology. A lot of different approaches have been put forward to deal with this, ranging from the conventional cosmological constant to various models of dark energy and beyond. Recently one very interesting approach to explaining the late time acceleration has been put forward, where the the expansion of the universe is driven by mergers with other "baby" universes and has been shown to be quite viable as well from the point of view of recent observational data. So in this work we examine the possibility of various rip scenarios and other future cosmological singularities in such "multiversal" scenario, probing such singularities for the first time in a multi universe scenario. We examine two models of such a baby universe merging cosmology, and show that remarkably no rip scenario or future cosmological singularity is possible in such models., Comment: v2, 17 pages with no figures, matches the published version in JCAP

Aligning large language models with human objectives is paramount, yet common approaches including RLHF suffer from unstable and resource-intensive training. In response to this challenge, we introduce ARGS, Alignment as Reward-Guided Search, a novel framework that integrates alignment into the decoding process, eliminating the need for expensive RL training. By adjusting the model's probabilistic predictions using a reward signal, ARGS generates texts with semantic diversity while being aligned with human preferences, offering a promising and flexible solution for aligning language models. Notably, ARGS demonstrates consistent enhancements in average reward compared to baselines across diverse alignment tasks and various model dimensions. For example, under the same greedy-based decoding strategy, our method improves the average reward by 19.56% relative to the baseline and secures a preference or tie score of 64.33% in GPT-4 evaluation. We believe that our framework, emphasizing decoding-time alignment, paves the way for more responsive language models in the future. Code is publicly available at: \url{https://github.com/deeplearning-wisc/args}., Comment: ICLR 2024

This paper investigates the ability of transformer-based models to learn structural recursion from examples. Recursion is a universal concept in both natural and formal languages. Structural recursion is central to the programming language and formal mathematics tasks where symbolic tools currently excel beyond neural models, such as inferring semantic relations between datatypes and emulating program behavior. We introduce a general framework that nicely connects the abstract concepts of structural recursion in the programming language domain to concrete sequence modeling problems and learned models' behavior. The framework includes a representation that captures the general \textit{syntax} of structural recursion, coupled with two different frameworks for understanding their \textit{semantics} -- one that is more natural from a programming languages perspective and one that helps bridge that perspective with a mechanistic understanding of the underlying transformer architecture. With our framework as a powerful conceptual tool, we identify different issues under various set-ups. The models trained to emulate recursive computations cannot fully capture the recursion yet instead fit short-cut algorithms and thus cannot solve certain edge cases that are under-represented in the training distribution. In addition, it is difficult for state-of-the-art large language models (LLMs) to mine recursive rules from in-context demonstrations. Meanwhile, these LLMs fail in interesting ways when emulating reduction (step-wise computation) of the recursive function., Comment: arXiv admin note: text overlap with arXiv:2305.14699

Employing the numerically accurate multiple Davydov Ansatz in combination with the thermo-field dynamics approach, we delve into interplay of the finite-temperature dynamics of holes and magnons in an antiferromagnet, which allows for scrutinizing previous predictions from self-consistent Born approximation while offering, for the first time, accurate finite-temperature computation of detailed magnon dynamics as a response and a facilitator to the hole motion. The study also uncovers pronounced temperature dependence of the magnon and hole populations, pointing to the feasibility of potential thermal manipulation and control of hole dynamics. Our methodology can be applied not only to the calculation of steady-state angular-resolved photoemission spectra, but also to the simulation of femtosecond terahertz pump-probe and other nonlinear signals for the characterization of antiferromagnetic materials., Comment: 28 pages, 5 figures

Spontaneous Hall conductivity has recently been reported in the triangular lattice antiferromagnet Co$_{1/3}$TaS$_2$ under a zero magnetic field. This phenomenon originates from the distinctive noncoplanar triple-Q magnetic ground state, possessing uniform real-space Berry curvature characterized by scalar spin chirality. We investigated the physical properties of Co$_{1/3}$TaS$_2$ by judiciously controlling the composition, revealing a drastic change in its bulk properties, even by slight variations in cobalt composition, despite the same crystal structure. For $0.299 < x < 0.325$, Co$_x$TaS$_2$ keeps all the characteristics of the ground state consistent with the previous studies -- two antiferromagnetic phase transitions at $T_{N1}$ and $T_{N2} (< T_{N1})$, a large spontaneous Hall conductivity (${\sigma}_{xy} (H=0)$), and a weak ferromagnetic moment along the c-axis. However, samples with $x > 0.330$ exhibit distinct bulk properties, including the absence of both ${\sigma}_{xy} (H=0)$ and the weak ferromagnetic moment. Our neutron diffraction data reveal that Co$_x$TaS$_2$ with $x > 0.330$ develops coplanar helical magnetic order with $q_{m1} = (1/3, 0, 0)$. This is entirely different from what has been seen in $x < 0.325$, explaining the observed composition dependence., Comment: 15 pages, 4 figures, accepted for publication in Phys. Rev. B

Ferroelectric nematic liquid crystals are formed by achiral molecules with large dipole moments. Its three-dimensional orientational order is universally described as unidirectionally polar. We demonstrate that the ground state of ferroelectric nematic unconstrained by externally imposed alignment directions is chiral, with left- and right-hand twists of polarization. Although the helicoidal deformations and defect walls separating domains of opposite handedness increase the elastic energy, the twists reduce the electrostatic energy and become weaker when the material is doped with ions. The study shows that the polar orientational order of molecules could trigger chirality in the soft matter with no chemically induced chiral centers., Comment: 24 pages, 13 figures

Trajectory optimization is a widely used technique in robot motion planning for letting the dynamics and constraints on the system shape and synthesize complex behaviors. Several previous works have shown its benefits in high-dimensional continuous state spaces and under differential constraints. However, long time horizons and planning around obstacles in non-convex spaces pose challenges in guaranteeing convergence or finding optimal solutions. As a result, discrete graph search planners and sampling-based planers are preferred when facing obstacle-cluttered environments. A recently developed algorithm called INSAT effectively combines graph search in the low-dimensional subspace and trajectory optimization in the full-dimensional space for global kinodynamic planning over long horizons. Although INSAT successfully reasoned about and solved complex planning problems, the numerous expensive calls to an optimizer resulted in large planning times, thereby limiting its practical use. Inspired by the recent work on edge-based parallel graph search, we present PINSAT, which introduces systematic parallelization in INSAT to achieve lower planning times and higher success rates, while maintaining significantly lower costs over relevant baselines. We demonstrate PINSAT by evaluating it on 6 DoF kinodynamic manipulation planning with obstacles., Comment: Under review

We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile's future motion, (iii) plan a minimum-time rapid trajectory that can evade obstacles and intercept the projectile, and (iv) execute the planned trajectory. These four steps must be performed under the manipulator's dynamic limits and extreme time constraints (<350ms in our setting) to successfully intercept the projectile. In addition, we want these trajectories to be smooth to reduce the robot's joint torques and the impulse on the platform on which it is mounted. To this end, we propose a kinodynamic motion planning framework that preprocesses smooth trajectories offline to allow real-time collision-free executions online. We present an end-to-end pipeline along with our planning framework, including perception, prediction, and execution modules. We evaluate our framework experimentally in simulation and show that it has a higher blocking success rate than the baselines. Further, we deploy our pipeline on a robotic system comprising an industrial arm (ABB IRB-1600) and an onboard stereo camera (ZED 2i), which achieves a 78% success rate in projectile interceptions., Comment: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2024

We study the effect of a Rashba spin-orbit coupling on the nodal superconducting phase of an Ising superconductor. Such nodal phase was predicted to occur when applying an in-plane field beyond the Pauli limit to a superconducting monolayer transition metal dichalcogenides (TMD). Generically, Rashba spin-orbit is known to lift the chiral symmetry that protects the nodal points, resulting in a fully gapped phase. However, when the magnetic field is applied along the $\Gamma -K $ line, a residual vertical mirror symmetry protects a nodal crystalline phase. We study a single-band tight-binding model that captures the low energy physics around the $\Gamma $ pocket of monolayer TMD. We calculate the topological properties, the edge state structure, and the current phase relation in a Josephson junction geometry of the nodal crystalline phase. We show that while the nodal crystalline phase is characterized by localized edge modes on non-self-reflecting boundaries, the current phase relation exhibits a trivial $2\pi $ periodicity in the presence of Rashba spin-orbit coupling., Comment: 9 pages, 9 figures

Pseudo-Hermitian field theories possess a global continuous ``similarity'' symmetry, interconnecting the theories with the same physical particle content and an identical mass spectrum. In their regimes with real spectra, within this family of similarity transformations, there is a map from the non-Hermitian theory to its Hermitian similarity partner. We promote the similarity transformation to a local symmetry, which requires the introduction of a new vector similarity field as a connection in the similarity space of non-Hermitian theories. In the case of non-Hermitian two-flavour scalar or fermion mixing, and by virtue of a novel IR/UV mixing effect, the effect of inhomogeneous non-Hermiticity then reveals itself via anomalous dispersion, instabilities and superluminal group velocities at very high momenta, thus setting an upper bound on the particle momentum propagating through inhomogeneous backgrounds characterised by Lagrangians with non-Hermitian mass matrices. Such a non-Hermitian extension of the Standard Model of particle physics, encoded in a weak inhomogeneity of the non-Hermitian part of the fermion mass matrix, may nevertheless provide us with a low-energy particle spectrum consistent with experimentally observed properties., Comment: 19 pages, 6 figures

We apply the Migdal-Eliashberg theory of superconductivity to heavy-fermion and mixed valence materials. Specifically, we extend the Anderson lattice model to a case when there exists a strong coupling between itinerant electrons and lattice vibrations. Using the saddle-point approximation, we derive a set of coupled nonlinear equations which describe competition between the crossover to a heavy-fermion or mixed-valence regimes and conventional superconductivity. We find that superconductivity at strong coupling emerges on par with the development of the many-body coherence in a Kondo lattice. Superconductivity is gradually suppressed with the onset of the Kondo screening and for strong electron-phonon coupling the Kondo screening exhibits a characteristic re-entrant behavior. Even though for both weak and strong coupling limits the suppression of superconductivity is weaker in the mixed-valence regime compared to the local moment one, superconducting critical temperature still remains nonzero. In the weak coupling limit the onset of the many body coherence develops gradually, in the strong coupling limit it emerges abruptly in the mixed valence regime while in the local moment regime the $f$-electrons remain effectively decoupled from the conduction electrons. Possibility of experimental realization of these effects in Ce-based compounds is also discussed., Comment: 9 pages, 5 figures

For partially ordered sets $X$ we consider the square matrices $M^{X}$ with rows and columns indexed by linear extensions of the partial order on $X$. Each entry $\left( M^{X}\right)_{PQ}$ is a formal variable defined by a pedestal of the linear order $Q$ with respect to linear order $P$. We show that all the eigenvalues of any such matrix $M^{X}$ are $\mathbb{Z}$-linear combinations of those variables.

Based on the earlier published theory (\textit{Nature Mat}. \textbf{18}, 223--228 (2019)), a comprehensive experimental investigation of multiferroic quantum critical behavior of (Eu,Ba,Sr)TiO$_3$ polycrystalline and single crystal samples was performed. Presence of the displacive ferroelectric quantum criticality is revealed through non-classical ($T^2$) temperature scaling of inverse dielectric susceptibility up to 60\,K. With increasing hydrostatic pressure, this ferroelectric quantum criticality is gradually suppressed. Inverse magnetic susceptibility follows classical Curie-Weiss law down to 4 K, but quantum fluctuations belonging to an antiferromagnetic phase transition ($T_{\mathrm{N}} < 0.8$ K) change its scaling below 3 K to $T^{(1.7\pm 0.1)}$ and $T^{(2.1\pm 0.2)}$ for samples containing 29\,\% and 25\,\% of Eu$^{2+}$ ions, respectively. Experimental indications of the coexisting ferroelectric and antiferromagnetic, i.e. multiferroic, quantum fluctuations and qualitative explanation why they could be seen only in the immediate proximity of $T_{\mathrm{N}}$ is given.

BSM physics, underlying the now standard inflationary cosmology with baryosynthesis and dark matter/energy, inevitably leads to features, going beyond this standard cosmological paradigm. The signatures of these features can be considered in the hints for the existence of antihelium component of cosmic rays and massive Primordial Black Holes (PBH), in the discovery of stochastic gravitational wave background (SGWB) and evidence for early galaxy formation as well as in the dark atom solution for the puzzles of direct dark mater searches. We discuss open problems of physics of dark atoms, which can explain positive result of DAMA/NaI and DAMA/LIBRA direct searches for dark matter, of the origin and evolution of antimatter domains in baryon asymmetrical Universe, of the Axion Like Particle (ALP) physics, which may explain the origin of SGWB, discovered by Pulsar Timing Array (PTA) facilities and early galaxy formation, favored by James Webb Space Telescope., Comment: Prepared for Proceedings of XXVI Bled Workshop "What comes beyond the Standard models?"

We compare two scenarios in a model where politicians offer local public goods to heterogeneous voters: one where politicians have access to data on voters and thus can target specific ones, and another where politicians only decide on the level of spending. When the budget is small, or the public good has a high value, access to voter information leads the winner to focus on poorer voters, enhancing voter welfare. With a larger budget or less crucial public goods, politicians target a narrow group of swing voters, which harms the voter welfare.

We study interacting classical magnetic and pseudoscalar fields in frames of the axion electrodynamics. A large scale pseudoscalar field can be the coherent superposition of axions or axion like particles. We consider the evolution of these fields in a thin spherical layer. Decomposing the magnetic field into the poloidal and toroidal components, we take into account their symmetry properties. The dependence of the pseudoscalar field on the latitude is accounted for the induction equation. Then, we derive the dynamo equations in the low mode approximation. The nonlinear evolution equations for the harmonics of the magnetic and pseudoscalar fields are solved numerically. As an application, we consider a dense axion star embedded in solar plasma. The behavior of the harmonics and their typical oscillations frequencies are obtained. We suggest that such small objects consisting of axions and confined magnetic fields can cause the recently observed flashes in solar corona contributing to its heating., Comment: 11 pages in LaTeX2e, 12 eps figures

We study a Bayesian persuasion model with two-dimensional states of the world, in which the sender (she) and receiver (he) have heterogeneous prior beliefs and care about different dimensions. The receiver is a naive agent who has a simplistic worldview: he ignores the dependency between the two dimensions of the state. We provide a characterization for the sender's gain from persuasion both when the receiver is naive and when he is rational. We show that the receiver benefits from having a simplistic worldview if and only if it makes him perceive the states in which his interest is aligned with the sender as less likely.

Rydberg excitons (analogues of Rydberg atoms in condensed matter systems) are highly excited bound electron-hole states with large Bohr radii. The interaction between them as well as exciton coupling to light may lead to strong optical nonlinearity, with applications in sensing and quantum information processing. Here, we achieve strong effective photon-photon interactions (Kerr-like optical nonlinearity) via the Rydberg blockade phenomenon and the hybridisation of excitons and photons forming polaritons in a Cu$_2$O-filled microresonators. Under pulsed resonant excitation polariton resonance frequencies are renormalised due to the reduction of the photon-exciton coupling with increasing exciton density. Theoretical analysis shows that the Rydberg blockade plays a major role in the experimentally observed scaling of the polariton nonlinearity coefficient as $\propto n^{4.4 \pm 1.8}$ for principal quantum numbers up to n = 7. Such high principal quantum numbers studied in a polariton system for the first time are essential for realisation of high Rydberg optical nonlinearities, which paves the way towards quantum optical applications and fundamental studies of strongly-correlated photonic (polaritonic) states in a solid state system.

This paper presents a systematic literature review (SLR) on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining, using the PRISMA framework. Given the rapid advancement of artificial intelligence (AI) and ML systems, understanding the "black-box" nature of these technologies has become increasingly critical. Focusing specifically on the domain of process mining, this paper delves into the challenges of interpreting ML models trained with complex business process data. We differentiate between intrinsically interpretable models and those that require post-hoc explanation techniques, providing a comprehensive overview of the current methodologies and their applications across various application domains. Through a rigorous bibliographic analysis, this research offers a detailed synthesis of the state of explainability and interpretability in predictive process mining, identifying key trends, challenges, and future directions. Our findings aim to equip researchers and practitioners with a deeper understanding of how to develop and implement more trustworthy, transparent, and effective intelligent systems for predictive process analytics.

Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for understanding the inner workings of neural networks. For a living system, such as a neuron, whose response to a stimulus is unknown and not differentiable, the only way to reveal these features is through a feedback loop that exposes it to a large set of different stimuli. The properties of these stimuli should be varied iteratively in order to maximize the neuronal response. To utilize this feedback loop for a biological neural network, it is important to run it quickly and efficiently in order to reach the stimuli that maximizes certain neurons' activation with the least number of iterations possible. Here we present a framework with an efficient design for such a loop. We successfully tested it on an artificial spiking neural network (SNN), which is a model that simulates the asynchronous spiking activity of neurons in living brains. Our optimization method for activation maximization is based on the low-rank Tensor Train decomposition of the discrete activation function. The optimization space is the latent parameter space of images generated by SN-GAN or VQ-VAE generative models. To our knowledge, this is the first time that effective AM has been applied to SNNs. We track changes in the optimal stimuli for artificial neurons during training and show that highly selective neurons can form already in the early epochs of training and in the early layers of a convolutional spiking network. This formation of refined optimal stimuli is associated with an increase in classification accuracy. Some neurons, especially in the deeper layers, may gradually change the concepts they are selective for during learning, potentially explaining their importance for model performance.

We introduce a new concept of logarithmic topological recursion that provides a patch to topological recursion in the presence of logarithmic singularities and prove that this new definition satisfies the universal $x-y$ swap relation. This result provides a vast generalization and a proof of a very recent conjecture of Hock. It also uniformly explains (and conceptually rectifies) an approach to the formulas for the $n$-point functions proposed by Hock., Comment: 30 pages; minor corrections and modifications; Section 3.11 on the new Family III of generalized double Hurwitz numbers added

Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods heavily rely on data exchangeability, a condition often violated in practice. Existing approaches for tackling non-exchangeability lead to methods that are not computable beyond the simplest examples. This work introduces a new efficient approach to CP that produces provably valid confidence sets for fairly general non-exchangeable data distributions. We illustrate the general theory with applications to the challenging setting of federated learning under data heterogeneity between agents. Our method allows constructing provably valid personalized prediction sets for agents in a fully federated way. The effectiveness of the proposed method is demonstrated in a series of experiments on real-world datasets., Comment: 29 pages

We develop a comprehensive framework for analyzing full record statistics, covering record counts $M(t_1), M(t_2), \ldots$, and their corresponding attainment times $T_{M(t_1)}, T_{M(t_2)}, \ldots$, as well as the intervals until the next record. From this multiple-time distribution, we derive general expressions for various observables related to record dynamics, including the conditional number of records given the number observed at a previous time and the conditional time required to reach the current record, given the occurrence time of the previous one. Our formalism is exemplified by a variety of stochastic processes, including biased nearest-neighbor random walks, asymmetric run-and-tumble dynamics, and random walks with stochastic resetting., Comment: 16 pages, 5 figures

Using first-principle numerical simulations, we find a new spatially inhomogeneous phase in rigidly rotating $N_c = 3$ gluon plasma. This mixed phase simultaneously possesses both confining and deconfining phases in thermal equilibrium. Unexpectedly, the local critical temperature of the phase transition at the rotation axis does not depend on the angular frequency within a few percent accuracy. Even more surprisingly, an analytic continuation of our results to the domain of real angular frequencies indicates a profound breaking of the Tolman-Ehrenfest law in the vicinity of the phase transition, with the confining (deconfining) phase appearing far (near) the rotation axis., Comment: 7 pages, 4 figures, movies on the phase evolution with increasing angular velocity are attached as ancillary files; acknowledgements and discussion added; figures updated; version accepted for publication in PLB

LiMn2O4 (LMO), cathodes present large stability when cycled in aqueous electrolytes, contrasting its behavior in conventional organic electrolytes in Lithium-ion batteries (LIBs). To elucidate the mechanisms underlying this distinctive behavior, we employ unconventional characterization techniques, including Variable Energy Positron Annihilation Lifetime Spectroscopy (VEPALS), Tip-Enhanced Raman Spectroscopy (TERS) and macro-Raman Spectroscopy (with mm-size laser spot). These still rather unexplored techniques in the battery field provide complementary information across different length scales, revealing previously hidden features. VEPALS offers atomic-scale insights, uncovering cationic defects and sub-nanometer pores that tend to collapse with cycling. TERS, operating at the nanometric range at the surface, captured the presence of Mn3O4 and its dissolution with cycling, elucidating dynamic changes during operation. Additionally, TERS highlights SO42- accumulation at grain boundaries. Macro-Raman Spectroscopy focuses on the micrometer scale, depicting small changes in the cathode's long-range order, suggesting a slow but progressive loss of crystalline quality under operation. Integrating these techniques provides a comprehensive assessment of LMO cathode stability in aqueous electrolytes, offering multifaceted insights into phase and defect evolution that can help to rationalize the origin of such stability when compared to conventional organic electrolytes. Our findings advance the understanding of LMO behavior in aqueous environments and provide guidelines for its development for next-generation LIBs.

In modern federated learning, one of the main challenges is to account for inherent heterogeneity and the diverse nature of data distributions for different clients. This problem is often addressed by introducing personalization of the models towards the data distribution of the particular client. However, a personalized model might be unreliable when applied to the data that is not typical for this client. Eventually, it may perform worse for these data than the non-personalized global model trained in a federated way on the data from all the clients. This paper presents a new approach to federated learning that allows selecting a model from global and personalized ones that would perform better for a particular input point. It is achieved through a careful modeling of predictive uncertainties that helps to detect local and global in- and out-of-distribution data and use this information to select the model that is confident in a prediction. The comprehensive experimental evaluation on the popular real-world image datasets shows the superior performance of the model in the presence of out-of-distribution data while performing on par with state-of-the-art personalized federated learning algorithms in the standard scenarios.

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

We characterized the dosimetric properties of a clinical linear accelerator configured to deliver ultra-high dose rate (UHDR) irradiation to mice and cell-culture FLASH radiobiology experiments. UHDR electron beams were controlled by a microcontroller and relay interfaced with the respiratory gating system. We produced beam collimators with indexed stereotactic mouse positioning devices to provide anatomically specific preclinical treatments. Treatment delivery was monitored directly with an ionization chamber, and charge measurements were correlated with radiochromic film at the entry surface of the mice. The setup for conventional (CONV) dose rate irradiation was similar but the source-to-surface distance was longer. Monte Carlo simulations and film dosimetry were used to characterize beam properties and dose distributions. The mean electron beam energies before the flattening filter were 18.8 MeV (UHDR) and 17.7 MeV (CONV), with corresponding values at the mouse surface of 17.2 MeV and 16.2 MeV. The charges measured with an external ion chamber were linearly correlated with the mouse entrance dose. Use of relay gating for pulse control initially led to a delivery failure rate of 20% ($+/-$ 1 pulse); adjustments to account for the linac latency improved this rate to <1/20. Beam field sizes for two anatomically specific mouse collimators (4x4 $cm^2$ for whole-abdomen and 1.5x1.5 $cm^2$ for unilateral lung irradiation) were accurate within <5% and had low radiation leakage (<4%). Normalizing the dose at the center of the mouse (~0.75 cm depth) produced UHDR and CONV doses to the irradiated volumes with >95% agreement. We successfully configured a clinical linear accelerator for increased output and developed a robust preclinical platform for anatomically specific irradiation, with highly accurate and precise temporal and spatial dose delivery, for both CONV and UHDR applications., Comment: Jinghui Wang and Stavros Melemenidis are co-first authors, and Emil Sch\"uler and Peter G. Maxim are co-senior/co-corresponding authors

Cyclops states are intriguing cluster patterns observed in oscillator networks, including neuronal ensembles. The concept of cyclops states formed by two distinct, coherent clusters and a solitary oscillator was introduced in [Munyayev {\it et al.}, Phys. Rev. Lett. 130, 107021 (2023)], where we explored the surprising prevalence of such states in repulsive Kuramoto networks of rotators with higher-mode harmonics in the coupling. This paper extends our analysis to understand the mechanisms responsible for destroying the cyclops' states and inducing new dynamical patterns called breathing and switching cyclops' states. We first analytically study the existence and stability of cyclops states in the Kuramoto-Sakaguchi networks of two-dimensional oscillators with inertia as a function of the second coupling harmonic. We then describe two bifurcation scenarios that give birth to breathing and switching cyclops states. We demonstrate that these states and their hybrids are prevalent across a wide coupling range and are robust against a relatively large intrinsic frequency detuning. Beyond the Kuramoto networks, breathing and switching cyclops states promise to strongly manifest in other physical and biological networks, including coupled theta-neurons.

With the help of a theoretical model and finite-difference-time-domain simulations based on the hydrodynamic-Maxwell model, we examine the effect of difference frequency generation in an array of L-shaped metal nano-particles characterized by intrinsic plasmonic nonlinearity. The outcomes of the calculations reveal the spectral interplay of the gain and loss in the vicinity of the fundamental frequency of the localized surface-plasmon resonances. Subsequently, we identify different array depths and pumping regimes facilitating parametric amplification and parametric down-conversion. Our results suggest that the parametric amplification regime becomes feasible on a scale of hundreds of nanometers and parametric downconversion on the scale of tens of nanometers, opening up new exciting opportunities for developing building blocks of photonic metasurfaces., Comment: 5 pages, 3 figures, journal article

We construct a new Yang-Mills 3D-solution on the space of negative scalar curvarure. We discuss a problem of non-abelian gauge symmetry is broken with the assumption that a scalar curvature of the domain is a negative small parameter. In this case we use the following fact: a geometrical scale related with Vassiliev's discriminant of magnetic lines coincids with a physical Kolmogorov scale. This gives an estimation of $\alpha$-effect by the dispersion of the asymptotic ergodic Hopf invariant in the limite with a negative scalar curvature parameter.

We present experimental observations of dilute ion spin ensemble defects in a low-loss single crystal cylindrical sample of CaWO$_4$ cooled to $30$ mK in temperature. Crystal field perturbations were elucidated by constructing a dielectrically loaded microwave cavity resonator from the crystal. The resonator exhibited numerous whispering gallery modes with high $Q$-factors of up to $3\times 10^7$, equivalent to a loss tangent of $\sim 3\times 10^{-8}$. The low loss allowed precision multi-mode spectroscopy of numerous high $Q$-factor photon-spin interactions. Measurements between 7 to 22 GHz revealed the presence of Gd$^{3+}$, Fe$^{3+}$, and another trace species, inferred to be rare-earth, at concentrations on the order of parts per billion. These findings motivate further exploration of prospective uses of this low-loss dielectric material for applications regarding precision and quantum metrology, as well as tests for beyond standard model physics.

Theories with more than one scalar field often exhibit phase transitions producing potentially detectable gravitational wave (GW) signal. In this work we study the semi-annihilating $\mathbb{Z}_3$ dark matter model, whose dark sector comprises an inert doublet and a complex singlet, and assess its prospects in future GW detectors. Without imposing limits from requirement of providing a viable dark matter candidate, i.e. taking into account only other experimental and theoretical constraints, we find that the first order phase transition in this model can be strong enough to lead to a detectable signal. However, direct detection and the dark matter thermal relic density constraint calculated with the state-of-the-art method including the impact of early kinetic decoupling, very strongly limit the parameter space of the model explaining all of dark matter and providing observable GW peak amplitude. Extending the analysis to underabundant dark matter thus reveals region with detectable GWs from a single-step or multi-step phase transition., Comment: 37 pages, 5 figures, references added, version accepted in JHEP

The impact of Next-to-Leading Order (NLO) QCD corrections to the differential distributions of J/psi and Upsilon mesons produced inclusively in gamma-gamma collisions is discussed for the kinematical conditions of LEP II for DELPHI and at the future Circular Electron-Positron Collider (CEPC). We take into account all sizeable contributions at LO in v^2 in NRQCD factorisation: 1) pure QED process gamma + gamma -> J/psi(3S^1_1) + \gamma up to alpha^3 alpha_s, 2) single-resolved-photon contributions up to alpha_s^4, 3) gamma+gamma -> J/psi + c c-bar up to alpha^2 alpha_s and 4) gamma+gamma -> J/psi + ggg up to alpha^2 alpha_s^3. We will also discuss the pure QED process as a contribution to the exclusive production in ultra-peripheral heavy-ion collisions (UPC) at the LHC., Comment: LaTeX, 6 pages, 16 figures, uses pos.sty. Talk given by Y. Yedelkina at "The European Physical Society Conference on High Energy Physics (EPS-HEP2023)", 21-25 August 2023, Hamburg, Germany; submitted to PoS - Proceedings of Science

Mechanistic growth models play a major role in bioprocess engineering, design, and control. Their reasonable predictive power and their high level of interpretability make them an essential tool for computer aided engineering methods. Additionally, since they contain knowledge about cell physiology, the parameter estimates provide meaningful insights into the metabolism of the microorganism under study. However, the assumption of time invariance of the model parameters is often violated in real experiments, limiting their capacity to fully explain the observed dynamics. In this work, we propose a framework for identifying such violations and producing insights into misspecified mechanisms. The framework achieves this by allowing kinetic and process parameters to vary in time. We demonstrate the framework's capabilities by fitting a hybrid model based on a simple mechanistic growth model for E. coli with data generated in-silico by a much more complex one and identifying missing kinetics.

Inferring parameters of macro-kinetic growth models, typically represented by Ordinary Differential Equations (ODE), from the experimental data is a crucial step in bioprocess engineering. Conventionally, estimates of the parameters are obtained by fitting the mechanistic model to observations. Fitting, however, requires a significant computational power. Specifically, during the development of new bioprocesses that use previously unknown organisms or strains, efficient, robust, and computationally cheap methods for parameter estimation are of great value. In this work, we propose using Deep Neural Networks (NN) for directly predicting parameters of mechanistic models given observations. The approach requires spending computational resources for training a NN, nonetheless, once trained, such a network can provide parameter estimates orders of magnitude faster than conventional methods. We consider a training procedure that combines Neural Networks and mechanistic models. We demonstrate the performance of the proposed algorithms on data sampled from several mechanistic models used in bioengineering describing a typical industrial batch process and compare the proposed method, a typical gradient-based fitting procedure, and the combination of the two. We find that, while Neural Network estimates are slightly improved by further fitting, these estimates are measurably better than the fitting procedure alone., Comment: 7 pages, 3 figures

We present radio and X-ray studies of A3444 and MS1455.0+2232, two galaxy clusters with radio minihalos in their cool cores. A3444 is imaged using the Giant Metrewave Radio Telescope (GMRT) at 333, 607 and 1300 MHz and the Very Large Array at 1435 MHz. Most of the minihalo is contained within r<120 kpc, but a fainter extension, stretching out to 380 kpc South-West of the center, is detected at 607 MHz. Using Chandra, we detect four X-ray sloshing cold fronts: three in the cool core at r=60, 120 and 230 kpc, and a fourth one at r=400 kpc - in the region of the southwestern radio extension - suggesting that the intracluster medium (ICM) is sloshing on a cluster-wide scale. The radio emission is contained within the envelope defined by these fronts. We also analyzed archival 383 MHz GMRT and Chandra observations of MS1455.0+2232, which exhibits a known minihalo with its bright part delineated by cold fronts inside the cool core, but with a faint extension beyond the core. Similarly to A3444, we find a cold front at r~425 kpc, containing the radio emission. Thus the entire diffuse radio emission seen in these clusters appears to be related to large-scale sloshing of the ICM. The radio spectrum of the A3444 minihalo is a power law with a steep index $\alpha=1.0\pm0.1$. The spectrum steepens with increasing distance from the center, as expected if the minihalo originates from re-acceleration of relativistic particles by the sloshing-induced turbulence in the ICM., Comment: 29 pages, 15 figures, accepted for publication in ApJ

Cultivation experiments often produce sparse and irregular time series. Classical approaches based on mechanistic models, like Maximum Likelihood fitting or Monte-Carlo Markov chain sampling, can easily account for sparsity and time-grid irregularities, but most statistical and Machine Learning tools are not designed for handling sparse data out-of-the-box. Among popular approaches there are various schemes for filling missing values (imputation) and interpolation into a regular grid (alignment). However, such methods transfer the biases of the interpolation or imputation models to the target model. We show that Deep Set Neural Networks equipped with triplet encoding of the input data can successfully handle bio-process data without any need for imputation or alignment procedures. The method is agnostic to the particular nature of the time series and can be adapted for any task, for example, online monitoring, predictive control, design of experiments, etc. In this work, we focus on forecasting. We argue that such an approach is especially suitable for typical cultivation processes, demonstrate the performance of the method on several forecasting tasks using data generated from macrokinetic growth models under realistic conditions, and compare the method to a conventional fitting procedure and methods based on imputation and alignment., Comment: 9 pages, 3 figures

Background: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Moreover, it is a frequent cause of distress and dissatisfaction during the early postoperative period. The tools used for predicting PONV at present have not yielded satisfactory results. Therefore, prognostic tools for the prediction of early and delayed PONV were developed in this study with the aim of achieving satisfactory predictive performance. Methods: The retrospective data of adult patients admitted to the post-anesthesia care unit after undergoing surgical procedures under general anesthesia at the Sheba Medical Center, Israel, between September 1, 2018, and September 1, 2023, were used in this study. An ensemble model of machine learning algorithms trained on the data of 54848 patients was developed. The k-fold cross-validation method was used followed by splitting the data to train and test sets that optimally preserve the sociodemographic features of the patients, such as age, sex, and smoking habits, using the Bee Colony algorithm. Findings: Among the 54848 patients, early and delayed PONV were observed in 2706 (4.93%) and 8218 (14.98%) patients, respectively. The proposed PONV prediction tools could correctly predict early and delayed PONV in 84.0% and 77.3% of cases, respectively, outperforming the second-best PONV prediction tool (Koivuranta score) by 13.4% and 12.9%, respectively. Feature importance analysis revealed that the performance of the proposed prediction tools aligned with previous clinical knowledge, indicating their utility. Interpretation: The machine learning-based tools developed in this study enabled improved PONV prediction, thereby facilitating personalized care and improved patient outcomes.