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Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as terahertz communications). Current wireless networks are designed to perform adequately across multiple scenarios so they lack the adaptability needed for specific use cases. Therefore, meeting the stringent requirements of next-generation applications incorporating technology advances and operating in novel scenarios will necessitate wireless specialized networks which we refer to as SpecNets. These networks, equipped with cognitive capabilities, dynamically adapt to the unique demands of each application, e.g., by automatically selecting and configuring network mechanisms. An enabler of SpecNets are the recent advances in artificial intelligence and machine learning (AI/ML), which allow to continuously learn and react to changing requirements and scenarios. By integrating AI/ML functionalities, SpecNets will fully leverage the concept of AI/ML-defined radios (MLDRs) that are able to autonomously establish their own communication protocols by acquiring contextual information and dynamically adapting to it. In this paper, we introduce SpecNets and explain how MLDR interfaces enable this concept. We present three illustrative use cases for wireless local area networks (WLANs): bespoke industrial networks, traffic-aware robust THz links, and coexisting networks. Finally, we showcase SpecNets' benefits in the industrial use case by introducing a lightweight, fast-converging ML agent based on multi-armed bandits (MABs). This agent dynamically optimizes channel access to meet varying performance needs: high throughput, low delay, or fair access. Results demonstrate significant gains over IEEE 802.11, highlighting the system's autonomous adaptability across diverse scenarios., Comment: 14 pages, 8 figures

Understanding the ultrafast vibrational relaxation following photoexcitation of molecules in a condensed phase is essential to predict the outcome and improve the efficiency of photoinduced molecular processes. Here, the vibrational decoherence and energy relaxation of a binuclear complex, [Pt$_2$(P$_2$O$_5$H$_2$)$_4$]$^{4-}$ (PtPOP), upon electronic excitation in liquid water and acetonitrile are investigated through direct adiabatic dynamics simulations. A quantum mechanics/molecular mechanics (QM/MM) scheme is used where the excited state of the complex is modelled with orbital-optimized density functional calculations while solvent molecules are described using potential energy functions. The decoherence time of the Pt-Pt vibration dominating the photoinduced dynamics is found to be $\sim$1.6 ps in both solvents. This is in excellent agreement with experimental measurements in water, where intersystem crossing is slow ($>10$ ps). Pathways for the flow of excess energy are identified by monitoring the power of the solvent on vibrational modes. The latter are obtained as generalized normal modes from the velocity covariances, and the power is computed using QM/MM embedding forces. Excess vibrational energy is found to be predominantly released through short-range repulsive and attractive interactions between the ligand atoms and surrounding solvent molecules, whereas solute-solvent interactions involving the Pt atoms are less important. Since photoexcitation deposits most of the excess energy into Pt-Pt vibrations, energy dissipation to the solvent is inefficient. This study reveals the mechanism behind the exceptionally long vibrational coherence of the photoexcited PtPOP complex in solution and underscores the importance of short-range interactions for accurate simulations of vibrational energy relaxation of solvated molecules.

We consider elliptic problems in multipatch IGA where the patch parameterizations may be singular. To deal with this issue, we develop a robust weak formulation that allows trimmed (cut) elements, enforces interface and Dirichlet conditions weakly, and does not depend on specially constructed approximation spaces. Our technique for dealing with the singular maps is based on the regularization of the Riemannian metric tensor, and we detail how to implement this robustly. We investigate the method's behavior when applied to a square-to-cusp parameterization that allows us to vary the singular behavior's aggressiveness. We propose a scaling of the regularization parameter to obtain optimal order approximation. Our numerical experiments indicate that the method is robust also for quite aggressive singular parameterizations.

Identifying optimal medical treatments to improve survival has long been a critical goal of pharmacoepidemiology. Traditionally, we use an average treatment effect measure to compare outcomes between treatment plans. However, new methods leveraging advantages of machine learning combined with the foundational tenets of causal inference are offering an alternative to the average treatment effect. Here, we use three unique, precision medicine algorithms (random forests, residual weighted learning, efficient augmentation relaxed learning) to identify optimal treatment rules where patients receive the optimal treatment as indicated by their clinical history. First, we present a simple hypothetical example and a real-world application among heart failure patients using Medicare claims data. We next demonstrate how the optimal treatment rule improves the absolute risk in a hypothetical, three-modifier setting. Finally, we identify an optimal treatment rule that optimizes the time to outcome in a real-world heart failure setting. In both examples, we compare the average time to death under the optimized, tailored treatment rule with the average time to death under a universal treatment rule to show the benefit of precision medicine methods. The improvement under the optimal treatment rule in the real-world setting is greatest (additional ~9 days under the tailored rule) for survival time free of heart failure readmission.

Research on school improvement has accumulated an extensive list of factors that facilitate turnarounds at underperforming schools. Given that contextual or resource constraints may limit the possibilities of putting all of these factors in place, an important question is what is necessary and sufficient to turn a school around. We use a qualitative comparative analysis (QCA) of 77 Swedish schools studied over 12 years to answer this question. Our core finding is that there is no "silver bullet" solution. There are, instead, several distinct combinations of factors that can enable a turnaround. The local school context is essential for which combinations of factors are necessary and sufficient for school turnaround. We discuss implications for research on school improvement and education policy.

We study reinforcement learning (RL) for decision processes with non-Markovian reward, in which high-level knowledge of the task in the form of reward machines is available to the learner. We consider probabilistic reward machines with initially unknown dynamics, and investigate RL under the average-reward criterion, where the learning performance is assessed through the notion of regret. Our main algorithmic contribution is a model-based RL algorithm for decision processes involving probabilistic reward machines that is capable of exploiting the structure induced by such machines. We further derive high-probability and non-asymptotic bounds on its regret and demonstrate the gain in terms of regret over existing algorithms that could be applied, but obliviously to the structure. We also present a regret lower bound for the studied setting. To the best of our knowledge, the proposed algorithm constitutes the first attempt to tailor and analyze regret specifically for RL with probabilistic reward machines., Comment: 35 pages

Generalizing previous results of Arezzo-Pacard-Singer, Seyyedali-Sz\'ekelyhidi and Hallam, we prove the invariance under smooth blowups of the class of weighted extremal K\"ahler manifolds, modulo a log-concavity assumption on the first weight. Through recent work of Di Nezza-Jubert-Lahdili and Han-Liu, this is obtained as a consequence of a general uniform coercivity estimate for the (relative, weighted) Mabuchi energy on the blowup, which applies more generally to any equivariant resolution of singularities of Fano type of a compact K\"ahler klt space whose Mabuchi energy is assumed to be coercive., Comment: 62 pages, no figures

For the almost complete intersection ideals $(x_1^2, \dots, x_n^2, (x_1 + \cdots + x_n)^k)$, we compute their reduced Gr\"obner basis for any term ordering, revealing a combinatorial structure linked to lattice paths, elementary symmetric polynomials, and Catalan numbers. Using this structure, we classify the weak Lefschetz property for these ideals. Additionally, we provide a new proof of the well-known result that the squarefree algebra satisfies the strong Lefschetz property. Finally, we compute the Betti numbers of the initial ideals and construct a minimal free resolution using a Mayer-Vietoris tree approach., Comment: 33 pages, 3 figures

Pregnancy loss is recognized as an important competing event in studies of prenatal medication use. However, a healthy live birth also precludes subsequent adverse pregnancy outcomes, yet these events are often censored. Using Monte Carlo simulation, we examine bias that results from failure to account for healthy live birth as a competing event in estimates of the total effect of prenatal medication use on pregnancy outcomes. We simulated data for 12 trials estimating the effect of antihypertensive initiation versus non-initiation on two outcomes: (1) composite fetal death or severe prenatal preeclampsia and (2) small-for-gestational-age (SGA) live birth. We used time-to-event methods to estimate absolute risks, risk differences and risk ratios. For the composite outcome, we conducted two analyses where non-preeclamptic live birth was (1) a censoring event and (2) a competing event. For SGA live birth, we conducted three analyses where fetal death and non-SGA live birth were (1) censoring events, (2) a competing event and censoring event, respectively; and (3) competing events. In all analyses, censoring healthy live births led to inflated absolute risk estimates as well as bias and imprecise treatment effect estimates. Studies of prenatal exposures on pregnancy outcomes should analyze healthy live births as competing risks to estimate unbiased total treatment effects., Comment: 33 pages, 4 figures

We consider the MIN-r-LIN(R) problem: given a system S of length-r linear equations over a ring R, find a subset of equations Z of minimum cardinality such that S-Z is satisfiable. The problem is NP-hard and UGC-hard to approximate within any constant even when r=|R|=2, so we focus on parameterized approximability with solution size as the parameter. For a large class of infinite rings R called Euclidean domains, Dabrowski et al. [SODA-2023] obtained an FPT-algorithm for MIN-2-LIN(R) using an LP-based approach based on work by Wahlstr\"om [SODA-2017]. Here, we consider MIN-r-LIN(R) for finite commutative rings R, initiating a line of research with the ultimate goal of proving dichotomy theorems that separate problems that are FPT-approximable within a constant from those that are not. A major motivation is that our project is a promising step for more ambitious classification projects concerning finite-domain MinCSP and VCSP. Dabrowski et al.'s algorithm is limited to rings without zero divisors, which are only fields among finite commutative rings. Handling zero divisors seems to be an insurmountable obstacle for the LP-based approach. In response, we develop a constant-factor FPT-approximation algorithm for a large class of finite commutative rings, called Bergen rings, and thus prove approximability for chain rings, principal ideal rings, and Z_m for all m>1. We complement the algorithmic result with powerful lower bounds. For r>2, we show that the problem is not FPT-approximable within any constant (unless FPT=W[1]). We identify the class of non-Helly rings for which MIN-2-LIN(R) is not FPT-approximable. Under ETH, we also rule out (2-e)-approximation for every e>0 for non-lineal rings, which includes e.g. rings Z_{pq} where p and q are distinct primes. Towards closing the gaps between upper and lower bounds, we lay the foundation of a geometric approach for analysing rings.

By combining Hartree-Fock with a neural-network-supported quantum-cluster solver proposed recently in the context of solid-state lattice models, we formulate a scheme for selective neural-network configuration interaction (NNCI) calculations and implement it with various options for the type of basis set and boundary conditions. The method's performance is evaluated in studies of several small molecules as a step toward calculations of larger systems. In particular, the correlation energy in the N$_2$ molecule is compared with published full CI calculations that included nearly $10^{10}$ Slater determinants, and the results are reproduced with only $4\cdot10^{5}$ determinants using NNCI. A clear advantage is seen from increasing the set of orbitals included rather than approaching full CI for a smaller set. The method's high efficiency and implementation in a condensed matter simulation software expands the applicability of CI calculations to a wider range of problems, even extended systems through an embedding approach., Comment: 15 pages, 4 figures, 2 Tables

Copper is a good CO2 electroreduction catalyst as products beyond CO form, but efficiency and selectivity is low. Experiments have shown that admixture of other elements can help, and computational screening studies have pointed out various promising candidates based on the adsorption of a single CO molecule as a descriptor. Our calculations of CO adsorption on surfaces where a first row transition metal atom replaces a Cu atom show that multiple CO molecules, not just one, bind to the substitutional atom. For Fe, Co, and Ni atoms, a decrease in binding energy is found, but the reverse trend, namely increasing bond strength, is found for V, Cr, and Mn and the first three CO molecules. Magnetic moment, charge, and position of the substitutional atom are also strongly affected by the CO adsorption in most cases. Magnetic moment is stepwise reduced to zero, and the outward displacement of the substitutional atom increased., Comment: 8 pages, 2 figures

In the third APOKASC catalog, we present data for the complete sample of 15,808 evolved stars with APOGEE spectroscopic parameters and Kepler asteroseismology. We used ten independent asteroseismic analysis techniques and anchor our system on fundamental radii derived from Gaia $L$ and spectroscopic $T_{\rm eff}$. We provide evolutionary state, asteroseismic surface gravity, mass, radius, age, and the spectroscopic and asteroseismic measurements used to derive them for 12,418 stars. This includes 10,036 exceptionally precise measurements, with median fractional uncertainties in \nmax, \dnu, mass, radius and age of 0.6\%, 0.6\%, 3.8\%, 1.8\%, and 11.1\% respectively. We provide more limited data for 1,624 additional stars which either have lower quality data or are outside of our primary calibration domain. Using lower red giant branch (RGB) stars, we find a median age for the chemical thick disk of $9.14 \pm 0.05 ({\rm ran}) \pm 0.9 ({\rm sys})$ Gyr with an age dispersion of 1.1 Gyr, consistent with our error model. We calibrate our red clump (RC) mass loss to derive an age consistent with the lower RGB and provide asymptotic GB and RGB ages for luminous stars. We also find a sharp upper age boundary in the chemical thin disk. We find that scaling relations are precise and accurate on the lower RGB and RC, but they become more model dependent for more luminous giants and break down at the tip of the RGB. We recommend the usage of multiple methods, calibration to a fundamental scale, and the usage of stellar models to interpret frequency spacings., Comment: 43 pages, 25 figures, submitted ApJSupp. Comments welcome. Data tables available on request from pinsonneault.1@osu.edu

We report tele-seismic waves detection from multiple earthquakes on an operational subsea cable from Iceland to Ireland. Using per-span laser interferometry with 100km spacing, we report clear detection of S-, P- and surface waves from multiple world-wide earthquakes, enabling seismic analysis for early warning applications., Comment: 3 pages, 2 figures. Conference submission

Frequency domain sweeps of array antennas are well-known to be time-intensive, and different surrogate models have been used to improve the performance. Data-driven model order reduction algorithms, such as the Loewner framework and vector fitting, can be integrated with these adaptive error estimates, in an iterative algorithm, to reduce the number of full-wave simulations required to accurately capture the requested frequency behavior of multiport array antennas. In this work, we propose two novel adaptive methods exploiting a block matrix function which is a key part of the Loewner framework generating system approach. The first algorithm leverages an inherent matrix parameter freedom in the block matrix function to identify frequency points with large errors, whereas the second utilizes the condition number of the block matrix function. Both methods effectively provide frequency domain error estimates, which are essential for improved performance. Numerical experiments on multiport array antenna S-parameters demonstrate the effectiveness of our proposed algorithms within the Loewner framework, where the proposed algorithms reach the smallest errors for the smallest number of frequency points chosen., Comment: 10 pages, 9 figures

We present an efficient momentum based perturbation scheme to evaluate polarizability tensors of small molecules and at the fraction of the computational cost compared to conventional energy based perturbation schemes. Furthermore, the simplicity of the scheme allows for the seamless integration into modern quantum chemistry codes. We apply the method to systems where the wavefunctions are described on a real-space grid and are therefore not subject to finite size basis set errors. In the grid-based scheme errors can be attributed to the resolution and the size of the grid-space. The applicability and generality of the method is exhibited by calculating polarizability tensors including the dipole-dipole and up to the quadrupole-quadrupole for a series of small molecules, representing the most common symmetry groups. By a direct comparison with standard techniques based on energy perturbation we show that the method reduces the number of explicit computations by a factor of 30. Numerical errors introduced due to the arrangement of the explicit point charges are eliminated with an extrapolation scheme to the effective zero-perturbation limit., Comment: 12 pages, 7 figures

Rate theory and DFT calculations of hydrogen evolution reaction (HER) on MoS2 with Co, Ni and Pt impurities show the significance of dihydrogen (H2*) complex where both hydrogen atoms are interacting with the surface. Stabilization of such a complex affects the competing Volmer-Heyrovsky (direct H2 release) and Volmer-Tafel (H2* intermediate) pathways. The resulting evolution proceeds with a very small overpotential for all dopants ($\eta$ = 0.1 to 0.2 V) at 25% edge substitution, significantly reduced from the already low $\eta$ = 0.27 V for the undoped edge. At full edge substitution, Co-MoS2 remains highly active ($\eta$ = 0.18 V) while Ni- and Pt-MoS2 are deactivated ($\eta$ = 0.4 to 0.5 V) due to unfavorable interaction with H2*. Instead of the single S-vacancy, the site of intrinsic activity in the basal plane was found to be the undercoordinated central Mo-atom in threefold S-vacancy configurations, enabling hydrogen evolution with $\eta$ = 0.52 V via a H2* intermediate. The impurity atoms interact favorably with the intrinsic sulfur vacancies on the basal plane, stabilizing but simultaneously deactivating the triple vacancy configuration. The calculated shifts in overpotential are consistent with reported measurements, and the dependence on doping level may explain variations in experimental observations.

This work studies offline Reinforcement Learning (RL) in a class of non-Markovian environments called Regular Decision Processes (RDPs). In RDPs, the unknown dependency of future observations and rewards from the past interactions can be captured by some hidden finite-state automaton. For this reason, many RDP algorithms first reconstruct this unknown dependency using automata learning techniques. In this paper, we show that it is possible to overcome two strong limitations of previous offline RL algorithms for RDPs, notably RegORL. This can be accomplished via the introduction of two original techniques: the development of a new pseudometric based on formal languages, which removes a problematic dependency on $L_\infty^\mathsf{p}$-distinguishability parameters, and the adoption of Count-Min-Sketch (CMS), instead of naive counting. The former reduces the number of samples required in environments that are characterized by a low complexity in language-theoretic terms. The latter alleviates the memory requirements for long planning horizons. We derive the PAC sample complexity bounds associated to each of these techniques, and we validate the approach experimentally., Comment: To appear in EWRL 2024

Electrochemical reduction is a promising way to make use of CO$_2$ as feedstock for generating renewable fuel and valuable chemicals. Several metals can be used in the electrocatalyst to generate CO and formic acid but hydrogen formation is an unwanted side reaction that can even be dominant. The lack of selectivity is in general a significant problem, but silver-based electrocatalysts have been shown to be highly selective for CO with over over 90% faradaic efficiency when the applied voltage is below -1 V vs. RHE. Hydrogen formation is then insignificant and little formate is formed even though it is thermodynamically favored. We present calculations of the activation free energy for the various elementary steps as a function of applied voltage at the three low index facets, Ag(111), Ag(100) and Ag(110), as well as experimental measurements on polycrystalline electrodes, to identify the reason for this high selectivity. The formation of formic acid is suppressed because of the low coverage of adsorbed hydrogen and kinetic hindrance to the formation of the HCOO* intermediate, while *COOH, a key intermediate in CO formation, is thermodynamically unstable until the applied voltage reaches -1 V vs. RHE, at which point the kinetics for its formation are more favorable than for hydrogen. The calculated results are consistent with experimental measurements carried out for acidic conditions and provide an atomic scale insight into the high CO selectivity of silver-based electrocatalysts.

We argue that some supersymmetric multiplets can naturally be equipped with the structure of an open-closed homotopy algebra. This structure is readily described through the pure spinor superfield formalism, which in particular associates a canonical multiplet for each choice of supersymmetry algebra. We study the open-closed homotopy algebra associated to (twists of) (resolutions of) the canonical multiplet, and show that it fits into a span of open-closed homotopy algebras, extending results of Cederwall et al. arXiv:2304.01258., Comment: 28 pages

The constraint satisfaction problem asks to decide if a set of constraints over a relational structure $\mathcal{A}$ is satisfiable (CSP$(\mathcal{A})$). We consider CSP$(\mathcal{A} \cup \mathcal{B})$ where $\mathcal{A}$ is a structure and $\mathcal{B}$ is an alien structure, and analyse its (parameterized) complexity when at most $k$ alien constraints are allowed. We establish connections and obtain transferable complexity results to several well-studied problems that previously escaped classification attempts. Our novel approach, utilizing logical and algebraic methods, yields an FPT versus pNP dichotomy for arbitrary finite structures and sharper dichotomies for Boolean structures and first-order reducts of $(\mathbb{N},=)$ (equality CSPs), together with many partial results for general $\omega$-categorical structures.

The fermionic von Neumann entropy, the fermionic entanglement entropy and the fermionic relative entropy are defined for causal fermion systems. Our definition makes use of entropy formulas for quasi-free fermionic states in terms of the reduced one-particle density operator. Our definitions are illustrated in various examples for Dirac spinors in two- and four-dimensional Minkowski space, in the Schwarzschild black hole geometry and for fermionic lattices. We review area laws for the two-dimensional diamond and a three-dimensional spatial region in Minkowski space. The connection is made to the computation of the relative entropy using modular theory., Comment: 36 pages, LaTeX, 2 figures

We argue that holomorphic twists of supersymmetric field theories naturally come with a symmetry $L_\infty$-algebra that nontrivially extends holomorphic symmetry. This symmetry acts on spacetime fields only up to homotopy, and the extension is only visible at the level of higher components of the action. We explicitly compute this for the holomorphic twist of ten-dimensional supersymmetric Yang-Mills theory, which produces a nontrivial action of a higher $L_\infty$-algebra on (a graded version) of five-dimensional affine space., Comment: 26 pages. Added additional references and minor clarifications

We introduce a novel approach to hierarchical reinforcement learning for Linearly-solvable Markov Decision Processes (LMDPs) in the infinite-horizon average-reward setting. Unlike previous work, our approach allows learning low-level and high-level tasks simultaneously, without imposing limiting restrictions on the low-level tasks. Our method relies on partitions of the state space that create smaller subtasks that are easier to solve, and the equivalence between such partitions to learn more efficiently. We then exploit the compositionality of low-level tasks to exactly represent the value function of the high-level task. Experiments show that our approach can outperform flat average-reward reinforcement learning by one or several orders of magnitude.

For an ideal $I$ in a Noetherian ring $R$, we introduce and study its conductor as a tool to explore the Rees algebra of $I$. The conductor of $I$ is an ideal $C(I)\subset R$ obtained from the defining ideals of the Rees algebra and the symmetric algebra of $I$ by a colon operation. Using this concept we investigate when adding an element to an ideal preserves the property of being of linear type. In this regard, a generalization of a result by Valla in terms of the conductor ideal is presented. When the conductor of a graded ideal in a polynomial ring is the graded maximal ideal, a criteria is given for when the Rees algebra and the symmetric algebra have the same Krull dimension. Finally, noting the fact that the conductor of a monomial ideal is a monomial ideal, the conductor of some families of monomial ideals, namely bounded Veronese ideals and edge ideals of graphs, are determined., Comment: 16 pages, 1 figure

Extended Reality (XR) has stringent throughput and delay requirements that are hard to meet with current wireless technologies. Missing these requirements can lead to worsened picture quality, perceived lag between user input and corresponding output, and even dizziness for the end user. In this paper, we study the capability of upcoming Wi-Fi 7, and its novel support for Multi-Link Operation (MLO), to cope with these tight requirements. Our study is based on simulation results extracted from an MLO-compliant simulator that realistically reproduces VR traffic. Results show that MLO can sustain VR applications. By jointly using multiple links with independent channel access procedures, MLO can reduce the overall delay, which is especially useful in the uplink, as it has more stringent requirements than the downlink, and is instrumental in delivering the expected performance. We show that using MLO can allow more users per network than an equivalent number of links using SLO. We also show that while maintaining the same overall bandwidth, a higher number of MLO links with narrow channels leads to lower delays than a lower number of links with wider channels.

Smart contracts, known for their immutable nature to ensure trust via automated enforcement, have evolved to require upgradeability due to unforeseen vulnerabilities and the need for feature enhancements post-deployment. This contradiction between immutability and the need for modifications has led to the development of upgradeable smart contracts. These contracts are immutable in principle yet upgradable by design, allowing updates without altering the underlying data or state, thus preserving the contract's intent while allowing improvements. This study aims to understand the application and implications of upgradeable smart contracts on the Ethereum blockchain. By introducing a dataset that catalogs the versions and evolutionary trajectories of smart contracts, the research explores key dimensions: the prevalence and adoption patterns of upgrade mechanisms, the likelihood and occurrences of contract upgrades, the nature of modifications post-upgrade, and their impact on user engagement and contract activity. Through empirical analysis, this study identifies upgradeable contracts and examines their upgrade history to uncover trends, preferences, and challenges associated with modifications. The evidence from analyzing over 44 million contracts shows that only 3% have upgradeable characteristics, with only 0.34% undergoing upgrades. This finding underscores a cautious approach by developers towards modifications, possibly due to the complexity of upgrade processes or a preference for maintaining stability. Furthermore, the study shows that upgrades are mainly aimed at feature enhancement and vulnerability mitigation, particularly when the contracts' source codes are accessible. However, the relationship between upgrades and user activity is complex, suggesting that additional factors significantly affect the use of smart contracts beyond their evolution.

We propose a new framework for formulating optimal transport distances between Markov chains. Previously known formulations studied couplings between the entire joint distribution induced by the chains, and derived solutions via a reduction to dynamic programming (DP) in an appropriately defined Markov decision process. This formulation has, however, not led to particularly efficient algorithms so far, since computing the associated DP operators requires fully solving a static optimal transport problem, and these operators need to be applied numerous times during the overall optimization process. In this work, we develop an alternative perspective by considering couplings between a flattened version of the joint distributions that we call discounted occupancy couplings, and show that calculating optimal transport distances in the full space of joint distributions can be equivalently formulated as solving a linear program (LP) in this reduced space. This LP formulation allows us to port several algorithmic ideas from other areas of optimal transport theory. In particular, our formulation makes it possible to introduce an appropriate notion of entropy regularization into the optimization problem, which in turn enables us to directly calculate optimal transport distances via a Sinkhorn-like method we call Sinkhorn Value Iteration (SVI). We show both theoretically and empirically that this method converges quickly to an optimal coupling, essentially at the same computational cost of running vanilla Sinkhorn in each pair of states. Along the way, we point out that our optimal transport distance exactly matches the common notion of bisimulation metrics between Markov chains, and thus our results also apply to computing such metrics, and in fact our algorithm turns out to be significantly more efficient than the best known methods developed so far for this purpose.

The relaxation time of several second generation molecular motors is analysed by calculating the minimum energy path between the metastable and stable states and evaluating the transition rate within harmonic transition state theory based on energetics obtained from density functional theory. Comparison with published experimental data shows remarkably good agreement and demonstrates the predictive capability of the theoretical approach. While previous measurements by Feringa and coworkers [Chem.\,Eur.\,J.\,(2017) 23, 6643] have shown that a replacement of the stereogenic hydrogen by a fluorine atom increases the relaxation time because of destabilization of the transition state for the thermal helix inversion, we find that a replacement of CH$_3$ by a CF$_3$ group at the same site shortens the relaxation time because of elevated energy of the metastable state without a significant shift in the transition state energy. Since these two fluorine substitutions have an opposite effect on the relaxation time, the two combined can provide a way to fine tune the rotational speed of a molecular motor.

The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 12 pages, 4 figures

Stateless model checking is a fully automatic verification technique for concurrent programs that checks for safety violations by exploring all possible thread schedulings. It becomes effective when coupled with Dynamic Partial Order Reduction (DPOR), which introduces an equivalence on schedulings and reduces the amount of needed exploration. DPOR algorithms that are optimal are particularly effective in that they guarantee to explore exactly one execution from each equivalence class. Unfortunately, existing sequence-based optimal algorithms may in the worst case consume memory that is exponential in the size of the analyzed program. In this paper, we present Parsimonious-OPtimal DPOR (POP), an optimal DPOR algorithm for analyzing multi-threaded programs under sequential consistency, whose space consumption is polynomial in the worst case. POP combines several novel algorithmic techniques, including (i) a parsimonious race reversal strategy, which avoids multiple reversals of the same race, (ii) an eager race reversal strategy to avoid storing initial fragments of to-be-explored executions, and (iii) a space-efficient scheme for preventing redundant exploration, which replaces the use of sleep sets. Our implementation in Nidhugg shows that these techniques can significantly speed up the analysis of concurrent programs, and do so with low memory consumption. Comparison to TruSt, a related optimal DPOR algorithm that represents executions as graphs, shows that POP's implementation achieves similar performance for smaller benchmarks, and scales much better than TruSt's on programs with long executions.

Asymptotic observables in quantum field theory beyond the familiar $S$-matrix have recently attracted much interest, for instance in the context of gravity waveforms. Such observables can be understood in terms of Schwinger-Keldysh-type 'amplitudes' computed by a set of modified Feynman rules involving cut internal legs and external legs labelled by time-folds. In parallel, a homotopy-algebraic understanding of perturbative quantum field theory has emerged in recent years. In particular, passing through homotopy transfer, the $S$-matrix of a perturbative quantum field theory can be understood as the minimal model of an associated (quantum) $L_\infty$-algebra. Here we bring these two developments together. In particular, we show that Schwinger-Keldysh amplitudes are naturally encoded in an $L_\infty$-algebra, similar to ordinary scattering amplitudes. As before, they are computed via homotopy transfer, but using deformation-retract data that are not canonical (in contrast to the conventional $S$-matrix). We further show that the $L_\infty$-algebras encoding Schwinger-Keldysh amplitudes and ordinary amplitudes are quasi-isomorphic (meaning, in a suitable sense, equivalent). This entails a set of recursion relations that enable one to compute Schwinger-Keldysh amplitudes in terms of ordinary amplitudes or vice versa., Comment: 27 pages. Published version: typos corrected and the review in sec. 5.1 includes clarifying remarks

We determine the influence of the Milky Way's warp on the kinematics of stars across the disc, and therefore measure its precession rate and line of nodes under different assumptions. We do this by applying Jeans' first equation to a model of a rigidly precessing warp. The predictions of these models are fit to the average vertical velocities of stars with measured line-of-sight velocities in Gaia DR3 data. We test models in which the warp's line of nodes and precession speed are fixed, and models in which they are allowed to vary linearly with radius. We also test models in which the velocity of stars radially in the disc is included in Jeans' equation. The kinematic data is best fit by models with a line of nodes that is 40 degrees offset from the Sun's Galactic azimuth, significantly leading the line of nodes found from the positions of stars. These models have a warp precession speed of around 13 km/s/kpc in the direction of Galactic rotation, close to other recent estimates. We find that including the velocity of stars radially in the disc in our kinematic model leads to a significantly worse fit to the data, and implausible warp parameters. We conclude that the Milky Way's warp appears to be rapidly precessing, but the structure and kinematics of the warped disc are not consistent within the approximation of a fixed, precessing, warp shape. This implies that the Milky Way's warp is dynamically evolving, which is a challenge to models of the warp's creation, and must be considered in the context of other known disturbances of the disc., Comment: Explainer video available at https://www.youtube.com/watch?v=8Wl81JC7l7k. A&A accepted. 13 pages, 10 figures

In this article, the effect a moving target has on the signal-to-interference-plus-noise-ratio (SINR) for high time-bandwidth noise radars is investigated. To compensate for cell migration we apply a computationally efficient stretch processing algorithm that is tailored for batched processing and suitable for implementation onto a real-time radar processor. The performance of the algorithm is studied using experimental data. In the experiment, pseudorandom noise, with a bandwidth of 100 MHz, is generated and transmitted in real-time. An unmanned aerial vehicle (UAV), flown at a speed of 11 m/s, is acting as a target. For an integration time of 1 s, the algorithm is shown to yield an increase in SINR of roughly 13 dB, compared to no compensation. It is also shown that coherent integration times of 2.5 s can be achieved., Comment: Presented at 2023 20th European Radar Conference (EuRAD)