No-regret learning dynamics ensure that a learner asymptotically achieves an average reward no worse than that of any fixed strategy. This no-regret guarantee does not determine the value of the asymptotic average reward. Indeed, it is possible for different no-regret learning dynamics to exhibit different asymptotic average rewards when facing the same environment while both assure the no-regret guarantee. This paper asks whether a "free-lunch" phenomenon can arise among no-regret algorithms. Namely, is it possible for one no-regret learning rule to uniformly outperform another no-regret learning rule across all payoff environments. Stated differently, can a learner regret not using a particular no-regret algorithm? We consider generalized replicator dynamics (RD) as a cascade interconnection between a linear time-invariant (LTI) system and the softmax nonlinearity. Varying this LTI system leads to different realizations of replicator dynamics, including so-called anticipatory RD, exponential RD, and other forms of higher-order RD. Setting the LTI system to be an integrator realizes standard RD, which is known to satisfy the no-regret property. Within this framework, we analyze and compare various realizations of these generalized realizations RD by varying the LTI system. We first formulate performance comparison as a passivity property of an associated comparison system and establish "local" dominance results, i.e., comparing the asymptotic performance near an equilibrium payoff vector. We then cast performance comparison between a form of anticipatory RD and standard RD as an optimal-control problem. We show that the minimal achievable cumulative reward gap is zero, thereby establishing global dominance of anticipatory RD across all payoff environments and establishing a "free lunch" among no-regret learning dynamics.

We numerically evaluate the two-loop QCD squared matrix element for in $q\bar{q}\to Z$ and $q\bar{q}\to Zγ$ with heavy top and bottom quarks circulating in a triangular fermion loop, by simultaneously subtracting infrared, ultraviolet, and threshold singularities directly in loop momentum space. This computation serves as an explicit demonstration that axial couplings can be included in the final state within the framework of arXiv:2510.18801. By formulating the entire calculation in four spacetime dimensions, with anomaly cancellation realised locally in loop momentum space, we bypass the complications associated with treating $γ^5$ in dimensional regularisation.

We numerically compute the two-loop QCD corrections to $Zγ$ production at the LHC mediated by light- and heavy-quark box loops. The calculation employs the pipeline of refs. arXiv:2407.18051 and arXiv:2510.18801, which performs Monte Carlo integration over spatial loop momenta after local subtraction of infrared, ultraviolet, and threshold singularities. We validate our results for partonic squared matrix elements with massless-quark loops against known benchmarks, extend them to include heavy-quark contributions, and compute the double-virtual corrections to $pp\to Zγ$ by performing the loop and phase space integrations simultaneously. This computation demonstrates the flexibility of the approach in handling both massless and massive final-state bosons, as well as additional mass scales in the loop.

Experimental data in particle and nuclear physics, particle astrophysics, and radiation protection dosimetry are collected using experimental facilities that consist of a complex system of sensors, electronics, and software. Measured spectra or cross sections are considered as Probability Density Functions (PDFs) that deviate from true PDFs due to resolution, bias, and efficiency effects. Unfolding is viewed as a procedure for estimating an unknown true PDF. Reliable estimates of the true PDF are necessary for testing theoretical models, comparing results from different experiments, and combining results from various research endeavors. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a blurred image, where the sharpness of the restored image serves as an indicator of quality. However, defining such external criteria can be challenging, particularly when a measurement has not been performed previously. This paper discusses various internal criteria for assessing the quality of the results independently of external information, as well as factors that influence the quality of the unfolded distribution.

There are many examples of `binary' partial groups in the literature: sets equipped an identity and a partially-defined binary operation, such that each element admits an inverse. We show that many of these may be regarded as partial groups in the sense of Chermak, and single out the largest class of such objects.

Scanning tunneling microscopy and spectroscopy have become indispensable tools for probing condensed matter at atomic length scales, yet achieving ultimate energy resolution remains a persistent challenge. At mK temperatures, the dynamical Coulomb blockade regime fundamentally limits spectroscopic precision through energy exchange between tunneling electrons and the electromagnetic environment. Here, we demonstrate that combining local electromagnetic shielding with low-pass filtering directly at the cryogenic scan head improves the energy resolution by nearly an order of magnitude, reaching benchmark values as low as 3.7$μ$eV at 10mK. We attribute this enhancement to efficient suppression of high-frequency radiation and capacitive shunting of the tunnel junction. Remarkably, this improved sensitivity reveals that the Josephson current couples to electromagnetic cavity modes of the centimeter-scale scan head, establishing a direct connection between atomic-scale tunneling processes and macroscopic cavity quantum electrodynamics. These advances open pathways for exploring ultra-low-energy phenomena with unprecedented precision.

In this paper we quantify the ability of the Laser Interferometer Space Antenna (LISA) to test the presence of non-tensorial polarizations as well as modifications to the tensor ones in gravitational waves emitted from massive black hole binaries. We employ the Parametrized Post-Einsteinian (PPE) formalism to model deviations from General Relativity (GR) for tensor, vector, and scalar polarizations. Our PPE parametrization is inspired by post-Newtonian waveforms from four modified gravity theories: Horndeski, Einstein-aether, Rosen's bimetric, and Lightman-Lee. We consistently implement these modifications across the inspiral, merger, and ringdown phases, ensuring proper waveform alignment and tapering. Subsequently, we perform Fisher forecasts to derive expected constraints on deviations from General Relativity and map these constraints to the parameter spaces of the four gravity theories. For tensor polarizations, LISA achieves constraints on amplitude modifications ranging between $\sim 10^{-4}-10^{-2}$ precision level, depending on the frequency evolution of the modifications, for systems with $10^5-10^7 {\, \rm M}_\odot$ at $z = 1$. We find that LISA can distinguish breathing and longitudinal scalar polarizations only for relatively light binaries with $M \lesssim 10^4 {\, \rm M}_\odot$, beyond which these modes become degenerate in the detector response. Importantly, constraints on vector polarizations are approximately 2-3 times more precise than for scalar polarizations. For both vector and scalar modes, amplitude measurements reach precisions ranging between $\sim 10^{-8}-10^{-2}$, depending on the frequency evolution of the modifications, for systems with $10^5-10^7 {\, \rm M}_\odot$ at $z = 1$. These results demonstrate LISA's potential to probe gravity in the strong-field regime via gravitational wave polarizations.

We are concerned with stable solutions to reaction-diffusion elliptic PDEs. We begin with regularity questions, first addressing the classical Laplacian. In joint work with Figalli, Ros-Oton, and Serra, we proved that stable solutions are smooth up to the optimal dimension 9, thereby solving an open problem posed by Brezis in the mid-1990s. We describe this result and also discuss related progress and open problems for the fractional Laplacian -- arising naturally in boundary reaction problems -- , the $p$-Laplacian, and minimal surfaces. We then turn to existence questions, starting with the Casten-Holland and Matano theorem for interior reactions, which states that no nonconstant stable solution exists in convex domains under zero Neumann boundary conditions. We present a recent result with Consul and Kurzke (forthcoming) establishing that the analogous statement fails for boundary reactions. This requires the development of a new Ginzburg-Landau theory for real-valued functions and the analysis of the half-Laplacian on the real line, for which we present new results and open problems.

In the present work we construct a novel generative architecture for systems with complex probability distributions. In general, these sampling tasks come with two challenges: resolving sign problems and efficient sampling. The architecture is based on physics-informed kernels (PIKs) introduced in arXiv:2510.26678, and aims at resolving both challenges. Key to the complex PIK-architecture is its probability-weight preserving property, which allows us to map the sampling task to one on a sign-problem free manifold with a simple distribution and efficient sampling. The potential of this novel architecture is demonstrated within applications to zero-dimensional field theories with complex couplings, as well as the real-time evolution of the quantum-mechanical harmonic oscillator.

Coherent detection of ultralight bosonic dark matter can be achieved by monitoring slow Rabi oscillations in superconducting qubits. We introduce an ancilla-assisted, gate-based protocol that enhances sensitivity to the hidden photon kinetic mixing parameter $ε$ using a single two-qubit gate, bypassing the need to maintain long-lived multi-qubit entangled states and remaining compatible with the limitations of modern quantum hardware. We characterized the increase in sensitivity accounting for decoherence, thermal occupation, errors in readout and reset, indicating up to a ten-fold reduction in the required integration time to reach the same exclusion limit on $ε$ achievable via Rabi-sampling experiments. Under plausible hardware assumptions and three years of data taking, the projected $95\%$ C.L. exclusion limit on the hidden photon mixing parameter reaches $ε\approx 1\times 10^{-14}$ across $2.5$-$6.0$ GHz ($10$-$25$ \textmu eV).

Background and Objectives: Longitudinal data are increasingly collected in clinical trials to provide information on treatment action and disease evolution. The trajectory of continuous biomarkers such as target hormone concentrations or viral loads can then be modelled in relationship to the occurrence of events such as recovery or hospitalisation. Other studies may include repeated measurements of discrete pain scores, number of episodes (count) or occurrence of events (survival). Non-linear mixed-effect models (NLMEM) can handle individual differences in trajectories while modelling the underlying population evolution and are the natural choice for their analysis. The saemix package for R is one of the few open-source solutions and the most flexible. In this paper, we extend it to accommodate a variety of models for non-Gaussian data. Methods: The saemix package estimates parameters through the Stochastic Approximation Expectation-Maximisation (SAEM) algorithm. Within the package, non-Gaussian models are specified by their log-likelihood functions, affording maximal control over model formulation. We extend estimation algorithms as well as exploratory and diagnostic plots for non-Gaussian data. Bootstrap approaches were implemented to estimate parameter uncertainty. To evaluate the performance of saemix, we performed a simulation study based on the toenail dataset, containing repeated binary data from a randomised clinical trial. Results: saemix showed good performance to recover the true parameter values in the simulation study, and was stable across different starting values for the parameters. An algorithm jointly searching for covariate and interindividual variability model was also implemented to build the covariate model and applied to categorical and survival-type data.

In this paper we show that steady states $u$ of the pressureless Euler equation which belong to $L^3_{loc}(\mathbb{R}^2,\mathbb{R}^2)$ are shear flows. This is achieved by combining results of degenerate Monge-Ampère-type equations with the theory of two dimensional transport equations. We also show that the problem of rigidity and flexibility for the associated differential inclusion is rigid for sequences equibounded in $L^{4+}$ and flexible for sequences equibounded in $L^{4-}$, thus displaying a gap in the rigidity exponent between the exact and the approximate problem.

We consider the coupled motion of a free rigid body immersed in an inviscid compressible isentropic fluid. By means of a vanishing viscosity limit, we obtain the local-in-time existence of a dissipative measure-valued solution to the model. Moreover, we establish the weak-strong uniqueness property of the obtained measure-valued solution. To our knowledge, this is the first mathematical result on compressible inviscid fluid-structure interaction. The key novel technique is the construction of a suitable approximation of the test function in the weak formulation of the inviscid system, as the space of test functions depends on the viscosity parameter.

The Primal-Dual Hybrid Gradient (PDHG) algorithm is a first-order method that can exploit GPUs to solve large-scale linear programming problems. The approach can often be faster than the alternatives, simplex and interior-point methods, typically at the cost of much lower accuracy. This paper looks at whether PDHG can be hybridized with an interior-point method to retain some of the speed advantages of the former while capturing the accuracy advantages of the latter.

In recent years, neutral atom quantum computers (NAQCs) have attracted a lot of attention, primarily due to their long coherence times and good scalability. One of their main drawbacks is their comparatively time-consuming control overhead, with one of the main contributing procedures being the detection of individual atoms and measurement of their states, each occurring at least once per compute cycle and requiring fluorescence imaging and subsequent image analysis. To reduce the required time budget, we propose a highly-parallel atom-detection accelerator for tweezer-based NAQCs. Building on an existing solution, our design combines algorithm-level optimization with a field-programmable gate array (FPGA) implementation to maximize parallelism and reduce the run time of the image analysis process. Our design can analyze a 256$\times$256-pixel image representing a 10$\times$10 atom array in just 115 $μ$s on a Xilinx UltraScale+ FPGA. Compared to the original CPU baseline and our optimized CPU version, we achieve about 34.9$\times$ and 6.3$\times$ speedup of the reconstruction time, respectively. Moreover, this work also contributes to the ongoing efforts toward fully integrated FPGA-based control systems for NAQCs.

This paper studies how the mean of the initial data $u_0$ affects the critical indices concerning local well-posedness for the following Majda-Biello systems: \[ \left\{\begin{aligned} & u_t + u_{xxx} + vv_x = 0 , \\ & v_t + αv_{xxx} + (uv)_x = 0 , \\ & (u,v) \mid_{t=0} = (u_0, v_0) \in H^s(\mathbb{T}) \times H^s(\mathbb{T}), \end{aligned}\right. \qquad x \in \mathbb{T}, \, t\in \mathbb{R}, \] where $\mathbb{T}$ refers to the periodic torus and the dispersion coefficient $α$ is restricted in $(0,4] \setminus \{1\}$ which corresponds to resonant cases. Previously, under the zero-mean assumption on $u_0$, Oh (Int. Math. Res. Not., (18):3516-3556, 2009) determined the critical indices $s^{*}(α)$ of the Sobolev regularity of the initial data for $C^3$ local well-posedness. In particular, Oh showed that \[ s^{*}(α) = \left\{ \begin{array}{lll} 1, & \text{for $α$ such that $\sqrt{12/α- 3} \in \mathbb{Q}$ }, \\ \frac12, & \text{for a.e. $α$ such that $\sqrt{12/α- 3} \notin \mathbb{Q}$ }. \end{array}\right. \] In this paper, by allowing the mean of $u_0$ to be non-zero, we find that the critical index $s^{*}(α)$ can be lowered from $1$ to $\frac12$ when $\sqrt{12/α- 3} \in \mathbb{Q}$. For other values of $α$, except in a set of zero measure, we also justify the critical index $s^{*}(α)$ to be $\frac12$ regardless of the mean of $u_0$. By subtracting the mean from $u_0$, the original Majda-Biello systems are slightly modified to contain first-order terms but with zero-mean initial data. The key ingredient in our proof is to introduce a refined Diophantine approximation theory to capture the essential resonance effect for the perturbed dispersive structure caused by these additional first-order terms.

This paper studies the impact of generative AI on U.S. households' task allocation at home, using detailed Internet browsing data from a large sample of home devices between 2021 and 2024. Leveraging pre-ChatGPT browsing patterns, we measure households' exposure to ChatGPT and use it as an instrument for ChatGPT adoption during the post-release period. Our IV estimates show that adopting generative AI substantially increases leisure browsing on home devices while leaving time spent on productive digital tasks unchanged. To examine mechanisms, we infer the purpose of households' ChatGPT use from surrounding internet activity and find that households primarily employ it for productive non-market tasks. Together, these results suggest that generative AI frees up leisure time by raising the efficiency of productive digital activities. Interpreting these findings through a standard time-allocation model implies economically large productivity gains from generative AI at home.

We introduce and study the problem of detecting short races in an observed trace. Specifically, for a race type $R$, given a trace $σ$ and window size $w$, the task is to determine whether there exists an $R$-race $(e_1, e_2)$ in $σ$ such that the subtrace starting with $e_1$ and ending with $e_2$ contains at most $w$ events. We present a monitoring framework for short-race prediction and instantiate the framework for happens-before and sync-preserving races, yielding efficient detection algorithms. Our happens-before algorithm runs in the same time as FastTrack but uses space that scales with $\log w$ as opposed to $\log |σ|$. For sync-preserving races, our algorithm runs faster and consumes significantly less space than SyncP. Our experiments validate the effectiveness of these short-race detection algorithms: they run more efficiently, use less memory, and detect significantly more races under the same budget, offering a reasonable balance between resource usage and predictive power.

As the energy storage ecosystem evolves beyond lithium, MXenes, a versatile family of 2D materials derived from MAX phases, have emerged as promising candidates for next-generation energy storage electrodes due to their tunable surface chemistry, large interlayer spacing, and excellent electronic conductivity. In this work, we use density functional theory to investigate Ti$_3$C$_2$ and V$_2$C MXenes as cathodes in Al-ion batteries. Four stacking configurations of the two-dimensional sheets and two different ion coordination sites are evaluated to understand their influence on ion intercalation and mobility. We find that the stacking configuration and surface chemistry critically impact interlayer spacing and electrochemical performance. O-terminated layers in an octahedral stacking exhibit remarkable structural stability with minimal interlayer expansion upon ion intercalation, particularly for Al intercalation in V$_2$C which exhibits an interlayer expansion of 0.1 angstrom, consistent with experimental findings. While octahedral stacking is observed to be energetically more favourable, it reduces ion mobility compared to prismatic stacking. Furthermore, O-terminated MXenes exhibit high theoretical specific capacities, reaching more than 270 mAh/g. F-terminated MXenes are considerably more unstable after intercalation and as a result exhibit much lower Al capacities. These findings highlight the importance of stacking configurations, termination and intercalant chemistry in MXenes for battery applications.

We consider $(1,1)$-surfaces, namely, minimal compact complex surfaces $S$ with $p_g (S) =K_S^2=1$: for these the bicanonical map is a covering of degree $4$ of the plane $\mathbb{P}^2$. And we answer a question posed by Meng Chen, whether they can contain a genus 2 pencil (this is the standard reason of failure of birationality of the bicanonical map). Our main theorem says that those which admit a genus 2 pencil form an irreducible subvariety of codimension $3$ in their moduli space $\frak M_{[1,1]}$; moreover, the general such surface admits exactly $12$ such pencils. The real fun is to relate this variety to the geometry of pencils of conics in the plane everywhere tangent to a cubic curve and a line. We investigate the corresponding variety $\mathcal{T}$ of triples and provide explicit equations using the classical theory of envelopes: among others, equations given in terms of the Weierstrass normal form of the cubic.

Ultra-faint dwarf galaxies (UFDs) show extreme dynamical mass-to-light ratios of approximately 100-5000 in solar units within the half-light radius, making them critical tests for cosmological models. However, it is a concern whether the line-of-sight (l.o.s.) velocity component of the orbital motion of undetected binary stars is significantly inflating the observed l.o.s. velocity dispersions and, consequently, UFDs dynamical mass estimates. We correct the current estimates of these quantities for UFDs to account for the presence of undetected binaries with single-epoch data. We use the latest binary population models in the solar neighborhood to compute the expected velocity distribution of binary stars. We then convolve this distribution with a Gaussian to model the l.o.s. velocity distribution of UFDs in a mixture model, in which the binary fraction is a free parameter. We apply this methodology to observed UFDs whose dynamical masses are potentially inflated by binaries. In order to generalize to the multi-epoch data case, we compute the velocity distribution of undetected binaries by applying the same cuts to the models as one would apply to the observed data to remove binaries. We find that estimated dynamical masses of UFDs decrease by a factor of 1.5 to 3 once undetected binaries are accounted for. These corrections significantly affect considerations about DM models based on these systems, even challenging the classification of Leo IV, Unions I and Sagittarius II as galaxies. We find that a dedicated multi-epoch campaign spanning one year could substantially mitigate the impact of binaries. Finally, we find that the expected level of binary-star contamination in DM halo density profile inferences from dynamical models of classical dwarf spheroidal galaxies is negligible.

Social media accounts engaging in online manipulation can change their behaviors for re-purposing or to evade detection. Existing detection systems are built on features that do not exploit such behavioral patterns. Here we investigate the degree to which change in behavior can serve as a signal for identifying automated or coordinated accounts. First, we use Behavioral Languages for Online Characterization (BLOC) to represent the behavior of a social media account as a sequence of symbols that represent the account's actions and content. Second, we segment an account's BLOC strings and measure the changes between consecutive segments. Third, we represent an account as a feature vector that captures the distribution of behavioral change values. Finally, the resulting features are used to train and test supervised classifiers. We apply the proposed method to two detection tasks aimed at automated behavior (social bots) and coordinated inauthentic behavior (information operations). Our results reveal that the distributions of behavioral changes tend to be consistent across authentic accounts, while social bots exhibit either very low or very high behavioral change. Coordinated inauthentic accounts exhibit highly similar distributions of behavioral change within the same campaign, but diverse across campaigns. These patterns allow our classifiers to achieve good accuracy in both tasks, demonstrating the effectiveness of behavioral change as a signal for identifying online manipulation.

This paper presents a deep Q-network (DQN)-based gain-scheduling framework for safety-critical quadcopter trajectory tracking. Instead of directly learning control inputs, the proposed approach selects from a finite set of pre-certified stabilizing gain vectors, enabling reinforcement learning to operate within a structured and stability-preserving control architecture. By exploiting the isotropic structure of the translational dynamics, feedback gains are shared across spatial axes to reduce dimensionality while preserving performance. The learned policy adapts feedback aggressiveness in real time, applying high authority during large transients and reducing gains near convergence to limit control effort. Simulation results using a high-fidelity nonlinear quadcopter model demonstrate accurate trajectory tracking, bounded attitude excursions, smooth transition to hover after the final time, and consistent reward improvement, validating the effectiveness and robustness of the proposed learning-based gain scheduling strategy.

Scholarly data are largely fragmented across siloed databases with divergent metadata and missing linkages among them. We present the Science Data Lake, a locally-deployable infrastructure built on DuckDB and simple Parquet files that unifies eight open sources - Semantic Scholar, OpenAlex, SciSciNet, Papers with Code, Retraction Watch, Reliance on Science, a preprint-to-published mapping, and Crossref - via DOI normalization while preserving source-level schemas. The resource comprises approximately 960GB of Parquet files spanning ~293 million uniquely identifiable papers across ~22 schemas and ~153 SQL views. An embedding-based ontology alignment using BGE-large sentence embeddings maps 4,516 OpenAlex topics to 13 scientific ontologies (~1.3 million terms), yielding 16,150 mappings covering 99.8% of topics ($\geq 0.65$ threshold) with $F1 = 0.77$ at the recommended $\geq 0.85$ operating point, outperforming TF-IDF, BM25, and Jaro-Winkler baselines on a 300-pair gold-standard evaluation. We validate through 10 automated checks, cross-source citation agreement analysis (pairwise Pearson $r = 0.76$ - $0.87$), and stratified manual annotation. Four vignettes demonstrate cross-source analyses infeasible with any single database. The resource is open source, deployable on a single drive or queryable remotely via HuggingFace, and includes structured documentation suitable for large language model (LLM) based research agents.

Trilayered Bi-2223 superconductor features the highest critical temperature $T_c$ among the bismuth-based cuprate collection and symbolizes an ideal prototype for studying intrinsic superconducting properties. The previous solid-state reaction method substantiated the growth of the high-quality Bi-2223 compounds but was accompanied by excessively laborious time and effort in terms of multiple grinding, pressing, as well as calcining stages, %causing risk of constituent loss, so finding a less tedious synthesis path is imperative. Here, we present an advanced sol-gel synthesis for assembling the multicomponent complexity of Bi1.4Pb0.6Sr2Ca2(Cu1-xLix)3O10 superconductors (Li-doped Bi-2223), with $x$ = 0.0--0.20, utilizing metallic cationic molecular mixing within the chemical Pechini polyesterization route followed by single-step pyrolysis and sintering stages. Although monovalent cations such as Li$^+$ pose limitations in establishing a perplex crosslinking network or chelating mechanism in the Pechini method, they represent a unique probe to elucidate the major chemical process during polymerization. We observe that a 5 molar~\% Li-doped sample pronounces the highest $T_c$ = 111.4 K among the series of samples, as confirmed by both ac susceptibility and dc resistivity measurements, and is equivalent to the value obtained by our preceding solid state fabrication. In addition, we showcase a rare observation of layer-by-layer crystalline phase growth under microstructure probe. Through analyzing the reliable ac susceptibility data at low magnetic fields in a wide range of frequency, we provide the quantum flux formation and flux creep mechanism by Anderson-Müller's model and Cole-Cole plot.

Software systems evolve continuously through frequent code changes, yet such changes often introduce unintended bugs despite extensive testing and code review. Existing testing approaches are largely constrained to predefined execution paths or rely on unguided exploration, leaving many change-induced issues undetected. To address this challenge, we present RippleGUItester, a change-driven testing system that treats a code change as the epicenter of a ripple effect and explores its broader, user-visible impacts via the GUI. Given a code change, RippleGUItester performs LLM-based change-impact analysis to generate and enrich realistic test scenarios, executes these scenarios on both pre-change and post-change versions of the system, and applies differential analysis to identify behavioral differences. Crucially, RippleGUItester employs multimodal bug detection, comparing visual GUI changes and interpreting them in the context of natural-language change intents to distinguish unintended bugs from intended behavioral updates. We evaluate our approach on hundreds of real-world code changes across four widely used software systems: Firefox, Zettlr, JabRef, and Godot. Our results show that the proposed approach uncovers bugs introduced by code changes that were missed by existing test suites, CI pipelines, and code review. In total, we identify 26 previously unknown bugs that still exist in the latest versions of the evaluated systems. After reporting, 16 bugs have been fixed, 2 have been confirmed, 6 are still under discussion, and 2 were marked as intended. We envision RippleGUItester being applied before or shortly after a code change is merged, enabling earlier detection of regressions.

We present an update on the determination of the leading-order hadronic vacuum polarisation contribution to the muon anomalous magnetic moment in isospin-symmetric QCD by the Extended Twisted Mass Collaboration. The calculation is based on five $N_f = 2+1+1$ gauge ensembles generated with Wilson-clover twisted-mass quarks at maximal-twist and near-physical pion masses, spanning four lattice spacings and two volumes. For the dominant quark-connected contributions, we employ two distinct valence-quark regularisations and present results for both the isovector and isoscalar components.

In this work, we investigate prompt ion drift orbit losses caused by shear Alfvén waves (SAW) in quasi-symmetric (QS) and quasi-isodynamic (QI) stellarators optimized for equilibrium confinement of energetic particles (EPs). We use the ideal reduced MHD model for SAW perturbations and study their impact on collisionless EP drift dynamics. We present a semi-analytical model for resonance between the passing EP and SAW, generalized to arbitrary quasi-symmetric configurations including the quasi-poloidal case relevant to QI equilibria. Analysis reveals that an increase in the number of field periods suppresses stochasticity in quasi-helical (QH) and quasi-isodynamic, but not quasi-axissymmetric (QA) stellarators. We show that wave-induced transitions between passing and trapped orbits cause significant losses in QA and QH, but not in QI configurations. For the considered equilibria at scales relevant to fusion power plants (FPPs), we numerically determine SAW amplitudes needed to induce prompt loss of fusion-born alpha particles. Using the weighted Birkhoff averaging technique, we confirm that the onset of prompt losses across all orbit classes occurs with the onset of stochasticity in ion motion. This motivates extending the stochasticity-onset criterion beyond passing orbits in future work.

Charge carriers in Dirac/Weyl semimetals with tilted anisotropic energy dispersion exhibit valley-dependent refraction and reflection at electrostatic barrier interfaces. Here, we show that an angled barrier interface provides a purely electrostatic route to valley filtering, producing finite valley-polarized conductance. We develop a generalized transfer-matrix formalism for the tilted, anisotropic Dirac Hamiltonian, extended to treat electrostatic barriers at arbitrary angles, and calculate the transmission in the rotated-barrier frame. We also present simulated valley-resolved trajectories in a finite device geometry, which clearly show that one valley is selectively transmitted, whereas the other is predominantly reflected by the angled barrier, without secondary effects such as real or pseudo-magnetic fields.

We adapt two results of Simon and collaborators to the setting of discrete-time unitary dynamics. We show that pure point spectrum precludes ballistic motion, and exhibit a family of examples showing that this is sharp within the class of extended Cantero--Moral-Velázquez (CMV) matrices: that is, there exist extended CMV matrices exhibiting pure point spectrum together with quantum dynamics as close to ballistic motion as one desires.