The dynamic programming approach is one of the most powerful ones in optimal control. However, when dealing with optimal control problems of stochastic Volterra integral equations (SVIEs) with completely monotone kernels, deep mathematical difficulties arise and it is still not understood. These very classical problems have applications in most fields and have now become even more popular due to their applications in mathematical finance under rough volatility. In this article, we consider a class of optimal control problems of SVIEs with completely monotone kernels. Via a recent Markovian lift \cite{FGW2024}, the problem can be reformulated as an optimal control problem of stochastic differential equations (SDEs) on suitable Hilbert spaces, which due to the roughness of the kernel, presents a generator of an analytic semigroup and unbounded control and diffusion operators. This analysis leads us to study a general class of optimal control problems of abstract SDEs on Hilbert spaces with unbounded control and diffusion operators. This class includes optimal control problems of SVIEs with completely monotone kernels, but it is also motivated by other models. We analyze the regularity of the associated Ornstein-Uhlenbeck transition semigroup. We prove that the semigroup exhibits a new smoothing property in control directions through a general observation operator $Γ$, which we call $Γ$-smoothing. This allows us to establish existence and uniqueness of mild solutions of the Hamilton-Jacobi-Bellman equation, establish a verification theorem, and construct optimal feedback controls. We apply these results to optimal control problems of SVIEs with completely monotone kernels. To the best of our knowledge these are the first results of this kind for this abstract class of infinite dimensional problems and for the optimal control of SVIEs with completely monotone kernels.

We analyze the modification of entanglement dynamics in the Grover algorithm when the qubits are subjected to single-qubit amplitude-damping or phase-flip noise. We compare quantum trajectories with full density-matrix simulations, analyzing the dynamics of averaged trajectory entanglement (TE) and operator entanglement (OE), in the respective state representation. Although not a genuine entanglement measure, both TE and OE are connected to the efficiency of matrix product state simulations and thus of fundamental interest. As in many quantum algorithms, at the end of the Grover circuit entanglement decreases as the system converges towards the target product state. While we find that this is well captured in the OE dynamics, quantum trajectories rarely follow paths of reducing entanglement. Optimized unraveling schemes can lower TE slightly, however we show that deep in the circuit OE is generally smaller than TE. This implies that matrix product density operator (MPDO) simulations of quantum circuits can in general be more efficient than quantum trajectories. In addition, we investigate the noise-rate scaling of success probabilities for both amplitude-damping and phase-flip noise in Grover's algorithm.

We prove that whenever $d=d(n)\to\infty$ and $n-d\to\infty$ as $n\to\infty$, then with high probability for any non-trivial initial colouring, the colour refinement algorithm distinguishes all vertices of the random regular graph $\mathcal{G}_{n,d}$. This, in particular, implies that with high probability $\mathcal{G}_{n,d}$ admits a canonical labelling computable in time $O(\min\{n^ω,nd^2+nd\log n\})$, where $ω<2.372$ is the matrix multiplication exponent.

We introduce a compact, physics-driven event representation, RMM-C46, designed to compress the high-dimensional rapidity mass matrix (RMM) into a low-dimensional, interpretable feature set suitable for physics-informed machine learning (ML) and quantum computing applications. The full RMM encodes detailed pairwise correlations among jets, b-jets, leptons, photons, and missing transverse energy but contains more than a thousand values per event, making it computationally heavy for large-scale training and incompatible with current low-qubit quantum devices. The proposed RMM-C46 input space for ML preserves the physical block structure of the RMM through aggregated invariant mass, rapidity difference, and transverse energy components, reducing the size of the original RMM by over an order of magnitude while maintaining interpretability. Applied to simulated proton-proton collisions at centre-of-mass energy of 13.6 TeV, these representations match or exceed the discriminative performance of the full RMM in both supervised and unsupervised ML tasks. Their compactness, stability, and physics transparency also make them naturally compatible with near-term quantum machine learning architectures. RMM-C46 provides a scalable, efficient, and quantum-ready alternative to the full RMM for next-generation collider physics analyses.

Body-centered Cubic (BCC) lattice structures demonstrate promising performance for applications that require simultaneous mechanical energy absorption and thermal management. However, current optimization approaches are typically confined to single-domain objectives, such as mechanical parameters like impact energy and peak stress, neglecting the role of multiple physics in real-world performance. To address this, we propose a multi-objective optimization framework for density-graded BCC lattices that effectively dissipates heat while maximizing absorbed impact energy. A parametric three-zone lattice configuration is investigated to explore various trade-offs between mechanical and thermal properties. Each design is evaluated through independent impact and forced-convection simulations using commercial solvers. Specific Energy Absorption (SEA) and peak stresses at the distal end quantify impact absorption performance, while the Nusselt number and pressure drop characterize thermal dissipation performance. Surrogate models constructed from this data enable multi-objective optimization via Goal Programming to identify an optimal design. Two Pareto-optimal lattice designs are identified with reduced pressure drop and peak stress, underlining the superiority of strategic density gradation. Analysis of the optimal designs reveals how material distribution and geometric design variables influence mechanical-thermal trade-offs, establishing quantitative design guidelines for lattice structures in this multi-physics application.

MRI measurements from a decade-old study of the physical properties of brain tissue observed a dynamic, pulsating fluid flow in the interstitial spaces of the brain attributed to the cardiac cycle. The effects of this cyclic flow pattern on the spatial distribution of molecules in the brain are modeled in this paper. The effects of oscillatory flow on the dispersion or volumetric transmission of a molecule that is advected by this flow is modeled by a mechanism hitherto neglected in the literature. An oscillatory random walk model is used to estimate the spread or effective diffusivity due to the oscillatory advection. Then, respiration effects are also estimated and the additional dispersion of molecules due to this are calculated in our model. Our model indicates that the observed oscillatory flow in the interstitial spaces due to cardiac as well as respiratory pulsatility can induce an effective diffusivity when the spread of the molecule is observed over times long compared with a cycle of the oscillation. This would help explain the high-volume transmission within the interstitium or brain parenchyma found in MRI measurements of a marker infused into the cerebrospinal fluid in human subjects that is well above what would be expected. Interstitial spaces should be viewed as a region of dynamic oscillatory flow driven by cardiac and respiratory cycles. This oscillatory flow could result in a significant dispersion of molecules and explain the higher-than-expected effective diffusion suggested in human studies. It may be possible to augment or slow this flow and concomitant spread by applying external forces.

Error-bounded lossy compression is essential for managing the massive data volumes produced by large-scale HPC simulations. While state-of-the-art compressors such as SZ and ZFP provide strong numerical error guarantees, they often fail to preserve topological structures (example, minima, maxima, and saddle points) that are critical for scientific analysis. Existing topology-aware compressors address this limitation but incur substantial computational overhead. We present TopoSZp, a lightweight, topology-aware, error-controlled lossy compressor that preserves critical points and their relationships while maintaining high compression and decompression performance. Built on the high-throughput SZp compressor, TopoSZp integrates efficient critical point detection, local ordering preservation, and targeted saddle point refinement, all within a relaxed but strictly enforced error bound. Experimental results on real-world scientific datasets show that TopoSZp achieves 3 to 100 times fewer non-preserved critical points, introduces no false positives or incorrect critical point types, and delivers 100 to 10000 times faster compression and 10 to 500 times faster decompression compared to existing topology-aware compressors, while maintaining competitive compression ratios.

The formation pathways of sulfur-bearing species in the interstellar medium are crucial to understand astrochemical processes in cold molecular clouds and to gain new insights about the sulfur budget in these regions. We aim to explore the recently detected, thioethanal (CH$_{3}$CHS) formation mechanisms from thioethanol (CH$_{3}$CH$_{2}$SH) as a precursor in addition to secondary sulfur products. The electronic structure methods and density functional theory for both gas-phase and ice-grain surface environments is employed. To mimic interstellar ice-mantles, we use medium (W6) and large amorphized (W22) water clusters as implemented in Binding Energy Evaluation protocol. A barrierless formation mechanism for CH$_{3}$CHS under low-temperature interstellar conditions is identified, in the gas phase. Surface environments modulate activation barriers in a site-specific manner, elucidated through both Langmuir-Hinshelwood and Eley-Rideal initiated surface reaction pathways. Compared to oxygen analogs, sulfur chemistry enables alternate pathways due to weaker S-H bonding, with a competing route forming ethane-1,1-di-thiol (CH$_{3}$CH(SH)SH), on the ice-grain surface, potentially reducing CH$_{3}$CHS yields. The first accurate binding energy for thioethanol on water ice is also reported, confirming its greater volatility than ethanol. The proposed mechanism offers a tentative hypothesis for the apparent mutual exclusive detections of the CH$_{3}$CH$_{2}$SH and CH$_{3}$CHS in TMC-1, Orion, and Sgr B2(N), that further requires validation through quantitative astrochemical modeling and also to distinguish this chemical differentiation from observational sensitivity limitations. These qualitative findings highlight the multifaceted chemical behavior of sulfur-bearing organics in the interstellar medium and support CH$_{3}$CH(SH)SH as promising astro-chemical targets.

Achieving controlled and directed motion of artificial nanoscale systems in three-dimensional fluid environments remains a key-challenge in active matter, primarily due to the prevailing thermal fluctuations that rapidly randomize the particle trajectories. While significant progress has been made with micrometer-sized particles, imparting sufficient mechanical energy, or self-propulsion, to nanometer-sized particles to overcome Brownian diffusion and enable controlled transport remains a major issue for emerging applications in nanoscience and nanomedicine. Here, we address this challenge by demonstrating the fuel-free, reversible, and tunable active behavior of gold-silica (Au-SiO2) Janus nanoparticles (radius R=33 nm) induced by optical excitation. Using single particle tracking, we provide direct experimental evidence of self-thermophoresis, clearly distinguishing active motion from thermal noise. These light-driven Janus nanoparticles constitute a minimal yet robust photothermal system for investigating active matter and its manipulation at the nanoscale.

We study the self-stabilizing leader election problem in anonymous $n$-nodes networks. Achieving self-stabilization with low space memory complexity is particularly challenging, and designing space-optimal leader election algorithms remains an open problem for general graphs. In deterministic settings, it is known that $Ω(\log \log n)$ bits of memory per node are necessary [Blin et al., Disc. Math. \& Theor. Comput. Sci., 2023], while in probabilistic settings the same lower bound holds for some values of $n$, but only for an unfair scheduler [Beauquier et al., PODC 1999]. Several deterministic and probabilistic protocols have been proposed in models ranging from the state model to the population protocols. However, to the best of our knowledge, existing solutions either require $Ω(\log n)$ bits of memory per node for general worst case graphs, or achieve low state complexity only under restricted network topologies such as rings, trees, or bounded-degree graphs. In this paper, we present a probabilistic self-stabilizing leader election algorithm for arbitrary anonymous networks that uses $O(\log \log n)$ bits of memory per node. Our algorithm operates in the state model under a synchronous scheduler and assumes knowledge of a global parameter $N = Θ(\log n)$. We show that, under our protocol, the system converges almost surely to a stable configuration with a unique leader and stabilizes within $O(\mathrm{poly}(n))$ rounds with high probability. To achieve $O(\log \log n)$ bits of memory, our algorithm keeps transmitting information after convergence, i.e. it does not verify the silence property. Moreover, like most works in the field, our algorithm does not provide explicit termination detection (i.e., nodes do not detect when the algorithm has converged).

Polyatomic molecules provide complex internal structures that are ideal for applications in quantum information science, quantum simulation, and precision searches for physics beyond the Standard Model. A key feature of polyatomic molecules is the presence of parity-doublet states. These structures, which generically arise from the rotational and vibrational degrees of freedom afforded by polyatomic molecules, are a powerful feature to pursue these diverse quantum science applications. Linear triatomic molecules contain $\ell$-type parity doublet states, which are predicted to exhibit robust coherence properties. We optically trap CaOH molecules, prepare them in $\ell$-type parity-doublet states, and realize a bare qubit coherence time of $T_2^* = 0.8(2)$ s. We suppress differential Stark shifts by employing molecular spectroscopy to cancel ambient electric fields, and characterize parity-dependent trap shifts, which are found to limit the coherence time. The parity-doublet coherence times achieved in this work are a defining milestone for the use of polyatomic molecules in quantum science.

We investigate the dynamics of a dense raft of millimeter-sized granular particles at a vertically vibrated air-water interface, which displays a rich set of patterns and particle dynamics as we vary the vibration amplitude, frequency, and particle packing fraction. While the classical parametric instability with standing waves still occurs over a certain parameter space, the measured wave dispersion relations indicate an increasing role in the raft's emerging elasticity at higher packing fractions, which induces a decrease in the effective surface tension and an increase in an out-of-plane bending modulus. At higher vibration frequencies and lower amplitudes, we also identified a regime without standing waves in which individual particles exhibit thermal-like motion and transition from diffusive to sub-diffusive transport as the packing fraction increases. Glassy behaviors such as spatial and temporal heterogeneity in particle dynamics occur as well, which is analogous to supercooled liquids. When the vibration amplitude is increased starting in this supercooled regime, a large cavity eventually forms inside the raft with its size and shape related to the vibration frequency and the injected vibration energy. The cavitation results in the coexistence of free surface water waves inside the cavity and thermal-like particle motion in the raft.

In this letter we evaluate one-loop all-plus gluon amplitudes of $\mathrm{SU}(N_c)$ gauge theory with $N_f$ fundamental fermions in the presence of a flavour instanton background. Fermion zero modes are regulated with a chiral mass term. This computation is performed by cancelling a twistorial 't Hooft anomaly via the Green-Schwarz mechanism. We find that the trace-ordered amplitude has the form of a Parke-Taylor factor multiplied by the Fourier transform of the instanton density evaluated on the total momentum of the gluons. A background flavour instanton modifies the leading soft gluon and photon theorem, generating a level equal to twice the instanton charge in the soft Kac-Moody symmetry. We discuss the implications of our results for amplitudes in the presence of dynamical instantons.

High Altitude Platforms (HAPs) are a major advancement in non-terrestrial networks, offering broad coverage and unique capabilities. They form a vital link between satellite systems and terrestrial networks and play a key role in next-generation communication technologies. This study reviews HAP network applications, focusing on advanced airborne communications, integrated sensing, and airborne informatics. Our survey assesses the current state of HAP-centric applications by examining data processing, network performance, computational and storage requirements, economic feasibility, and regulatory challenges. The analysis highlights the evolving role of HAPs in global communication and identifies future research directions to support their deployment.

The transverse-momentum spectrum of Drell-Yan lepton pairs at small $p_T$ probes non-perturbative QCD effects, including intrinsic partonic transverse momentum and initial-state soft-gluon radiation. The novel approach PDF2ISR is employed to study the transverse-momentum spectrum of Drell-Yan lepton pairs in the small-$p_T(\ell\ell)$ region. This framework is particularly well suited for such investigations, as it provides a systematic treatment of the dominant non-perturbative effects and their interplay. In order to extract robust physical conclusions, a detailed analysis of the technical aspects involved in the simulation of the intrinsic transverse momenta of partons inside the proton, as well as the remnant recoil schemes, was carried out. Furthermore, different approaches for the treatment of the strong coupling at low scales were investigated and confronted with available experimental data. This comparison enabled an assessment of the sensitivity of the experimental measurements to the chosen low-scale behaviour of $α_s$.

The stability of chemically complex nanoparticles is governed by an immense configurational space arising from heterogeneous local atomic environments across surface and interior regions. Efficiently identifying low-energy configurations within this space remains a central challenge for first-principles-based materials discovery, particularly when the available reference data are limited. Here, we introduce a data-efficient and physically interpretable machine-learning framework based on a fragmented, layer-resolved descriptor that explicitly decomposes nanoparticles into surface, intermediate, and core environments using a topology-driven definition. This representation preserves a compact and fixed feature dimensionality while retaining spatial resolution, enabling controlled emphasis on different regions of the nanoparticle through physically motivated weighting schemes. Coupled with gradient-boosted decision tree models and a ranking-based learning strategy, the proposed framework enables accurate identification of the most stable nanoparticle configurations using only a few hundred density functional theory reference calculations. Ranking performance metrics demonstrate near-saturation of correlation, high top-k recall, and rapidly vanishing regret at moderate training-set sizes, highlighting the strong data efficiency of the approach. Beyond predictive performance, layer-weighting and SHAP-based interpretability analyses reveal how surface segregation, coordination topology, and local chemical disorder contribute differently to stability across spatial regions of the nanoparticle. These insights provide a transparent physical interpretation of the learned models and establish a natural pathway toward active learning-driven exploration of complex nanoparticle configurational spaces.

We introduce millimeter-wave silicon photonic crystal cavities as a versatile platform for the perturbative sensing of nanoscale materials. This dielectric-based platform is compatible with strong magnetic fields, opening avenues for studying quantum materials in extreme environments where superconducting cavities cannot operate. To establish the platform's performance, we cryogenically characterize a silicon photonic crystal cavity at 4.3 K, achieving a total quality factor exceeding $10^5$ for a 96 GHz mode. As a proof-of-concept for its sensing capabilities, we position a hexagonal boron nitride-multilayer graphene (hBN-MLG) heterostructure at an electric-field antinode of the cavity and measure the perturbative response at room temperature. The heterostructure induces a significant change in the cavity's resonance, from which we extract a total sample conductivity of approximately $5.1\times10^6$~S/m. These results establish silicon photonic crystal cavities as a promising platform for sensitive, on-chip spectroscopy of nanoscale materials at millimeter-wave frequencies.

The phase evolution of gravitational waves encodes critical information about the orbital dynamics of binary systems. In this work, we test the robustness of parameterized tests against unmodeled deviations from general relativity. We demonstrate that these parameterized tests are flexible and sensitive in detecting generic deviations in the waveform using the Cutler-Vallisneri bias formalism. This universality arises from examining the inherent geometry of the waveform signal and understanding how biases manifest. We show how Bayes factors are governed by the intrinsic geometry of the waveform signal manifold when parameterized tests are used to approximate generic violations of GR. We use the singular value decomposition to propose templates that are orthogonal to parameterized tests, identifying degeneracies and enhancing the detection of potential deviations. More broadly, the geometric framework developed here clarifies -- at a fundamental level -- how subtle waveform effects (including orbital eccentricity, spin precession, waveform systematics, and instrumental glitches) can mimic one another in data, and when they are intrinsically distinguishable.

We present high-precision spectroscopy of the ground-state hyperfine structure of HD$^+$ at 4~T. We determine the bound-electron $g$ factor, $g_{e,\mathrm{bound}} = -2.002\,278\,540\,96(40)$, to a relative uncertainty of $2\times$10$^{-10}$, the most precise determination of a bound-electron $g$ factor of a molecular ion to date. The experimental value agrees with recently developed ab initio theory that now includes quantum-electrodynamical effects up to order $α^5$ and has reduced the theoretical uncertainty by three orders of magnitude [O. Kullie \textit{et al.}, Phys. Rev. A 112 052813 (2025)]. In addition, we extract the scalar spin-spin interaction coefficients $E_4$~=~925\,395.758(41)$\,$kHz (electron-proton) and $E_5$~=~142\,287.821(22)$\,$kHz (electron-deuteron), which show a moderate tension with another state-of-the-art theoretical prediction [M. Haidar \textit{et al.}, Phys. Rev. A 106 042815 (2022)].

Many biological microswimmers can modulate their swimming gait to achieve directional control of motility, especially when performing steering towards specific directional cues. This can be achieved without the need for obvious morphological or structural asymmetries in the form of the organism, or in the number or organisation of propulsion-generating appendages such as cilia. In this work, we identify and validate a core principle of asymmetric turning in biflagellate microswimmers: propulsive forces interact constructively to drive translation whilst interacting destructively to drive rotation. We explore the ramifications of this tunable biflagellar swimming mechanism across a range of systems, from a simple, back-of-the-envelope model to a detailed computational representation of an exemplar swimmer. This leads to a markedly general quantitative relation between key drivers of asymmetry, such as ciliary beat frequency, and the curvature of emergent trajectories. We discuss how the model green alga Chlamydomonas reinhardtii, which actuates its two cilia in a symmetric breaststroke for forward swimming, may exploit this feature for phototaxis. Finally, we validate our predictions in a Chlamydomonas-inspired robophysical model, implementing closed-loop control to achieve phototactic turning.

Large language models (LLMs) demonstrate strong performance on standard digital logic and Boolean reasoning tasks, yet their reliability under locally redefined semantics remains poorly understood. In many formal settings, such as circuit specifications, examinations, and hardware documentation, operators and components are explicitly redefined within narrow scope. Correct reasoning in these contexts requires models to temporarily suppress globally learned conventions in favor of prompt-local definitions. In this work, we study a systematic failure mode we term semantic override, in which an LLM reverts to its pretrained default interpretation of operators or gate behavior despite explicit redefinition in the prompt. We also identify a related class of errors, assumption injection, where models commit to unstated hardware semantics when critical details are underspecified, rather than requesting clarification. We introduce a compact micro-benchmark of 30 logic and digital-circuit reasoning tasks designed as verifier-style traps, spanning Boolean algebra, operator overloading, redefined gates, and circuit-level semantics. Evaluating three frontier LLMs, we observe persistent noncompliance with local specifications, confident but incompatible assumptions, and dropped constraints even in elementary settings. Our findings highlight a gap between surface-level correctness and specification-faithful reasoning, motivating evaluation protocols that explicitly test local unlearning and semantic compliance in formal domains.

The synchronization of rhythms is ubiquitous in both natural and engineered systems, and the demand for data-driven analysis is growing. When rhythms arise from limit cycles, phase reduction theory shows that their dynamics are universally modeled as coupled phase oscillators under weak coupling. This simple representation enables direct inference of inter-rhythm coupling functions from measured time-series data. However, strongly rhythmic chaos can masquerade as noisy limit cycles. In such cases, standard estimators still return plausible coupling functions even though a phase-oscillator model lacks a priori justification. We therefore extend the phase description to the chaotic oscillators. Specifically, we derive a closed equation for the phase difference by defining the phase on a Poincaré section and averaging the phase dynamics over invariant measures of the induced return maps. Numerically, the derived theoretical functions are in close agreement with those inferred from time-series data. Consequently, our results justify the applicability of phase description to coupled chaotic oscillators and show that data-driven coupling functions retain clear dynamical meaning in the absence of limit cycles.

With automated systems increasingly issuing search queries alongside humans, Information Retrieval (IR) faces a major shift. Yet IR remains human-centred, with systems, evaluation metrics, user models, and datasets designed around human queries and behaviours. Consequently, IR operates under assumptions that no longer hold in practice, with changes to workload volumes, predictability, and querying behaviours. This misalignment affects system performance and optimisation: caching may lose effectiveness, query pre-processing may add overhead without improving results, and standard metrics may mismeasure satisfaction. Without adaptation, retrieval models risk satisfying neither humans, nor the emerging user segment of agents. However, datasets capturing agent search behaviour are lacking, which is a critical gap given IR's historical reliance on data-driven evaluation and optimisation. We develop a methodology for collecting all the data produced and consumed by agentic retrieval-augmented systems when answering queries, and we release the Agentic Search Queryset (ASQ) dataset. ASQ contains reasoning-induced queries, retrieved documents, and thoughts for queries in HotpotQA, Researchy Questions, and MS MARCO, for 3 diverse agents and 2 retrieval pipelines. The accompanying toolkit enables ASQ to be extended to new agents, retrievers, and datasets.

Event shape variables, constructed from the four-momenta of the final-state objects in an event, are sensitive to the predictions of quantum chromodynamics in multijet production. A measurement of five event shape variables is presented, using proton-proton collision data collected at a centre-of-mass energy of 13 TeV with the CMS detector during 2016$-$2018, corresponding to an integrated luminosity of 138 fb$^{-1}$. The variables are evaluated using the charged particles inside jets. After correcting for detector effects, their distributions are compared with the results from the predictions from a number of models for multijet production. Overall, there is general agreement between several theoretical predictions and the data.

The aim of this work is to apply a semi-implicit (SI) strategy within a Rosenbrock-type and IMEX linear multistep (LM) framework to a sequence of 1D time-dependent partial differential equations (PDEs) with high order spatial derivatives. This strategy provides great flexibility to treat these equations, and allows the construction of simple lienarly implicit schemes without any Newton iteration. Furthermore, the SI schemes so designed do not require the severe time-step restrictions typically encountered when using explicit methods for stability, i.e., $Δt = \mathcal{O}(Δx^k)$ for the $k$-th order PDEs with $k\ge 2$. For space discrertization, this strategy is combined with finite difference schemes. We provide example of methods up to order $p = 4$, and we illustrate the effectiveness of the schemes with appllications to dissipative, dispersive, and biharmonic-type equations. Numerical experiments show that the proposed schemes are stable and achieve the expected orders of accuracy.

In the three-body problem with positive energy, solutions which avoid triple collision have the property that the size of the triangle formed by the bodies tends to infinity as $t\rightarrow \pm\infty$. Furthermore, the triangles have well-defined asymptotic shapes $s_\pm$. The scattering problems asks which asymptotic shape $s_+$ can occur for a given choice of $s_-$. Previous work shows that this can be viewed as the problem of finding heteroclinic orbits connecting equilibrium points on a boundary manifold ``at infinity'' and some results were obtained for solutions which avoid collisions. The goal of this paper is to study the scattering effect of binary and near-triple collisions in a simple setting -- the isosceles three-body problem. The details depend on the mass parameters but in many cases, a fixed isosceles initial shape $s_-$ scatters to essentially all possible isosceles shapes $s_+$.

The concept of a common local spin equilibrium for both spin-1/2 and spin-1 particles is incorporated into a thermal model of particle production in heavy-ion collisions at the top RHIC energies. We show that an effective spin polarization tensor leading to a correct description of the longitudinal spin polarization of $Λ$ hyperons simultaneously yields a positive alignment of vector mesons ($ϕ$ and $K^{*0}$) that grows monotonically with transverse momentum and centrality. Similar trends can be seen in the data, suggesting a possible common mechanism for longitudinal spin polarization and alignment. However, model calculations are insufficient to explain the data in a fully quantitative way. The correlation found between the magnitude of the $Λ$ longitudinal polarization and vector meson alignment suggests further more elaborate investigations of this issue.

UV slopes ($β$) are a powerful diagnostics for galaxies at the Epoch of Reionization, tracing star formation, ISM ionization, and the escape fraction $f_{esc}$ of ionizing photons. Studies at low and intermediate z find a gradual $β$ reddening with time and steeper slopes for fainter galaxies, however recent JWST studies reveal a flattening of this trend at $z>7$. We want measure $β$ for galaxies at $z>7.5$ using the strong lensing around massive galaxy clusters to observe high-redshift and faint galaxies. The low-brightness regime is of particular interest for reionization, as most of the recent models of this process posit that numerous faint galaxies are the prime drivers of reionization. We use NIRCam and NIRSpec data from CANUCS, Technicolor, JUMPS, Silver Bullet, UNCOVER and MEGASCIENCE across 7 strong lensing fields. We find galaxies down to $M_{UV}\sim-16$ and 7.5<z<12.5. We measure \b{eta} with a forward-modelling procedure and estimate $f_{esc}$ for a subsample with emission line data using a relation, calibrated from a low-z sample, with UV slope, galaxy size and H$β$ equivalent width. We find 378 galaxies (45 with spectrum), yielding average values $β=-2.3\pm0.4$, $z=8.5\pm1.0$, and $M_{UV}=-18\pm1$. We find no significant $β$ evolution across our redshift range, suggesting a flattening of the $β-z$ trend above $z\sim7.5$. We find a weak negative trend between $β$ and $M_{UV}$. For 14 galaxies we estimate an average $f_{esc}=0.26\pm0.22$. The flat trend of $β$ at $z>7.5$ suggests similar properties between $300$ and $600 Myr$ after the Big Bang. The weak trend between $β$ and $M_{UV}$ suggests an analogous composition for low- and high-mass galaxies' ISM, likely due to a lack of time for dust buildup. While average $f_{esc}$ is higher than necessary to ionize the IGM by z~6, the model extrapolated at low-z may overestimate its value.

Natural Language Processing (NLP) tools support requirements engineering (RE) tasks like requirements elicitation, classification, and validation. However, they are often developed from scratch despite functional overlaps, and abandoned after publication. This lack of interoperability and maintenance incurs unnecessary development effort, impedes tool comparison and benchmarking, complicates documentation, and diminishes the long-term sustainability of NLP4RE tools. To address these issues, we postulate a vision to transition from monolithic NLP4RE tools to an ecosystem of reusable, interoperable modules. We outline a research roadmap towards a software reference architecture (SRA) to realize this vision, elaborated following a standard methodological framework for SRA development. As an initial step, we conducted a stakeholder-driven focus group session to elicit generic system requirements for NLP4RE tools. This activity resulted in 36 key system requirements, further motivating the need for a dedicated SRA. Overall, the proposed vision, roadmap, and initial contribution pave the way towards improved development, reuse, and long-term maintenance of NLP4RE tools.

Brownian motion provides access to hydrodynamic properties of nanoscale objects independent of their optical resolvability. Here, we present a diffusion-based approach to infer effective particle size distributions of DNA-functionalized magnetic nanoparticles (MNPs), consisting of a magnetic core and a polystyrene shell, in a regime where direct geometric sizing is limited by optical diffraction. Using multi-particle tracking microscopy, we analyze the Brownian dynamics of MNPs grafted with double-stranded DNA (dsDNA) of varying contour length under low-salt conditions. A physically motivated model is introduced that relates dsDNA contour length to an effective hydrodynamic diameter via an attenuated corona description. The measured diffusion coefficient distributions exhibit a systematic and monotonic dependence on dsDNA length in quantitative agreement with the model. While the tracked objects are predominantly dsDNA-mediated agglomerates rather than isolated nanoparticles, clustering does not obscure the length-dependent signal. Instead, the dsDNA corona determines the hydrodynamic scaling, whereas agglomeration mainly introduces an offset and distribution broadening. These results demonstrate that Brownian dynamics enables robust readout of biomolecular length scales even far below the optical resolution limit. The distribution-based approach is inherently tolerant to polydispersity and aggregation, making diffusion-based tracking a simple and promising strategy for future biotechnological and biomedical assays.