We study the computational complexity of constrained nonnegative Gram feasibility. Given a partially specified symmetric matrix together with affine relations among selected entries, the problem asks whether there exists a nonnegative matrix $H \in \mathbb{R}_+^{n\times r}$ such that $W = HH^\top$ satisfies all specified entries and affine constraints. Such factorizations arise naturally in structured low-rank matrix representations and geometric embedding problems. We prove that this feasibility problem is $\exists\mathbb{R}$-complete already for rank $r=2$. The hardness result is obtained via a polynomial-time reduction from the arithmetic feasibility problem \textsc{ETR-AMI}. The reduction exploits a geometric encoding of arithmetic constraints within rank-$2$ nonnegative Gram representations: by fixing anchor directions in $\mathbb{R}_+^2$ and representing variables through vectors of the form $(x,1)$, addition and multiplication constraints can be realized through inner-product relations. Combined with the semialgebraic formulation of the feasibility conditions, this establishes $\exists\mathbb{R}$-completeness. We further show that the hardness extends to every fixed rank $r\ge 2$. Our results place constrained symmetric nonnegative Gram factorization among the growing family of geometric feasibility problems that are complete for the existential theory of the reals. Finally, we discuss limitations of the result and highlight the open problem of determining the complexity of unconstrained symmetric nonnegative factorization feasibility.

We introduce 2DIO, a microbenchmark creating cache-accurate, stressful I/O traces. While existing tools are limited to generating traces with well-behaved, concave hit ratio curves, 2DIO produces ones with tunable complex cache behaviors, particularly performance cliffs and plateaus. Our framework encodes a workload as a compact parameter triplet, capturing both short-term recency and long-term frequency. This parsimonious parameterization allows researchers to easily translate individual adjustments into predictable cache effects across various eviction policies, and enables the parameter space to be "swept" for exhaustive exploration of desired cache behavior, or to mimic real traces by calibrating parameters to match observed behaviors. The tuned parameters are portable, meaning if the scale of the system under evaluation changes, so too will the footprint and length of the trace, while the relative cache behaviors are preserved. Evaluations demonstrate 2DIO's ability to generate traces across a continuum of "what-if" cache behaviors and to reproduce real-world ones with high accuracy.

Trapped-ion Quantum Charge-Coupled Device (QCCD) architectures promise scalability through interconnected trap zones and dynamic ion transport; however, this transport capability creates a complex compilation challenge: how to move qubits efficiently without degrading fidelity. We introduce a routing strategy that turns this challenge into an advantage by exploiting operational parallelism across traps while adapting to both algorithmic structure and device topology through a configurable multi-parameter scoring mechanism. Across a broad suite of benchmarks and QCCD layouts, the method consistently reduces ion-transport overhead and improves execution fidelity, outperforming state-of-the-art routing techniques. These results highlight that explicitly balancing movement overhead and execution parallelism under architectural constraints is key to unlocking the full potential of modular trapped-ion quantum processors.

Reinforcement learning (RL) models have shown the capability of learning complex behaviors, but quantitatively assessing those behaviors - which is critical for safety assurance and the discovery of novel strategies - is challenging. By viewing RL models as control systems, we hypothesize that data-driven approximations of their associated Koopman operators may provide dynamical information about their behavior, thus enabling greater interpretability. To test this, we apply the Koopman with control framework to RL models trained on several standard benchmark environments and demonstrate that properties of the fit linear control models, such as stability and controllability, evolve during training in a task dependent manner. Comparing these metrics across different training epochs or across differently optimized RL models enables an understanding of how they differ. In addition, we find cases where - even when the reward achieved by the RL model is static - the stability and controllability is nonetheless evolving, predicting increased reward with further training. This suggests that these metrics may be able to serve as hidden progress measures, a core idea in mechanistic interpretability. Taken together, our results illustrate that the Koopman with control framework provides a comprehensive way in which to analyze and interpret the behavior of RL models, particularly across training.

[Background] Well-formulated Software Engineering (SE) research problems are essential for bridging the gap between industry-academia. Lean Research Inception (LRI) aims to support this activity. [Goal] Apply LRI to support SE students in formulating practice-aligned research problems. [Method] We conducted a case study with 60 students and 7 faculty advisors of a Brazilian university. [Results] Students reported benefits in reasoning (60%), clarity and definition (61.7%), contextualization (60%), and communication (50%). Advisors also observed clearer and more structured problems (57.1%) with a high recommendation rate (85.7%). [Conclusion] LRI can be a promising approach to support practice-aligned research problem formulation in SE education.

The jet mass of W bosons decaying to a quark-antiquark pair is measured in W+jets events from proton-proton collisions at a center-of-mass energy of 13 TeV. The data used were collected by the CMS experiment at the CERN LHC and correspond to an integrated luminosity of 138 fb$^{-1}$. Hadronic decays of W bosons with high momenta produce strongly collimated decay products due to the large Lorentz boost, and are reconstructed as single large-radius jets. These jets have a characteristic substructure that is exploited to distinguish them from the large background of quark- and gluon-initiated jets. The jet mass is computed using the soft-drop algorithm, which suppresses soft wide-angle radiation that leads to a broadening of the jet mass distribution. For the first time, unfolded measurements are presented of the double-differential W+jets cross section as a function of the jet transverse momentum and soft-drop mass. From these distributions, the W boson mass is obtained, with a value of 80.83 $\pm$ 0.55 GeV, achieving the smallest uncertainty available today from an all-jets final state at a hadron collider.

We report our ongoing lattice QCD study of radiative leptonic decays of the charged pseudoscalar mesons $D$, $D_s$, $B$, and $B_c \to \ell ν_\ell γ$. We carry out our analysis on a single JLQCD ensemble with lattice spacing $a=0.044~\text{fm}$. This work is a step towards a complete QCD+QED lattice calculation of these modes, aimed at reducing theoretical uncertainties in the extraction of $|V_{cd}|$ and $|V_{cs}|$ and providing first-principles estimates of the corresponding form factors in the $B$ sector.

We extend the semi-Lagrangian discontinuous Galerkin (SLDG) method of Einkemmer to velocity grids with adaptive mesh refinement (AMR) and to three-dimensional velocity space. The original SLDG formulation assumes uniform cell widths, which permits the overlap matrices to be precomputed once per fractional shift and reused for every cell. On an adaptively refined mesh, neighboring cells may differ in size, invalidating this assumption. We develop a hybrid sweep strategy: conforming cells in the mesh interior use precomputed per-level overlap matrices (the fast path), while nonconforming cells at refinement boundaries evaluate generalized overlap integrals on the fly (the slow path). A compressed sparse row (CSR) pencil data structure organizes the dimensional splitting along each velocity coordinate, with weighted accumulation for coarse cells that appear in multiple pencils. The method is extended from one to three velocity dimensions using tensor-product DG elements on hexahedral cells provided by PETSc's PetscFE class. We verify the solver on the standard Landau damping benchmark in 1X+3V, demonstrating correct damping rates, exact mass conservation, and convergence behavior with polynomial degree and AMR refinement level.

We investigated the lattice dynamics of the unconventional superconductor LiFeAs using inelastic neutron scattering experiments and density-functional theory (DFT) calculations. By comparing the neutron scattering intensities with lattice-dynamics simulations we can identify the polarization symmetry of all modes along the main-symmetry directions yielding a complete experimental picture of the phonon dispersion. Overall there is good agreement between the experimental and DFT results, which renders an overlooked strong electron phonon coupling unlikely. Our DFT calculations reveal only a small averaged electron-phonon coupling constant. The transversal acoustic in-plane branches exhibit a normal dispersion for small propagation vectors indicating the absence of a nematic instability. Several modes exhibit considerable hardening upon cooling that can be attributed to the anisotropic shrinking of the LiFeAs lattice.

Ground Level Enhancements (GLEs) probe the earliest, highest-energy solar energetic particles and thus provide a unique window onto particle release and transport from the low corona to 1 AU. We present a uniform, event-resolved analysis of the early anisotropy for ten well-observed GLEs, combining consistently reconstructed neutron-monitor pitch-angle distributions (PADs) with Parker-spiral footpoint mapping. We find a clear, monotonic decline of initial anisotropy with increasing magnetic connection angle: well-connected events exhibit strong, persistent forward-directed beams, while poorly connected events show systematically weaker and more rapidly decaying anisotropies. This relationship holds across a wide range of flare classes and CME speeds, demonstrating that magnetic connectivity and interplanetary transport, rather than eruption magnitude, dominate the directional properties of the earliest relativistic arrivals at Earth. A principal component analysis was applied to time-resolved spectral and angular parameters to separate source-driven changes from transport effects. By explicitly identifying and removing secondary sunward (back-scattered) components-attributable to scattering and reflection from solar-wind structures and transient interplanetary features-from the PAD fits, we isolate the intrinsic relaxation of the primary forward beam and show that apparent departures from simple exponential decay are frequently attributable to reflected or delayed populations rather than prolonged source injection. The empirical anisotropy--connection-angle relation reported here provides an event-resolved, quantitative benchmark that constrains focused-transport and shock-acceleration models and offers immediate operational value: rapid footpoint estimates can meaningfully limit expected initial beaming and directional radiation risk.

In this paper, the potential capacity and spatial efficiency of future autonomous lane-free traffic in urban environments are explored using a combination of analytical and simulation-based approaches. For lane-free roadways, a simple analytical approach is employed, which shows not only that lane-free traffic offers a higher capacity than lane-based traffic for the same street width, but also that the relationship between capacity and street width is continuous under lane-free traffic. To test the potential capacity and properties of lane-free signal-free intersections (automated intersection management), two approaches were simulated and compared, including a novel approach which we call OptWULF. This approach uses a multi-agent conflict-based search approach with a low-level planner that uses a combination of optimization and simple window-based reservation. With these simulations, we confirm the continuous relationship between capacity and street width for intersection scenarios. We also show that OptWULF results in an even utilization of the entire drivable area of the street and intersection area. Furthermore, we show that OptWULF is capable of handling asymmetric demand patterns without any substantial loss in capacity compared to symmetric demand patterns.

Quantum Fisher information (QFI) is a measure of multipartite quantum entanglement that can be obtained from inelastic neutron scattering data on quantum magnets. In this work, we demonstrate that the QFI can distinguish an unconventional quantum critical point (QCP) with fractionalization and emergent gauge structure from conventional ones within the Landau paradigm. We compute the QFI, via large-scale quantum Monte Carlo (QMC) simulations and exact diagonalization, in a kagome lattice quantum spin liquid (QSL) model with an XY and a cluster-Ising interactions. When the XY interaction is ferromagetic, the QFI obtained by QMC reveals a large anomalous dimension, which is a fingerprint of the (2+1)d XY$^\ast$ universality class for the transition from the ferromagnetic phase to the $\mathbb{Z}_2$ QSL. The investigation of thermal and dynamical properties of QFI is further extended to the case of antiferromagnetic XY interaction via exact diagonalization. In this regime, a transition to a possibly distinct QSL phase is suggested via both entanglement-based probes, such as QFI and genuine multipartite negativity, and analyses of the energy spectrum and structure factors. These results not only demonstrate the versatility of QFI in identifying QSL states and unconventional QCPs but also provide useful guidance for future theoretical and experimental studies of frustrated magnets.

This paper proposes low-overhead and low-complexity channel estimation (CE) of frequency-domain equalization aided faster-than-Nyquist (FTN) signaling. In the proposed CE scheme, the concept of pilot superimposition is employed, where the FTN block is designed to superimpose pilot symbols with information symbols, and thus, no dedicated time and frequency resources nor guard bands are required, resulting in a 50% reduction of the overhead. Furthermore, interference induced by the pilot superimposition is eliminated by invoking a novel scheme, referred to as spectral interference alignment, where a data-dependent sequence is subtracted from transmitted information symbols. The theoretical mean-square error (MSE) of the proposed CE is derived, which verifies that the MSE is no longer affected by interference due to the pilot superimposition.

The unexpected collapse of the Carola Bridge in Dresden, Germany, provides a rare opportunity to characterise how urban network traffic adapts to an unexpected infrastructure disruption. This study develops a data-driven analytical framework using traffic data from the Dresden traffic management system to assess the short-term impacts of the disruption. By combining statistical comparisons of pre- and post-collapse motorised traffic distributions, peak-hour shifts, and Park-and-Ride data analyses, the framework reveals how traffic dynamics and traveller choices adjust under infrastructure disruption. Results reveal that the two closest bridges, the Albert and Marien Bridges, absorb the majority of the diverted motorised traffic. In particular, the daily traffic volume on the Albert bridge increases by up to 81%, which is equivalent to 3.5 hours of traffic operating with maximum flow. Peak hours on critical links are significantly prolonged, reaching up to 250 minutes. Besides redistribution, the overall daily motorised traffic crossing the Elbe river declines by approximately 8,000 vehicles, while Park-and-Ride usage increases by up to 188%, suggesting a potential travel mode shift after the disruption. The study reveals the patterns of traffic redistribution following an unexpected disruption and provides insights for resilience planning and emergency traffic management.

The Arthur-Nimue-Merlin degrees are a generalization of the Turing degrees introduced by Kihara as a tangible description of the partially ordered set of Lawvere-Tierney topologies on the effective topos (equivalently, subtoposes of the effective topos). They are defined in terms of a three-player game that introduces both angelic and demonic non-determinism into oracle queries. We construct an order embedding of the Turing degrees with their order reversed into the Arthur-Nimue-Merlin degrees, whose image we call the "co-Turing degrees"; we then study the order relationship of these co-Turing degrees with the (naturally embedded) Turing degrees within the Arthur-Nimue-Merlin degrees.

Five-year cancer survival rates are widely reported and often interpreted to mean that early detection saves lives, that a late fatal diagnosis would have been prevented by earlier detection, and that increasing survival over time proves better treatment. This expository article explains why such inferences are not supported by survival statistics alone. A simple Markov model of tumor progression, calibrated to patterns like those in SEER data, shows that high survival after early diagnosis, large gaps between early and late stage, and improving survival can all appear even when treatment is ineffective and screening does not reduce mortality. The discussion ties these points to the clinical literature and argues that randomized trials and mortality outcomes are needed to support screening and treatment claims; five-year survival alone provides little actionable evidence and is easily misread.

Atomic electron tomography (AET) determines the three-dimensional (3D) coordinates and chemical identities of individual atoms from a series of scanning transmission electron microscopy images taken at different tilt angles. However, under the low dose conditions required to mitigate beam damage, the reduced signal-to-noise ratio forces a trade off among accuracy, robustness, and throughput, which ultimately limits the broader application of AET. Here, we introduce a physics aware, two stage neural networks (PANN) that incorporates physical constraints throughout its workflow to achieve accurate AET under low-dose imaging. First, a global local 3D ResUNet refines the initial reconstruction and corrects geometric distortions in the volume. Second, the local density around each identified atom is encoded using 3D Zernike moments. These feature descriptors, along with the atomic coordinates are then processed by a graph attention Transformer to classify the elemental species. We benchmark the PANN workflow using a dataset of 42,588 reconstructed volumes, covering diverse noise models, materials morphologies, and dose settings. Under low dose conditions, PANN significantly improves performance, reducing the atomic coordinates error and leading to an increase in the atomic recovery rate. The framework's performance on experimental lose dose AET data across nanoparticles of varying morphology and composition demonstrate robust generalization. We anticipate this approach will extend the applicability of AET, particularly in investigating materials sensitive to electron dose or chemical state, including halide perovskites, zeolite, and quantum dot.

In this work, we demonstrate interfacial charge transfer-driven transport enhancement in few-layer graphene monolayer MoS2 vertical heterostructure field-effect transistor. Raman scattering and Raman intensity mapping results confirm the successful stacking of FL graphene on ML MoS2. Pronounced photoluminescence (PL) quenching of MoS2 and spectral redshift in the heterostructure suggest efficient interlayer charge transfer and strong electronic coupling at the vdW interface. Electrical measurements show enhanced drain current, field-effect mobility, and conductivity in Gr-MoS2 device compared to pristine MoS2 transistor with Ag contacts. The energy band considerations under equilibrium and gate bias conditions suggest improved Fermi-level alignment and reduced effective Schottky barrier effects at the graphene-MoS2 interface, enabling efficient carrier injection. Temperature-dependent transport (300-400 K) reveals phonon-dominated mobility and conductivity degradation in both devices; however, the heterostructure exhibits significantly suppressed performance degradation. The mobility enhancement factor increases from ~1.6 at 300 K to ~4.0 at 400 K, accompanied by a corresponding improvement in conductivity stability, demonstrating superior thermal robustness for the Gr-MoS2 heterostructure. The power-law analysis indicates that transport in pristine MoS2 is influenced by both intrinsic phonon scattering and additional thermally activated extrinsic processes such as contact and interfacial effects, whereas the weaker temperature dependence in the Gr-MoS2 device reflects moderated extrinsic contributions and transport behaviour approaching a predominantly phonon-limited regime. These findings demonstrate graphene contact engineering as a viable pathway toward improved performance and thermally stable two-dimensional semiconductor electronics.

Determination of orientation in the imaged sample/scene has a large application potential when the anisotropy of properties is analysed, usually, under a linearly polarised illumination. This study combined several improvements of microscopy imaging: use of an incoherent white illumination source (a lamp) with a spectral filter to define a spectral window, a plastic circular polariser to image with circularly polarised light (instead of linear), and a 4-pol. camera with integrated polarisers for simultaneous acquisition of images at four pi/4-azimuth shifts. In the transmission mode, a high-fidelity readout of form birefringent optical elements, q-plates, was achieved using a fitting procedure based on the analytical expression of S0 (intensity) Stokes parameters at the pixel level. In the reflection mode, the S0 fit was used to determine the azimuth orientation of the q-plates, as well as a generic Amp x cos(2Q1 - 2Q0) + Offset fit (at pixel level) applied to images taken at four Q1 azimuths. The 4-pol. analysis in reflection under circularly polarised illumination is discussed.

Millimeter-wave (mmWave) and terahertz (THz) massive MIMO systems often rely on predefined beamforming codebooks, which are usually suboptimal in Non-Line-of-Sight (NLoS) conditions and for hardware-limited transceivers. Reinforcement Learning (RL) enables adaptive, data-driven codebook design without explicit Channel State Information (CSI), but the robustness of such algorithms in practical conditions is underexplored. This paper introduces a robust multi-agent RL framework that learns beam codebooks directly from environmental feedback, eliminating the need for prior channel knowledge. Our method is well-suited for real-world deployments facing unpredictable propagation and hardware constraints. We conduct a comprehensive analysis of three off-policy algorithms, Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor-Critic (SAC), evaluating their resilience to hardware impairments and feedback noise. Simulations show that SAC consistently outperforms deterministic methods, achieving superior beamforming gains and stability in NLoS scenarios, even under severe impairments. These results demonstrate the promise of RL-based codebook design for robust mmWave/THz massive MIMO systems.

We develop a new numerical technique for approximating solutions of the Navier-Stokes equations on moving domains. The method aims at simulating an incompressible fluid past an object whose motion is assigned a priori using a level-set function. The proposed approach relies on a space discretization based on the ghost finite element method (ghost-FEM), which allows computations on unfitted meshes and avoids costly remeshing as the domain evolves in time. Time integration is performed using an IMplicit-EXplicit (IMEX) scheme to address the nonlinearity of the convective term, ensuring high-order accuracy for incompressible flows. The error introduced by the geometrical approximation is handled using the Shifted Boundary Method, which allows higher order approximations of boundary conditions on unfitted meshes. Dirichlet boundary conditions are imposed weakly by means of Nitsche's method. The associated stabilization parameter is chosen by solving a generalized eigenvalue problem, ensuring stability and accuracy of the numerical scheme. We present a series of numerical experiments designed to validate the accuracy of the proposed method, as well as comparisons with established benchmark problems involving moving boundaries.

This paper is a contribution to the supersymmetry gap problem for supergravity backgrounds $(M,g,F)$ in $11$ dimensions. We study restrictions on the curvature of $(M,g,F)$ and, using the bijective correspondence between the space of certain filtered deformations of Lie superalgebras and the space of highly supersymmetric supergravity backgrounds, we establish the following general rigidity result: if the $4$-form $F$ has rank $\operatorname{rk}(F)\leq 6$, Euclidean support, and the space $\mathfrak{k}_{\bar 1}$ of Killing spinors has dimension $\dim\mathfrak{k}_{\bar 1}> 26$ then $(M,g,F)$ is locally isometric to the maximally supersymmetric Minkowski spacetime or Freund Rubin background $\mathrm{AdS}_7\times\mathrm{S}^4$. The same rigidity result but with finer estimates on $\dim\mathfrak{k}_{\bar 1}$ is provided for certain types of $\mathfrak k_{\bar 1}$ and specific orbits of the $4$-form under the action of the Lorentz group.

Hydrogen-rich supernovae can efficiently accelerate particles when the expanding ejecta interact with the surrounding circumstellar medium (CSM), producing high-energy (TeV--PeV) neutrinos. In this work we investigate the nearby SN~2024ggi, whose proximity and clear signatures of ejecta--CSM interaction make it a promising candidate for studying high-energy ($ν$) emission. We apply a new semi-analytical model that consistently links the electromagnetic and neutrino emission components, allowing us to constrain the main explosion parameters, including the kinetic energy, ejecta mass, progenitor radius, and nickel yield. The predicted high-energy ($ν$) fluence at Earth peaks at TeV energies and remains below the sensitivity of current detectors. However, the modeling establishes a robust framework for interpreting future signals from nearby interacting supernovae and fine-tuning observational strategies for next-generation multi-messenger facilities such as IceCube-Gen2 and KM3NeT/ARCA.

Party-Hecke algebras are introduced as a two-parameter deformation of party algebras, where one parameter deforms the party generators and the other deforms the elementary transpositions. We construct a basis for this algebra and show that it can be realized as a quotient of the algebra of braids and ties. Furthermore, we study the party monoid and its relationship with the tied symmetric monoid and their associated algebras.

Structured-core thulium-doped fibers were developed to reduce heat load, enable shorter-wavelength operation, and achieve a pedestal-free design. In a proof-of-principle experiment, laser slope efficiencies of 52% at 1907 nm and 54% at 1940 nm were achieved with respect to absorbed power.

We present predictions for the ultraperipheral heavy-ion reaction ${\rm Pb} {\rm Pb} \to {\rm Pb} {\rm Pb} D \bar{D}$, where $D$ refers to either $D^0$ or $D^+$, limiting to low invariant mass of $D \bar{D}$ and at energies available at the LHC. The calculation of the $γγ\to D \bar{D}$ subprocess is done including the continuum mechanisms and the $χ_{c0}(3860)$ and $χ_{c2}(3930)$ resonant contributions. These states are considered as candidates for the first excited $P$-wave charmonia $χ_{c0}(2P)$ and $χ_{c2}(2P)$. We compare our results for the $γγ\to D \bar{D}$ process with the $D \bar{D}$ invariant mass and angular distributions measured by the Belle and BaBar experiments in $e^{+}e^{-}$ collisions. Then, we present first calculations for ultraperipheral Pb-Pb collisions (UPCs) within the equivalent photon approximation in the impact-parameter space. Both, the total cross section and several differential distributions for experimental cuts corresponding to the ALICE, ATLAS, CMS, and LHCb experiments are presented. For instance, the nuclear cross sections for the $D^{0} \bar{D}^{0}$ and $D^{+} D^{-}$ channel at $\sqrt{s_{NN}} = 5.02$ TeV are found to be approximately 100--132 $μ$b and 29 $μ$b, respectively, taking into account the cuts on pseudorapidities and transverse momenta of the final state charmed mesons ($|η_{D}| < 2.5$, $p_{t,D} > 0.2$ GeV) and for $M_{D \bar{D}} < 4.3$ GeV. Results for $\sqrt{s_{NN}} = 5.36$ TeV are also presented. Cross section for the ${\rm Pb} {\rm Pb} \to {\rm Pb} {\rm Pb} (γγ\to χ_{c0,2}(2P) \to D \bar{D})$ processes is sufficiently large for experimental studies. The back-to-back correlation between charm mesons can be used to separate the exclusive process under consideration from other background processes, and could provide valuable insight into the properties of the excited quarkonia.

Transition-metal complexes (TMCs) are promising photosensitizers for Type~I photodynamic therapy (PDT), where electron-transfer processes can generate reactive oxygen species under hypoxic conditions. Yet identifying candidates with the required ground- and excited-state redox energetics remains challenging across the vast chemical space of TMCs. Here, we develop a data-efficient active learning (AL) framework for the discovery of Type~I active TMC photosensitizers by combining a chemically structured design space of over 2.1 million Ru(II), Os(II), and Ir(III) complexes with targeted DFT calculations and pretrained atomistic representations. With only 300 quantum-chemical evaluations, the approach efficiently enriches candidates within a mechanistically defined optimal redox region. Analysis of the viable complexes reveals chemical design principles linking metal identity, ligand framework, substituent pattern, and physicochemical properties to Type~I photoreactivity, including a pronounced preference for Os(II)-based complexes and electronically asymmetric ligand environments along with combination of electronic donating and accepting substituents. More broadly, the strategy presented herein provides a scalable, mechanism-guided route for the rational design of transition-metal photocatalysts for applications spanning biomedicine, solar energy conversion, and photoredox chemistry.

Discriminating between noisy quantum processes is a central primitive for quantum communication, metrology, and computing. While discrimination limits for finite-dimensional channels are well understood, the continuous-variable setting, particularly under experimentally relevant energy constraints, remains significantly less developed. In this work, we establish an energy-constrained chain rule for the Belavkin-Staszewski channel divergence, which yields a fundamental upper bound on the error exponents achievable by fully adaptive, energy-constrained quantum channel discrimination protocols. We then derive efficiently computable bounds on asymmetric error exponents for energy-constrained discrimination of bosonic dephasing and loss-dephasing channels. Specifically, we show that three operationally relevant quantities -- the measured relative entropy, the Umegaki relative entropy, and the geometric Renyi divergence -- admit semidefinite program (SDP) formulations when the input energy is bounded and the Hilbert space is suitably truncated. Applying these tools, we demonstrate that optimal probes for these channels under energy constraints are Fock-diagonal, and we also enable numerically precise evaluation of bounds on achievable error exponents across discrimination strategies ranging from separable to fully adaptive. The resulting SDPs provide practical benchmarks for quantum-limited sensing in low-energy bosonic platforms.

Group recommendation systems play a pivotal role in supporting collective decisions across various contexts, from leisure activities to organizational team-building. Existing group recommendation approaches typically use either handcrafted aggregation rules (e.g. mean, least misery, weighted sum) or neural aggregation models (e.g. attention-based deep learning frameworks), yet both fall short in distinguishing leader-dominated from collaborative groups and often misrepresent true group preferences, especially when a single member disproportionately influences group choices. To address these limitations, we propose the Dual-stream Adaptive Leadership Identification (DALI) framework, which uniquely combines the symbolic reasoning capabilities of Large Language Models (LLMs) with neural network-based representation learning. Specifically, DALI introduces two key innovations: a dynamic rule generation module that autonomously formulates and evolves identification rules through iterative performance feedback, and a neuro-symbolic aggregation mechanism that concurrently employs symbolic reasoning to robustly recognize leadership groups and attention-based neural aggregation to accurately model collaborative group dynamics. Experiments conducted on the Mafengwo travel dataset confirm that DALI significantly improves recommendation accuracy compared to existing frameworks, highlighting its capability to dynamically adapt to complex, real-world group decision environments.

Formal patterns are formally specified solutions to frequently occurring distributed system problems that are generic, executable, and come with strong qualitative and/or quantitative formal guarantees. A formal pattern is a generic system transformation which transforms a usually infinite class of systems in need of the pattern's solution into enhanced versions of such systems that solve the problem in question. In this paper we demonstrate the application of formal patterns to protocol dialects. Dialects are methods for hardening protocols so as to endow them with light-weight security, especially against easy attacks that can lead to more serious ones. A lingo is a dialect's key security component, because attackers are unable to ''speak'' the lingo. A lingo's ''talk'' changes all the time, becoming a moving target for attackers. In this paper we present several formal patterns for both lingos and dialects. Lingo formal patterns can make lingos stronger by both transforming them and by composing several lingos into a stronger lingo. Dialects themselves can be obtained by the application of a single dialect formal pattern, generic on both the chosen lingo and the chosen protocol.