We investigate finite volume effects on the transverse momentum spectra of charged pions produced in the most central heavy-ion collisions at RHIC and LHC energies. A cylindrically symmetric finite volume Boltzmann-Gibbs blast-wave model is employed that fully incorporates the finite longitudinal extent of the fire cylinder at kinetic freeze-out. The model applies Planck transformations to convert the local rest frame temperature and chemical potential of each fluid element into laboratory frame values, ensuring full Lorentz covariance. This approach is compared with the conventional infinite volume blast-wave model, in which the thermodynamic parameters remain defined in the local rest frame while the particle momenta are expressed in the laboratory frame. Both models are fitted to the experimental transverse momentum distributions of charged pions measured by the HADES, STAR, PHENIX, and ALICE collaborations over the center-of-mass energy range $\sqrt{s_{NN}} = 2.4$ GeV to $5.44$ TeV. The finite volume model with Planck transformed laboratory frame parameters yields temperature values fully consistent with relativistic thermodynamics (except for a small anomaly at $\sqrt{s_{NN}} = 193$ and $200$ GeV) and produces realistic fire cylinder volumes several times larger than the initial nuclear overlap volume. In contrast, the conventional infinite volume model yields unphysical results: infinite volume, infinite maximum half-length, and maximum longitudinal flow velocity equal to the speed of light at all energies. These findings demonstrate that a proper treatment of finite system size, together with the correct Lorentz (Planck form) transformation of the thermodynamic variables, is essential for the reliable extraction of freeze-out parameters in heavy-ion collisions.

A successive vertex ordering of a graph is a linear ordering of its vertices in which every vertex except the first has at least one neighbour appearing earlier. Such orderings arise naturally in incremental growth and connectivity-preserving constructions, where vertices are added sequentially and must attach to the existing structure. We derive an exact formula for the number of successive vertex orderings of any finite connected graph. The formula is obtained via an inclusion--exclusion argument over independent sets and depends on two explicit combinatorial parameters, one of which is defined recursively. The result applies to all finite connected graphs without requiring regularity or symmetry assumptions. We also express the enumeration as a weighted generating polynomial over independent sets; its value at $x = -1$ recovers the total count of successive orderings, and the $k$-th derivative at this point encodes the number of orderings in which exactly $k$ vertices have no earlier neighbour.

The definition of \NP\ requires, for each member language~$L$, a polynomial-time checking relation~$R$ and a constant~$k$ such that $w \in L \iff \exists y\,(|y| \leq |w|^k \wedge R(w,y))$. We show that this biconditional instantiates, for each member language, Hilbert's triple: a sound, complete, decidable proof system in which truth-in-$L$ and bounded provability coincide by fiat. We show further that the polynomial-time restriction on~$R$ does not exclude Gödel's proof-checking relation, which is itself polynomial-time and fits the definition as a literal instance. Hence \NP, taken as a totality over all polynomial-time~$R$, contains languages for which the biconditional asserts a property that Gödel's First Incompleteness Theorem prohibits. The semantic definition of \NP\ is unsatisfiable, for the same reason that Hilbert's Program is.

Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs), but simultaneously exposes a critical vulnerability to knowledge poisoning attacks. Existing attack methods like PoisonedRAG remain detectable due to coarse-grained separate-and-concatenate strategies. To bridge this gap, we propose RefineRAG, a novel framework that treats poisoning as a holistic word-level refinement problem. It operates in two stages: Macro Generation produces toxic seeds guaranteed to induce target answers, while Micro Refinement employs a retriever-in-the-loop optimization to maximize retrieval priority without compromising naturalness. Evaluations on NQ and MSMARCO demonstrate that RefineRAG achieves state-of-the-art effectiveness, securing a 90% Attack Success Rate on NQ, while registering the lowest grammar errors and repetition rates among all baselines. Crucially, our proxy-optimized attacks successfully transfer to black-box victim systems, highlighting a severe practical threat.

We develop a unified mathematical method for the pole structure of frequency-domain Green's functions and the associated quasinormal spectra in radial boundary value problems reducible to the Gauss hypergeometric equation. By systematically employing connection formulas for Kummer solutions, we construct an explicit quantization function that encodes arbitrary linear asymptotic boundary conditions. We demonstrate that the frequency-dependent spectral factor entering the residue formula is controlled algebraically by the closed-form Digamma derivative of this quantization function, bypassing integral evaluation. Furthermore, we establish the simultaneous vanishing of the quantization function and its first derivative as a direct algebraic criterion for double-pole QNMs. The formalism is successfully benchmarked against the exact BTZ black hole spectrum and provides an analytic diagnostic for the exceptional lines and nearly double-pole excitations in the Nariai/Pöschl-Teller limit.

Magnon thermal Hall effect in insulating magnets is the manifestation of Berry curvature in magnon bands, which is formulated using the emergent gauge fields that act on magnons as a fictitious magnetic field. In ferromagnets, it is commonly accepted as the outcome of U(1) gauge fields generated by Dzyaloshinskii-Moriya interactions and spin textures, but this mechanism is often suppressed by symmetry-enforced cancellations in many lattice geometries, known as a no-go rule. As a result, antiferromagnetic insulators have long been considered as unfavorable platforms for the effect. We show that antiferromagnets with multiple magnetic sublattices naturally host non-Abelian SU(N) gauge fields in magnon band structures, providing a robust rule-to-go mechanism. The noncommutativity of these gauge fields prevents Berry-curvature cancellation and guarantees a nonvanishing thermal Hall response. As a minimal realization, we demonstrate that a coplanar 120$^{\circ}$ antiferromagnet with Dzyaloshinskii-Moriya interactions constitutes a canonical SU(3) platform for the magnon thermal Hall effect. We provide a table of so-far-known two-dimensional lattice geometries and variants of magnetic structures, along with the corresponding gauge fields, providing a unified guideline for identifying magnetic materials, including antiferromagnets and altermagnets, that host thermal Hall transport.

We provide a comprehensive analysis of GW190711_030756 and GW200114_020818, two of the most significant binary black hole merger candidates in the IAS catalog, with probabilities of astrophysical origin $p_{\rm astro}=0.99$ and $0.71$, respectively, and signal-to-noise ratios of approximately $10.0$ and $13.4$. We employ numerical relativity surrogate models to infer both the source properties and the remnant properties of these two candidates. We find that both GW190711_030756 and GW200114_020818 are asymmetric-mass binaries, with inferred mass ratios of $0.35^{+0.32}_{-0.15}$ and $\leq 0.20$. In addition, GW200114_020818 is inferred to have a source-frame total mass of approximately $220M_{\odot}$ and highly spinning black holes, with primary (secondary) dimensionless spin magnitudes of $0.96^{+0.03}_{-0.07}$ ($0.84^{+0.13}_{-0.34}$), closely resembling GW231123_135430. We further find that GW200114\_020818 has a confidently negative effective inspiral spin of $χ_{\rm eff}=-0.60^{+0.22}_{-0.13}$ and exhibits strong spin precession, characterized by an effective precession parameter of $χ_{\rm p}=0.60^{+0.21}_{-0.19}$. GW200114_020818 (when considered alongside GW231123_135430) points towards an emerging population of massive, rapidly spinning BBH mergers. While GW231123_135430 is consistent with mergers in globular clusters, producing systems like GW200114_020818 in such environments remains difficult even under hierarchical merger scenarios. The probability that the remnant black hole of GW190711_030756 (GW200114_020818) is retained in its host environment is $0.079$ ($0.0002$), $0.62$ ($0.965$), and $0.997$ ($1$) if the merger occurred in a globular cluster, a nuclear star cluster, or an elliptical galaxy, respectively.

With the widespread application of artificial intelligence technologies in face recognition and other fields, data privacy security issues have received extensive attention, especially the \textit{right to be forgotten} emphasized by numerous privacy protection laws. Existing technologies have proposed various unlearning methods, but they may inadvertently leak the categories of unlearned data. This paper focuses on the category unlearning scenario, analyzes the potential problems of category leakage of unlearned data in multiple scenarios, and proposes four attack methods from the perspectives of model parameters and model inversion based on attackers with different knowledge backgrounds. At the level of model parameters, we construct discriminative features by computing either dot products or vector differences between the parameters of the target model and those of auxiliary models trained on subsets of retained data and unrelated data, respectively. These features are then processed via k-means clustering, Youden's Index, and decision tree algorithms to achieve accurate identification of the forgotten class. In the model inversion domain, we design a gradient optimization-based white-box attack and a genetic algorithm-based black-box attack to reconstruct class-prototypical samples. The prediction profiles of these synthesized samples are subsequently analyzed using a threshold criterion and an information entropy criterion to infer the forgotten class. We evaluate the proposed attacks on four standard datasets against five state-of-the-art unlearning algorithms, providing a detailed analysis of the strengths and limitations of each method. Experimental results demonstrate that our approach can effectively infer the classes forgotten by the target model.

A study has been carried out to evaluate the performance stability of Gas Electron Multiplier (GEM) chamber prototypes in the laboratory using $^{55}$Fe radiation source with Argon and CO$_2$ gas mixture. This research focuses on the characterisation of the GEM detector's gain, efficiency (count rate with radioactive source), and energy resolution under varying operational conditions. A patch on the detector has been subjected to continuous and absolutely uninterrupted radiation for about 98 days. The gain and energy resolution of the detector are measured along with the ambient parameters temperature (t), pressure (p) and relative humidity (RH). In addition to that, the long-term behaviour of the count rate with a strong radioactive source are also studied. This work is very relevant for Micro Pattern Gaseous Detectors (MPGD) such as GEM before installing on large experiment. The experimental setup, methodology, and results are presented in this article.

We introduce Poisson-response tensor-on-tensor regression (PToTR), a novel regression framework designed to handle tensor responses composed element-wise of random Poisson-distributed counts. Tensors, or multi-dimensional arrays, composed of counts are common data in fields such as international relations, social networks, epidemiology, and medical imaging, where events occur across multiple dimensions like time, location, and dyads. PToTR accommodates such tensor responses alongside tensor covariates, providing a versatile tool for multi-dimensional data analysis. We propose algorithms for maximum likelihood estimation under a canonical polyadic (CP) structure on the regression coefficient tensor that satisfy the positivity of Poisson parameters and then provide an initial theoretical error analysis for PToTR estimators. We also demonstrate the utility of PToTR through three concrete applications: longitudinal data analysis of the Integrated Crisis Early Warning System database, positron emission tomography (PET) image reconstruction, and change-point detection of communication patterns in longitudinal dyadic data. These applications highlight the versatility of PToTR in addressing complex, structured count data across various domains.

In recent years, magnetically-frustrated triangular and honeycomb lattice cobaltates have seen extensive study in the pursuit of a quantum spin liquid (QSL) state in a real material. In this work, we describe the hydroflux synthesis of K$_2$Co$_2$(TeO$_{3}$)$_{3}$ $\cdot$ 2.5 H$_2$O (KCoTOH), a novel zemannite-type antiferromagnet (AFM) possessing structural elements of both triangular dimer and honeycomb structural motifs. Bulk magnetometry and specific heat data support the onset of long-range AFM order below $T_\text{N}$ = 7.6(1) K, with neutron diffraction and muon spin relaxation ($μ$SR) measurements placing the majority of the ordered moment within the pseudo-honeycomb plane. We resolve three unique oscillation frequencies from the zero-field $μ$SR spectra, additionally suggesting a remarkably low level of structural disorder in as-grown KCoTOH crystals. Whereas interactions between dimerized chains of Co$^{2+}$ cations are typically observed to be negligible or ferromagnetic in nature, the largely planar ordering motif observed in KCoTOH is instead stabilized by net antiferromagnetic interactions through bridging tellurite groups. This work highlights the potential of hydroflux synthesis methods in the stabilization of magnetic materials possessing novel and potentially more frustrated lattice geometries.

Bicycle safety is important for bikeability and transportation efficiency. However, conventional surveys often fall short in capturing how people actually perceive cycling environments because they rely heavily on respondents' recall rather than in-the-moment experience. By leveraging large language models (LLMs), this study proposes a new method of combining video-based surveys with a conversational AI chatbot to collect human perceptions of cycling safety and the reasons behind these perceptions. The paper developed the AI chatbot using a modular LLM architecture, integrating prompt engineering, state management, and rule-based control to support the structure of human-AI interaction. This paper evaluates the feasibility of the proposed video-based conversational chatbot using complete responses from sixteen participants to the pilot survey across nine street segments in New York City. The method feasibility was assessed using a seven-point scale rating for user experience (i.e., ease of use, supportiveness, efficiency) and a five-point scale for chatbot usability (i.e., personality, roboticness, friendliness), yielding positive results with mean scores of 5.00 out of 7 (standard deviation = 1.6) and 3.47 out of 5 (standard deviation = 0.43), respectively. The data feasibility was assessed using multiple techniques: (1) Natural language processing (NLP), such as KeyBERT, for overall safety and feature analysis to extract built-environment attributes; (2) K-means clustering for semantic analysis to identify reasons and suggestions; and (3) regression to estimate the effects of built-environment and demographic variables on perceived safety outcomes. The results show the potential of AI chatbots as a novel approach to collecting data on human perception, behavior, and future visions for transport planning.

I present here PowerSpectR, an R package for computing and visualizing median-based radial Fourier power spectra from imaging data. Power spectra provide a representation of spatial structure by decomposing contributions across spatial scales, and the resulting slopes can serve as compact, low-dimensional summaries of morphological complexity across images. PowerSpectR provides a workflow for estimating these slopes, combining edge-effect mitigation through Hann windowing, Fourier-domain analysis, and radial binning with azimuthal median statistics. The use of median aggregation helps to reduce sensitivity to bright compact sources, masking artifacts, and other localized features that can bias standard estimators. PowerSpectR is released under the MIT license at \href{https://github.com/RafaelSdeSouza/PowerSpectR}{this repository}.

We present a fully covariant and gauge-invariant formulation of electromagnetic wave propagation in static, spherically symmetric black hole spacetimes, developed entirely within Schwarzschild-like coordinates. Start ing from the source-free Maxwell equations on a curved background, electromagnetic perturbations are de composed according to parity and systematically reduced to gauge-invariant dynamical variables without introducing auxiliary coordinate transformations or horizon-regular variables. Both axial and polar sectors are shown to obey the same parity-independent master equation, and their exact isospectrality emerges nat urally as a direct consequence of Maxwell theory in four dimensions. By eliminating first-derivative terms through an appropriate field redefinition, the radial dynamics is cast into a Helmholtz-type equation, which motivates the introduction of an effective, position- and frequency-dependent refractive index encoding grav itational redshift, curvature effects, and angular momentum within a unified optical framework. Specializing to the Schwarzschild geometry, we obtain the refractive index in closed analytical form and analyze its behavior in the near-horizon, intermediate, and asymptotic regimes. The resulting description provides a transparent and physically intuitive interpretation of electromagnetic evanescence, and propagation in black hole spacetimes, and establishes a robust foundation for wave-optical, semiclassical, and numerical studies in more general static gravitational backgrounds.

In this article, we study the convergence of the empirical spectral measure of twisted Toeplitz matrices subject to small random perturbations. We show that the empirical spectral measure converges weakly in probability to the push-forward of the Lebesgue measure by the symbol. The symbol of the twisted Toeplitz matrices is assumed to be smooth in frequency, and only piecewise H{ö}lder continuous with respect to the position variable with discontinuities of jump type.

Early initiation of alcohol, nicotine, cannabis, and other substances predicts later substance use disorders and related psychopathology. We integrate time-varying environmental factors with polygenic risk scores (PRS) in a longitudinal framework to identify determinants of substance initiation in adolescence. Using data from the Adolescent Brain Cognitive Development (ABCD) Study with repeated assessments over approximately four years, we defined time-to-event outcomes for first use of alcohol, nicotine, cannabis, and any substance. We constructed high-dimensional panels of time-varying environmental covariates across family, school, neighborhood, behavioral, and health domains, alongside time-invariant covariates and PRS for alcohol, cannabis, nicotine, and general substance use disorders. Time-varying Cox models with clustered standard errors were applied. Univariate analyses showed broad associations between earlier initiation and multiple environmental domains, including impulsivity, sleep disturbance, parental monitoring, caffeine use, and school functioning. In multivariable models, a smaller set of predictors remained robust, particularly impulsivity traits, parental monitoring, and selected health and lifestyle factors. PRS were positively associated with earlier initiation, with the strongest and most consistent effects for nicotine-related genetic risk. Secondary analyses using marginal structural models suggested that higher parental monitoring is protective, whereas higher impulsivity and caffeine exposure are associated with increased risk. These results demonstrate that integrating dynamic environmental exposures with genetic liability can identify key risk factors for adolescent substance initiation and highlight actionable targets for prevention.

Designing high-performance error-correcting codes at short blocklengths is critical for low-latency communication systems, where decoding is governed by finite-length and graph-structural effects rather than asymptotic properties. This paper presents a global discrete optimization framework for constructing short-block linear codes by directly optimizing parity-check matrices. Code design is formulated as a constrained binary optimization problem with penalties for short cycles, trapping-set-correlated substructures, and degree violations. We employ a hybrid strategy combining tunneling-augmented simulated annealing (TASA) with classical local refinement to explore the resulting non-convex space. Experiments at blocklengths 64-128 over the AWGN channel show 0.1-1.3 dB SNR gains over random LDPC codes (average 0.45 dB) and performance within 0.6 dB of Progressive Edge Growth. In constrained regimes, the method enables design tradeoffs unavailable to greedy approaches. However, structural improvements do not always yield decoding gains: eliminating 1906 trapping set patterns yields only +0.08 dB improvement. These results position annealing-based global optimization as a complementary tool for application-specific code design under multi-objective constraints.

Alphanumeric identifiers such as manufacturer part numbers (MPNs), SKUs, and model codes are ubiquitous in e-commerce catalogs and search. These identifiers are sparse, non linguistic, and highly sensitive to tokenization and typographical variation, rendering conventional lexical and embedding based retrieval methods ineffective. We propose a training free, character level retrieval framework that encodes each alphanumeric sequence as a fixed length binary vector. This representation enables efficient similarity computation via Hamming distance and supports nearest neighbor retrieval over large identifier corpora. An optional re-ranking stage using edit distance refines precision while preserving latency guarantees. The method offers a practical and interpretable alternative to learned dense retrieval models, making it suitable for production deployment in search suggestion generation systems. Significant gains in business metrics in the A/B test further prove utility of our approach.

Metal Laser Powder Bed Fusion (PBF-LB/M) is a leading additive manufacturing technique in which part quality and grain morphology are highly dependent on process parameters. Numerous studies of process variations, such as laser power, scan speed, and spot diameter, have demonstrated that they strongly influence melt pool dynamics; however, the effects of powder layer height and geometric variations remain less well understood. In this article, we focus on variations in powder layer height and part geometry to study their influence on melt pool dynamics. We employed a high-fidelity multiphysics simulation framework based on the open source finite volume method (FVM) solver package `LaserBeamFoam' built on `OpenFOAM' to study the variations in different melt pool metrics -- melt pool depth, width, bead height, overlap depth, overlap width, solidified area, and dilution area. The solver captures coupled phenomena of heat transfer, fluid flow, vaporization, recoil pressure, Marangoni convection, and realistic laser reflection behavior to accurately model the melt pool dynamics. Simulations are performed for different powder layer heights and geometric dimensions for direct comparison with benchmark experiments conducted at the National Institute of Standards and Technology (NIST) in 2025. Quantitative validation against NIST experiment demonstrates excellent agreement in all the melt pool metrics. These results highlight the predictive capability of physics-based PBF-LB models, paving the way for process optimization, defect mitigation, and the integration of simulation into digital twin frameworks for additive manufacturing.

Digital Video Broadcasting Satellite, Second Generation and its extension DVBS2X are widely used in modern satellite communications, where synchronization relies on physical layer headers, pilot symbols, and optional superframe structures but lacks defined implementation methods. This work explores the use of external synchronization to enhance DVBS2 performance by using GPS disciplined oscillators, and a hardware software in the loop satellite channel model emulating Low Earth Orbit propagation. We evaluate scenarios with and without Doppler shifts and radio frequency interference, comparing synchronized and unsynchronized cases. Results show that external synchronization significantly improves bit error rate, frame error rate, and signal-to-noise ratio, subsequently reducing the frames required for reliable synchronization and enabling higher throughput in future satellite communication systems.

Spectral analysis at the infrared (IR) spectral range is introduced with assignment of synthetic red-green-blue (RGB) colours defined by adjustable wavelength and bandwidth. The RGB bands were selected at the phase-specific absorbance A or reflectance R bands of olivine and related materials, which can be formed via high temperature annealing (HTA) of natural minerals up to 1500 C. Natural olivines were collected from quarry at volcanic site in Mortlake, Victoria, Australia and spectrally characterised during IR-THz spectroscopy beamtime experiments at Australian Synchrotron. Phase changes in HTA natural olivines were traced by correlation of optical IR 4-polarisation spectroscopy, X-ray energy dispersive spectroscopy and magnetisation. After HTA, olivine samples were magnetized via precipitation of Fe-rich oxides.

In this paper, we provide an explicit description of the Schubert classes in the equivariant $K$-theory of weighted Grassmann orbifolds. We introduce the `twisted factorial Grothendieck polynomials', a family of symmetric polynomials by specializing the factorial Grothendieck polynomials, and prove that they represent the Schubert classes in the equivariant $K$-theory of the weighted Grassmann orbifolds. We give an explicit formula for the restriction of the Schubert classes to any torus fixed point in terms of twisted factorial Grothendieck polynomials. We give an explicit formula for the structure constants with respect to the Schubert basis in the equivariant $K$-theory of weighted Grassmann orbifolds. Eminently, we describe `twisted Grothendieck polynomials' and prove that these represent the Schubert classes in the $K$-theory of the weighted Grassmann orbifold. As a consequence, we describe the structure constants in the $K$-theory of weighted Grassmann orbifolds.

Federated recommendations (FRs) have emerged as an on-device privacy-preserving paradigm, attracting considerable attention driven by rising demands for data security. Existing FRs predominantly adapt ID embeddings to represent items, making the quality of item embeddings entirely dependent on users' historical behaviors. However, we empirically observe that this pattern leads to suboptimal recommendation performance under high data sparsity scenarios, due to its strong reliance on historical interactions. To address this issue, we propose a novel method named FedUTR, which incorporates item textual representations as a complement to interaction behaviors, aiming to enhance model performance under high data sparsity. Specifically, we utilize textual modality as the universal representation to capture generic item knowledge, and design a Collaborative Information Fusion Module (CIFM) to complement each user's personalized interaction information. Besides, we introduce a Local Adaptation Module (LAM) that adaptively exploits the off-the-shelf local model to efficiently preserve client-specific personalized preferences. Moreover, we propose a variant of FedUTR, termed FedUTR-SAR, which incorporates a sparsity-aware resnet component to granularly balance universal and personalized information. The convergence analysis proves theoretical guarantees for the effectiveness of FedUTR. Extensive experiments on four real-world datasets show that our method achieves superior performance, with improvements of up to 59% across all datasets compared to the SOTA baselines.

Interactions between stellar and planetary magnetic fields are expected to produce observable radio and optical signals modulated by their orbital periods, but direct detections remain elusive. We analyze 17 years of spectroscopic data of the GJ 436 system. This M2.5 V star hosts a transiting Neptune-sized planet in a close-in, inclined orbit. The data shows repeated enhancements of the stellar chromospheric activity at approximately the same phase of its 8-year activity cycle modulated by a combination of the planet's orbital period and the stellar rotation. We interpret this modulation as star-planet interaction. We propose a new geometrical model to interpret these signals, then, estimate the power of the interaction and, from models, estimate the magnetic field of GJ 436 b to be between 6 and 110G. This finding opens new pathways to detect star-planet interactions and to investigate planetary magnetic fields and their implications on atmospheric retention and detectability.

Lung cancer clinical decision support demands precise reasoning across complex, multi-stage oncological workflows. Existing multimodal large language models (MLLMs) fail to handle guideline-constrained staging and treatment reasoning. We formalize three oncological precision treatment (OPT) tasks for lung cancer, spanning TNM staging, treatment recommendation, and end-to-end clinical decision support. We introduce LungCURE, the first standardized multimodal benchmark built from 1,000 real-world, clinician-labeled cases across more than 10 hospitals. We further propose LCAgent, a multi-agent framework that ensures guideline-compliant lung cancer clinical decision-making by suppressing cascading reasoning errors across the clinical pathway. Experiments reveal large differences across various large language models (LLMs) in their capabilities for complex medical reasoning, when given precise treatment requirements. We further verify that LCAgent, as a simple yet effective plugin, enhances the reasoning performance of LLMs in real-world medical scenarios.

Polylab is a MATLAB toolbox for multivariate polynomial scalars and polynomial matrices with a unified symbolic-numeric interface across CPU and GPU-oriented backends. The software exposes three aligned classes: MPOLY for CPU execution, MPOLY_GPU as a legacy GPU baseline, and MPOLY_HP as an improved GPU-oriented implementation. Across these backends, Polylab supports polynomial construction, algebraic manipulation, simplification, matrix operations, differentiation, Jacobian and Hessian construction, LaTeX export, CPU-side LaTeX reconstruction, backend conversion, and interoperability with YALMIP and SOSTOOLS. Versions 3.0 and 3.1 add two practically important extensions: explicit variable identity and naming for safe mixed-variable expression handling, and affine-normal direction computation via automatic differentiation, MF-logDet-Exact, and MF-logDet-Stochastic. The toolbox has already been used successfully in prior research applications, and Polylab Version 3.1 adds a new geometry-oriented computational layer on top of a mature polynomial modeling core. This article documents the architecture and user-facing interface of the software, organizes its functionality by workflow, presents representative MATLAB sessions with actual outputs, and reports reproducible benchmarks. The results show that MPOLY is the right default for lightweight interactive workloads, whereas MPOLY-HP becomes advantageous for reduction-heavy simplification and medium-to-large affine-normal computation; the stochastic log-determinant variant becomes attractive in larger sparse regimes under approximation-oriented parameter choices.

Long secondary periods (LSPs) in luminous red giants remain the only major class of long-period stellar variability without a secure physical origin. Competing hypotheses include binaries with dusty companions and oscillatory convective dipole modes. We identify the physical and geometric conditions under which oscillatory convective dipole modes produce distinctive harmonic signatures that contrast with those expected from binary systems, and apply this diagnostic to a filtered subset of the OGLE-III LSP sample to identify examples consistent with oscillatory convective dipole modes. We model the geometric flux modulation from oscillatory convective dipole modes and map the range of inclinations, temperature amplitudes, and observing wavelengths for which harmonic features are observable. Using OGLE-III I-band light curves, we require statistically significant power at both sequence D and its harmonic, keeping a filtered sample of 249 stars (2.1% of the ridge-selected sample). We apply iterative Lomb-Scargle and weighted Fourier decomposition to isolate the fundamental and harmonic components. The relative phase ($Δϕ$) between these distinguishes secondary maxima predicted by an inclined dipole from secondary minima caused by eclipsing or ellipsoidal binary systems. The majority of high amplitude stars in the filtered subset show $Δϕ$ consistent with secondary minima produced by binary systems. However, a small but statistically non-negligible subset exhibits $Δϕ$ consistent with secondary maxima that are difficult to reconcile by eclipsing or ellipsoidal binaries, and instead match the geometric predictions for highly inclined, non-rotating oscillatory convective dipole modes with temperature amplitudes consistent with published models.

Breaking negative mental health cycles, including rumination and recurring regrets, requires reflection that translates awareness into behavioral change. Grounded in the Transtheoretical Model (TTM) and Gross's Emotion Regulation (ER) Process Model, we examine how Technologies Supporting Self-Reflection (TSR) bridge reflection and action. In a 15-day in-the-wild study (N = 20), participants used a voice-based journaling system to capture regrets and wishes and engaged in WhatIf-Planning, a novel structured reflection module integrating counterfactual thinking with if-then planning. Participants were randomized to either a free-form condition or a Gross-guided condition, which maps the five processes of Gross's ER model into explicit journaling prompts. We contribute: (1) a unified reflection-to-action TSR system that operationalizes the Preparation stage of TTM to bridge Contemplation and Action, and (2) triangulated empirical evidence from an in-the-wild journaling study that first operationalizes Gross's Process Model, revealing effects on coping flexibility and emotion regulation in daily life. Results show significant pre-post improvements in coping flexibility, indicating adaptive self-regulation across conditions, with the Gross-guided group generating more counterfactual alternatives, articulating concrete if-then action plans, and implementing more plans for self-driven change.

We present a composition-based approach to building correctby-construction database backing stores. In previous work, we specified the behaviour of several store variants and proved their correctness and equivalence. Here, we derive a Java implementation: the simplicity of the specification makes manual construction straightforward. We leverage spec-guaranteed store equivalence to compose performance features, then demonstrate practical value with CobbleDB, a reimplementation of RocksDB's levelled storage.

D. B. Brückner et al. [Phys. Rev. Lett. 125, 058103 (2020)] have described a novel method for inferring the dynamics of systems governed by an underdamped Langevin equation in the presence of measurement noise. While this is a significant achievement, the paper also presents a number of significant errors. These are explained and corrected in this note.