Mobile applications rely on complex backends that introduce significant security risks, yet developers often lack the tools to assess these risks effectively. This paper presents AndroScanner, an automated pipeline for detecting vulnerabilities in Android application backends through combined static and dynamic analysis. AndroScanner extracts backend API calls from APK files using apktool, Androguard, and Frida-based dynamic instrumentation, then vets them against the OWASP API Security Top 10 using APIFuzzer. We evaluate AndroScanner on two Android applications: a purposely vulnerable bank application and a production recruitment application with over 50,000 downloads on Google Play Store. Across both applications, AndroScanner extracted 24 APIs and identified 5 vulnerabilities, including a previously unreported zero-day Excessive Data Exposure vulnerability (ranked 3rd in the OWASP API Security Top 10) in the production application. The vulnerability was responsibly disclosed to the development team prior to publication. AndroScanner is available upon request to assist developers in identifying and remediating backend security risks before deployment.

In this paper we study higher-order difference equations which can be written as follows: $$ \mathbf{J} (y_0,y_1,...)^T = λ^N (y_0,y_1,...)^T, $$ where $\mathbf{J}$ is a $(2N+1)$-diagonal bounded banded matrix ($\mathbf{J}=(g_{m,n})_{m,n=0}^\infty$, $| g_{m,n} |< C$, $C>0$; and $g_{k,l}=0$ if $|k-l|>N$), $y_j$s are unknowns, $λ$ is a complex parameter, $N\in\mathbb{N}$. It is assumed that all $g_{k,k+N}$ and $g_{l-N,l}$ are nonzero. Two special cases are considered: \noindent \textit{Case A}: The matrix $\mathbf{J}$ is complex symmetric, i.e. $\mathbf{J} = \mathbf{J}^T$. \noindent \textit{Case B}: The matrix $\mathbf{J}$ is such that $g_{k,k+N}=1$, $k=0,1,2,...$. Notice that this condition can be attained by changing $y_j$s by their multiples. In both cases there exists a \textit{positive} matrix measure $M$ on a circle in the complex plane such that polynomial solutions satisfy some orthogonality relations. Namely, in case~A this is related to a $J$-orthogonality in the Hilbert space $L^2(M)$ ($J$ is a complex conjugation). In case~B we have a left $J$-orthogonality in $L^2(M)$. As a tool, a related matrix moment problem is studied. A complex rank-one perturbation of a free Jacobi matrix is discussed.

Scalable estimation of quantum states with readout errors is a central challenge in large multiqubit systems. Existing overlapping-tomography methods improve scalability by working with local subsystems, but they usually assume known or separately calibrated measurements. At the same time, readout-estimation methods model measurement errors without enforcing consistency among overlapping regional states. In this context, the present paper introduces a unified framework for joint regional quantum state tomography with readout errors. A multiqubit system is partitioned in overlapping regions, each region is assigned to a local density operator and a local confusion matrix, and neighboring regions are coupled through reduced-state consistency on shared subsystems. This leads to a structured bilinear optimization problem. To solve it, a distributed alternating method is developed in which the state-update step is handled by the alternating direction method of multipliers (ADMM), while the confusion-matrix updates are carried out locally in parallel. Analytical guarantees are also established, including a sufficient condition for local identifiability, local quadratic growth of the population misfit, and convergence of the inner state-update procedure. Simulations on Ring, Ladder, Torus, and Hub graph geometries show that joint estimation improves state recovery over fixed-readout reconstruction, recovers a substantial portion of oracle performance, and reveals a clear tradeoff between state estimation performance, communication, and computation.

A criterion for proving a strong form of propagation of chaos on the path space, known as entropy chaos, for a general interacting diffusion system is proposed. Our analysis focuses on the class of conservative diffusions introduced by Carlen, which are characterized by infinitesimal characteristic pairs, that is, a time-marginal probability density and a current velocity field. A key property of this broad class is that the processes remain diffusions under time-reversal. We prove that, given a suitable bound on the relative entropy (with respect to the Wiener measure) and the weak convergence of both drifts and fixed-time marginal densities, strong entropy chaos at the process level is achieved in the infinite particle limit, provided the limit drift satisfies a specific regularity condition. This stochastic framework encompasses various singular interacting particle systems and their related asymptotic scenarios.

The paper is devoted to the description of the varieties of complex 5-dimensional nilpotent Jordan superalgebras. We find all representatives for the isomorphism classes, using the Jordan normal form, results of simultaneous matrix triangularization, the Jordan-Kronecker theorem for a pair of skew-symmetric bilinear forms and similar arguments developed for $Δ$-modules by Burde and Grunewald. We also provide a complete geometric classification, determining the irreducible components of the corresponding varieties and describing all possible degenerations and non-degenerations between these superalgebras, in particular, applying some $\mathbb{Z}_2$-graded subspaces as invariants.

Recently, a semi-microscopic structure model has been presented to study the structure of a weakly-bound, two-body nucleus with a deformed core, including Pauli-blocking effects. The model has been successfully applied within the adiabatic distorted wave approximation (ADWA) reaction framework to study the reactions $^{16}$C(d, p)$^{17}$C, restricting the analysis to bound states of the residual $^{17}$C nucleus. In these calculations, the structure of $^{17}$C is described using the recently presented semimicroscopic Nilsson+AMD model (NAMD), considering different Pauli-blocking methods. In the present work, the analysis is extended to unbound states of this nucleus with the aim of constraining the location of the $1d_{3/2}$ single-particle strength and infer the $N=16$ shell-gap. Comparing the measured energy differential cross section for this reaction with calculations in which the position of the $1d_{3/2}$ orbital is arbitrarily varied, we conclude that a large shell-gap (>5 MeV) is required, in agreement with recently reported value from [J. Lois-Fuentes et al., Phys. Lett. B 867, 139600 (2025)].

Platonov in 1991 conjectured that adjoint groups are rational as varieties over arbitrary infinite fields, and as a consequence have weak approximation. The rationality part of the conjecture was disproved by Merkurjev in 1996, but the question about weak approximation remained open. We settle this in the negative.

The paper considers the problem of finding a submatrix $X_{\mathcal{S}} \in \mathbb{R}^{m \times k}$ in a matrix $X \in \mathbb{R}^{m \times n}$, such that the spectral or Frobenius norm of $X_{\mathcal{S}}^† X$ is limited, which guarantees it provides a good representation of the whole matrix. Such bounds can be reached by applying greedy algorithms, maximizing the submatrix volume. We suggest a modification of a greedy volume maximization, which performs column exchanges asymptotically faster for $n \gg m$ than the known alternatives, while guaranteeing the same bounds on $X_{\mathcal{S}}^† X$. In addition, we prove a new upper bound on the number of required exchanges, which is applicable to the new algorithm as well as to other greedy volume maximization algorithms.

Decades of advocacy for reproducibility and replication have advanced open, transparent practices in the sciences. However, traditional notions of reproducibility fit poorly with design-oriented visualization research, where insights emerge through subjective, situated, and iterative work. So how can we ensure rigor and transparency in processes that are inherently unreproducible? To introduce transparency in design-oriented research, we propose to focus on traceability: surfacing the origin and development of research contributions based on rich sets of artifacts documenting the design process. We investigated traceability through a collaborative autoethnographic reflection that builds on several years of work exploring ways to make design-oriented research transparent. This exploration includes an experiment to build a tool to support traceability, which we called tRRRacer. The tRRRacer tool provided a testbed for us to operationalize the three tenets of a traceable process: (1) Record abundant, annotated artifacts representative of research activities; (2) Report curated research threads that articulate rationale and evolution of the process, allowing others to (3) Read via interfaces that help retrace claims and assess plausibility. Reflecting on our experiences, we contribute a theorization of traceability and reflections on how we might support it.

We study independent sets in strong powers of circulant graphs using a transfer matrix formulation. The compatibility constraints separate into intra-layer and inter-layer components, yielding a transfer operator that is equivariant under the dihedral group action. The characteristic polynomial of the transfer operator factors into an \emph{anomalous} component (arising from the trivial isotypic component, with rational coefficients) and a \emph{cyclotomic} component (arising from nontrivial Fourier modes, splitting over the maximal real cyclotomic subfield). We show that the spectral radius is attained in the trivial isotypic component, so the dominant exponential growth is governed by a low-dimensional orbit-compressed operator. The independence polynomial is computed exactly for strong cylinders and tori, with the cyclotomic sector contributing a sparse correction confined to high-weight coefficients. All results are verified for $C_7$.

Correlated transition metal oxides present exciting prospects as switches or memory and storage devices owing to the possibility to control electronic properties using various external stimuli. While their complex behaviour is known to stem from interplay between electronic correlations, atomic structure and orbital physics, they remain poorly understood on the microscopic level. Here, we investigate such origins as a function of temperature and pressure in the transition metal oxide Ti3O5. We find that the insulating room-temperature phase is characterized by one-dimensional zig-zag chains composed by two types of titanium dimers forming orbital selective valence bonds. At the thermal phase transition, one type of titanium dimer breaks up, resulting in an insulator to metal transition with a large orbital repopulation between the two states. Moreover, optical spectroscopy reveals that an additional pressure-driven insulator to metal transition occurs in Ti3O5 at room temperature. The phenomenology of this novel pressure-induced metallic transition is completely different from the insofar studied transitions and results from a competition between intra- and inter-dimer hopping. Our combined results suggest that Ti3O5 is a prototypical correlated transition metal oxide, where both correlations as well as orbital interactions need to be considered to fully understand the evolution of the electronic states.

We consider the Cauchy problem for the Korteweg--de Vries equation with real initial data $q$ that is both $L^1$ and $L^2$ summable and supported on (0,\infty). Using the left reflection coefficient and Hankel operators on the Hardy space $H^2$, we derive a trace-type representation for the corresponding solution. The proof is based on approximation by compactly supported potentials, uniform convergence of the associated reflection coefficients away from the origin, and continuity properties of the resulting Hankel operators. This yields a rigorous inverse scattering construction for a class of summable half-line supported initial data beyond the standard short-range setting.

Hypergraph partitioning is a pervasive NP-hard problem, and accelerating its computation on GPU can both slice time-to-solution and raise quality of results. In this work, we implement a multi-level hypergraph partitioning algorithm on GPU targeting a specific set of problem constraints: bounded per-partition size and distinct inbound hyperedges. Manipulating hypergraphs requires long orders of nested iterations, and enforcing these constraints introduces further set operations amidst them. Hence, we design algorithms around our problem's specifics, materializing the hypergraph's incidence structure in memory and exploiting set sparsity. Our results show competitive speedups as high as 940x and 2-26% better results in connectivity over a sequential multi-level partitioner.

We formulate a method to co-optimize power system capacity planning decisions and policy investments that shape electricity load patterns. To this end, we leverage a gradient-based solution technique that enables the efficient solution of operation-aware planning models. To compute gradients with respect to the conditions that define daily electricity demand profiles, we introduce and formalize the concept of differentiable scenario generation and show that generative machine learning models satisfy the mathematical requirements needed to compute consistent gradients. We demonstrate the feasibility of the proposed approach through numerical experiments using a diffusion model-based scenario generator and a stylized generation and capacity expansion planning model.

While conventional oscillation experiments measure neutrino mixing parameters with high precision, these measurements are strictly confined to sub-TeV scales. At higher energies, renormalization-group effects can cause these parameters to evolve with the transferred momentum, $Q$. High-energy and ultra-high-energy astrophysical neutrinos, spanning TeV to EeV energies, probe high values of $Q$ unreachable by conventional experiments, offering an unprecedented test of high-energy mixing. We use the flavor composition of these neutrinos -- the relative proportions of $ν_e$, $ν_μ$, and $ν_τ$ -- to constrain this evolution, both phenomenologically and within dimension-6 Standard Model Effective Field Theory. We account for astrophysical uncertainties -- an unavoidable requirement to obtain realistic results, even though this weakens the bounds. Although present IceCube measurements lack the sensitivity to detect this running, we forecast that upcoming multi-detector combinations will place unprecedented bounds on the high-energy evolution of neutrino mixing.

Toxic interactions during code reviews can undermine teamwork and hinder productivity in software engineering (SE) teams. While prior studies explore toxicity detection and empirical investigation, they lack real-time detoxification tools to support the SE community. To address this gap, we present ToxiShield, a browser extension for GitHub pull requests that is built using three modules: i) Toxicity Filter -- to identify whether a text is toxic, ii) Communication coach -- to facilitate just-in-time fine-grained toxicity categorization with explanations, and iii) The Reframer -- that generates a revised, constructive alternative of a toxic text. For each module, we trained and evaluated multiple deep learning and Large Language Models (LLMs) to identify the best choice. A BERT-based binary detection model, trained on 38,761 code review samples, achieves 98% accuracy and an F1-score of 97% and is the selected one for the Toxicity Filter module. For the Communication Coach, prompt-tuned Claude 3.5 Sonnet achieved the best performance with 39% MCC and 42% F1 in multiclass toxicity classification with detailed reasoning. For Reframer, we evaluated five LLMs using a fine-tuning strategy on a dataset of 10,120 code review comments. The fine-tuned Llama 3.2 model achieves 95.27% style transfer accuracy, 97.03% fluency, 67.07% content preservation, and an 84% J-score. We further validated ToxiShield through a human evaluation using the Technology Acceptance Model with 10 participants, confirming its perceived usefulness and ease of adoption. ToxiShield sets a benchmark for advancing constructive communication in software engineering, driving inclusivity and healthier collaboration in open-source communities.

Propensity score weighting approaches have been widely implemented in clinical research to estimate the effects of a treatment or exposure while mitigating the risk of confounding in the absence of random assignment. In practice, when working with large electronic health records (EHR) or administrative datasets to evaluate health quality outcomes at the institutional level, or evaluate supportive care interventions for a wide range of hospitalized patients, it may be advisable to stratify the propensity score weighting approach by indication, reason for admission, or other clinical risk factors due to the potential for substantial heterogeneity across subgroups of patients with complex care needs. A stratified approach may be appropriate if (i) prognosis differs substantially between patient subgroups such that achieving balance in the composition of these strata between exposure/treatment groups should be prioritized, (ii) likelihood of exposure differs substantially across clinical subgroups, or (iii) the covariate-exposure associations are expected to differ substantially between subgroups (i.e. there are covariate-subgroup interactions in the exposure/treatment propensity model). For example, we may want to evaluate the impact of prophylactic anticoagulant use for venous thromboembolism prevention in elderly patients admitted to hospital for a wide array of conditions. The purpose of this article is to outline an approach to implementing propensity score weighting with stratification by clinical groups. We also provide guidance on best practices with particular focus on EHR and administrative medical data, and population health settings.

We study the metric dimension (strong and weak) of infinite graphs. In particular, our main interest is characterizing infinite graphs with finite dimension. Our main results: (1) graphs with more than one end have infinite strong dimension; (2) for graphs with a finite number of cycles, the weak dimension is finite if and only if the graph has finitely many vertices of degree three, and the strong dimension is finite if and only if the graph has one end and finitely many vertices of degree three.

When considering a model selection or, more generally, an aggregation approach for adaptive statistical inference, it is often necessary to compute estimators over a wide range of model complexities including unnecessarily large models even when the true data-generating process is relatively simple, due to the lack of prior knowledge. This requirement can lead to substantial computational inefficiency. In this work, we propose a novel framework for efficient model aggregation called the early-stopped aggregation (ESA): instead of computing and aggregating estimators for all candidate models, we compute only a small number of simpler ones using an early-stopping criterion and aggregate only these for final inference. Our framework is versatile and applies to both Bayesian model selection, in particular, within the variational Bayes framework, and frequentist estimation, including a general penalized estimation setting. We investigate adaptive optimal property of the ESA approach across three learning paradigms. We first show that ESA achieves optimal adaptive contraction rates in the variational Bayes setting under mild conditions. We extend this result to variational empirical Bayes, where prior hyperparameters are chosen in a data-dependent manner. In addition, we apply the ESA approach to frequentist aggregation including both penalization-based and sample-splitting implementations, and establish corresponding theory. As we demonstrate, there is a clear unification between early-stopped Bayes and frequentist penalized aggregation, with a common "energy" functional comprising a data-fitting term and a complexity-control term that drives both procedures. We further present several applications and numerical studies that highlight the efficiency and strong performance of the proposed approach.

Traditional Retrieval-Augmented Generation (RAG) approaches generally assume that retrieval and generation occur on powerful servers removed from the end user. While this reduces local hardware constraints, it introduces significant drawbacks: privacy concerns regarding data access, recurring maintenance and storage costs, increased latency, and the necessity of an internet connection. On-device RAG addresses these challenges by executing the entire pipeline locally, making it ideal for querying sensitive personal information such as financial documents, contact details, and medical history. However, on-device deployment necessitates a delicate balance between limited memory and disk space. Specifically, the context size provided to the generative model must be restricted to manage KV cache and attention memory usage, while the size of stored embeddings must be minimized to preserve disk space. In this work, we propose a unified model that compresses the RAG context and utilizes the same representations for retrieval. This approach minimizes disk utilization compared to using separate representations, while significantly reducing the context size required for generation. With an average of 1/10 of the context, our model matches the performance of a traditional RAG reader without increasing storage requirements compared to a multi-vector retrieval model. This approach represents the first model to unify retrieval and context compression using a shared model and representation. We believe this work will inspire further consolidation of distinct models to optimize on-device performance.

The guiding center formalism is employed to analyze the motion of a charged relativistic particle in an inhomogeneous magnetic field, subject to magnetic mirroring and energy loss due to cooling. The governing equation for the evolution of the magnetic moment is derived. An example representing a neutron star (pulsar or magnetar) magnetosphere is presented to illustrate typical particle orbits. Notably, radiative losses are most pronounced near a trapped particle's turning point. Depending on the initial particle's pitch angle, energy loss can become catastrophic, resulting in the rapid migration of the particle into the loss cone and subsequent precipitation onto a neutron star. Conversely, particles with a larger pitch angle remain temporarily trapped and form a gradually decaying "cooled-loss-cone" or "funnel'' distribution, characterized by the maximum momentum space particle density being located at the edge of the loss cone. The size of the loss cone is energy-dependent and scales as $α_{c} \propto γ^{3/10}$. Synchrotron losses are strongest in a well-localized region of the magnetosphere, about a few hundred to a thousand star radii under typical pulsar and magnetar conditions. This region is a plausible site for synchrotron radiation originating in the outer magnetosphere, and could also be responsible for non-polar coherent pulsar emission, as well as weak fast radio bursts.

Range functions are a fundamental tool for certified computations in geometric modeling, computer graphics, and robotics, but traditional range functions have only quadratic convergence order ($m=2$). For ``superior'' convergence order (i.e., $m>2$), we exploit the Cornelius--Lohner framework in order to introduce new bivariate range functions based on Taylor, Lagrange, and Hermite interpolation. In particular, we focus on practical range functions with cubic and quartic convergence order. We implemented them in Julia and provide experimental validation of their performance in terms of efficiency and efficacy.

By using a probabilistic technique based on the exponential change of measure we find a precise tail asymptotic behavior of some perpetuities with distributions close to the Dickman distribution.

We previously reported apparent ideal diamagnetism at ambient conditions in a graphene-n-heptane-permalloy system. At the same time, the experiments revealed inconsistent behavior, including signal freezing and occasional paramagnetic responses. Further measurements performed without graphene produced similar signals, indicating that graphene is not responsible for the observed effects. The results suggest that magnetic field redistribution caused by inhomogeneities in the permalloy foil and experimental geometry can mimic ideal diamagnetism in sub-milligauss measurements. These findings revise the interpretation of our earlier results and emphasize caution in interpreting ultra-low-field magnetic measurements.

This paper presents a framework for binary autoregressive time series in which each observation is a Bernoulli variable whose success probability evolves with past outcomes and probabilities, in the spirit of GARCH-type dynamics, accommodating nonlinearities, network interactions, and cross-sectional dependence in the multivariate case. Existence and uniqueness of a stationary solution is established via a coupling argument tailored to the discontinuities inherent in binary data. A key theoretical result, further supported by our empirical illustration on S&P 100 data, shows that, under a rare-events scaling, aggregates of such binary processes converge to a Poisson autoregression, providing a micro-foundation for this widely used count model. Maximum likelihood estimation is proposed and illustrated empirically.

We describe a method to prove new integral inequalities for stable minimal hypersurfaces in Euclidean space. As an application, we give a simple proof that complete, two sided, stable minimal hypersurfaces in $\mathbb{R}^4$ are hyperplanes. A core part of the argument hinges on the fact that stable minimal hypersurfaces in non-negatively curved spaces are examples of manifolds with a spectral Ricci curvature lower bound; in particular, we prove a sharp pointwise gradient estimate for the Green kernel on non-parabolic manifolds with spectral Ricci lower bounds, extending previous work by Colding.

Climate change has intensified extreme weather and wildfire conditions globally. Canada experienced record-breaking wildfires in 2023 and 2025, burning millions of hectares and severely impacting the Prairie provinces, with Manitoba facing its worst season in 30 years. These events highlight the urgent need to understand and mitigate escalating fire risks. While existing research largely focuses on wildfire management approaches, few studies have explored the relationship between user network traffic and wildfire activity, despite the potential of such correlations to provide valuable spatiotemporal insights into wildfire dynamics. This paper investigates the relationship between wildfire intensity and network performance during the 2025 Manitoba wildfire season, using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-derived Fire Radiative Power data and large-scale Speedtest measurements. We found statistically significant correlations between wildfire intensity and several network performance metrics in both the province-wide and region-wide case studies, as measured by Spearman's correlation coefficients ($ρ$) and corresponding p-values. Throughput-related metrics showed inverse correlations with wildfire intensity (e.g., download speed: $ρ= -0.214$, $p\_value = 0.004$), whereas latency-related metrics showed positive correlations (e.g., round-trip time latency: $ρ= 0.162$, $p\_value = 0.0308$). The findings suggest satellite fire indicators and network performance metrics together can reveal vulnerabilities during extreme environmental events and support diaster response and recovery efforts.

We study log-concavity properties of real sequences $(a_n)_{n \ge 0}$ satisfying a $d$-th order linear recurrence whose coefficients are linear functions of $n$; the so-called P-recursive (or holonomic) sequences. Writing the recurrence in companion-matrix form $\mathbf{v}_{n+1} = M_n\,\mathbf{v}_n$ with $M_n = nA + B$, we show that the log-concave operator value $\mathcal{L}(a_n) = b_n \coloneqq a_n^2 - a_{n+1}a_{n-1}$ is a quadratic form in the state vector $\mathbf{v}_n$, and identify the matrix $Q_n = Q^{(0)} + nQ^{(1)}$ whose positive semi-definiteness gives a sufficient condition for log-concavity. For the class of second-order recurrences with constant coefficients, we prove a tight (necessary and sufficient) criterion for the sequence to be $\infty$-log-concave, a consequence of the fact that $\mathcal{L}(a_n)$ is itself a geometric sequence so that $\mathcal{L}^2(a_n) = 0$ identically. We obtain analogous tight criteria for sequences fixed by $\mathcal{L}$, and for P-recursive sequences satisfying a dominant-root asymptotic behaviour. We leave some further insight in case this criteria break down in full generality.

We present a theory of ballistic N/F/S and S/F/S junctions with a uniformly precessing magnetization, which generates long-range equal-spin superconducting correlations [Takahashi et al., Phys. Rev. Lett. 99, 057003 (2007), Houzet, Phys. Rev. Lett. 101, 057009 (2008)]. The non-equilibrium distribution of Andreev bound states leads to a strongly non-sinusoidal current-phase relationship for large precession angles. We derive detailed results for ballistic junctions involving partially and fully polarized ferromagnets. In the fully polarized half-metal limit, the magnetization precession switches the junction from an "off" state with vanishing subgap current to an "on" state with finite Andreev conductance and finite Josephson current.

New data for the total inclusive helicity-dependent cross section for the proton and deuteron were obtained in the photon energy interval 200-1400 MeV. The experiment was performed at the A2 tagged-photon facility of the Mainz Microtron (MAMI) using a circularly polarized photon beam and longitudinally polarized proton and deuteron targets. The reaction products were detected using the large-acceptance Crystal Ball/TAPS calorimeter, which covers 97% of the full solid angle. These new results, obtained with fine energy binning, significantly expand both the quantity and the quality of the available data for these observables and enable a detailed comparison with state-of-the-art theoretical calculations. From the combination of the results for the deuteron and the proton, important information could also be extracted for the free neutron. Based on these data, and using existing models to evaluate the missing contributions from unmeasured photon energy regions, the validity of the Gerasimov-Drell-Hearn (GDH) sum rule has been verified for the proton, the neutron, and the deuteron. These new data provide a precise experimental benchmark for theoretical models used to study nucleons, both in their free state and when embedded in the nuclear medium.