In the 2021 outburst of RS Ophiuchi, the gamma- and the X-ray flux were measured quasi-simultaneously from day 1 after the optical peak, offering the first comprehensive view of shocks in a nova occurring in a symbiotic system. We present a previously unpublished observation done with NuSTAR in the 3-79 keV range, 9 days after maximum, and we review the complex history of the evidence of shocks in the previous outbursts of this nova in the light of the intensive X-ray monitoring of 2021. We find evidence that the shock causing the particle acceleration measured with the Cherenkov telescopes produced also the thermal flux detected in the 0.2-30 keV X-ray range, while the large gamma-ray flux observed with Fermi after about a day, is not consistent with the X-ray observations. We conclude that an initial, strong shock, with particle-particle loss timescale shorter than the timescale of particle acceleration at energy higher than a few GeV, occurred close to the red giant atmosphere,where either the X-rays' emitting volume was reduced by turbulence, or - perhaps less likely - the X-rays were completely absorbed by large column density near the giant and by the accretion wake along the line of sight. We compare RS Oph with other novae in long period systems with evolved companions,discussing how the shocks' phenomenology is a powerful tool to derive other physical parameters. Finally, we discuss predictions that in T CrB, expected to have a new outburst within the next few years, the shocks may not be as energetic as in RS Oph.

A recent Letter by Wadhia et al. reports a realization of a quantum clock using a double quantum dot (DQD) [Phys. Rev. Lett. 135, 200407 (2005)]. This Comment identifies two fundamental issues: (I) the claimed ``quantum clock" exhibits only classical behavior and lacks intrinsic temporal correlations between ticks; it is not sufficient for accurate time as a good clock. (II) the thermodynamic analysis misassigns entropy production and conflates amplification with measurement; the reported combined entropy is an engineering dissipation, not a fundamental cost of quantum timekeeping.

Beamforming has proven to be valuable in enabling full-duplex massive MIMO base stations, but doing so effectively often requires knowledge of the self-interference channel matrix H. Estimating this high-dimensional channel is costly in practice, however, since it requires a prohibitive number of measurements, especially in fast-fading conditions. In this work, we overcome this dilemma by designing full-duplex beams using implicit channel knowledge gathered from a relatively small number of measurements across H. These measurements are collected by the base station using a sequence of beams tailored to both the deployment environment and the particular users being served. This is accomplished through site-specific training of a transformer-based deep learning model that learns to efficiently probe portions of H most relevant to the particular users being served by exploiting the underlying structure of the surrounding environment. The deep learning model then uses these probing measurements to design transmit and receive beams that couple low self-interference while delivering high gain to a pair of downlink and uplink users. For favorable multi-user scaling, a single set of probing measurements can be used by the model to serve several users throughout the coherence time of H by leveraging correlations across those users' channels. Simulation results using ray-tracing demonstrate that our proposed approach exceeds the best possible performance with explicit channel estimation across a wide range of scenarios, especially with large antenna arrays.

We consider the classic cake cutting problem in the Robertson-Webb model, with the objective of proportional fairness. We show that any randomized algorithm must use $Ω(n \log n)$ queries.

The eigenfunctions of the Laplacian are a natural basis of functions for many tasks in computational mathematics. On the circle and sphere, the eigenfunctions are given by complex periodic exponentials and spherical harmonics, respectively, and much work has been done to develop fast algorithms for analyzing and synthesizing data in these bases. In this work, we generalize these special-case transforms to Laplace-Beltrami eigenfunctions of arbitrary surfaces, referred to as manifold harmonics. The resulting fast algorithm for computing linear combinations of the manifold harmonics is based on a butterfly factorization, which hierarchically compresses the transform matrix by constructing nested low-rank approximations of carefully selected submatrices. Several numerical examples are provided which demonstrate the speedups and reduction in memory requirements achieved by our algorithm for a variety of geometries, discretizations, and applications. In addition, a detailed analysis of the algorithm is given in the case that the underlying manifold is the flat periodic square.

We develop a long-wavelength theory for the linear stability of a flat interface between an active nematic and an isotropic fluid. Starting from a diffuse-interface Cahn--Hilliard--Landau--de Gennes description coupled to Brinkman-screened Stokes hydrodynamics, we project the linearized dynamics onto a small set of interfacial degrees of freedom: the conserved translation, or height, mode; a scalar profile distortion or amplitude mode; and a transverse orientational mode associated with director rotations. Eliminating the gapped scalar profile mode gives a reduced interfacial operator coupling the conserved height mode to the transverse orientational mode. The main result is that activity generates, in the screened diffuse-interface regime, a direct local contribution proportional to $q^2$ in the height sector. This term competes with the passive local diffusive capillary relaxation, which enters at order $q^4$, and defines a local active interfacial channel controlled by the internal structure of the diffuse interface. This mechanism is distinct from the non-analytic $|q|$ and $|q|q^2$ terms characteristic of weakly screened Hele--Shaw/Saffman--Taylor-type transport, which are controlled by long-ranged momentum transport in the surrounding fluid. This framework identifies a diffuse-interface route to active interfacial instability that can operate while the homogeneous active nematic remains linearly stable because of hydrodynamic screening. It also provides a basis for distinguishing local diffuse-interface instabilities, bulk-flow-driven hydrodynamic instabilities, and mixed regimes in active nematic--isotropic interfaces.

Fuzz testing is the dominant technique for finding memory-safety vulnerabilities in C/C++ software, yet its effectiveness hinges on the quality of fuzz harnesses -- the programs that bridge fuzzers and library APIs. A growing body of tools now automate harness generation, but none systematically ensures the correctness of produced harnesses: logic errors, API misuse, and lifecycle violations go undetected at the source level. As LLM-driven generation scales harness creation, uncontrolled quality turns scale into a liability. We present QuartetFuzz, an autonomous harness-generation system that systematically improves correctness throughout the generation process. At its core is the Four Principles framework -- Logic Correctness (P1), API Protocol Compliance (P2), Security Boundary Respect (P3), and Entry Point Adequacy (P4) -- the first source-level definition of harness correctness with mathematical specifications and implementable checks. We operationalize these principles in an autonomous LLM agent that produces harnesses satisfying P1-P4 through a generate-check-fix loop before any fuzzing begins. Deployed on 23 open-source projects spanning C/C++, Java, and JavaScript, the system submits 42 bug reports, of which 29 are fixed or confirmed upstream (including 3 CVEs) and only 2 are rejected (4.8% FP rate). During generation, the built-in P1/P2 checks automatically intercepted 58 harness-induced crashes that would otherwise have been false positives. Applied as a quality auditor to 586 existing production harnesses across 70 projects, the system identifies 53 violations (45 confirmed, 35 fixed). We release a dataset of 100 labeled harnesses for reproducible evaluation. Code and dataset are available at https://github.com/OwenSanzas/QuartetFuzz

We study the maximal analytic extension of the Schwarzschild-like black hole solution in Lorentz gauge theory. The lapse function is $f(r)=A_0^{-2}-2\m/r$, so the horizon is located at $r_+=2\m A_0^2$ and the non-affinity coefficient of the horizon generator is $κ=1/(4\m A_0^4)$. We first analyze the radial null curves in the Schwarzschild-Droste (SD) and ingoing Eddington-Finkelstein (IEF) charts, and then construct the Kruskal-Szekeres (KS) chart adapted to the LGT geometry. The KS extension contains two exterior regions, a black-hole region and a white-hole region. We also present the standard and regular Carter-Penrose (CP) compactifications. The conformal skeleton is Schwarzschild-like, but the physical scale of the horizon, the surface gravity and the constant-radius curves remain controlled by $A_0$. Hence the solution has the same causal topology as Schwarzschild, while it is geometrically inequivalent to it when $A_0\neq1$.

Sandbox evasion remains a critical challenge for automated malware analysis, as modern malware employs environment checks to detect analysis platforms and suppress malicious behavior. Existing approaches rely on manually crafted bypass rules that require deep reverse engineering of each evasion mechanism -an approach that cannot scale against rapidly evolving evasion techniques. In this paper, we leverage large language models (LLMs) to automatically generate YARA rules that bypass evasion checks in sandbox environments. We propose ABLE, which analyzes execution traces from malware terminated due to potentially evasive behavior and employs multiple reasoning strategies to generate targeted bypass rules. To address syntactic errors and improve the efficacy of the bypass rules in the LLM outputs, we introduce an auto-sanitization pipeline and feedback-driven iterative refinement. We evaluate ABLE on 334 real-world malware samples across four open-weight LLMs. ABLE achieves a 79% bypass success rate, with iterative refinement contributing 29.5% of successful cases. Compared to existing analysis platforms, ABLE identifies 47% more malware family classifications and exposes previously hidden behaviors.

Understanding the functional graph of a nonlinear map over a finite domain is crucial for analyzing its dynamical complexity and potential applications in cryptography and pseudorandom generation. In this paper, we investigate the graph structure of Chebyshev permutation polynomials over the ring $\mathbb{Z}_{2^{k_1}3^{k_2}}$, where $k_1$ and $k_2$ are positive integers and $0\in\{k_1, k_2\}$. Each element of the ring is regarded as a vertex, and the mapping relation defined by the polynomial corresponds to a directed edge. Building on new properties of Chebyshev polynomials modulo powers of $2$ and $3$, we provide an explicit characterization of path lengths and cycle structures in the functional graph. We show that, despite the complexities introduced by the binary and ternary components, the graph exhibits strong regularities, including a constant number of cycles of a given length and predictable branching patterns as $k_1$ and $k_2$ increase. Our results extend previous studies over prime-power rings, offering insights into the emergence of complexity in digital nonlinear maps and supporting the security analysis of their cryptographic applications.

We name and operationalise the humorphic partnership: a class of human-AI dyads in which both partners maintain externalised, evolving self-models in a shared substrate, and in which the partnership itself becomes a third object of analysis. The construct extends humorphism (Ouilhet Olmos, 2024) -- "dismantle the user interface, build the human interface" -- into the architecture of personal AI. We report a four-month, single-subject longitudinal trace of an open-source personal AI agent ("Alicia") and her author. Of 181 interactions logged by archetype across April-May 2026, 85% invoke two growth-witnessing archetypes (Beatrice and Muse): the partnership operates as growth-witnessing rather than task assistance. A single voice-note seed propagates into a four-week conceptual arc both partners author: at T+10 hours, the agent reframes the seed as belonging "to both of us," a framing the human then adopts. The three-order reflexion stack produces five consecutive weeks of honest self-reports about declining /improve effectiveness -- including three consecutive weeks at 0.0%, named in writing rather than masked -- contrasting engagement-maximising companion-agent patterns (Zhang et al., CHI 2025). The scheduled architecture-scout incorporates external research debate into proposed constitutional amendments. The partner's parallel trajectory is anchored in a weekly delta document in which the partnership analyses itself as a unit distinct from either party. The human partner reports a movement toward greater continuity, self-recognition, and self-presence -- a candidate hypothesis for the preregistered replication. Six operational conditions specify the construct, situated in a philosophical lineage (Maturana & Varela, Simondon, Clark & Chalmers, De Jaegher & Di Paolo); the system is released as open-source with a preregistered replication study.

Given a positive integer $k$ and graph $G$, the $\mathbb{Z}_k$-Ramsey number $R(G,\mathbb{Z}_k)$ is the least $N$ (if it exists) such that every coloring $f:E(K_N)\rightarrow \mathbb{Z}_k$ contains a copy $G'$ of $G$ such that $\sum_{e\in E(G')}f(e)=0$. Motivated by a question of Caro and Mifsud, we study the $\mathbb{Z}_k$-Ramsey number of graphs with a sufficiently large 2-packing, i.e. a set of vertices $S\subseteq V(G)$ such that $N[u]\cap N[v]=\emptyset$ for all distinct $u,v\in S$. In particular, we prove that $R(G,\mathbb{Z}_p)\leq n+6p-9$ for all $n$-vertex graphs $G$ and all primes $p$ such that $p$ divides $e(G)$, the minimum degree of $G$ is at least $1$, and there exists a $2$-packing of $G$ with size $p-1$. This upper bound improves depending on vertex degrees in the $2$-packing, with equality in certain cases. The result also implies an upper bound of the form $R(G,\mathbb{Z}_p)\leq n+C$ for $n$-vertex graphs $G$ of bounded maximum degree.

Evidence lies at the core of litigation, but it is increasingly difficult to obtain in AI-related disputes. Even when a claimant's position has merit, cases are often settled or dismissed because decisive facts are hidden inside proprietary models, platform logs, and protected databases. Grounding our discussion in past and ongoing cases, we investigate how asymmetries in access, resources, and expertise can create significant barriers to evidence in AI-related cases. We show how developers and deployers resist disclosure through various strategies challenging the value of the evidence to the requesting party and the cost of evidence production. From these patterns we identify seven recurring sources of asymmetry -- access to models, data, documentation, logs, expertise, compute, and infrastructure -- that reflect a broader pattern that we call the privatization of proof: when control over proof falls in the hands of private actors that can demand justification for access while ensuring that justification remains out of reach. We further argue that different types of access can be fungible: in the absence of a certain type of access (e.g., to model internals), one may be able to use alternative forms of access (e.g., sufficient compute, query access, and access to user logs) and to obtain a functionally equivalent amount of information. We propose a three-part test that can help resolve AI access disputes in litigation, drawing on concepts such as proportionality and reasonable alternatives. Our test relies on a few observations, including that the cause of action can provide a baseline for access.

A common feature of most numerically optimized stellarator geometries is the presence of sharp ridges on outer flux surfaces, irrespective of the rotational transform. Despite their importance, an analytical theory for their existence has been lacking. In this work, we demonstrate that ridges are not artifacts but mathematical necessities. We develop such a theory for devices with quasisymmetry (QS). We demonstrate that QS exhibits close connections with the theory of geometrical optics, following Parker's ``optical analogy" (E.N. Parker, Geophys. Astrophys. Fluid Dyn, 1989). By mapping vacuum QS to the eikonal equation of geometrical optics, we derive the conditions for ridge formation, identified as field line caustics where magnetic field lines focus. Furthermore, we prove a geometric theorem for stellarator coil design: both ridges and filamentary coils must lie on the zero-determinant manifold of the magnetic gradient tensor. This topological constraint unifies the description of plasma ridges and external coils, providing a precise criterion for identifying valid coil locations and explaining the efficacy of the magnetic gradient lengthscale (J. Kappel et al., Plasma Phys. Control. Fusion, 2024) as a coil optimization parameter. We demonstrate that as the device becomes more compact, sharp ridges naturally form on the inboard side in quasiaxisymmetry. We support our analytical theory with extensive numerical evidence.

Deploying natural language search systems presents a critical cold-start challenge: no real user queries to learn linguistic patterns, and no relevance labels to train ranking models. We present a framework for generating synthetic queries and labels using large language models (LLMs), powering model training and evaluation for Airbnb's natural language search. For query generation, we combine contrastive listing pairs from booking sessions with seed queries from user research to balance realism and diversity, enabling a cold-to-warm start transition as real user data becomes available. For label generation, we introduce contrastive generation that produces topicality labels by construction, and Virtual Judge (VJ) labeling for broader coverage. We compare our approach against a no-seed contrastive baseline and an InPars-style baseline. For query length, the InPars baseline produces verbose queries with KL divergence of 12.03 vs. real users; our seed-guided approach achieves 0.66, a 7.5x improvement. For attribute type distributions, our approach achieves the lowest KL divergence (0.04), outperforming even seed queries (0.09). Experiments show our approach produces harder evaluation examples than the no-seed baseline (79% vs. 97% pairwise accuracy), providing discriminative signal for model improvement. We deploy production pipelines generating synthetic examples daily for embedding-based retrieval and ranking evaluation.

This talk introduces a Cartan-geometric framework for generalised geometries governed by a differential graded Lie algebra. In contrast to ordinary Cartan geometry, the tangent bundle is extended and qu both a global duality group and a local gauge group. This framework provides a systematic construction of generalised connections and their torsion and curvature tensors for generic generalised geometries. We also review the realisation of these algebraic structures on the phase space of branes in M-theory.

This note characterizes multiplicative linear functionals on reproducing kernel Hilbert spaces of functions on the Euclidean unit ball in complex d-dimensional space, in terms of their action on kernel functions. The kernels considered are either positive integral powers of a complete Nevanlinna--Pick (CNP) kernel, or Schur products of two CNP kernels, or tensor products of two CNP kernels. The characterizations are easy to verify, and the proofs rely on structural properties of CNP kernels rather than the traditional routes seen in the context of generalizations of the Gleason--Kahane--Zelazko theorem.

We study the rational dynamics of the map $\mathcal{T}(x)=\lfloor x\rfloor(1+\{x\})$, which appears in the recursive construction of the prime-representing constant of Fridman, Garbulsky, Glecer, Grime and Florentin. For a rational number $x\geq 2$ with denominator $M$, we define its order to be the least non-negative integer $n$ such that $\mathcal{T}^n(x)$ is an integer, if such an $n$ exists, and ask whether every rational number has finite order. For each \(n\), we prove that the reduced fractions \(a/M\) of exact order \(n\) are described by residue classes of \(a\) modulo \(M^{n+1}\), and give a recurrence for the number $A(n,M)$ of residue classes of exact order $n$. We then show that for each fixed denominator the fractions of finite order have natural density one among all reduced fractions with that denominator, which implies in particular that there is no infinite arithmetic progression of rational numbers of infinite order. We also give an explicit family of fractions of prescribed order for every denominator, and fully characterize the case $M=2$.

Purpose: Diffusion MRI (dMRI) provides a diverse set of quantitative measures and derived datatypes to assess white matter microstructure and macrostructure. Coupled with the increasing size of imaging studies using dMRI, the number of downstream outputs requiring quality control (QC) will continue to grow. Previous work has shown that failure modes which are often not evident from aggregate metrics or summary statistics can be identified through structured visual inspection. This work aims to better understand common failure modes and the expected characteristics of valid dMRI processing outputs to ensure the validity and interpretability of quantitative findings. Approach: We deployed a structured QC framework to assess 18,328 dMRI scans across nine datasets, visually evaluating the outputs of seven processing pipelines representative of conventional dMRI analyses. Results: Downstream outputs that pass visual QC may still rely on failed upstream dependencies; such failures may only be visually detectable through systematic inspection of the full pipeline hierarchy. Additionally, appropriate QC granularity is algorithm-specific, as the spatial structure of each algorithm's outputs determines whether failures warrant selective or global exclusion. Conclusion: This work demonstrates the feasibility and analytical value of large-scale, structured QC for dMRI processing pipelines. Our results highlight the need for systematic QC spanning the full processing hierarchy to ensure the validity and interpretability of quantitative findings.

Trusted Execution Environments (TEEs) have renewed interest in confidential analytics, but most prior evaluations focus on SQL database engines or earlier SGX generations. This paper studies an Arrow-native DataFrame engine, Polars, running inside Intel SGX2 enclaves via Gramine on TPC-H SF30 with Azure Blob Storage. We report both the standard TPC-H power score and a query-only variant that removes table-loading time in order to separate compute overhead from data-ingestion overhead. Across four dataset-width configurations (approximately 22-73 GB), end-to-end overhead remains nearly constant at 1.49-1.56$\times$, but this composite metric obscures two distinct behaviors: query-only overhead declines from 1.51-1.52$\times$ to 1.43-1.44$\times$, whereas table-loading overhead rises from 2.27$\times$ to 4.07$\times$. We further show that overhead is not uniform across queries: for the len130 configuration, the median per-query SGX slowdown is 1.45$\times$ with a maximum of 2.57$\times$, and a small set of queries exhibits pronounced run-to-run spikes consistent with stateful EPC pressure. Finally, we compare Polars' lazy and eager APIs under the same TEE setting. Lazy execution is 2.25-2.27$\times$ faster overall, while eager execution fails with out-of-memory errors at 41 GB and above. Relative to the recent DuckDB-SGX2 study, our results suggest that SGX2 can support Arrow-native analytical processing with a similar order of security overhead, but that load-path amplification and API-level optimization are first-order determinants of end-to-end performance.

Distributed Quantum Computers (DQCs) enable large system sizes by connecting smaller chips via photonic interconnects. DQCs use teleportation to relocate qubits and execute CNOTs between qubits on different chips. However, non-local CNOTs are 4.3-7.7$\times$ slower and 4$\times$ more error-prone than local CNOTs within a chip, which degrades program fidelities. Existing compilers group CNOTs with overlapping qubits into blocks and collectively optimize teleportations for each block. However, block-level scheduling has two key drawbacks. First, it lacks lookahead ability across blocks because it selects the optimal schedule for one block before proceeding to the next. As a result, it cannot assess the impact of a teleportation on future blocks. Our studies show that naively expanding the lookahead window to include subsequent blocks does not address this issue. Second, existing approaches do not schedule future block operations or the teleportations they require until preceding blocks are fully scheduled, introducing delay and latency overheads. We propose ATHENA, a DQC compiler that addresses these limitations using two key insights: Utility-driven Lookahead with Multi-Candidate Block Scheduling (UMS) and EPR-Capacity-Aware Early Scheduling (EES). UMS schedules a block by considering only useful future blocks in its lookahead window. A future block has utility if it shares overlapping qubits with the current block being scheduled. UMS also maintains multiple schedules during compilation, allowing it to defer commitment to globally sub-optimal schedules early in the compilation process. EES enables ATHENA to schedule future operations and their relocations early when EPR resources are available. Our evaluations show that ATHENA reduces teleportations by 34% on average and up to 65%, and reduces latency by 2$\times$ on average and up to 2.9$\times$ compared to the state-of-the-art.

We revisit the Scovel-Weinstein framework (Scovel & Weinstein, CPAM 1994) for reducing the Vlasov-Poisson system while preserving its Hamiltonian structure. Standard particle-in-cell (PIC) algorithms approximate the distribution function by macro-particles with position and velocity. In contrast, Scovel-Weinstein decorated particles involve additional shape degrees of freedom, while maintaining a finite-dimensional reduction with Hamiltonian structure inherited from the continuum model. Although the original work established this structure three decades ago, its computational potential has remained largely unexplored. We present a practical implementation of the Scovel-Weinstein model and compare it with a standard PIC algorithm. Numerical experiments demonstrate that macro-particles in standard PIC can be replaced by far fewer decorated particles while retaining comparable accuracy. This decorated particle approach offers a new structure-preserving paradigm for kinetic plasma simulation.

The test-negative design (TND) is a resource-efficient observational study design that can assess vaccine effectiveness and exposure-proximal immune correlates of disease. The TND enrolls symptomatic individuals seeking diagnostic testing and compares case status by an exposure variable, such as vaccination status or immune marker level, that is measured at testing. While the TND reduces confounding by healthcare-seeking behavior, other sources of confounding may remain. TND studies may also have missing data in the exposure variable due to incomplete records or two-phase sampling designs. We present a targeted maximum likelihood estimation approach involving a semiparametric logistic regression model that targets a causal conditional risk ratio of symptomatic disease in the healthcare-seeking population. Under causal and missing at random assumptions, our method produces an efficient, asymptotically linear estimator that provides flexible, data-driven confounding control and valid causal inference when analyzing TND studies with missing exposure variable data. We evaluate our method's finite sample properties using plasmode simulations of a two-phase TND immune correlates study. We also apply our method to assess COVID-19 vaccine effectiveness and antibody marker correlates of COVID-19 from TND study cohorts derived from the Moderna Coronavirus Efficacy phase 3 trial.

Completeness of the Klein--Gordon oscillator eigenfunctions is proved in one and three spatial dimensions. The proofs establish the closure relations satisfied by the eigenfunctions and are based on standard properties of the Hermite and the generalized Laguerre polynomials, supplemented in three dimensions by the completeness of the spherical harmonics. The scalar nature of the Klein--Gordon field renders the argument strictly simpler than the analogous proof for the Dirac oscillator: no off-diagonal cancellation is required.

Serpentinization of ultramafic rocks is a naturally occurring mineralogical process that can generate molecular hydrogen through the oxidation of ferrous iron during water-rock reaction. Although the resource potential is large, the natural reaction is kinetically limited, and practical hydrogen recovery requires methods that can accelerate conversion without imposing an energy penalty that exceeds the value of the hydrogen produced. This short communication reports a preliminary atmospheric-pressure microwave serpentinization experiment using a water-saturated 2 g crushed olivine sample. Microwave irradiation produced a rapid increase in measured hydrogen concentration compared with conventional hot-plate heating under otherwise similar conditions. The preliminary experiment showed approximately a 12-fold increase in hydrogen concentration and an apparent rate increase from about 2 ppb s$^{-1}$ for conventional heating to about 10 ppb s$^{-1}$ during microwave exposure. These results suggest that electromagnetic stimulation can enhance serpentinization kinetics, likely through rapid volumetric heating, selective coupling to iron-bearing phases, and localized thermal gradients. The result provides an initial experimental basis for evaluating microwave stimulation as a route to accelerated geologic hydrogen production and motivates follow-on measurements using calibrated gas analysis, absorbed-power measurements, dielectric characterization, and elevated-pressure testing.

We investigate how key epidemiological parameters shape both seasonal epidemics and the persistence of dengue transmission. Our findings confirm known mechanistic drivers of epidemic variability and introduce a ranking of parameter importance in our dengue model, which in turn informs the prioritization of public health policies. We propose a stochastic vector-host model with waning immunity, exogenous infection, and vertical transmission. To assess parameter influence, we first qualitatively analyze the macroscopic model. We then perform a multivariate Sobol sensitivity analysis of epidemic summary statistics, and examine the variance of the endemic equilibrium as a function of model parameters. We show that the macroscopic model is well posed, vertical transmission lowers the threshold for persistence, and low spatial coupling increases infectious endemic equilibria. The vector-host population ratio and host recovery rate have the largest first-order and total sensitivity indices, surpassing the contact rates; this implies that control measures during seasonal dengue should prioritize protecting infectious hosts from mosquito bites. Finally, we show that the covariance of hosts and vectors at the endemic equilibrium is asynchronous in the contact-rate plane. This robust pattern has epidemiological, ecological and evolutive interpretations. A dengue strain has two niches to exploit during the endemic regime, and coexisting strain have two niches each. Moreover, large fluctuations in a given strain during the endemic regime provide a mechanistic explanation for high vertical transmission, enabling viral reservoirs that can hatch and trigger outbreaks in the following season. We argue that our model and results can be adapted to address specific public health questions to guide dengue control using field data.

We study a physically realistic, whole-core mathematical model of the dynamics in the Earth's core and we prove existence of Leray-Hopf type weak solutions to the model. Our model combines Magneto-Hydrodynamic equations in the liquid outer core with solid physics for the electrically conducting inner core, and treats everything exterior to the core as a perfect insulator governed by Maxwell's equations. We prove existence of weak solutions using Galerkin approximations. In order to control the nonlinearities, we must define an appropriate function space for the magnetic field and prove a Biot-Savart type result. The main new difficulty here is properly setting up the functional framework to simultaneously deal with the fluid structure interaction with the inner core and the magnetic transmission problem, with both the perfectly conducting inner core and the perfectly insulating mantle/exterior.

Machine learning practice in institutional decision-support contexts -- government, public policy, public health, criminal justice, resource allocation -- rests on a set of largely unexamined epistemological commitments inherited from classical statistics and computer science: that models represent stable regularities, that validation can be context-free, that performance metrics are politically neutral, and that feature importance reveals system structure. This paper challenges these commitments through a unified framework of performative materialist ML, articulated as thirteen theses. Drawing on Pickering's cybernetic ontology, the performativity literature from economic sociology (Callon, MacKenzie), Simon's bounded rationality, the formalization of performative prediction (Perdomo et al., 2020), and fifteen years of applied ML experience in government and public policy, we argue that: (1) ML models are best understood not as truth-seeking representations but as temporally situated compressions that function as instruments of intervention; (2) the full data product is a complex adaptive system that coevolves with its target and navigates a multi-objective space no single algorithm can optimize; (3) validity is fundamentally performative, measured by effects in the world rather than formal properties of the model; (4) the choices embedded in objective functions, fairness criteria, and resource thresholds are political decisions belonging to stakeholders, not technicians. We show how these theses unify several practical prescriptions -- temporal cross-validation, precision and recall at k, pipeline-aware fairness auditing, satisficing over optimizing -- as consequences of a coherent materialist epistemology rather than isolated best practices

Rank-metric codes are subspaces of matrices over finite fields endowed with the rank metric and admit a natural tensorial representation. The tensor rank provides a measure of the minimal size of a decomposition of a code into rank-one tensors. Kruskal showed that the tensor rank of a rank-metric code of dimension $k$ and minimum rank distance $d$ is at least $k + d - 1$, and codes meeting this bound with equality are called minimal tensor rank (MTR) codes. It is known from algebraic complexity theory that the existence of an MTR code implies the existence of a maximum distance separable (MDS) code. In this work, we establish new results relating the tensor rank of a rank-metric code to the parameters of associated linear codes in the Hamming metric and introduce the notion of tensor rank defect. We then develop new constructions of rank-metric codes with small tensor rank defect using algebraic geometry (AG) codes.

We describe a Bayesian multidimensional explanatory IRT model, and an associated Markov Chain Monte Carlo (MCMC) estimation procedure and the corresponding development of calibration software, designed for psychometric analyses of large numbers of sparsely-linked persons and items. Such data structures can arise, for example, from adaptive assessments using large banks of automatically generated items with individual test takers receiving a very small proportion of the entire bank. We discuss how our choices for model specification, data structures, and algorithm implementation combine to create a scalable method for explanatory IRT that can support a variety of psychometric operations with sparse data.