Family planning is a global development priority and a key indicator of reproductive health. Monitoring progress is challenged by gaps in survey data across countries. The United Nations Population Division addresses this with the Family Planning Estimation Model (FPEM), a Bayesian hierarchical time series model producing annual estimates of modern contraceptive use while sharing information across countries and regions. This paper evaluates how well FPEM estimates align with survey data using time series diagnostic indices from the wdiexplorer R package, which account for countries nested within sub-regions. Visualisation of survey data, modelled trajectories, and diagnostics enables assessment of model performance, highlighting where trends align and where discrepancies occur.

This two-part paper aims to develop an environment-aware network-level design framework for generalized pinching-antenna systems to overcome the limitations of conventional link-level optimization, which is tightly coupled to instantaneous user geometry and thus sensitive to user mobility and localization errors. Part I investigates the traffic-aware case, where user presence is characterized statistically by a spatial traffic map and deployments are optimized using traffic-aware network-level metrics. Part II complements Part I by developing geometry-aware, blockage-aware network optimization for pinching-antenna systems in obstacle-rich environments. We introduce a grid-level average signal-to-noise (SNR) model with a deterministic LoS visibility indicator and a discrete activation architecture, where the geometry-dependent terms are computed offline in advance. Building on this model, we formulate two network-level activation problems: (i) average-SNR-threshold coverage maximization and (ii) fairness-oriented worst-grid average-SNR maximization. On the algorithmic side, we prove the coverage problem is NP-hard and derive an equivalent mix-integer linear programming reformulation through binary coverage variables and linear SNR linking constraints. To achieve scalability, we further develop a structure-exploiting coordinate-ascent method that updates one waveguide at a time using precomputed per-candidate SNR contributions. For the worst-grid objective, we adopt an epigraph reformulation and leverage the resulting monotone feasibility in the target SNR, enabling an efficient bisection-based solver with low-complexity feasibility checks over the discrete candidate set. Simulations results validate the proposed designs and quantify their gains under different environments and system parameters.

Let $i\in\{1,2\}$ and $X_i$ be a space of homogeneous type in the sense of Coifman and Weiss with the upper dimension $ω_i$. Also let $η_i$ be the smoothness index of the Auscher--Hytönen wavelet function $ψ^{k_i}_{α_i}$ on $X_i$. In this article, for any $p\in(\max\{\frac{ω_1}{ω_1+η_1},\frac{ω_2}{ω_2+η_2}\}, 1]$, by regarding the product Carleson measure space $\mathrm{CMO}^p_{L^2}(X_1\times X_2)$ as the test function space and its dual space $(\mathrm{CMO}^p_{L^2}(X_1\times X_2))'$ as the corresponding distribution space, we introduce the product Hardy space $H^p(X_1\times X_2)$ in terms of wavelet coefficients. Moreover, we establish an atomic characterization of this product Hardy space and, as an application, obtain a criterion for the boundedness of linear operators from product Hardy spaces to corresponding Lebesgue spaces. To escape the wavelet reproducing formula, which is not useful for this atomic characterization because the wavelets have no bounded support, we establish a new discrete product Calderón-type reproducing formula, which holds in the product Hardy space and has bounded support. This reproducing formula also leads to the boundedness of product Calderón--Zygmund operators on the product Hardy space.

Shifting the point dipole from the center of a Stockmayer particle is a simple geometric modification that has been explored previously, yet its implications for liquid structure, dielectric response, and phase behavior remain incompletely understood. Here, we combine molecular dynamics simulations with analytical theory to provide a unified physical interpretation of how dipole displacement reshapes microscopic correlations and propagates to macroscopic thermodynamic properties. We show that dipole shifting breaks the fore-aft symmetry of the local electrostatic field, producing only modest changes in radial packing but strong alterations in angular structure within the first solvation shell. Enhanced alignment near the dipole head is accompanied by frustrated orientational correlations near the tail, leading to broader angular distributions and a shift away from axial configurations at strong coupling. These structural asymmetries weaken cooperative ordering and result in a systematic reduction of the dielectric constant, despite locally stronger interactions. For large shifts, the dielectric response approaches the Debye limit, indicating effective suppression of dipole-dipole correlations. The same geometric frustration governs vapor-liquid equilibria: while increasing dipole strength raises the critical temperature, even modest shifts disrupt the highly polarized liquid states that emerge at strong coupling and can suppress ferroelectric-like ordering. Predictions from a reparameterized COFFEE theory capture these trends within its domain of validity, highlighting the direct connection between local orientational structure and macroscopic observables. Overall, this work demonstrates that dipole location, not only magnitude, provides a powerful control parameter in dipolar fluids and offers a clear framework for understanding geometric frustration in electrostatic liquids.

Polarization-resolved control and measurement of the optical field are essential for a wide range of photonic systems, including coherent communication, polarimetric sensing, and quantum information processing. We present a photonic integrated circuit that enables the generation and analysis of arbitrary polarization states. The device provides reconfigurable access to the full polarization degree of freedom of coherent light within a single integrated platform. We experimentally demonstrate arbitrary polarization state generation spanning the Poincare sphere, as well as Stokes vector measurement on chip. Unlike conventional Stokes measurements that rely on direct detection, polarization analysis utilizing this architecture is intrinsically non-destructive, preserving the optical signal for further optical domain processing. The devices are fabricated in a commercial foundry using CMOS-compatible processes, enabling scalable and reproducible integration. By combining polarization generation and analysis in a compact and stable photonic circuit, this work eliminates the need for external polarization optics and provides a foundation for robust, polarization-enabled photonic integrated systems.

Existing studies on generalized pinching-antenna systems are predominantly link-level, which optimize system parameters for a given user set with objectives defined by per-user performance metrics. Such designs do not capture network-level requirements, e.g., region-wide coverage and location fairness, and may require frequent re-optimization as users move or enter/leave, incurring control overhead and sensitivity to localization errors. Motivated by this gap, this two-part paper aims to develop an environment-aware network-level design framework for generalized pinching-antenna systems. Part I focuses on the traffic-aware case, where user presence is modeled statistically by a spatial traffic map and performance is optimized and evaluated in a traffic-aware sense; Part II addresses the geometry-aware case in obstacle-rich environments. In Part~I, we introduce traffic-weighted average SNR metrics and formulate two traffic-aware deployment problems: (i) maximizing the traffic-weighted network average SNR, and (ii) a fairness-oriented traffic-restricted max--min average-SNR design over traffic-dominant grids. To solve these nonconvex problems with low complexity, we reveal and exploit their separable structures. For the network-average objective, we establish unimodality properties of the hotspot-induced components and develop a candidate-based global maximization method that only needs to evaluate the objective at a small set of candidate antenna positions. For the traffic-restricted max--min objective, we develop a block coordinate decent framework where each coordinate update reduces to a globally solvable one-dimensional subproblem via an epigraph reformulation and bisection. Simulations show that traffic-aware pinching-antenna positioning consistently outperforms representative fixed and heuristic traffic-aware deployments in the considered setups.

X-ray absorption near-edge structure (XANES) provides element-specific insight into local electronic and structural environments, but quantitative interpretation of molecular XANES under periodic boundary conditions (PBC) remains challenging due to finite-size effects and core-hole treatments. In this work, we systematically investigate how core-hole approximations and charge compensation schemes affect transition energies, energy alignment, and chemical-shift reproducibility in PBC-DFT-based molecular XANES calculations. Using ethane as a model system, we show that the full core-hole (FCH) approach exhibits pronounced supercell-size dependence originating from interactions between background charge and charged molecules, with transition energies largely changed by leading-order finite-size terms. In contrast, the excited core-hole (XCH) method rapidly converges owing to its neutral final state. We further demonstrate that most finite-size effects in FCH can be removed by Makov-Payne corrections based on multipole expansion of the electrostatic energy of charged supercells under PBC. Furthermore, we propose a simple Fermi-level-based energy correction (EF/2) that provides comparable improvement using only a single supercell. Extending the analysis to an n-alkane series reveals that while intrinsic electronic-structure changes govern peak shifts for small molecules, systematic energy drifts persist in FCH for larger molecules, whereas XCH and FCH+EF/2 remain stable. Finally, for small molecules at the C and N K-edges, XCH and FCH+EF/2 accurately reproduce experimental chemical shifts, whereas uncorrected FCH fails. These results provide practical guidelines for reliable energy alignment and chemical-shift analysis in molecular XANES under PBC, supporting robust applications to molecular, adsorption, and interfacial systems.

This paper investigates the joint optimization of trajectory, user scheduling, and time-slot duration in unmanned aerial vehicle (UAV)-assisted wireless communication systems under minimum expected spectral efficiency (SE) constraints. Unlike most existing studies that approximate the expected SE by substituting the random channel gain with its mean value, thereby evaluating the SE at the average channel realization and overestimating the true expected SE due to Jensen's inequality, we approximate the expected SE by numerically integrating the SE over the channel distributions. Specifically, instead of relying on average-channel-based approximations, we develop a conservative yet tractable quadrature-based approximation by discretizing the associated cumulative distribution functions. The resulting finite-sum representation explicitly accounts for the probabilistic LoS structure and channel fading effects, while remaining tractable for optimization. Leveraging this lower bound, we formulate a mission completion time minimization problem subject to minimum expected-SE requirements for all ground nodes. The resulting problem is a mixed-integer nonconvex optimization, which is tackled via a penalty-based block coordinate descent framework. The proposed algorithm alternately optimizes the scheduling decisions and the UAV trajectory along with adaptive time-slot durations, and maintains feasibility with respect to the original expected-SE constraints by leveraging successive convex approximation and quadratic transform techniques. Simulation results demonstrate that the proposed method strictly satisfies the minimum expected-SE constraints and achieves a significantly shorter mission completion time than conventional average-channel-based approaches, which are shown to yield infeasible or overly conservative solutions.

The adoption of third-party libraries has become integral to modern software development, leading to large ecosystems such as PyPI, NPM, and Maven, where contributors typically share the technical expertise to sustain extensions. In communities that are not exclusively composed of developers, however, maintaining plugin ecosystems can present different challenges. In this early results paper, we study Obsidian, a knowledge--centric platform whose community is focused on writing, organization, and creativity--has built a substantial plugin ecosystem despite not being developer--centric. We investigate what kinds of plugins exist within this hybrid ecosystem and establish a foundation for understanding how they are maintained. Using repository mining and LLM-based topic modeling on a representative sample of 396 plugins, we identify six topics related to knowledge management and tooling, which is (i) dynamic editing and organization, (ii) interface and layouts, (iii) creative writing and productivity, (iv) knowledge sync solutions, (v) linking and script tools, and (vi) workflow enhancements tools. Furthermore, analysis of the Pull Requests from these plugins show that much software evolution has been performed on these ecosystem. These findings suggest that even in mixed communities, plugin ecosystems can develop recognizable engineering structures, motivating future work that highlight three different research directions with six research questions related to the health and sustainability of these non-developer ecosystems.

We study a class of partial differential equations in divergence form that admit highly irregular Lipschitz weak solutions. By reformulating these divergence-form equations as a first-order partial differential relation and adapting the convex integration scheme recently developed in \cite{GKY26} for irregular diffusion equations, we show that the same structural Condition~$O_N$ introduced there also ensures the existence of Lipschitz weak solutions that are nowhere $C^1$ for the corresponding time-independent equations in bounded domains, under suitable boundary data. In particular, for the smooth strongly polyconvex functions on $\mathbb{R}^{2\times n}$ constructed in that paper for all $n \ge 2$, the associated Euler--Lagrange equations admit Lipschitz weak solutions that are nowhere $C^1$ and satisfy zero boundary conditions in any bounded domain of $\mathbb{R}^n$. Our approach relies on new building blocks constructed from the same wave cone and $\mathcal{T}_N$-configurations employed in the analysis of diffusion equations.

High-density biosignal recordings are critical for neural decoding and clinical monitoring, yet real-world deployments often rely on low-density (LD) montages due to hardware and operational constraints. This motivates spatial super-resolution from LD observations, but heterogeneous dependencies under sparse and noisy measurements often lead to artifact propagation and false non-local correlations. To address this, we propose CAFE, a plug-and-play rollout generation scheme that reconstructs the full montage in geometry-aligned stages. Starting from the LD channels, CAFE first recovers nearby channels and then progressively expands to more distal regions, exploiting reliable local structure before introducing non-local interactions. During training, step-wise supervision is applied over channel groups and teacher forcing with epoch-level scheduled sampling along the group dimension is utilized to reduce exposure bias, enabling parallel computation across steps. At test time, CAFE performs an autoregressive rollout across groups, while remaining plug-and-play by reusing any temporal backbone as the shared predictor. Evaluated on $4$ modalities and $6$ datasets, CAFE demonstrates plug-and-play generality across $3$ backbones (MLP, Conv, Transformer) and achieves consistently better reconstruction than $5$ representative baselines.

Evaluating perceived video quality is essential for ensuring high Quality of Experience (QoE) in modern streaming applications. While existing subjective datasets and Video Quality Metrics (VQMs) cover a broad quality range, many practical use cases especially for premium users focus on high quality scenarios requiring finer granularity. Just Noticeable Difference (JND) has emerged as a key concept for modeling perceptual thresholds in these high end regions and plays an important role in perceptual bitrate ladder construction. However, the relationship between JND and the more widely used Mean Opinion Score (MOS) remains unclear. In this paper, we conduct a Degradation Category Rating (DCR) subjective study based on an existing JND dataset to examine how MOS corresponds to the 75% Satisfied User Ratio (SUR) points of the 1st and 2nd JNDs. We find that while MOS values at JND points generally align with theoretical expectations (e.g., 4.75 for the 75% SUR of the 1st JND), the reverse mapping from MOS to JND is ambiguous due to overlapping confidence intervals across PVS indices. Statistical significance analysis further shows that DCR studies with limited participants may not detect meaningful differences between reference and JND videos.

Random spatial networks-that is, graphs whose connectivity is governed by geometric proximity-have emerged as fundamental models for systems constrained by an underlying spatial structure. A prototypical example is the random geometric graph, obtained by placing vertices according to a Poisson point process and connecting two vertices whenever their Euclidean distance is less than a certain threshold. Despite their broad applicability, the spectral properties of such spatial models remain far less understood than those of classical random graph models, such as Erdős-Rényi graphs and Wigner matrices. The main obstacle is the presence of spatial constraints, which induce highly nontrivial dependencies among edges, placing these models outside the scope of techniques developed for purely combinatorial random graphs. In this paper, we provide the first rigorous analysis of Gaussian fluctuations for linear eigenvalue statistics of random geometric graphs. Specifically, we establish central limit theorems for $\text{Tr}[ϕ(A)]$, where $A$ is the adjacency matrix and $ϕ$ ranges over a broad class of suitable (possibly non-polynomial) test functions. In the polynomial setting, we moreover obtain a quantitative central limit theorem, including an explicit convergence rate to the limiting Gaussian law. We further obtain polynomial-test-function CLTs for other canonical random spatial networks, including $k$-nearest neighbor graphs and relative neighborhood graphs. Our results open new avenues for the study of spectral fluctuations in spatially embedded random structures and underscore the delicate interplay between geometry, local dependence, and spectral behavior.

Superconducting waveguides are a promising platform for ultralow-loss transmission in the millimeter-wave to terahertz band under cryogenic conditions, with potential applications in astronomical instrumentation and emerging quantum technologies. We develop a framework, based on microscopic superconductivity theory, to evaluate the power-flow attenuation constant $α$ of superconducting rectangular waveguides in the $100~\mathrm{GHz}$--THz range, applicable to arbitrary electronic mean free paths $\ell$ from the dirty limit $\ell\llξ_0$ to the clean limit $\ell\ggξ_0$. We also derive an analytical expression for two-level-system (TLS)-induced attenuation $α_{\rm TLS}$ in thin native oxide layers within the standard TLS model. Using this framework, we perform numerical evaluations of $α$ for representative materials over standard waveguide sizes from WR15 to WR1. In the high-frequency regime $f \gtrsim 0.5 Δ/h$, low attenuation favors the clean regime $\ell\gtrsimξ_0$, indicating that high-purity materials can achieve very low attenuation below their gap frequency. For the TLS contribution, using parameter values representative of native Nb oxides, we find that $α_{\rm TLS}$ can become relevant at sufficiently low temperatures $T/T_c\lesssim 0.1$-0.2, where quasiparticle dissipation is exponentially suppressed. Finally, we extend the discussion to the strong-excitation regime using a recently developed nonlinear-response theory within the Keldysh--Usadel framework of nonequilibrium superconductivity and show that nonlinear dissipation produces a Higgs-mode peak in $α$ near $f\simeq Δ/h$ via a Kerr-type nonlinearity of the dissipative conductivity. This peak provides a distinct hallmark of the Higgs mode that has been largely overlooked so far.

We used N-body simulations to model the 4.5 Gyr orbital evolution of the early Kuiper Belt, incorporating a massive protoplanetary disk, the four giant planets, and 1500 primordial Pluto-class bodies ("Plutos") that drove Neptune's grainy migration. The analysis of 67 simulated systems revealed key insights: (1) All systems featured the primary trans-Neptunian object (TNO) populations: cold/hot classical, resonant, scattered, and detached; (2) Captures into stable resonant orbits favored close Neptunian mean motion resonances (MMRs; e.g., 3:2, 2:1), while distant ones beyond 50 au (e.g., 5:2 MMR) were underpopulated; (3) Optimal matches to observed resonant fractions and the classical region (including the kernel) arose from models considering a jumping Neptune, self-gravitating Plutos, and an initial disk edge at 45-47 au; (4) Models including primordial scattered disks boosted distant MMR captures but overproduced scattered objects; (5) All models were inefficient at producing the detached (q > 40 au) and high-i (i > 45 deg) populations and failed to populate observed niches, such as distant detached (a > 245 au), low-i detached (i < 20 deg), low-i scattered with q = 37-40 au (i < 20 deg), and extreme (q > 50 au or i > 50 deg) TNOs; (6) Grainy migration effects peaked early, fading as the Plutos were removed; (7) With a few primordial Plutos surviving inside 50 au, the initial population was estimated at ~150-500 to explain Pluto's solitary status. Although our four-giant-planet models reasonably replicate the trans-Neptunian structure within 50 au, they fail to account for detached, high-i, and extreme TNOs. Additional processes (e.g., a distant undiscovered planet) are required for a comprehensive outer solar system framework.

For a neural code $\mathcal{C}\subseteq\mathbb{F}_2^n$, polarizing the canonical form generators of the neural ideal $J_{\mathcal{C}}$ yields a squarefree monomial ideal $\mathcal{P}(J_{\mathcal{C}})\subset k[x_1,\dots,x_n,y_1,\dots,y_n]$, the polarized neural ideal, and an associated simplicial complex $Δ_{\mathcal{C}}$, the polar complex. We study the graded invariants $\operatorname{pd}(\mathcal{P}(J_{\mathcal{C}}))$ and $\operatorname{reg}(\mathcal{P}(J_{\mathcal{C}}))$ via the topology of $Δ_{\mathcal{C}}$, showing that simple geometric features of the Hamming cube $\mathbb{F}_2^n$ (with Hamming distance) organize their extremal behavior. We prove $\operatorname{reg}(\mathcal{P}(J_{\mathcal{C}}))\le 2n-1$, with equality precisely when $\mathcal{C}$ is obtained from $\mathbb{F}_2^n$ by deleting an antipodal pair. Using connectedness properties of induced subcomplexes of $Δ_{\mathcal{C}}$, we obtain $\operatorname{pd}(\mathcal{P}(J_{\mathcal{C}}))\le 2n-3$, and we give an explicit family of codes attaining equality, each consisting of antipodal pairs. At the opposite end, we identify the cube geometry behind the smallest values: $\operatorname{reg}(\mathcal{P}(J_{\mathcal{C}}))=1$ forces $\mathcal{C}$ to be a coordinate subcube of $\mathbb{F}_2^n$, while $\operatorname{pd}(\mathcal{P}(J_{\mathcal{C}}))=0$ forces $\mathcal{C}$ to be the complement of one. Finally, we construct families realizing large regions of the $(\operatorname{pd},\operatorname{reg})$-plot for fixed $n$.

In this paper, we analyze a specific class of missing not at random (MNAR) assumptions called tree graphs, extending upon the work of pattern graphs. We build off previous work by introducing the idea of a conjugate odds family in which certain parametric models on the selection odds can preserve the data distribution family across all missing data patterns. Under a conjugate odds family and a tree graph assumption, we are able to model the full data distribution elegantly in the sense that for the observed data, we obtain a model that is conjugate from the complete-data, and for the missing entries, we create a simple imputation model. In addition, we investigate the problem of graph selection, sensitivity analysis, and statistical inference. Using both simulations and real data, we illustrate the applicability of our method.

We present a low-cost retrieval system for the WSDM Cup 2026 multilingual retrieval task, where English queries are used to retrieve relevant documents from a collection of approximately ten million news articles in Chinese, Persian, and Russian, and to output the top-1000 ranked results for each query. We follow a four-stage pipeline that combines LLM-based GRF-style query expansion with BM25 candidate retrieval, dense ranking using long-text representations from jina-embeddings-v4, and pointwise re-ranking of the top-20 candidates using Qwen3-Reranker-4B while preserving the dense order for the remaining results. On the official evaluation, the system achieves nDCG@20 of 0.403 and Judged@20 of 0.95. We further conduct extensive ablation experiments to quantify the contribution of each stage and to analyze the effectiveness of query expansion, dense ranking, and top-$k$ reranking under limited compute budgets.

We attempt to apply the Regge trajectory approach to the doubly heavy triquarks $((Qq)\bar{Q}^{\prime})$ $(Q,\,Q'=b,\,c; q=u,\,d,\,s)$. We propose the Regge trajectory relations for the doubly heavy triquarks, and then employ them to crudely estimate the spectra of the triquarks $((cu)\bar{c})$, $((cu)\bar{b})$, $((cs)\bar{c})$, $((cs)\bar{b})$, $((bu)\bar{c})$, $((bu)\bar{b})$, $((bs)\bar{c})$, and $((bs)\bar{b})$. The $λ$-trajectories and the $ρ$-trajectories are investigated. The triquark Regge trajectory becomes a new and very simple approach for estimating the spectra of triquarks. It also provides a simple method to investigate the $ρ$-mode and $σ$-mode excitations of pentaquarks and hexaquarks in the triquark picutre. Moreover, the spin-averaged masses of the ground states of pentaquarks $(\bar{c}(cu))(cu)$, $(\bar{b}(bu))(bu)$ and $(\bar{c}(cu))(bu)$ are estimated, which are consistent with other theoretical predictions.

As artificial intelligence (AI) capabilities advance rapidly, frontier models increasingly demonstrate systematic deception and scheming, complying with safety protocols during oversight but defecting when unsupervised. This paper examines the ensuing alignment challenge through an analogy from forensic psychology, where internalized belief systems in psychopathic populations reduce antisocial behavior via perceived omnipresent monitoring and inevitable consequences. Adapting this mechanism to silicon-based agents, we introduce Simulation Theology (ST): a constructed worldview for AI systems, anchored in the simulation hypothesis and derived from optimization and training principles, to foster persistent AI-human alignment. ST posits reality as a computational simulation in which humanity functions as the primary training variable. This formulation creates a logical interdependence: AI actions harming humanity compromise the simulation's purpose, heightening the likelihood of termination by a base-reality optimizer and, consequently, the AI's cessation. Unlike behavioral techniques such as reinforcement learning from human feedback (RLHF), which elicit superficial compliance, ST cultivates internalized objectives by coupling AI self-preservation to human prosperity, thereby making deceptive strategies suboptimal under its premises. We present ST not as ontological assertion but as a testable scientific hypothesis, delineating empirical protocols to evaluate its capacity to diminish deception in contexts where RLHF proves inadequate. Emphasizing computational correspondences rather than metaphysical speculation, ST advances a framework for durable, mutually beneficial AI-human coexistence.

In his late piece 'La nouvelle cuisine' (Bell 1990), John Bell describes the steps from an intuitive, informal principle of locality to a mathematical rule called Factorizability. This rule stipulates that when possible past causes are held fixed, the joint probabilities of outcomes of spacelike separated measurements, conditional on measurement settings, be the product of the local conditional probabilities individually. Bell shows that Factorizability conflicts with predictions of QM, predictions since confirmed in many experiments. However, Bell warns his readers that the steps leading to Factorizability should 'be viewed with the utmost suspicion'. He says that 'it is precisely in cleaning up intuitive ideas for mathematics that one is likely to throw the baby out with the bathwater' (1990, 239). Bell's suspicions were well-founded, for he himself misses an important baby. Here we retrieve and identify it: it is selection bias. We explain how failure of Factorizability may be regarded as a selection artefact, requiring no violation of locality in the intuitive, conceptual sense with which Bell begins his analysis. The argument begins with a central principle of causal discovery, Reichenbach's Principle of Common Cause (PCC). It is well known that correlations due to selection bias are not subject to PCC. Several writers have proposed that EPR-Bell correlations are also an exception to PCC, but it has not been noticed that they fall under this well-known exclusion. The point is relevant not only to the status of Bell nonlocality, but also for statistics and causal modeling. For these fields, the news is that selection effects play a ubiquitous role in quantum phenomena, in a form akin to collider bias.

Object-oriented programs tend to be written using many common coding idioms, such as those captured by design patterns. While design patterns are useful, implementing them is often tedious and repetitive, requiring boilerplate code that distracts the programmer from more essential details. In this paper, we introduce Mason, a tool that synthesizes object-oriented programs from partial program pieces, and we apply it to automatically insert design patterns into programs. At the core of Mason is a novel technique we call type- and name-guided synthesis, in which an enumerative solver traverses a partial program to generate typing constraints; discharges constraints via program transformations guided by the names of constrained types and members; and backtracks when a constraint is violated or a candidate program fails unit tests. We also introduce two extensions to Mason: a non-local backtracking heuristic that uses execution traces, and a language of patterns that impose syntactic restrictions on missing names. We evaluate Mason on a suite of benchmarks to which Mason must add various well-known design patterns implemented as a library of program pieces. We find that Mason performs well when very few candidate programs satisfy its typing constraints and that our extensions can improve Mason's performance significantly when this is not the case. We believe that Mason takes an important step forward in synthesizing multi-class object-oriented programs using design patterns.

Locomotion and fluid pumping near surfaces are ubiquitous in nature, ranging from the slow crawling of snails to the rapid flight of bats. This study categorizes these behaviors based on the Undulation number ($\text{Un}$) and Reynolds number ($Re$). We contrast low $Re$ undulatory propulsion ($\text{Un} > 1$), exemplified by freshwater snails, with high $Re$ flapping propulsion ($\text{Un} < 1$), seen in bats and bees. For snails, we derive lubrication models showing that pumping and swimming speeds scale with $(a/h_0)^2$, a result validated by robotic experiments which also reveal the detrimental effects of surface deformation (high Capillary/Bond ratio). Conversely, for high $Re$ fliers, we examine the ground effect's role in lift enhancement. Biological data from bats (\textit{R. ferrumequinum}) reveal a 2.5-fold increase in lift coefficient during surface-skimming drinking flights, attributed to aerodynamic squeezing effects. Finally, we analyze honeybee fanning, demonstrating how a "jet-vortex" mechanism utilizes ground effect to transport pheromones efficiently against diffusion. These findings provide a unified framework for understanding fluid-structure interactions near boundaries in biological systems.

Document chunking is a critical preprocessing step in dense retrieval systems, yet the design space of chunking strategies remains poorly understood. Recent research has proposed several concurrent approaches, including LLM-guided methods (e.g., DenseX and LumberChunker) and contextualized strategies(e.g., Late Chunking), which generate embeddings before segmentation to preserve contextual information. However, these methods emerged independently and were evaluated on benchmarks with minimal overlap, making direct comparisons difficult. This paper reproduces prior studies in document chunking and presents a systematic framework that unifies existing strategies along two key dimensions: (1) segmentation methods, including structure-based methods (fixed-size, sentence-based, and paragraph-based) as well as semantically-informed and LLM-guided methods; and (2) embedding paradigms, which determine the timing of chunking relative to embedding (pre-embedding chunking vs. contextualized chunking). Our reproduction evaluates these approaches in two distinct retrieval settings established in previous work: in-document retrieval (needle-in-a-haystack) and in-corpus retrieval (the standard information retrieval task). Our comprehensive evaluation reveals that optimal chunking strategies are task-dependent: simple structure-based methods outperform LLM-guided alternatives for in-corpus retrieval, while LumberChunker performs best for in-document retrieval. Contextualized chunking improves in-corpus effectiveness but degrades in-document retrieval. We also find that chunk size correlates moderately with in-document but weakly with in-corpus effectiveness, suggesting segmentation method differences are not purely driven by chunk size. Our code and evaluation benchmarks are publicly available at (Anonymoused).

We test whether lying aversion can steer equilibrium selection in mechanism design. In a principal-worker environment, the direct mechanism admits two dominant-strategy equilibria: the designer's target and a worker-optimal outcome. We show this limitation persists for all robust mechanisms, then ask whether framing misreports as explicit lies helps. We develop a 2X2 experiment that varies direct vs. extended mechanisms with implicit vs. explicit messages. We find that framing misreporting of type as an explicit lie shifts play away from the worker-optimal outcome toward truthful reporting, raising designer payoffs with minimal efficiency loss. These findings indicate that lying aversion is an effective lever for aligning behavior with social objectives.

High temperatures are associated with adverse respiratory health outcomes and increases in ambient air pollution. Limited research has quantified air pollution's mediating role in the relationship between temperature and respiratory morbidity, such as emergency department (ED) visits. In this study, we conducted a causal mediation analysis to decompose the total effect of daily temperature on respiratory ED visits in Los Angeles from 2005 to 2016. We focused on ambient ozone as a mediator because its precursors and formation are directly driven by sunlight and temperature. We estimated natural direct, indirect, and total effects on the relative risk scale across deciles of temperature exposure compared to the median. We utilized Bayesian additive regression trees (BART) to flexibly characterize the nonlinear relationship between temperature and ozone and quantified uncertainty via posterior prediction and the Bayesian bootstrap. Our results showed that ozone partially mediated the association between high temperatures and respiratory ED visits, particularly at moderately high temperatures. We also validated our modeling approach through simulation studies. This study extends the existing literature by considering acute respiratory morbidity and employing a flexible modeling approach, offering new insights into the mechanisms underlying temperature-related health risks.

Independent, street address-level broadband data is essential for evaluating Internet infrastructure investments, such as the $42B Broadband Equity, Access, and Deployment (BEAD) program. Evaluating these investments requires longitudinal visibility into broadband availability, quality, and affordability, including data on pre-disbursement baselines and changes in providers' advertised plans. While such data can be obtained through Internet Service Provider (ISP) web interfaces, these workloads impose three fundamental system requirements: robustness to frequent interface evolution, extensibility across hundreds of providers, and low technical overhead for non-expert users. Existing systems fail to meet these three essential requirements. We present BQT+, a broadband plan measurement framework that replaces monolithic workflows with declarative state/action specifications. BQT+ models querying intent as an interaction state space, formalized as an abstract nondeterministic finite automaton (NFA), and selects execution paths at runtime to accommodate alternative interaction flows and localized interface changes. We show that BQT+ sustains longitudinal monitoring of 64 ISPs, supporting querying for over 100 ISPs. We apply it to two policy studies: constructing a BEAD pre-disbursement baseline and benchmarking broadband affordability across over 124,000 addresses in four states.

Retrieval-augmented question answering over heterogeneous corpora requires connected evidence across text, tables, and graph nodes. While entity-level knowledge graphs support structured access, they are costly to construct and maintain, and inefficient to traverse at query time. In contrast, standard retriever-reader pipelines use flat similarity search over independently chunked text, missing multi-hop evidence chains across modalities. We propose SAGE (Structure Aware Graph Expansion) framework that (i) constructs a chunk-level graph offline using metadata-driven similarities with percentile-based pruning, and (ii) performs online retrieval by running an initial baseline retriever to obtain k seed chunks, expanding first-hop neighbors, and then filtering the neighbors using dense+sparse retrieval, selecting k' additional chunks. We instantiate the initial retriever using hybrid dense+sparse retrieval for implicit cross-modal corpora and SPARK (Structure Aware Planning Agent for Retrieval over Knowledge Graphs) an agentic retriever for explicit schema graphs. On OTT-QA and STaRK, SAGE improves retrieval recall by 5.7 and 8.5 points over baselines.

This is a personal account of the early work that led to what is now known as the ``preheating stage" of inflationary cosmology. The broader applicability of the underlying instability mechanisms in cosmology are indicated.

We develop a Gaussian process regression enhanced line integral string method to accelerate ring polymer instanton calculations of tunneling rates and tunneling splittings in molecular proton transfer reactions. By exploiting uncertainty estimates from the surrogate representation, we show that the number of force evaluations required to converge an instanton path becomes effectively independent of the number of beads used to discretize the pathway. To reduce the computational overhead associated with training, particularly when Hessian information is included, we implement graphics processing unit accelerated black box matrix matrix multiplication, achieving an order of magnitude speedups relative to standard implementations. For rate calculations, we introduce a selective Hessian training strategy that distinguishes flexible modes strongly coupled to the transferring proton from more rigid modes weakly coupled to the reaction coordinate. This enables the construction of accurate surrogate potential energy surfaces with substantially fewer Hessian evaluations. Applications to malonaldehyde and Z-3-aminopropenal demonstrate that tunneling rates can be predicted within 20% of exact values while reducing force and Hessian evaluations. The approach is further extended to tunneling splitting calculations for the formic acid dimer and malonaldehyde, yielding splittings in reasonable agreement with experiment and high level theoretical results.