We develop an exact-curved Lagrange finite element framework for the Poisson problem on two-dimensional curved domains. The element map is factorised as $ F_K=Ψ_K\circΦ_{T_K}$, where $Φ_{T_K}$ maps the reference triangle to an affine core and $Ψ_K$ maps the affine core to the physical curved element. This factorisation separates affine scaling from curvature effects and allows the interpolation analysis to be carried out first on the affine core and then transferred to the exact curved element. For conforming linear Lagrange elements, we prove local $L^2$- and $H^1$-interpolation estimates on exact curved triangles. The estimates are expressed in terms of transported directional derivatives on the physical element, and the constants are independent of the anisotropic shape of the affine core under the stated semi-regularity assumptions. These interpolation estimates are then applied to derive energy-norm and $L^2$-error estimates for the Poisson problem. Numerical results on the unit disk illustrate the difference between straight-sided and curved geometric representations: the curved geometry reduces the geometric error substantially, while the leading finite element error remains governed by the $\mathbb{P}^1$ approximation.

In this paper, we study maximal Cohen-Macaulay sheaves on closures of minimal nilpotent orbits in simple Lie algebras. For singularities of type $A_n$, we first classify vector bundles on their symplectic resolutions whose pushforwards are maximal Cohen-Macaulay. We then construct equivariant maximal Cohen-Macaulay sheaves via irreducible representations of the stabilizer group. We compare these two approaches in the case of maximal Cohen-Macaulay Weil divisors, and extend the equivariant construction to the classical types $B_n$, $C_n$, and $D_n$. Finally, we formulate the construction for an arbitrary simple Lie algebra and carry it out explicitly in the exceptional cases.

Umbral dots (UDs) are small-scale convective intrusions in the umbral core of sunspots and pores. Different methods have been used in the past to determine the physical properties of UDs. One of the methods typically used is multi-level tracking (MLT), which tags spatial structures at equi-spaced intensity levels from the highest level while progressing downward. A modified approach to the regular MLT is explored in this article that also uses the local intensity maxima with a change in the threshold condition to enclose a UD, such that diffuse UDs do not appear extended than they visually appear. The physical properties of UDs from these two MLT approaches are compared. The methods are implemented on high-resolution blue continuum images of four sunspots from the 50-cm Solar Optical Telescope on board Hinode. In addition, we introduce a density-based, spatial clustering routine for the first time to ascertain the differences resulting from the two tracking methods. The modified MLT approach yields an effective diameter with median values ranging from 250-310 km which is on average 70-90 km smaller than the regular MLT approach. The lower effective diameter in the modified method is associated with a reduced UD fill fraction of 12%-13% while the regular method yields 17-19%. However, these differences are still within the range of values cited by earlier works. On the other hand, the histogram of the mean intensity of UDs from both methods is nearly identical. The spatial clustering of UDs from both methods also shows very similar results. There is, however, a preferential spatial concentration of UDs, particularly at locations where the umbral core is highly irregular and in the vicinity of faint light bridges. The dependency of the localized clustering of UDs on the evolutionary phase of the sunspot and its magnetic complexity needs to be further explored.

Agentic systems have been widely studied to automate software engineering jobs such as bug fixing. As these systems increasingly tackle complex tasks, understanding where and why they fail becomes essential for iterative refinement and operational reliability. Existing automated failure diagnosis approaches leverage task execution trajectories, yet their effectiveness degrades substantially as trajectory length and complexity increase. For repository-level coding tasks specifically, trajectories are laden with noise, such as redundant program structure and verbose code context. Moreover, these trajectories are very long, while long-context reasoning remains a known weakness of LLMs. To address these two challenges, we propose TrajAudit, the first failure diagnosis framework for repository-level coding trajectories. TrajAudit employs an investigator agent supported by two modules: one filters failure-irrelevant information through pattern matching and keyword detection, and the other generates a preliminary diagnosis from test failure reports as prior knowledge, helping the agent handle noisy long contexts. The investigator agent can further invoke tools to retrieve filtered content on demand, ensuring that critical information is preserved while noise is minimized. We also introduce RootSE, a benchmark of 93 real-world agentic failure instances sourced from software maintenance tasks, representing the most complex trajectory diagnosis benchmark to date. Experiments on RootSE show that TrajAudit outperforms all existing baselines by over 24.4 percentage points in localization accuracy, while reducing token consumption by at least 18%, demonstrating its practical effectiveness. We hope this work draws community attention to failure management in agentic software engineering and provides a foundational resource for future research.

Speculative decoding has emerged as a promising lossless approach for accelerating Large Language Models (LLMs). As reasoning LLMs increasingly suffer from decode-stage overhead and approximation-based methods degrade accuracy, lossless speculative decoding has become essential for efficient inference. However, existing methods still struggle to deliver strong low-batch performance without additional training, limiting practical deployment on consumer devices. To address this challenge, we propose Cassandra, an algorithm-hardware co-designed self-speculative decoding framework optimized for low-batch scenarios. Cassandra constructs a high-performance, training-free draft model through fine-grained data selection. Using optimized pruning and mantissa truncation, it identifies the most salient values in both model weights and the Key-Value (KV) cache, enabling rapid candidate token generation before full-precision parallel verification. Unlike prior self-speculative decoding methods based on layer skipping or structured KV compression, Cassandra achieves significantly higher efficiency. To further reduce the overhead of format conversion between Cassandra representations and standard floating-point formats, we also introduce a lightweight encoder-decoder hardware module designed for seamless integration with commercial GPUs and NPUs. Experimental results show that Cassandra achieves up to 2.41x speedup over the BF16 baseline without additional training. Furthermore, on Llama 3 8B running on an NVIDIA GeForce RTX 4090, Cassandra generates 1.81x more tokens under the same memory budget compared to Eagle-3, a state-of-the-art speculative decoding method.

Antimicrobial photodynamic therapy (aPDT) is a promising modality for inactivation of antibiotic resistant bacteria, relying on the activation of a photosensitizer (PS) by light of a specific wavelength. This process results in the formation of reactive oxygen species, which ultimately induce cell death. However, aPDT in its conventional form, is limited by the shallow penetration of visible light, restricting its effectiveness for treatment of soft tissue and orthopaedic tissues. To overcome this limitation, near-infrared (NIR) absorbing PS can be used. However, poor stability in vivo after injection, ineffective microbial targeting due to hydrophilic nature and off-site tissue damage are the issues with use of NIR absorbing bare PSs. This issue can be mitigated by combining NIR light with a upconverting nanoparticles (UCNPs), which mediate in conversion of NIR into visible light for effective PS activation. In this study, LaF$_3$:Er$^{3+}$,Yb$^{3+}$ nanoparticles were synthesized using a hydrothermal method and coated with Rose Bengal (RB), a promising hydrophilic PS for aPDT, to evaluate the potential for NIR-triggered aPDT. Characterization of the synthesized UCNPs confirmed the crystalline structure, size distribution and successful RB functionalization. Photophysical studies demonstrated efficient energy transfer between UCNPs and RB, leading to singlet oxygen ($^1$O$_2$) generation in vitro. Antibacterial studies against Methicilin resistant Staphylococcus aureus (MRSA), a superbug of implicated soft tissue and orthopaedic infections, revealed significant photo-bactericidal efficacy upon NIR irradiation, indicating the potential of RB-coated UCNPs for aPDT applications.

We use the connective formal group law to define a one-parameter ($β$-)deformation of the motivic Segre classes of Schubert cells in the $d$-step flag variety. This $β$-deformation specializes to the motivic Segre classes of Schubert cells when $β=1$ and to the Segre-Schwartz-MacPherson classes of Schubert cells when $β=0$. We define rational function representatives for the $β$-deformed classes in the $d=1$ case in terms of a solvable lattice model, and we prove a combinatorial formula for the structure constants in the $β$-deformed basis in the $d=1$ case using Knutson-Tao puzzles. The proof of the puzzle formula involves intertwiners for representations of the multi-parameter quantum group of type $\widehat{a}_2$. We show that our $β$-deformations can be viewed as quotients of canonical elements in a quotient of the equivariant algebraic cobordism ring of the cotangent bundle of the flag variety by proving that the canonical elements satisfy a GKM type condition.

As transistor scaling approaches its fundamental physical limits in the Angstrom era, two-dimensional (2D) semiconductors have emerged as the promising channel material candidates for future computing. While the device physics of 2D semiconductors have been rigorously explored, translating these nanodevices into fully functional integrated circuits remains a largely uncharted frontier. This review bridges the gap between material- and device-centric breakthroughs and circuit-level chip design in 2D semiconductors, a valley of death that has so far prevented translation of high-performance individual transistors into functional chips. We track the evolution of 2D semi-conductor field-effect transistors from basic Boolean logic families and standard cells to complex chip architectures, including recent milestones in RISC-V and monolithic CMOS microprocessors. Critically, we highlight the indispensable role of multiscale compact modeling, spanning semiclassical, quantum-hybrid and data-driven approaches, as the necessary link between device physics and the electronic design automation workflows for scalable chip development. By summarizing recent breakthroughs and identifying the bottlenecks in both fab and fabless trajectories of 2D semiconductors, this review shall provide insights that motivates the translation of proof-of-concept 2D transistors into fully functional computing chips, paving a way towards future Angstrom era computing technology empowered by 2D semiconductors.

FRB 190520B is a repeating fast radio burst source whose large dispersion measure (DM) and temporal broadening suggest a dense and evolving local environment. In this work, we test the possibility that FRB 190520B originates from the core-collapse of a massive star so that its central engine is embedded in a supernova remnant (SNR) expanding into a wind environment, whose evolution is described by the self-similar solution. We use the observed DM and scattering timescale of FRB 190520B to constrain the physical parameters of its surrounding SNR and host-galaxy DM. Twenty typical cases are considered, arising from four ejecta profiles and five scattering prescriptions. It is found that only 6 cases are retained and provide acceptable fits. All retained cases have a shallow ejecta profile and a young source age of $t_0=79.8$--$169.8~{\rm yr}$. The ejecta mass is inferred to be large for all six cases, while the kinetic energy and mass-loss rate span a wide range. The secular DM evolution is reproduced better than the detailed scattering evolution. The up-drift behavior of the scattering residual suggests an additional component or more complicated structures inside the SNR. All retained cases are self-consistent within the adopted scattering theory and the circum-burst medium becomes transparent for GHz bursts before the inferred source ages.

Randomly connected neural networks have long served as a theoretical tool for studying collective dynamics in neural populations, yet quantitative comparisons to experiments remain limited. Recent technological advances have made it possible to resolve population-wide correlations across neurons, and minimal models such as random neural networks predict their generic structure. Whether the two agree quantitatively remains untested. In this work, we examine whether a minimally structured random neural network can account for the low dimensionality of activity in neural population recordings by building on recent developments in Dynamical Mean-Field Theory and incorporating two additional experimentally relevant features into the model: finite measurement time and variability across behavioral contexts. We show that, when these factors are included, the dimensionality measured from large-scale recordings is consistent with the values predicted by random models. However, current recording durations make it difficult to use dimensionality to discriminate among connectivity structures. We further show that analytically predicted dimensionality varies non-monotonically with external input strength, and that the orientation similarity between neural manifolds recorded under different behavioral contexts can be more sensitive to network structure than dimensionality is. Together, these results provide quantitative guidance for experimental design to infer the connectivity structure underlying population activity.

Randomized zeroth-order methods are classically analyzed in expectation, but a black-box Markov conversion can give misleading high-probability guarantees, in particular by forcing the finite-difference smoothing radius to shrink with the confidence parameter. This paper gives a direct finite-horizon high-probability analysis of a two-query Gaussian finite-difference method for deterministic objectives with Lipschitz gradients. The method uses the classical two-point estimator together with the normalized stepsize \(η_t=1/(4L\norm{\bu_t}^2)\). We prove that it finds an \(\varepsilon\)-suboptimal point with probability at least \(1-δ\) using \(\cO((dL/μ)\log(1/\varepsilon)+\log(1/δ))\) function queries under strong convexity, subject to an explicit finite-difference smoothing-radius condition. We also establish high-probability guarantees for smooth convex objectives under a level-set distance-to-solution radius condition and a pathwise smoothing-radius condition. For lower-bounded smooth non-convex objectives, the trajectory average is certified in stationarity with \(\cO(LΔ_0(d+\log(1/δ))/\varepsilon)\) function queries. The proofs combine lower-tail bounds for adaptive sums of Gaussian directional projections with upper-tail bounds for accumulated finite-difference smoothing errors.

In Global Navigation Satellite System (GNSS)-denied environments, terrestrial signals of opportunity (SoOP) offer an alternative for positioning, but synchronization impairments such as clock offsets, drift, and multipath limit performance. This paper proposes a receiver-centric multi-channel time-difference-of-arrival (TDOA) localization framework based on Digital Audio Broadcasting (DAB) signals. The method exploits the DAB null symbol for coarse timing and the phase reference symbol (PRS) for fine synchronization, followed by sub-sample time-of-arrival (TOA) estimation. A double-difference formulation removes inter-receiver clock offsets, while a peak-to-sidelobe ratio (PSR)-based weighting improves robustness. A bias correction step mitigates errors due to multipath. Finally, a coordinated-turn extended Kalman filter (CT-EKF) further refines position estimates. Results show improved accuracy over conventional TDOA with Gauss-Newton estimation, especially in challenging conditions.

Recursive QAOA (RQAOA) solves combinatorial optimization problems by using shallow quantum circuits to estimate pairwise correlations and recursively eliminate variables until a classical solver can handle the residual instance. Each elimination step requires measurement shots, and the total shot cost grows with the number of recursive stages. On near-term quantum devices, increasing shot counts can translate directly into greater exposure to hardware-level noise sources such as readout errors and decoherence, making shot-efficient execution not merely a cost-reduction measure but a factor with direct implications for solution reliability. While shot reduction has been studied broadly across NISQ algorithms, step-wise measurement control inside the recursive loop of RQAOA has received little attention. We formulate this step-wise allocation as a sequential decision problem and propose two strategies for depth-1 RQAOA on weighted Max-Cut instances. A hand-crafted heuristic assigns shots based on local indicators of step difficulty, and a tabular Double Q-learning agent learns a residual policy that adjusts this baseline under a Lagrangian-constrained objective. Both methods are evaluated under a fixed-cap fairness protocol that equalizes the per-step budget across all strategies, and the elimination rule itself is kept unchanged so that the contribution of adaptive measurement control can be isolated. On a diverse set of weighted graph instances spanning a range of sizes and structures, the heuristic reduces total shots by approximately 23% relative to uniform allocation, and the RL policy achieves a 36% reduction with a lower effective shots per success ratio than both baselines. The improvement persists on problem sizes not seen during training, suggesting that reinforcement learning can discover efficient, instance-adaptive measurement strategies in recursive quantum optimization.

The super-Arrhenius dynamic slowdown in fragile supercooled liquids remains one of the central unresolved questions in condensed matter physics. In this study, we analyze particle jump dynamics in a prototypical fragile glass-forming liquid, the Kob-Andersen Lennard-Jones (KALJ) model. Using the displacement of jumping particles as the reaction coordinate, we demonstrate the emergence of non-Poissonian dynamics as the temperature decreases. In the mildly supercooled regime, the outer region of the first coordination shell of a jumping particle exhibits a significant distribution shift during the jump motion. By comparing the survival probability with its slow-fluctuation limit using this distribution as a slow variable, we confirm that particles in this region modulate the jump dynamics, enhance the jump rate fluctuations, and thereby induce the dynamic slowdown as supercooling proceeds. As the temperature decreases, this behavior extends to the outer regions of the second coordination shell and beyond, intensifying the dynamic slowdown. This spatial growth of the slow variables responsible for dynamic disorder exhibits close correspondence with an increase in the static correlation length. These results provide a microscopic mechanism for the super-Arrhenius dynamic slowdown in the KALJ model.

FuzzPilot is a controller for AFL++ that moves expensive reasoning out of the mutation hot path. When coverage plateaus, it snapshots the corpus, prepares candidate mutation recipes, evaluates them in short isolated AFL++ micro-campaigns, and promotes only recipes with positive validation reward. Recipes are JSON data, not generated code: a native custom mutator consumes operator weights, byte ranges, corpus-selection rules, and dictionary tokens. Candidate recipes can come from local rules or from a language-model agent, with Ghidra-derived constants and decompiled context as target hints. This preprint reports a deliberately narrow cJSON evaluation. We compare vanilla AFL++ and the full FuzzPilot agent over five 14,400 s repetitions per arm. cJSON is saturated: baseline AFL++ reaches the exposed 269-edge ceiling at a median of about 2,500 s. The experiments therefore do not show that language-model proposals improve coverage or generalize beyond cJSON. Within this scope, FuzzPilot preserves throughput (median execs_per_sec about 1.06x baseline), shows a descriptively shorter median plateau (1,384 s versus 2,532 s), but the difference is not statistically significant at N=5 (Mann-Whitney p=0.42). The validation gate evaluated 20 model-proposed recipes and promoted none because all rewards were zero. The observed plateau reduction is more likely due to controller snapshot and restart machinery than to the model or recipe mutator. This version is best read as an auditable implementation report and baseline for ongoing non-saturated-target evaluation.

While negative capacitance has been demonstrated in ferroelectric-dielectric heterostructures in the form of capacitance enhancement, all experimental evidence, to date, suggests the existence of domains therein. Here, we address the question: what are the conditions to achieve ideal, domain-free negative capacitance in ferroelectric-dielectric heterostructures? Our main claim is that for given thicknesses of the ferroelectric and the dielectric layers, there is a critical value of domain wall energy parameter -- above which the system would be stabilized in an ideal and robust domain-free negative capacitance state and would be robust against domain formation. Our analyses suggest that to achieve ideal negative capacitance, efforts should lie in understanding the means to control the domain wall energy on all fronts, both theory and experiments via high throughput design, discovery, and engineering of ferroelectrics.

Safety filters constructed from control barrier functions (CBFs) are commonly appended to pre-trained neural network controllers to enforce safety requirements. However, this decoupled design with hand-tuned, fixed CBF parameters often fails to adapt to the underlying controller, yielding overly conservative solutions. Thus, given a valid CBF, we address these limitations by jointly learning a neural network controller and neural-network-parameterized CBF parameters, enforcing the resulting affine safety constraints by construction and avoiding an online quadratic program (QP) safety filter at run time. To further improve computational efficiency and scalability, we introduce a lightweight projection architecture that enforces constraints without full constraint enumeration. Extensive simulation evaluations demonstrate reliable, scalable safety constraint satisfaction at reduced computational cost.

Individualized randomized experiments are central to online platforms for optimizing personalized decisions in complex environments. In two-sided markets, however, standard treatment effect estimation is often invalid due to strong temporal and cross-unit interference, a challenge compounded when only aggregated data are available because of privacy or system constraints. To address these issues, we identify the Global Average Treatment Effect (GATE) using only group-level data from treatment and control groups. We first establish identification conditions based on aggregated observations, and then propose the Individualized Randomized Experiment Varying Coefficient Decision Process (IRE-VCDP) model, which accounts for interference through supply-demand dynamics. Building on this framework, we develop a complete procedure for estimation and statistical inference of the GATE, along with theoretical guarantees for the proposed test. Extensive simulations and real-world experiments using data from a leading ridesharing platform demonstrate the effectiveness of our approach.

With the rapid development of satellite communication and navigation, there is an urgent need to integrate both technologies to achieve reliable communication and precise navigation services within the same satellite system. By combining multi-/uni-cast (MUC) and non-orthogonal multiple access (NOMA) technologies, we propose a novel MUC-NOMA-based integrated navigation and communication (INAC) signal structure, in which the navigation and communication signals share a common pseudo noise (PN) sequence, thereby integrating satellite communication and navigation at the signal level. According to different power allocation strategies, two scenarios are defined: multi-cast-oriented (MO-) INAC and uni-cast-oriented (UO-) INAC, where a greater portion of power is assigned to either the multi-cast or the uni-cast signal, respectively. To mitigate co-channel interference, we employ successive interference cancellation (SIC) at the receiver and design a signal processing algorithm for the proposed INAC signal. Then, closed-form expressions are subsequently derived for the bit error rates (BER) of both the navigation and communication signals, along with the positioning accuracy of the navigation signal. To gain further insights, the impacts of power allocation factors and communication rates are evaluated. Our analysis results show that: i) In the MO-INAC scenario, the positioning and BER performance of navigation signal are excellent when more power is assigned to the multi-cast signal; ii) In the UO-INAC scenario, interference in the shared resources is reduced when more power is assigned to the uni-cast signal; iii) The ranging accuracy decreases as the communication data rate increases. Numerical results confirm the superior BER and positioning accuracy of the MO-INAC scenario for MEO satellites.

Thermal transport in low-dimensional semiconductors is crucial for advancing thermal management in nanoelectronics, quantum devices, and thermoelectric devices. Recent molecular dynamics (MD) studies have identified a nonmonotonic dependence of thermal conductivity (k) on diameter in ultrathin silicon nanowires (NWs). However, classical MD methods are limited at low temperatures and in strongly confined regimes. This work introduces a fully quantum-mechanical perspective on this anomaly by employing anharmonic non-equilibrium Green's function (NEGF) simulations combined with density-functional-theory-trained neuroevolution potentials. For [001]- and [110]-oriented NWs, k decreases with diameter d to a minimum at d_c = 6.24 nm and 5.50 nm, respectively, then rises with d, for a temperature range of 10-300 K. At room temperature, this behavior arises from dominant momentum-conserving normal scattering relative to Umklapp processes in confined regimes, thereby enabling Poiseuille-like hydrodynamic phonon flow that competes with boundary scattering. At cryogenic temperatures, strong radial confinement quantizes the phonon spectrum, and only low-frequency phonons (< 2 THz) significantly contribute to heat transport through quasi-ballistic propagation of long-wavelength modes, as demonstrated by the spectral thermal conductance. In contrast to classical MD, which is inaccurate at low temperatures due to overexcitation of high-frequency vibrations by Boltzmann statistics, neglect of quantum suppression, and overestimation of thermal conductivity in thinner NWs with stronger quantum confinement, the NEGF framework provides quantitative accuracy even at low temperatures, such as 10 K.

Logical T state preparation is a major overhead source in fault tolerant architectures built from stabilizer operations. Existing protocols, however, are reported under different code families, noise models, postselection rules, and cost conventions, making direct comparison difficult. We compare three representative preparation routes: magic state distillation, magic state cultivation, and code switching, using currently available results. Rather than reducing heterogeneous data to a single cost metric, we retain source native cost units and record output error, single attempt cost, expected cost per accepted output, footprint, latency, and reporting completeness for each configuration. Within the current dataset, distillation reaches the lowest output error regime; code switching achieves the lowest reported single attempt cost and the smallest explicit footprint among the compatible rows; and recent RP2 cultivation results add low cost cultivation points with output errors between 1e-6 and 1e-9. As a simple algorithm level case study, we also examine the reported preparation routes under an error budget motivated by Shor factoring algorithm, in order to relate single state preparation costs to full workload requirements. The resulting comparison clarifies the trade offs currently supported across the literature, while remaining bounded by the conventions and coverage of the underlying papers.

Multi-agent systems increasingly expose explicit workflow structure: agents, tools, tool-access rules, restrictions, and delegation paths. Existing evaluations rely largely on end-to-end task success, benchmark scores, final-response quality, or prompt-level checks, which provide limited evidence that this declared coordination structure has actually been exercised. This makes it difficult to assess test-suite adequacy or detect structural regressions in tool access, restrictions, and inter-agent delegation. We address this gap with a structural testing approach for multi-agent workflow specifications. The approach represents each workflow as a typed coordination graph, derives coverage obligations over reachable agents, allowed tool edges, restricted tool edges, and delegation edges, and uses coverage-driven generation with DSPy-based scenario realization to produce executable tests. The graph fixes what must be covered; DSPy realizes those obligations as natural-language scenarios whose witnesses are checked at runtime. We implement the approach for OpenAI Agents SDK-style workflows and evaluate it on ten SDK-derived benchmarks comprising 49 reachable agents, 47 tools, and 403 structural obligations. Generated scenarios witness 54/75 allowed-tool obligations and 36/48 delegation obligations within a bounded refinement budget. The adversarial restricted-tool criterion elicits 23/248 restricted-call violations, separating workflows whose restrictions hold under probing from workflows with concrete misrouting failures. These results show that structural coverage provides a useful adequacy layer for multi-agent workflow testing: it does not replace semantic or end-to-end evaluation, but reveals whether declared agents, tool-access rules, restrictions, and delegation paths have been exercised.

Dark matter constitutes roughly one-fourth of the Universe, yet its physical nature remains unknown. Warm dark matter (WDM), a class of dark matter candidates, has non-negligible velocity dispersion that suppresses the formation of small-scale cosmic structures. Current constraints therefore rely mainly on small-scale probes such as the Lyman-alpha (Ly$α$) forest and Milky Way observations of satellite galaxies and stellar streams. We propose a novel large-scale probe based on long-lived "reionization relics": because the thermal and dynamical evolution of the intergalactic medium depends on the local reionization redshift, patchy reionization imprints additional large-scale fluctuations in Ly$α$ forest opacity and post-reionization HI traced by 21 cm intensity mapping. The strength of these imprints depends on WDM through both small-scale gas evolution and WDM-driven changes in the reionization history. For example, the Ly$α$ (21 cm) power spectrum in 3 keV WDM differs from cold dark matter by ~19% (~19%) at $k=0.05\,{\rm Mpc^{-1}}$ at z=4 (z=5.5) when reionization relics are included. Using Ly$α$ forest with a covariance model designed to mimic the capabilities of the Dark Energy Spectroscopic Instrument (DESI), we forecast a constraint of $m_{\rm WDM}>5.0\,{\rm keV}$ (95%), which improves to $m_{\rm WDM}>7.1\,{\rm keV}$ when combined with 21 cm intensity-mapping observations from the Square Kilometre Array (SKA). The next-generation surveys can further strengthen the current best lower bounds from 9.7 to 39 keV.

The pinching-antenna systems (PASS), which dynamically activate and relocate the pinching-antennas (PAs) along the dielectric waveguide, offer unprecedented potential for integrated positioning and communication. The multi-waveguide-based uplink positioning approaches for indoor environments are first proposed in this paper, and the downlink communication performance is analyzed. Two possible scenarios, multi-waveguide single-PA (MWSP) and multi-waveguide multi-PA (MWMP), are considered under the assumptions of line-of-sight channels and a single, stationary user. For the MWSP scenario, the received signal strength indication (RSSI)-based ranging method and the MWSP-based least square (LS) positioning algorithm are developed. To gain deeper insights, a comprehensive error analysis of the LS positioning algorithm is conducted. Subsequently, for the MWMP scenario, the closed-form expression of the superposed signal is derived. According to the signal power, the MWMP-based grid search algorithm is proposed and the estimation error of proposed algorithm is analyzed. Then, based on the user's positioning result, the PAs are relocated to provide downlink communication service, and the achievable data rate of MWSP and MWMP scenarios are analyzed. Numerical results validate the correctness of our analysis, which show that: i) For the MWSP scenario, a smaller geometric dilution of precision (GDoP) leads to a lower average positioning error. Furthermore, even when the GDoP is large, the regions where the distances to PAs are nearly equal achieve the best accuracy. ii) For the MWMP scenario, non-parallel waveguide deployment improves positioning accuracy, although errors increase with the number of PAs. iii) The noise has a serious double-impact on data rate. There is a trade-off between positioning accuracy and communication performance.

This paper introduces state-robust equilibrium (SRE), a local validity test for Nash predictions in finite-strategy population games when the payoff-relevant aggregate state may be misspecified. The reported prescription and payoff map are held fixed; only the state used to evaluate payoff comparisons varies. SRE is equivalent to local best-response invariance, absence of structural exposure, and validity along every vanishing interior aggregate-state error. In affine games, the tangent-cone, normal-cone, and linear-program tests characterize exposure and identify the exposing population, the pure strategy, and the aggregate-state direction. The main implication is a sharp negative result: robust mixing requires local payoff identity on the support; in generic affine games, SRE reduce to strict pure Nash equilibria, although weak boundary equilibria can survive through feasible-set protection. In affine games with polyhedral local uncertainty regions, the same inequalities yield a deterministic finite diagnostic for reported-state validity.

This paper studies causal discovery for a directed acyclic graph under a structural equation model with additive heteroscedastic errors. We first establish new identifiability results for location-scale noise models, showing that heteroscedasticity can be leveraged to recover causal directions. Based on these insights, we propose a novel iterative procedure, Residual Simultaneous Quantile Estimation (RESQUE), where each iteration consists of a residual-construction stage and a composite quantile regression stage, enabling recursive identification of sink nodes via the invariance of conditional scale coefficients across quantiles. We then establish its theoretical guarantees for recovering topological order and graph structure, even when the number of variables diverges with the sample size. Simulation studies and application to benchmark datasets show that RESQUE performs favorably compared with existing methods, especially when causal information is partly encoded in the variance component. These results highlight exploiting structured variance signals for causal discovery and provide a principled framework for multivariate causal discovery beyond mean-based modeling.

Gomez's Hamburger (IRAS 18059-3211, GoHam) is a massive, edge-on protoplanetary disk that is potentially gravitationally unstable and hosts an overdensity that may be the site of a forming giant planet, making it a particularly interesting source for the study of planet formation in the direct collapse scenario. In this study, we present a molecular inventory of GoHam's disk combining several Submillimeter Array observations for a wideband survey at an angular resolution on the order of ~1 arcsecond. We detect 11 different molecules, including 15 individual lines, and measure their disk-integrated fluxes. We also infer column densities for several species over a range of fixed excitation temperatures. We find that the molecular inventory of GoHam and the inferred column densities for select molecules are broadly consistent with the general population of large protoplanetary disks. We explore the putative gravitational instability (GI) in GoHam's disk via possible enhancements in the gas-phase H$_2$CO abundance, but find no definitive evidence of GI. The results of this study can guide future, higher-resolution studies of GoHam, as well as efforts to characterize the giant protoplanet candidate GoHam b.

Solar flares represent one of the most intense forms of solar activity. Understanding the evolution of physical parameters in the solar atmosphere during flares is key to studying flare mechanisms and improving prediction capabilities. However, directly measuring quantities such as electron number density, temperature, and plasma velocity remains difficult. Here, we introduce a novel fully connected neural network, trained on synthetic data from the Radiative Hydrodynamics Code (RADYN) simulations, to perform rapid inversion of physical parameters from H$α$ spectral profiles. The spectral data were processed to align with the observational resolution of the CHASE satellite, enabling seamless application of the model to real-world observations. Results demonstrate a high degree of consistency with RADYN simulations, achieving low errors under diverse flare conditions. Furthermore, we applied the developed model to analyze CHASE observations of a class X7.1 solar flare on October 1, 2024. The results reveal reasonable spatial and temporal evolution of key parameters throughout different flare phases. This work demonstrates the potential of deep learning techniques for fast and reliable spectral inversion, providing new tools for solar flare diagnostics based on H$α$ data.

Twisted moire superlattices in van-der-Waals heterostructures provide a powerful platform for engineering correlated states through moire-band reconstruction. However, whether globally coherent electronic orders can be continuously manipulated at the nanoscale remains largely unexplored. Reconstructed moire structures in small-angle and near-commensurate regime feature continuously varying local environments, offering new opportunities for nanoscale manipulation of correlated phases. Here, we report the modulation of charge density wave (CDW) states in a twisted vortex moire superlattice formed between monolayer VTe2 and superconducting NbSe2. Scanning tunneling microscopy/spectroscopy reveals that the intrinsic long-range CDW of monolayer VTe2 is reconstructed into inequivalent local phases with distinct stability and coherence within a single moire unit cell, including suppressed CDW order and enhanced short-range CDW correlations persisting to room temperature. First-principles calculations show that the reconstructed CDW landscape originates from strong local strain variation, where compressive strain substantially stabilizes the charge order. Furthermore, the modulated CDW states exhibit competing interplay with proximity-induced superconductivity. Our results establish vortex moire superlattices as a versatile platform for nanoscale manipulation of correlated electronic orders in low-dimensional quantum materials.

Win statistics, including the win ratio, net benefit, and win odds, summarize treatment effects on hierarchical composite endpoints by sequentially comparing patient pairs on component outcomes ordered by clinical importance, proceeding to lower priority components only when higher priority ones are tied. Restricting comparisons to a pre-specified clinical horizon yields well defined estimands separated from the censoring mechanism, and it is critically important to address right censoring during estimation. Existing inverse probability of censoring weighting methods discard indeterminate pairs entirely, incurring avoidable efficiency loss that grows with censoring and restriction horizon length. We propose a novel estimator that replaces the confirmation of higher priority ties with a conditional tie probability given the observed data, recovering partially observed pairs as fractional contributions. Large sample theory is developed based on two-sample U-statistics with estimated nuisance functions, and closed-form sandwich variance estimators are obtained for the win ratio, net benefit, and win odds under our new weighting scheme. Simulations demonstrate sizable efficiency gains growing sharply from light censoring to high censoring rate based on our new estimator, and we further apply our estimator to reanalyze a completed randomized clinical trial.