To accommodate classical communication systems with progressively increasing transmission rates, quantum access networks (QAN) have undergone systematic and protocol-level optimizations in recent years, where quantum passive optical network (QPON) architectures are gaining significant attention due to their simple structure. It is challenging for the previous QAN based on active protocols or Stokes operator coding protocols to achieve high-speed linear modulation with high extinction ratio and stability under practical conditions. In this work, we propose and experimentally demonstrate a downstream fully passive quantum access network protocol using passive state preparation (PSP) with free-space and single-mode fiber hybrid channels, and the final key generation rate is up to a record-breaking 19.48 Mbps per quantum network unit. The proposed PSP-QPON scheme extends the scope of PSP-CVQKD from point-to-point to point-to-multi-point networks, which enables high-key-rate, high-stability, and low-resource-consumption implementation. Moreover, the network channel in this experiment is fully compatible with access networks in classical optical communications, which allows integration with existing optical infrastructure without the need for additional modifications, providing a promising solution for local area network quantum access network at home or a mobile terminal.

This work introduces Q3SAT-GPT, a generative model for discovering quantum circuits for the Max-E3-SAT problem. Our method learns from high-performing QAOA-style ansätze to directly generate candidate circuits. To create high-quality supervision, we also introduce Mosaic Adaptive QAOA (MosaicADAPT-QAOA), an adaptive strategy for constructing low-depth QAOA circuits by selecting subsets of mixer operators in each step, rather than inserting operators sequentially. The resulting circuits serve as training data for the generative model, allowing it to learn effective circuit design patterns while eliminating the need for costly variational optimization at inference time. Experiments show that our framework attains strong solution quality with shallow circuits and scales significantly better than both our adaptive construction procedure and conventional variational baselines. Our results establish generative modeling as a high-performance route toward the scalable discovery of quantum optimization circuits, demonstrating that these models can effectively internalize circuit logic while providing a foundation for future, instance-aware inductive biases. Reproducibility: The source code is available at https://github.com/pratimugale/Q3SAT-GPT.

In this work, we aim to advance the Universal Rotation Curve (URC) paradigm by leveraging new data and extending its observational domain. Building on previous studies that established the URC using optical rotation curves reaching the galaxy optical radius $R_{opt}$, we exploit the SPARC sample's extended HI rotation curves to construct the URC out to $2R_{opt}$. This crucial extension enables us to investigate the mass distribution of spiral galaxies in a region dominated by dark matter an important step to better constrain galaxy mass models and to explore the nature of the dark matter particle. We find that the URC constructed from the SPARC sample's extended HI rotation curves maintains its universal character out to $2R_{opt}$, with the double-normalized rotation curves collapsing onto a single profile. This extended URC provides new insights into the interplay between baryonic matter and dark matter in shaping galaxy rotation curves, particularly in the outer regions where dark matter dominates. Our results not only reinforce the URC paradigm but also refine our understanding of the mass distribution in spiral galaxies, offering new constraints on galaxy mass models and implications for the nature of dark matter.

Revealing the interaction topology underlying strategic behavior is fundamental to prediction, intervention, and policy design in networked systems. Yet the interaction matrix is often unobservable, and passive observation of repeated actions fails to provide sufficient excitation for reliable recovery. This paper studies topology recovery in repeated linear-quadratic network games under decaying active probing, where probing inputs are injected into a subset of players and the unknown interaction matrix is inferred from the resulting action trajectories. We first characterize a structural recoverability condition that determines when noiseless probing experiments can make the interaction matrix identifiable. We then show that, under suitable stability and controllability assumptions, a concrete decaying probing signal guarantees exact finite-step recovery while preserving convergence of the repeated-play process. To handle decision perturbations, we further develop a reweighted sparse estimator that achieves almost-sure consistency together with finite-time exact support recovery. These results clarify what can be recovered in both noiseless and perturbed settings.

This paper presents an updated n=1 error field penetration threshold scaling, which increases fit quality compared to previous error field scaling laws, is produced from an expanded database, and exhibits reduced uncertainty in projections to future conventional tokamaks. It improves confidence in tokamak engineering tolerances, which are a significant driver of cost and time constraints on device construction. We add J-TEXT data, new JET data, and create the scaling using only conventional tokamak Ohmic and L-mode experiments. Since H-mode plasmas are more resilient to error field penetration, this scaling predicts what is likely the most dangerous regime of error field penetration for new tokamak designs. These decisions improve confidence in the error field penetration threshold scaling and its application in the construction and design decisions of any future conventional tokamak or FPP.

Label-free identification and real-time tracking of biochemical substances became critical for molecular diagnostics and chemical analysis, yet conventional resonant terahertz metasurface sensing relies on a single resonance, limiting spectral selectivity and dynamic capability. Here, we suggest multiresonant membrane metasurfaces and implement them for simultaneous static molecular fingerprint retrieval and dynamic reaction monitoring within a single pixel. We consider a membrane metasurface supporting multiple quasi-bound states in the continuum designed at target frequencies and enabling the tailoring of the field enhancement and frequency-selective interaction with target analytes. As a proof-of-concept, we achieve label-free detection of the dual fingerprint absorption features of pefloxacin at 0.78 THz and 0.99 THz, and real-time tracking of vitamin C oxidation and denaturation under ambient conditions. The kinetic profiles extracted from the THz amplitude evolution show excellent agreement with nonlinear reaction models, demonstrating quantitative biochemical tracking capabilities. Our results establish a versatile and scalable THz photonic platform that unifies static fingerprint identification and dynamic reaction monitoring, paving the way toward integrated on-chip biochemical analytics and multifunctional metasurface sensors.

Cross-national comparison of research funding projects is increasingly important for science policy and strategic planning, but language differences remain a major obstacle. In particular, KAKENHI project descriptions are written primarily in Japanese, whereas projects from major overseas funding agencies, such as NSF, NIH, and UKRI, are documented in English. This study investigates whether multilingual sentence embeddings can support meaningful cross-lingual comparison of research funding projects, with particular attention to the semantic effects of translating Japanese texts into English. For each KAKENHI project, we construct two representations: the original Japanese text and its machine-translated English version, both embedded in a shared semantic space using a multilingual Sentence-BERT model. We then compare their distances and nearest-neighbor relationships with respect to projects from English-language funding agencies. The results show that the Japanese and translated English representations of the same KAKENHI project are, on average, located closer to one another than to native English projects, indicating substantial cross-lingual alignment. However, the overlap of nearest neighbors between the two representations is limited, averaging 2.9 out of 10. This suggests that multilingual embeddings capture semantic similarity across languages to a meaningful extent, while language differences and translation still affect the local structure of the embedding space. These findings suggest that multilingual embeddings provide a useful basis for large-scale exploratory comparison of funding projects across countries and agencies. At the same time, they offer an empirical reference for assessing semantic drift when Japanese research project data are translated into English for international analysis.

For the two-loop next-to-leading-order electroweak (NLO EW) corrections to $gg \rightarrow ZH$, the light-fermion contributions can be classified into eight distinct topologies. Using the canonical differential-equations method, we perform an analytic computation of the master integrals (MIs) associated with the four planar topologies. Canonical bases are constructed using the Magnus-expansion method, and the resulting alphabets consist of algebraic symbol letters involving nontrivial radicals. We develop a systematic framework for identifying the radical structures of the canonical MIs, enabling their organization into suitable subsystems and, whenever possible, their representation in terms of Goncharov polylogarithms (GPLs) up to $\mathcal{O}(ε^4)$. Only a few MIs at $\mathcal{O}(ε^3)$ and $\mathcal{O}(ε^4)$ are instead represented as one-fold integrals over GPLs, due to the presence of nested square roots that obstruct the simultaneous rationalization of all radicals.

Epidemics have shaped human history, often with devastating consequences, motivating the development of mathematical models to understand and control their dynamics. Among the many aspects of epidemic behavior, the conditions that lead to epidemic extinction stand out as a central-if not the fundamental-question in epidemic modeling. In this work, we study epidemic extinction in a continuous SIRS (Susceptible-Infected-Recovered-Susceptible) model governed by a system of ordinary differential equations (ODEs). The model includes vaccination as a time-dependent process and considers the reinfection of recovered individuals through waning immunity. We analyze how different parameter regimes -- particularly infection, recovery, and immunity loss rates -- affect the persistence or extinction of the epidemic. Special attention is given to the limitations of continuous population models, in which the infected fraction can fall below the equivalent of a single individual, leading to nonphysical outcomes such as unrealistically long persistence or artificial secondary peaks. By comparing the continuous SIRS dynamics with expected real-world thresholds for extinction, we highlight the importance of incorporating stochasticity or discrete effects to accurately describe epidemic fade-out.

Collective motions in strongly interacting magnets involve many spins and are often described in terms of integer-spin excitations. However, in certain cases, the collective motion can behave as if these integer excitations break apart into smaller, particle-like entities with unusual properties. Such fractionalized excitations in quantum magnets are commonly associated either with topological order in two dimensions or with criticality in one dimension. It remains unclear how these distinct mechanisms are connected across a dimensional crossover. Here we investigate the Ti-based quantum antiferromagnet, $Cs_{8}LiNa_{3}Ti_{12}F_{48}$, in which $Ti^{3+}$ ($3d^{1}$, $S=1/2$) ions interact antiferromagnetically within distorted kagome planes. Our inelastic neutron scattering study on a single crystal reveals a frustrated network of weakly coupled spin-$1/2$ chainsaws, realizing a regime of dimensional frustration in which interchain couplings fail to establish coherent two-dimensional order. The magnetic excitation spectrum exhibits a strong continuum spanning the full measured momentum and energy phase space. In addition, the dynamic spin correlation function displays rod-like scattering in momentum space, indicating a quasi-one-dimensional nature of the magnetic correlations. These results point to fractionalized excitations with intrinsically directional character, demonstrating that signatures of one-dimensional criticality can persist within a two-dimensional lattice. Our findings establish anisotropic fractionalization as a distinct organizing principle for quantum-disordered states.

Retrieval-augmented generation (RAG) systems are frequently evaluated via fact-based metrics, yet standard implementations retrieve passages or static propositions. This unit mismatch between evaluation and retrieval objects hinders maintenance when corpora evolve and fails to capture superseded facts or source disagreements. We propose NuggetIndex, a retrieval system that stores atomic information units as managed records, so called nuggets. Each record maintains links to evidence, a temporal validity interval, and a lifecycle state. By filtering invalid or deprecated nuggets prior to ranking, the system prevents the inclusion of outdated information. We evaluate the approach using a nuggetized MS MARCO subset, a temporal Wikipedia QA dataset, and a multi-hop QA task. Against passage and unmanaged proposition retrieval baselines, NuggetIndex improves nugget recall by 42%, increases temporal correctness by 9 percentage points without the recall collapse observed in time-filtered baselines, and reduces conflict rates by 55%. The compact nugget format reduces generator input length by 64% while enabling lightweight index structures suitable for browser-based and resource-constrained deployment. We release our implementation, datasets, and evaluation scripts

Regression models with both high-dimensional responses and covariates have attracted growing attention. Standard multivariate regression models become inadequate when the response variables depend not only on observed covariates but also on latent variables that capture key unobserved characteristics. To draw statistical inferences on covariate effects while accounting for latent variables, we consider a high-dimensional generalized latent variable model that accommodates mixed-type responses and allows for flexible dependence between covariates and latent variables, which is more suitable for many real-world applications than existing methods that either rely on a linear regression form or restricted assumptions on the dependence between covariates and latent variables. We develop an alternating algorithm that iteratively updates the regression parameters and the latent variables, transforming an intractable nonconvex problem into a sequence of tractable convex subproblems. Theoretically, we provide algorithmic guarantees by establishing statistical consistency of the resulting estimator and deriving an error bound for it. Further, building on this estimator, we construct a debiased estimator for the covariate effect and establish its asymptotic normality. The effectiveness of the proposed method is demonstrated through an application to evaluating the fairness of the Programme for International Student Assessment (PISA).

Twisted bilayer graphene aligned with hexagonal boron nitride (TBG/hBN) hosts rich topological and correlated quantum phases, such as (fractional) Chern insulators, whose character is dictated by the topology of the moiré flat band. This topology is highly sensitive to several material parameters in the continuum model, yet a systematic understanding of their combined influence has been lacking. Here, we present a comprehensive study of topological phase transitions in TBG/hBN by varying the interlayer hopping strengths ($w_0, w_1$) and hBN-induced staggered potential, both with and without the hBN moiré potential. We map out Chern number phase diagrams across a broad, experimentally relevant parameter space, revealing a progressive enrichment of the topological landscape including multiple high-Chern number ($C$ = 3, 4, and 5) states. Each transition is linked to distinct band-inversion mechanisms at generic $C_3$-symmetric k points, high-symmetry momenta, or parabolic touchings, clearly reflecting in the evolution of the Berry curvature. Our results offer theoretical insights that help interpret existing experimental observations, elucidate the mechanisms driving these topological phase transitions and facilitate the exploration of topological states in TBG/hBN and related moiré systems.

We establish two consequences of the Kawamata--Morrison--Totaro cone conjecture, and prove them unconditionally in all dimensions. First, for a K-trivial variety, the natural action of its automorphism group on the set of ample divisor classes of fixed volume has only finitely many orbits. Second, the number of (isomorphism classes of) minimal models for a given K-trivial variety is finite if these models admit a bounded polarization.

Android residential proxy applications represent a growing class of potentially-unwanted programs (PUPs) that covertly route third-party traffic through end-user devices, enabling ad fraud, credential abuse, and evasion of geolocation controls by sophisticated threat actors. Attributing an unknown APK to a specific proxy network remains challenging due to code reuse, SDK embedding, and obfuscation across proxy families. We present a static-analysis pipeline for automated proxyware family attribution, extracting graph-structured representations (control-flow and function-call graphs) and behavioral signatures from a labeled corpus of 3,365 Android proxy apps spanning four commercial proxy networks. We evaluate Weisfeiler-Lehman graph kernel features alone and fused with binary capability vectors across multiple classifiers. Using 5-fold DEX-grouped cross-validation to prevent data leakage, SGD achieves a macro F1 of 0.985 on the expanded dataset. To support explainability, we map classifier decisions to automatically generated Yara rules, achieving per-family accuracies up to 88.45\% after filtering non-discriminative signatures. Finally, we discuss these results in the context of the broader ecosystem. We find that from the expanded dataset, the majority of applications (51.4\%) still available through APKPure still contain embedded proxy SDK code. Further analysis of developer accounts reveals that 23 developers are responsible for other applications also containing such functionality, suggesting continuous and ongoing commercial relationships between proxy providers and developers.

Winds and jets are symbiotic when the accretion rate is low, according to black hole accretion theory. Both components are potentially important for active galactic nucleus (AGN) feedback, but previous works typically include only jets with free parameters. We perform hydrodynamical simulations of an isolated elliptical galaxy with both jets and winds included. The key features discriminating our simulations from others are that our simulations resolve the Bondi radius for reliable black hole accretion rate calculation and use parameters from GRMHD simulations. By selectively activating jets and winds, we examine their individual and combined effects. We find that effective AGN feedback, which is capable of generating strong turbulence and subsequently increasing central gas entropy and suppressing cool gas condensation and star formation, occurs only when both jets and winds operate simultaneously. The physical mechanism is the interaction between winds and jets: this interaction produces strong shear at their interface, leading to turbulence via the Kelvin-Helmholtz instability. In contrast, neither jets nor winds alone can generate strong turbulence due to the insufficient shear. The turbulence produced by wind-jet interaction is predominantly solenoidal in nature, giving rise to a broad energy spectrum approximately following a Kolmogorov-like power law and a dissipation rate $\sim 10^{-27}\,\mathrm{erg\,cm^{-3}\,s^{-1}}$ in the interstellar medium, consistent with observations. Our findings highlight the importance of simultaneously considering both jets and winds in studying the effects of AGN feedback in the evolution of elliptical galaxies.

Thermal plasma properties play a critical role in plasma simulations and plasma-related applications. However, their strong nonlinear dependence on temperature, pressure, and gas composition makes accurate and efficient evaluation challenging. In this work, an operator learning-based model, termed DeepPropNet, is proposed for fast prediction of thermodynamic and transport properties of thermal plasmas. Two architectures are developed, including a single-property model (S-DeepPropNet) and a Mixture of Experts (MoE)-based multi-property model (MoE-DeepPropNet). The proposed models learn the nonlinear mapping from plasma operating conditions to physical properties based on high-fidelity datasets. The MoE architecture enables efficient multi-property prediction within a unified framework. Predictions are performed for binary SF6-N2 and ternary C4F7N-CO2-O2 mixtures. The results show that the proposed models achieve high accuracy, with relative L2 errors on the order of 10-3 to 10-2, while maintaining strong generalization capability under unseen conditions. The applicability of DeepPropNet is further demonstrated by coupling with finite volume method (FVM) and physics-informed neural networks (PINNs). The results indicate that DeepPropNet provides an efficient and scalable approach for plasma property prediction and plasma simulations.

Quantum geometry encoded in the momentum space structure of electronic wavefunctions, governs charge dynamics through Berry curvature, enabling unconventional transport and optical responses. In topological semimetals, this geometry is sampled over Fermi pockets, suggesting electrical control by Fermi surface tuning, yet such control has remained largely limited to DC transport. Here we show that electrostatic gating of the 3D Dirac semimetal Cd3As2 reshapes Fermi pockets surrounding photoinduced Floquet Weyl nodes, enabling electrical control of terahertz (THz) emission chirality. Gate tuning selectively modulates the Berry curvature driven linearly polarized THz component by up to 60% and 49% at positive and negative bias, respectively, while the orthogonal linearly polarized photon-drag component remains unchanged. With the two orthogonal fields intrinsically phase-locked at \sfracπ{2} by the excitation geometry, the selective gate-tuned amplitude control enables the polarization tuning across the Poincaré sphere, achieving near-circular polarization (χ\approx-42°) at +10 V. These results establish Fermi surface tuning as a general route to programmable quantum geometric control of chiral terahertz emission.

In this paper we provide a geometric condition satisfied by certain closed subsets of the Riemann sphere which implies that their hyperbolic convex hulls in $\mathbb{H}^3$ have infinite volume. As a corollary, we characterize continua in the Riemann sphere whose hyperbolic convex hulls have infinite volume, answering a question of Danny Calegari. Furthermore, we give a geometric characterization of planar self-similar sets whose hyperbolic convex hulls have infinite volume.

This paper studies whether solutions of a class of nonlinear feedback systems remain bounded over time. The systems we consider arise naturally in synthetic biology, where the antithetic feedback controller regulates a biological process through a delayed feedback loop. Our main result is that every trajectory of such a system is bounded. The key insight is simple: if the regulated state grows too large for too long, the feedback loop will eventually respond and push it back down. More precisely, we show that whenever the state exceeds a threshold and remains there long enough, the feedback signal becomes strong enough to force the state to decrease. We then show that once this happens, the feedback remains strong enough to keep the state from growing unbounded. The proof works directly with differential inequalities and does not require constructing a Lyapunov function, making the mechanism transparent and easy to interpret. The boundedness result can be understood as a time-domain small-gain effect, where the delayed feedback ultimately counteracts any persistent growth in the system.

Existing research gives conditions for when the outer automorphism group of a graph product of primary cyclic groups $W_Γ$ is finite, virtually abelian, or large. We seek to prove a set of conditions for when this outer automorphism group is virtually cyclic. To this end, we study the finite index subgroup $\text{Out}^0(W_Γ)$, which is generated by specific partial conjugations. The presence or absence of Coxeter and non-Coxeter separating intersections of links (SILs), separating triple intersections of links (STILs), and flexible separating intersections of links (FSILs) in $Γ$ determines algebraic properties of $\text{Out}^0(W_Γ)$. We identify each SIL with a pair of partial conjugations in $\text{Out}^0(W_Γ)$ and place restrictions on the SILs in $Γ$ to ensure that $\text{Out}^0(W_Γ)$ is virtually $\mathbb{Z}$ both when $Γ$ is connected or disconnected. In particular, this applies to the study of right-angled Coxeter groups. This paper is a slightly shorter version of the author's master's thesis from Tufts University.

Counterintuitively, the S&P 500 Index rose between January 1, 2022, and December 29, 2023, while exchange-traded funds (ETFs) seeking to deliver 2x and 3x daily returns of the index delivered substantially negative returns. Roughly two-thirds of the difference between the returns of the index and the levered ETFs can be attributed to compounding and volatility. The remaining difference is explained by the covariance between the ETFs' deviations from constant leverage and the index's return.

We construct and analyze a thermodynamic extension of the recently proposed information geometric regularization of Cao and Schäfer. The construction extends their shock-mitigating Hessian metric geometry using the Shannon entropy to constrain the regularized motion based on a thermodynamic length. Reformulating the equations in terms of mass and specific entropy explicitly connects the thermodynamic state to a position in the diffeomorphism group, allowing for a derivation of the regularized equations using an information geometric mechanics formalism based on geodesics on a Hessian manifold with a dual affine connection. The dynamics are defined using a pullback geometry for the Levi--Civita connection, describing constrained geodesic motion, and the cubic Amari--Chentsov tensor describing the information geometric correction. This new compressible fluid model introduces an anisotropic stress tensor to the momentum equation that vanishes along isentropic directions and an additional elliptic equation coupled to the barotropic regularization. Numerical simulations in one and two spatial dimensions demonstrate that the geometrically consistent incorporation of a thermodynamic constraint mitigates cusp singularities previously observed in other approaches while still maintaining the benefits of an inviscid regularization.

We demonstrate stable operation of a thin-film lithium tantalate (TFLT) modulator at very high operating temperatures. We show that the electro-optic modulation and bandwidth of the TFLT modulators are not affected by high-temperature operation, and both waveguide and resonant modulators are DC-bias stable even at 120°C. At higher temperatures, we even observe 10% reduction of the Vπ of the modulator. Our results position TFLT modulators as a strong candidate for uncooled operation in co-packaged optics.

Quantum secret sharing schemes are a family of quantum cryptographic protocols which provide secure quantum encodings, mapping one secret to multiple shares of information such that the original secret cannot be accessed without an authorized set of shares present for decoding. In this work, we describe a protocol that enables sender-anonymity during the secret decoding process. By using permutation-invariant QEC codes along with a set of anonymous quantum transmission algorithms, we construct a quantum anonymous secret sharing scheme that achieves sender-anonymity. We quantify information leakage in ramp quantum secret sharing schemes via the quantum conditional min-entropy, justifying it as a valid measure of leaked information by relating it to the Knill-Laflamme quantum error correction conditions. Finally, we evaluate several permutation-invariant codes using this measure to make observations on the information leakage of intermediate shares for each quantum anonymous secret sharing scheme.

In this paper, we prove that smooth Calabi--Yau hypersurfaces of degree $d$ over complete unramified discrete valuation rings with residue characteristic $p$ are perfectoid split if $p$ is larger than the relative dimension and $p\nmid d$. We also show that unramified lifts of smooth Fano hypersurfaces over fields of characteristic $p>0$ are globally $+$-regular if $p\ge \dim X$ and $p\nmid d$.

Behavioural distances provide a quantitative approach to comparing the states of transition systems, moving beyond traditional Boolean notions of equivalence. In this paper, we develop a sound and complete axiomatisation of behavioural distance for nondeterministic processes using Milner's charts, a model that generalises finite-state automata by incorporating variable outputs. Charts provide a compelling setting for studying behavioural distances because they shift the focus from language equivalence to bisimilarity. Their axiomatic study lays the groundwork for quantitative analysis of more expressive models, such as weighted transition systems. To formalise this approach, we adopt string diagrams as our syntax of choice. String diagrams closely mirror the graphical structure of charts, while providing a rigorous formalism that supports inductive reasoning and compositional semantics. Unlike traditional algebraic syntaxes, which require additional mechanisms such as binders and substitution, string diagrams offer a variable-free representation where recursion naturally decomposes into simpler components. This makes them well-suited for reasoning about behavioural distances and aligns with broader efforts to axiomatise automata-theoretic equivalences through a unified diagrammatic framework.

Multi-band sensing has emerged as a key enabler of integrated sensing and communication (ISAC), one of the six primary usage scenarios defined for IMT-2030 (6G). The introduction of frequency range 3 (FR3, 7-24 GHz), comprising non-contiguous sub-bands across a wide frequency span, further reinforces the importance of multi-band operation. In such scenarios, frequency-dependent clutter, collectively referred to as dense multipath components (DMC), must be carefully considered. Building on prior literature and our experimental observations, this paper analyzes the impact of DMC on multi-band fusion ISAC systems by investigating Cramér-Rao bound (CRB)-based fundamental limits and the performance of our proposed multi-band estimator. Numerical results show that multi-band processing, especially in DMC-dominated scenarios, can substantially reduce estimation error and boost system resilience when channel statistics vary.

Beta-gallium oxide (β-Ga2O3) holds enormous potential for medium voltage range power electronic applications. This work reports VBr > 10 kV/Ron,sp = 43 mΩ*cm2 class edge terminated vertical heterojunction diodes (HJDs) with e-beam/sputtered nickel oxide (NiOx) stack on epitaxial (011) β-Ga2O3. The power figure of merit (PFOM) of the HJD exceeds 2.3 GW/cm2. The extracted parallel plane breakdown field is > 5.3 MV/cm, which is the highest reported electric field for thick (011) β-Ga2O3 epitaxial drift layer.

Evaluating text-to-SQL systems remains largely fragile: correctness is typically judged by executing predicted and gold SQL queries on a single static database, even though the same queries may behave differently under alternative database instances. This raises a broader language modeling question: Can large language models synthesize semantically meaningful, schema-consistent relational data directly from a natural language question? If so, such generation can serve as a controlled mechanism for stress-testing text-to-SQL systems beyond fixed benchmark databases. We introduce SynSQL, a framework that synthesizes test databases conditioned on question-schema alignment rather than gold SQL queries. SynSQL decomposes the task into three stages: (1) schema selection, (2) question-guided data synthesis, and (3) constraint-aware critique with iterative refinement, framing database construction as structured generation under semantic and relational constraints. Across ten text-to-SQL models on Spider, BIRD, and Spider 2.0, SynSQL-generated databases reveal performance drops of 3-14% compared to static evaluation, exposing errors masked by benchmark artifacts. We further analyze generation quality, constraint adherence, and failure modes, highlighting both the promise and limitations of LLMs in structured data synthesis. Our findings position synthetic database generation as a new lens for studying LLM reasoning, controllability, and robustness in structured environments.