With the growing demand for wireless spectrum, dynamic spectrum sharing (DSS) frameworks such as the Citizens Broadband Radio Service (CBRS) have emerged as practical solutions to improve utilization while protecting incumbent users (IUs) such as military radars. However, current incumbent protection mechanisms face critical limitations. The Environmental Sensing Capability (ESC) requires costly sensor deployments and remains vulnerable to interference and security risks. Alternatively, the Incumbent Informing Capability (IIC) requires IUs to disclose their identities and operational parameters to the Spectrum Coordination System (SCS), creating linkable records that compromise operational privacy and mission secrecy. We propose IU-GUARD, a privacy-preserving spectrum sharing framework that enables IUs to access spectrum without revealing their identities. Leveraging verifiable credentials (VCs) and zero-knowledge proofs (ZKPs), IU-GUARD allows IUs to prove their authorization to the SCS while disclosing only essential operational parameters. This decouples IU identity from spectrum access, prevents cross-request linkage, and mitigates the risk of centralized SCS data leakage. We implement a prototype, and our evaluation shows that IU-GUARD achieves strong privacy guarantees with practical computation and communication overhead, making it suitable for real-time DSS deployment.

Biological systems, particularly the human brain, achieve remarkable energy efficiency by abstracting information across multiple hierarchical levels. In contrast, modern artificial intelligence and communication systems often consume significant energy overheads in transmitting low-level data, with limited emphasis on abstraction. Despite its implicit importance, a formal and computational theory of information abstraction remains absent. In this work, we introduce the Degree of Information Abstraction (DIA), a general metric that quantifies how well a representation compresses input data while preserving task-relevant semantics. We derive a tractable information-theoretic formulation of DIA and propose a DIA-based information abstraction framework. As a case study, we apply DIA to a large language model (LLM)-guided video transmission task, where abstraction-aware encoding significantly reduces transmission volume by $99.75\%$, while maintaining semantic fidelity. Our results suggest that DIA offers a principled tool for rebalancing energy and information in intelligent systems and opens new directions in neural network design, neuromorphic computing, semantic communication, and joint sensing-communication architectures.

The correlation between inner super-Earths (SEs) and outer cold Jupiters (CJs) provides an important constraint on the formation and dynamical evolution of planetary systems. Previous studies have suggested a positive connection between these two populations, particularly around metal-rich stars, and proposed that nearly all CJ-hosting stars may also harbor inner SEs. In this work, we use TESS transits to investigate the occurrence of hot SEs in systems with known CJs detected by radial velocity (RV). Out of a statistical sample of 132 CJ systems, we identify five transiting hot super-Earths ($1$-$4R_\oplus$, $P<10\mathrm{d}$) around four stars, including one new candidate (TOI-6965.01) around HD 50554. To enable statistical analysis, we first validate the two candidates around HD 50554 using TESS photometry, archival RV measurements, and Gaia astrometry. After accounting for detection sensitivity and geometric transit probability, we find that the presence of CJs enhances the occurrence rate of hot super-Earths by a factor of $8.1^{+4.3}_{-3.2}$ relative to field stars, with the case of no enhancement ruled out at the 99.9% confidence level. Taking into account the average multiplicity of hot SEs, we find that about 87% of CJ systems host at least one hot SE. Our results provide strong supporting evidence for a positive SE-CJ correlation. We also briefly explore the correlation around metal-poor hosts and for specific sub-populations (e.g., warm super-Earths or cold super-Jupiters).

Cuprate superconductors display robust in-plane electronic correlations but exceptionally fragile interlayer coherence. We suggest that even weak vertically correlated disorder (arising from interstitial-oxygen staging, twin boundaries, extended strain fields, or defect-pinned charge textures) can impose a layer-dependent modulation of the interlayer tunneling amplitude t(z). Because the bare interlayer coupling is intrinsically small, such modulations generate an effectively multichannel c-axis electrodynamic response, consistent with multi-component Josephson plasma resonances, nonmonotonic c-axis resistivity, redistribution of bilayer magnetic spectral weight, and field-enhanced vertical CDW correlations. We propose a phenomenological framework in which the organization of disorder, rather than its magnitude, governs the effective interlayer coupling and its electrodynamic signatures. This viewpoint unifies diverse c-axis anomalies across several cuprate families, suggesting that controlling vertical disorder correlations offers a viable pathway for tuning dimensionality and interlayer coherence in high-Tc superconductors.

We establish the convergence theory of multiplicative Diophantine approximation for all non-degenerate, smooth manifolds. We also settle said convergence theory for all affine subspaces satisfying a highly generic and essentially optimal Diophantine condition. This answers a question of Beresnevich and Velani from 2005, while simultaneously sharpening results of Kleinbock and Margulis on the strong extremality of non-degenerate manifolds, and of Kleinbock on the strong extremality of affine subspaces.

We compute the dual Steenrod algebra for Bredon homology with constant coefficients $\underline{\mathbb Z}$ and $\underline{\mathbb Z}/2$ in the category of modules over $MU^{((G))}$, the norm to $G=C_{2^n}$ of $MU_{\mathbb R}$. Using this and an equivariant version of the Greenlees--Serre spectral sequence, we give a spectral sequence computing the $RO$-graded homotopy of the Eilenberg--Mac Lane spectrum $H\underline{\mathbb F}_2\otimes H\underline{\mathbb Z}$.

We investigate instability phenomena for linear evolution equations within the framework of $C_0$--semigroups on infinite--dimensional spaces. We show that Devaney chaos, being formulated in purely topological terms, may depend on the choice of topology and therefore fail to capture intrinsic dynamical behavior. To address this issue, we introduce a trajectory--based notion of relative chaos, defined with respect to a reference solution and measured in a fixed, physically meaningful norm. This criterion is independent of topological refinements and is shown to be strictly weaker than classical Devaney chaos. Its relevance is illustrated on boundary--driven reaction--diffusion--transport semigroups.

Multiscale simulations utilizing high-fidelity, microscopic Monte Carlo models to provide the nonlinear response for continuum models can easily become computationally intractable. Surrogate models for the high-fidelity Monte Carlo models can overcome this but come with some challenges. One such challenges arise by the sampling noise in the underlying Monte Carlo data, which leads to uncontrolled errors possibly corrupting the surrogate even though it would be highly accurate in the case of noise-free data. Another challenge arises by the 'curse of dimensionality' when the response depends on many macro-variables. These points are addressed by a novel noise-balanced sparse grids interpolation approach which, in a quasi-optimal fashion, controls the amount of Monte Carlo sampling for each data point. The approach is complemented by a multilevel on-the-fly construction during the multiscale simulation. Besides its efficiency, a particularly appealing feature is the ease of use of the approach with only a single hyperparameter controlling the whole surrogate construction - from the surrogate's accuracy with guaranteed convergence to which data needs to be created with which accuracy. The approach is demonstrated on challenging examples from heterogeneous catalysis, coupling microscopic kinetic Monte Carlo models into macroscopic reactor simulations.

Despite the great success Kohn-Sham density functional theory (KS-DFT) has achieved, the delocalization error remains a major challenge for commonly used density functional approximations (DFAs), resulting in systematic errors in ionization energies, electron affinities, band structures, and charge distributions. A recently developed localized orbital scaling correction (LOSC) method, namely linear response LOSC (lrLOSC), addresses these challenges by incorporating a functional correction that includes the screening effect and orbital localization within the LOSC framework. The method has been shown to provide accurate descriptions of bulk systems and core-level binding energies in small molecular systems. In this work, we extend the applicability of lrLOSC to a broader range of molecular systems, spanning various sizes, with a focus on the corrections to valence orbital energies and total energies. To enable the calculation of large chemical systems, we developed an efficient implementation of lrLOSC with computational costs comparable to standard KS-DFT calculations. Numerical results show that, while screening provides modest improvements for small molecules, it becomes critical for achieving high accuracy in larger molecules, from linear to three-dimensional systems. With the screening effect well captured in a unified way, lrLOSC provides accurate descriptions for a wide range of chemical systems, including organic molecular systems of varying sizes and transition-metal oxide complexes, establishing it as a powerful tool for enhancing the reliability of computational simulations of chemical systems.

Let $(M,h)$ be a compact Kähler manifold. Under a suitable spectral positivity assumption we prove that $M$ is simply connected, projective, uniruled and $h^{p,0}(M)=\{0\}$ for each $p>0$. Then, in the second part of this paper, we focus on Riemannian manifolds and we provide an appropriate spectral positivity assumption which guarantees that a compact and oriented even dimensional Riemannian manifold $(M,g)$ is a simply connected rational homology sphere.

We present a reference-free computational wavefront sensor based on binary amplitude modulation and phase retrieval. The method employs Digital Micro-mirror Device as a programmable amplitude modulator and reconstructs the complex optical field from multiple far-field intensity measurements using the Reweighted Amplitude Flow algo-rithm with Optimal Spectral Initialization. Unlike classical pupil-plane wavefront sen-sors, the proposed architecture does not include any wavelength-specific optical elements, enabling straightforward adaptation across a broad spectral range. The achievable spatial resolution of the reconstructed wavefront is scalable with the modulator resolution. We experimentally demonstrate wavefront reconstruction at 650 nm and at 2116 nm, where commercial wavefront sensors are not widely available. The reconstructed wavefront is validated against a commercial lateral shearing interferometer at 650 nm, and the method is further integrated into a closed-loop adaptive optics system using a deformable mirror. The approach is particularly suited for applications requiring high spatial resolution and large dynamic range in slowly varying or quasi-static laser fields, where computational reconstruction speed is not of the primary concern.

For $p>1$, we study subordination phenomena for local and non-local regular $p$-energies on metric measure spaces. Under suitable geometric assumptions, we show that if a local regular $p$-energy satisfies a Poincaré inequality together with a cutoff Sobolev inequality with scaling function $Ψ$, then all associated stable-like non-local $p$-energies with scaling functions strictly below $Ψ$ are regular and satisfy the corresponding non-local cutoff Sobolev inequalities. Moreover, if a stable-like non-local regular $p$-energy with scaling function $Ψ$ satisfies the corresponding non-local cutoff Sobolev inequality, then the same conclusion holds for all associated stable-like non-local $p$-energies with scaling functions below $Ψ$. These results provide a non-linear extension of the classical subordination principle beyond the Dirichlet form framework.

In this paper, we study deformation quantization of symplectic vector fields à la Fedosov. We show that each symplectic vector field can be quantized to a derivation of the deformed star algebra. Moreover, we show that this quantization yields a non-abelian $2$-cocycle on the Lie algebra of symplectic vector fields with values in the deformed star algebra. Therefore, we can quantize any Lie algebra action by symplectic vector fields.

Recent cosmological tensions pose difficulties for $Λ$CDM. Forthcoming facilities will be able to check whether these tensions result from systematic effects or indeed with the $Λ$CDM model itself. However, these new data will primarily probe gravitational interactions and provide only limited information about non-gravitational interactions. Distinguishing between competing models that make similar predictions yet rely on fundamentally different principles, therefore requires suitably diverse physical tests. Observational constraints on spacetime variations of fundamental constants fill this need. The fine-structure constant, $α= e^2/\hbar c$, can be measured using absorption systems towards bright quasars using the Many Multiplet method, and using atomic doublets from line emitting gas in galaxies. A spectroscopic facility such as the WST could produce more than 100,000 new measurements of $α$ from quasars together with a million measurements from galaxies. When combined with other probes, such a large and homogeneous dataset of $α$ measurements would provide unprecedented constraints on physics beyond $Λ$CDM.

Integral discriminants provide a simple and fundamental model for non-Gaussian integrals, associated with homogeneous polynomials of degree r in n variables. We argue that, in this context, the study of correlators is equally if not more important. In this paper, we study a natural class of correlators in this model -- the invariant correlators. We suggest a general method to compute invariant correlators using differential operators that act on the partition function. This method allows to compute general invariant correlators in terms of the fundamental invariants. Moreover, in some cases the correlators appear to be simply polynomials in the invariants. This could be an interesting manifestation of superintegrability phenomenon in the theory of integral discriminants.

A review of the $σ$-model approach to derivation of effective string equations of motion for the massless fields is presented. We limit our consideration to the case of the tree approximation in the closed bosonic string theory.

Near-field multi-antenna wireless communication has attracted growing research interest in recent years. Despite this development, most of the current literature on antennas and reflecting structures relies on simplified models, whose validity for real systems remains unclear. In this paper, we introduce a physically consistent near-field model, which we use to evaluate commonly used models. Our results indicate that common models are sufficient for basic beamfocusing, but fail to accurately predict the sidelobes and frequency dependence of reflecting structures.

We study the star hull of planar Brownian motion and bridge. Roughly speaking, this is the smallest starshaped set (with respect to the origin) that contains the trace of the path. In particular, we prove that the expected areas of the star hulls are $\frac{3π}{8}$ and $\fracπ{4}$ for planar Brownian motion and bridge, respectively. Our proofs rely on a detailed analysis of the first hitting time and place of a horizontal ray $\mathcal{R}_ρ: = [ρ,\infty)\times\{0\}$ by planar Brownian motion starting at the origin. After deriving a remarkably simple Laplace transform of this joint law, we uncover via a probabilistic argument a surprising conditional structure: conditionally on the first hitting place being the point $(x,0)\in \mathcal{R}_ρ$, the hitting time is distributed as the first passage time to the level $x$ of one-dimensional Brownian motion starting at $0$.

In large scale agile systems development, the lack of a unified requirements engineering (RE) process is a major challenge, exacerbated by the absence of high level guiding principles for effective requirements management. To address this challenge, we conducted a five year longitudinal case study with Grundfos AB, in collaboration with the Software Centre in Sweden. RE principles were first derived through qualitative data collection spanning more than 25 sprints, approximately 320 weekly synchronisation meetings, and seven cross-company, company-specific workshops between 2019 and 2024. These activities engaged practitioners from diverse roles, representing several hundred developers across domains. In late 2024, five in depth focus groups with senior leaders at Grundfos provided retrospective validation of the principles and assessed their strategic impact. We aim to (1) empirically examine RE principles in large scale agile system development, (2) explore their benefits in practice within the case company, and (3) identify a set of transferable RE principles for large scale contexts. Using thematic analysis, six key RE principles architectural context, stakeholder-driven validation and alignment, requirements practices in large-scale agile organisations. evolution with lightweight documentation, delegated requirements management, organisational roles and responsibilities, and a shared understanding of requirements are derived. The study was further validated through crosscompany expert evaluation with three additional multinational organisations (Bosch, Ericsson, and Volvo Cars), which are directly responsible for largescale requirements management. Together, these efforts provide a scalable and adaptable foundation for improving requirements practices in largescale agile organisations.

We study the graph-theoretic properties of the trace of random walks on pseudorandom graphs. We show that for any $\varepsilon>0$, there exists a constant $C$ such that the cover time of an $(n,d,λ)$-graph $G$ with $d/λ\ge C$ is at most $(1+\varepsilon)n\log n$, meaning the expected number of steps needed to reach all vertices at least once is at most $(1+\varepsilon)n\log n$ regardless of the starting vertex. Furthermore, we prove that with high probability, the trace of a random walk of length $(1+\varepsilon)n\log n$ on $G$ is Hamiltonian, regardless of the starting vertex. These results also hold for random $d$-regular graphs with sufficiently large $d$. These findings answer two questions proposed by Frieze, Krivelevich, Michaeli, and Peled [PLMS, 2018]. Notably, our results imply a bound on a stronger version of the cover time: with high probability, all vertices are covered after $(1+\varepsilon)n\log n$ steps, regardless of the starting vertex. Our proofs rely on the spectral properties of the adjacency matrix and the graph expansion. All results are asymptotically optimal.

We propose a constructive algorithm for identifying complete data distributions in graphical models of missing data. The complete data distribution is unrestricted, while the missingness mechanism is assumed to factorize according to a conditional directed acyclic graph. Our approach follows an interventionist perspective in which missingness indicators are treated as variables that can be intervened on. A central challenge in this setting is that sequences of interventions on missingness indicators may induce and propagate selection bias, so that identification can fail even when a propensity score is invariant to available interventions. To address this challenge, we introduce a tree-based identification algorithm that explicitly tracks the creation and propagation of selection bias and determines whether it can be avoided through admissible intervention strategies. The resulting tree provides both a diagnostic and a constructive characterization of identifiability under a given missingness mechanism. Building on these results, we develop recursive inverse probability weighting procedures that mirror the intervention logic of the identification algorithm, yielding valid estimating equations for both the missingness mechanism and functionals of the complete data distribution. Simulation studies and a real-data application illustrate the practical performance of the proposed methods. An accompanying R package, flexMissing, implements all proposed procedures.

Finding, counting, or determining the existence of Nash equilibria, where players must play optimally given each others' actions, are known to be computational intractable problems. We ask whether weakening optimality to the requirement that each player merely avoid worst responses -- arguably the weakest meaningful rationality criterion -- yields tractable solution concepts. We show that it does not: any solution concept with this minimal guarantee is ``as intractable'' as pure Nash equilibrium. In general games, determining the existence of no-worst-response action profiles is NP-complete, finding one is NP-hard, and counting them is #P-complete. In potential games, where existence is guaranteed, the search problem is PLS-complete. Computational intractability therefore stems not only from the requirement of optimality, but also from the requirement of a minimal rationality guarantee for each player. Moreover, relaxing the latter requirement gives rise to a tractability trade-off between the strength of individual rationality guarantees and the fraction of players satisfying them.

Micro-structural models of contagion and systemic risk emphasize that shock propagation is inherently multi-channel, spanning counterparty exposures, short-term funding and roll-over risk, securities cross-holdings, and common-asset (fire-sale) spillovers. Empirical implementations, however, often rely on stylized or simulated networks, or focus on a single exposure dimension, reflecting the practical difficulty of reconciling heterogeneous granular collections into a coherent representation with consistent identifiers and consolidation rules. We close part of this gap by constructing an empirically grounded multilayer network for euro area significant banking groups that integrates several supervisory and statistical datasets into layer-consistent exposure matrices defined on a common node set. Each layer corresponds to a distinct transmission channel, long- and short-term credit, securities cross-holdings, short-term secured funding, and overlapping external portfolios, and nodes are enriched with balance-sheet information to support model calibration. We document pronounced cross-layer heterogeneity in connectivity and centrality, and show that an aggregated (flattened) representation can mask economically relevant structure and misidentify the institutions that are systemically important in specific markets. We then illustrate how the resulting network disciplines standard systemic-risk analytics by implementing a centrality-based propagation measure and a micro-structural agent-based framework on real exposures. The approach provides a data-grounded basis for layer-aware systemic-risk assessment and stress testing across multiple dimensions of the banking network.

This paper studies a fluid-antenna-enabled integrated bistatic sensing and backscatter communication system for future networks where connectivity, power delivery, and environmental awareness are jointly supported by the same infrastructure. A multi-antenna base station (BS) with transmitting fluid antennas serves downlink users, energizes passive tags, and illuminates radar targets, while a spatially separated multi-antenna reader decodes tag backscatter and processes radar echoes to avoid the strong self-interference that would otherwise obscure weak returns at the BS. The coexistence of tags and targets, however, induces severe near--far disparities and multi-signal interference, which can be mitigated by fluid antennas through additional spatial degrees of freedom that reshape the multi-hop channels. We formulate a transmit-power minimization problem that jointly optimizes the BS information beamformers, sensing covariance matrix, reader receive beamformers, tag reflection coefficients, and fluid-antenna (FA) positions under heterogeneous quality of service constraints for communication, backscatter, and sensing, as well as energy-harvesting and FA geometry requirements. To tackle the resulting non-convex problem, we develop an alternating-optimization block-coordinate framework that solves four tractable subproblems using semidefinite relaxation, majorization--minimization, and successive convex approximation. Numerical results show consistent transmit-power savings over fixed-position antennas and zero-forcing baselines, achieving about 13.7% and 54.5% reductions, respectively.

Finding optimal solutions of conflicting objectives is a daily matter in many industrial applications, with multi-objective optimization trying to find the best solutions to them. The advent of quantum computing has led to researchers wondering if the promised exponential advantage can be obtained for these problems by variational quantum multi-objective optimization (QMOO) algorithm. Here, we improve it by introducing a Pareto Archive and dominated solutions substitution, clearly improving in hyper-volume convergence at additional quantum and classical cost. We propose the use of RMNK-landscapes as a unifying testbed for benchmarking QMOO, as it is common in classical multi-objective field. By devising a generic classical-to-quantum mapping of these landscapes, we perform a numerical hyperparameter tuning of QMOO, significantly enhancing its performance. Finally, we compare QMOO against well-known classical solvers for multi-objective tasks, NSGA-II/III, showing comparable results in small instances. Our results demonstrate that QMOO, when carefully tuned for the task at hand, might be advantageous on harder problems than its classical counterparts.

Recent advances in quantum computing and the increasing availability of quantum hardware have substantially enhanced the practical relevance of quantum approaches to discrete optimization. Among these, the Quadratic Unconstrained Binary Optimization (QUBO) formulation provides a unifying modeling framework for a broad class of $\mathbf{NP}$-hard problems and is naturally suited to quantum computing and quantum-inspired algorithms. Location science, network design, and logistics represent core application domains of discrete optimization, combining high practical impact with substantial computational challenges. In this work, we develop QUBO formulations for several fundamental problems in these domains, including a nonlinear integer formulation of the Discrete Ordered Median Problem (DOMP). Beyond their modeling relevance, these QUBO formulations serve as representative benchmark problems for assessing quantum algorithms and quantum hardware. We further derive a tight bound for the penalty parameter ensuring equivalence between the QUBO formulation and its underlying integer program. Finally, we conduct a comprehensive computational study using QAOA, WS-QAOA, and classical heuristics for QUBO instances of the $p$-Median Problem and the Fixed-Charge Facility Location Problem (FCFLP), and introduce two effective warm-start strategies for WS-QAOA based on its linear programming relaxation.

Ultrawide-bandgap semiconductors are attractive for solar-blind ultraviolet (UV) detection owing to their intrinsically low noise and high spectral selectivity, yet their deployment in large-area, high-density electronic imaging systems remains limited by a fundamental trade-off between material quality, device speed, and compatibility with high-density planar silicon readout circuits. Here, we report a membrane-enabled integration platform based on transferable single-crystalline beta-Ga2O3 that overcomes these constraints at the system level. By exploiting the weak interplanar bonding of beta-Ga2O3 (100) plane, we obtain wafer-scale freestanding single-crystalline membranes that enable vertically integrated photodiodes with sub-microsecond, non-persistent photoresponse and high UV-visible rejection. Crucially, we introduce a stitching-based membrane assembly strategy that decouples array resolution from the size of the source single-crystalline substrate, allowing high-resolution photodetector arrays to be integrated onto silicon thin-film-transistor backplanes. The modular assembled active-matrix UV imaging arrays exhibit uniform solar-blind response without image lag, in stark contrast to arrays based on amorphous or polycrystalline films. Beyond beta-Ga2O3, this membrane-enabled and stitching-based modular integration strategy provides a general route toward high-speed, high-resolution electronic imaging systems using transferable single-crystalline semiconductors.

In the present work, we experimentally realize CoVGe for the first time and investigate its structural, magnetic, and transport properties, supported by theoretical calculations. The material crystallizes in a cubic structure and exhibits a very low magnetic moment of 0.13 μB per formula unit at 5 K. The temperature dependence of electrical resistivity suggests half-metallic behaviour. Magnetoresistance shows a positive, non-saturating linear field dependence at low temperature that gradually weakens with increasing temperature. The combination of low magnetic moment and unusual magnetotransport behaviour positions CoVGe as a promising platform for exploring spin-dependent transport in Heusler-based materials.

Multiple-environment Markov decision processes (MEMDPs) equip an MDP with several probabilistic transition functions (one per possible environment) so that the state is observable but the environment is not. Previous work studies two semantics: (i) the universal semantics, where an adversary picks the environment; and (ii) the prior semantics, where the environment is drawn once before execution from a fixed distribution. We clarify the relation between these semantics. For parity objectives, we show that the qualitative questions, i.e. value one, coincide, and we develop a new algorithm for the general value of MEMDP with prior semantics. In particular, we show that the prior value of an MEMDP with a parity objective can be approximated to any precision with a space efficient algorithm; equivalently, the associated gap problem is decidable in PSPACE when probabilities are given in unary (and in EXPSPACE otherwise). We then prove that the universal value equals the infimum of prior values over all beliefs. This yields a new algorithm for the universal gap problem with the same complexity (PSPACE for unary probabilities, EXPSPACE in general), improving on earlier doubly-exponential-space procedures. Finally, we observe that MEMDPs under the prior semantics form an important tractable subclass of POMDPs: our algorithms exploit the fact that belief entropy never increases, and we establish that any POMDP with this property reduces effectively to a prior-MEMDP, showing that prior-MEMDPs capture a broad and practically relevant subclass of POMDPs.

We define trajectory predictive control (TPC) as a family of output-feedback indirect data-driven predictive control (DDPC) methods that represent the output trajectory of a discrete-time system as a linear function of the recent input/output history and the planned input trajectory. This paper shows that for different choices of the trajectory predictor, TPC encompasses a wide variety of DDPC methods, including subspace predictive control (SPC), closed-loop SPC, $γ$-DDPC, causal-$γ$-DDPC, transient predictive control, and others. This paper introduces a trajectory predictor that corresponds to a linear state-space model with the recent input/output history as the state. With this state-space predictor, TPC is a special case of linear model predictive control and therefore inherits its mature theory. In numerical experiments, TPC performance approaches the limit of oracle $H_2$-optimal control with perfect knowledge of the underlying system model. For TPC with small training datasets, the state-space predictor outperforms other predictors because it has fewer parameters.