arXiv:2604.23904v3 Announce Type: replace-cross
Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference. We show that fully generative tabular synthesizers, including GAN- and LLM-based models, can preserve predictive utility while distorting average treatment effect (ATE) estimates. The failure is structural: ATE preservation requires both a realistic covariate law and an accurate treatment-effect contrast, whereas prediction loss penalizes treatment-effect error only through an overlap-weighted term. Thus, under imbalance or limited overlap, a generator may reproduce dominant observed outcomes while underlearning intervention-relevant contrasts. We formalize this mismatch through sensitivity and loss-decomposition results. Motivated by this causal analysis and intuition, we propose a hybrid synthetic-data framework for causal inference that generates covariates while modeling treatment and outcome mechanisms separately. We evaluate the framework in three settings: ATE preservation under fully generative versus hybrid synthesis, augmentation for practical positivity problems, and diagnostic simulation engines for comparing OR, IPW, AIPW, and TMLE before real-data analysis. We also stress-test the hybrid construction across settings that vary overlap, covariate dimension, seed sample size, and treatment-effect complexity, including a logistic outcome-model misspecification check. Across controlled simulation experiments, hybrid synthesis improves causal fidelity relative to fully generative baselines; the ACTG application shows improved predictive fidelity and potential for finite-sample estimator benchmarking. LLM-based hybrid synthesis is often more faithful than CTGAN in settings where causal fidelity can be assessed.
Science Journals
arXiv:2607.14756v1 Announce Type: new
Abstract: This research investigates the potential of Vision-Language Models (VLMs) to infer building typologies: Construction, Current Use, and Storeys from Google Street View (GSV) images. Predictions generated by VLMs are compared with inference by human experts (civil engineers and architects) as a source of manually labelled ground-truth data. We evaluate several state-of-the-art VLMs, including GPT-4o, Claude 3.5 Sonnet, and Gemini 2.0 Flash. By applying different scaling strategies and prompting techniques, we found that Chain-of-Thought prompts provide an overall more stable model performance. We also investigate the reasoning behind VLMs' building-typology predictions by examining the probabilities of keywords appearing in AI explanations. This enabled us to analyse patterns in these reasonings and identify key themes driving both agreements and disagreements between VLM and expert labels. We find that AI tends to focus on visual indicators, whereas human experts place greater emphasis on broader contextual cues and domain knowledge, in addition to visual cues. Overall, VLM can approximate experts' capability in building-typology classification at scale, with an average accuracy of approximately 70%. The study demonstrates the VLM's potential for AI automation in tasks that require pattern recognition and object identification in an urban context. AI have the potential to serve as complementary and collaborative tools for urban analysis, leveraging their strengths in understanding visual patterns. This study contributes to the exploration of the efficiency and scalability of AI visual prediction and provides insights into the reasoning processes that could support automation processes in urban analysis and prediction.
arXiv:2607.15156v1 Announce Type: new
Abstract: Frailty assessments are crucial to evaluate the risk of adverse events and the health and social care needs of older adults, yet their administration remains resource-intensive and typically relies on coarse clinical outcomes, such as task completion times, which may overlook biomechanical indicators of functional decline. To address this, we present a robotic framework that guides older adults through standardised frailty and fall-risk tests while capturing clinical scores and additional frailty-related metrics, offering a deeper insight into a user's condition. The system uses a Behaviour Tree architecture that coordinates perception, decision-making, interaction, and measurement modules. Using vision-based skeleton tracking, the robot evaluates established clinical tests, including the Short Physical Performance Battery (SPPB) and the Timed Up and Go (TUG). The framework was co-designed with healthcare professionals and evaluated in situ during six months in a rehabilitation centre's research lab with N=81 older adults. Robot-derived measurements were compared against therapist assessments and clinical reference instruments, including a gait analysis walkway and an inertial measurement unit (IMU). Results showed excellent agreement for most test completion times and gait-related parameters ($ICC > 0.9$). And, substantial agreement for the overall SPPB score comparing the robot and the therapist ($k = 0.67$) and moderate agreement comparing the robot and the IMU ($k=0.55$). The findings highlight that social robots can provide reliable and objective frailty assessments in healthcare settings while enabling the collection of relevant mobility indicators beyond conventional outcomes.
arXiv:2403.12261v2 Announce Type: replace-cross
Abstract: The interaction of radiation defects with grain boundaries (GBs) governs damage tolerance in refractory materials for extreme environments. Tungsten (W), a leading plasma-facing material for fusion, will be subjected to coupled ion and neutron irradiation that degrades both surface and bulk properties. In this study, molecular dynamics (MD) simulations are employed to examine defect evolution under successive 1 keV displacement cascades at a W surface in nano-bicrystals containing Sigma3 and Sigma5 symmetric tilt GBs. The free surface biases interstitial accumulation toward surface planes, reducing bulk interstitial populations, while vacancy saturation is driven by cascade overlap. When cascades indirectly interact with GBs, defect accumulation becomes strongly dependent on GB character. The higher energy Sigma5 boundary acts as a more effective defect sink for interstitials relative to the coherent Sigma3 boundary. This behavior arises from its larger interstitial segregation energy and enhanced strain field, which promote trapping via thermal migration and focused collision sequences. The deeper trap states of the Sigma5 GB suppress interstitial emission and limit recovery, whereas the shallower traps and mobile crowdion configurations in Sigma3 GBs enable dynamic defect recombination. These results highlight the critical role of GB structure and grain size in controlling radiation damage evolution in tungsten with behavior that applies broadly to refractory BCC metals.
arXiv:2403.17961v2 Announce Type: replace-cross
Abstract: We give a categorical formulation of Kraus' "magic trick" for recovering information from truncated types. Rather than type theory, we work in Van den Berg-Moerdijk path categories with a univalent universe, and rather than propositional truncation we work with arbitrary cofibrations, which includes truncation as a special case. We show, using Kraus' argument that any cofibration with homogeneous domain is a monomorphism. We give some simple concrete examples in groupoids to illustrate the interaction between homogeneous types, cofibrations and univalent fibrations.
arXiv:2404.14732v2 Announce Type: replace-cross
Abstract: I propose a general quantitative framework to evaluate the quality, track the historical development, and guide future optimization of photovoltaic (PV) absorbers at any development level, both lab-made and computer-simulated. The framework is centered around a PV figure of merit designed to include efficiency limitations that are not captured by classic detailed balance methods derived from the Shockley-Queisser limit. A more stringent set of figure-of-merit-driven efficiency limits are calculated for 28 experimentally synthesized PV absorbers and 10 PV computationally modeled absorbers. Among early-stage absorbers, this analysis reveals very large differences in their likelihood of achieving high PV efficiencies in the future. Since the proposed figure of merit is instantly evaluated from a single equation, it can be a suitable objective function for closed-loop research on PV materials in autonomous labs, while also providing a quantitative bridge between computationally determined material properties and PV efficiency.
arXiv:2405.05774v4 Announce Type: replace-cross
Abstract: We develop further the theory of monoidal bicategories by introducing and studying bicategorical counterparts of the notions of a linear exponential comonad, as considered in the study of linear logic, and of a codereliction transformation, introduced to study differential linear logic via differential categories. As an application, we extend the differential calculus of Joyal's analytic functors to analytic functors between presheaf categories, just as ordinary calculus extends from a single variable to many variables.
arXiv:2410.04909v5 Announce Type: replace-cross
Abstract: Gibbs state preparation, or Gibbs sampling, is a key computational technique extensively used in physics, statistics, and other scientific fields. Recent efforts for designing fast mixing Gibbs samplers for quantum Hamiltonians have largely focused on commuting local Hamiltonians (CLHs), a non-trivial subclass of Hamiltonians which include highly entangled systems such as the Toric code and quantum double model. Most previous Gibbs samplers relied on simulating the Davies generator, which is a Lindbladian associated with the thermalization process in nature.
Instead of using the Davies generator, we design a different Gibbs sampler for various CLHs by giving a reduction to classical Hamiltonians, in the sense that one can efficiently prepare the Gibbs state for some CLH $H$ on a quantum computer as long as one can efficiently do classical Gibbs sampling for the corresponding classical Hamiltonian $H^{(c)}$. We demonstrate that our Gibbs sampler is able to replicate state-of-the-art results as well as prepare the Gibbs state in regimes which were previously unknown, such as the low temperature region, as long as there exists fast mixing Gibbs samplers for the corresponding classical Hamiltonians. Our reductions are as follows.
- If $H$ is a 2-local qudit CLH, then $H^{(c)}$ is a 2-local qudit classical Hamiltonian.
- If $H$ is a 4-local qubit CLH on 2D lattice and there are no classical qubits, then $H^{(c)}$ is a 2-local qudit classical Hamiltonian on a planar graph. As an example, our algorithm can prepare the Gibbs state for the (defected) Toric code at any non-zero temperature in $O(n^2 poly(log n))$ time.
- If $H$ is a 4-local qubit CLH on 2D lattice and there are classical qubits, assuming that quantum terms are uniformly correctable, then $H^{(c)}$ is a constant-local classical Hamiltonian.
arXiv:2412.00289v4 Announce Type: replace-cross
Abstract: Quantum Error Correction (QEC) is regarded as the most promising path to quantum advantage. The success of QEC relies on achieving quantum gate fidelities below the error threshold of the QEC code, while accurately decoding errors through classical processing of the QEC stabilizer measurements. In this paper, we uncover the critical system-level requirements from a controller-decoder system (CDS) necessary to successfully execute the next milestone in QEC: a non-Clifford circuit. Using a representative non-Clifford circuit, of Shor factorization algorithm for the number 21, we convert the logical-level circuit to a QEC surface code circuit and finally to the physical level circuit. By taking into account realistic implementation aspects using typical superconducting qubit processor parameters, we reveal a broad range of core requirements from any CDS aimed at performing error corrected quantum computation. Our findings indicate that the controller-decoder closed-loop latency must remain within tens of microseconds, achievable by distributing decoding data into several decoders while ensuring fast communication between decoders and with the controller. By extending existing simulation techniques, we simulate the complete fault-tolerant factorization circuit at the physical level, demonstrating that near-term hardware performance in the scale of 0.1% physical error rates and 1000 qubits, are sufficient for a successful circuit execution. Overall, the requirements outlined here set the stage for near- and medium-term experimental realizations of non-Clifford QEC circuits.
arXiv:2412.20556v2 Announce Type: replace-cross
Abstract: We study distributionally robust optimization (DRO) for robust inference when the worst-case distribution is continuous, leading to significant computational challenges due to the infinite-dimensional nature of the optimization problem. Unlike traditional discrete DRO approaches, which often suffer from scalability issues, limited generalization, and costly worst-case inference, our framework exploits Brenier's theorem to characterize the least favorable distribution as the pushforward of a transport map from a continuous reference measure. This characterization motivates our study of the minimax problem in Wasserstein space. We propose an iterative algorithmic framework with multiple variants and establish global convergence guarantees under mild assumptions, deriving complexity bounds in terms of subgradient evaluations and inexact Jordan-Kinderlehrer-Otto updates. Numerical results with neural network-based transport maps demonstrate that the proposed method enables both stable training of robust classifiers and effective worst-case inference for classification tasks.
arXiv:2502.17602v2 Announce Type: replace-cross
Abstract: We study a class of stochastic nonsmooth optimization problems in which an outer variable minimizes the expectation of a pointwise maximum. This minimization--expectation--maximization (minEmax) problem arises in Wasserstein distributionally robust optimization and adversarially robust training, and it cannot in general be reformulated as a finite-dimensional minimax problem when the underlying distribution is not empirical. We propose a stochastic smoothing proximal gradient method based on log-mean-exp smoothing of the value function. Under compactness and Lipschitz-type assumptions, we present nonasymptotic analysis in terms of Goldstein stationarity and show that every almost-sure cluster point generated by our method is a Clarke stationary point; by Clarke regularity, such a point is also directional stationary for the original problem. Numerical experiments on newsvendor, robust regression, and adversarially robust learning problems show that the proposed method is competitive with existing baselines.
arXiv:2607.15095v1 Announce Type: new
Abstract: The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large Language Models (LLMs) open new avenues for computational political science, the neutrality and helpfulness biases instilled by Reinforcement Learning from Human Feedback (RLHF) prevent them from sustaining steadfast partisan behaviour. We present a multi-agent framework that reconciles factual grounding with ideological alignment by combining Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Retrieval-Augmented Generation (RAG): DPO instils aggressive party-specific personas, while a per-party RAG pipeline keeps each agent bounded to its official manifesto. We operationalize the framework on the 2019 Flemish election, deploying the partisan agents in a hub-and-spoke negotiation arbitrated by a formateur. To make the emergent negotiation interpretable, we introduce a Multi-Layered Information Lineage Topology (MILT) that traces every clause in the final agreement back to its manifesto origin and classifies it into five provenance states, a Coalition Influence Score (CIS) that aggregates these traceable contributions to identify which party shaped the agreement, and a real-world grounding pass that benchmarks each simulated provision against the historically adopted coalition agreement. Across three independent simulations the framework yields a stable winner and ranking (N-VA ahead of CD\&V and Open Vld), and manifesto-anchored lineage reliably predicts real-world materialization whereas hallucinated content does not. The result is a transparent, scalable testbed for the ex-ante exploration of party compatibility and formateur-mediated compromise.
A system-level approach to generalized feedback Nash equilibrium seeking in partially observed games
arXiv:2503.24159v2 Announce Type: replace-cross
Abstract: This work proposes an algorithm for seeking generalized feedback Nash equilibria (GFNE) in noncooperative dynamic games. The focus is on cyber-physical systems with dynamics which are linear, stochastic, potentially unstable, and partially observed. We employ System Level Synthesis (SLS) to reformulate the problem as the search for an equilibrium profile of closed-loop responses to noise, which can then be used to reconstruct a stabilizing output-feedback policy. Under this setup, we leverage monotone operator theory to design a GFNE-seeking algorithm capable to enforce closed-loop stability, operational constraints, and communication constraints onto the control policies. This algorithm is amenable to numerical implementation and we provide conditions for its convergence. We demonstrate our approach in a simulated experiment on the noncooperative stabilization of a decentralized power grid.
arXiv:2504.00978v2 Announce Type: replace-cross
Abstract: Over the course of the past decade, advances in the radial velocity and transit techniques have enabled the detection of rocky exoplanets in the habitable zones of nearby stars. Future observations with novel methods are required to characterize this sample of planets, especially those that are non-transiting. One proposed method is the Planetary Infrared Excess (PIE) technique, which would enable the characterization of non-transiting planets by measuring the excess infrared flux from the planet relative to the star's spectral energy distribution. In this work, we predict the efficacy of future observations using the PIE technique by potential future observatories such as the MIRECLE mission concept. To do so, we conduct a broad suite of 21 General Circulation Model (GCM) simulations with ExoCAM of seven nearby habitable zone targets for three choices of atmospheric composition with varying partial pressure of CO$_2$. We then construct thermal phase curves and emission spectra by post-processing our ExoCAM GCM simulations with the Planetary Spectrum Generator (PSG). We find that all cases have distinguishable carbon dioxide and water features assuming a 90$^\circ$ orbital inclination. Notably, we predict that CO$_2$ is potentially detectable at 15 $\mu\mathrm{m}$ with MIRECLE for at least four nearby known non-transiting rocky planet candidate targets in the habitable zone: Proxima Cenaturi b, GJ 1061 d, GJ 1002 b, and Teegarden's Star c. Our ExoCAM GCMs and PSG post-processing demonstrate the potential to observationally characterize nearby non-transiting rocky planets and better constrain the potential for habitability in our Solar neighborhood.
arXiv:2504.02500v2 Announce Type: replace-cross
Abstract: Quantum processing units (QPUs) built on superconducting Josephson junctions remain the most industrially mature route to fault-tolerant quantum computing, but the field has moved substantially since early 2025. This paper provides an updated overview of Josephson-junction QPUs, grounded in the quantum-mechanical principles, superposition, entanglement, and decoherence, that any qubit implementation must contend with, and in the physics of Cooper pairing and quantum tunneling that make a Josephson junction behave as a controllable qubit. We examine the engineering challenges of scaling these devices, including crosstalk, classical control-interface bottlenecks, and quantum error correction, and discuss the first below-threshold surface-code demonstrations on superconducting hardware. We survey recent materials advances, near-term computational results, and applications beyond computing, and compare Josephson-junction QPUs against trapped-ion, photonic, and neutral-atom alternatives, the latter having emerged as a serious fourth architecture since our original analysis. We close with the current public roadmaps toward fault-tolerant superconducting quantum computers and a calibrated assessment of how much progress those roadmaps still require.
arXiv:2504.04177v2 Announce Type: replace-cross
Abstract: We describe the physical processes that affect the formation, trapping, and outgassing of O2 at Europa and Ganymede. Following Voyager measurements of their ambient magnetospheric plasmas, laboratory data indicated that observed ions, mostly ejected from volcanic Io, would in turn impact and sputtering their surfaces, decomposing the ice producing thin oxygen atmospheres. Subsequently, Europa and Ganymede's O2 atmospheres were inferred from O aurora, condensed O2 bands identified at 5773 and 6225 Angstroms, and their atmospheres were shown to have a dusk/dawn enhancement, confirmed by recent Juno data. Although plasma produces these observables, processes that occur within the topmost surface are not well understood. Here, we note that the incident plasma particles produce nonequilibrium defect density locally in the surface ice grains. Defect diffusion within these grains leads to the formation of voids and molecular products, some of which are volatile. Although some volatiles are released into the satellite atmospheres, others are trapped at defect sites or trapped in voids, creating bubbles whose lifetimes are limited by the plasma-induced destruction rate. We discuss how trapping competes with annealing of the radiation damage, and how hemispheric differences at Europa and Ganymede, roughly determine the observed trend with latitude of O2 bands. We discuss the relative importance of condensed O2 and O2 adsorbed on regolith grains as atmospheric sources, accounting for dusk/dawn enhancements and temporal variability reported in condensed O2 band depths. Since plasma-induced damage and thermal annealing timescales drive oxidant variability on icy moons (likely also Callisto, Dione, and Rhea), they can help determine volatile downwelling, a potentially metabolic source for their oceans, and upwelling of other trapped oxidants (e.g. CO2) suggestive of ongoing geologic activity.
arXiv:2504.14659v3 Announce Type: replace-cross
Abstract: Minimum mean square error (MMSE) estimation is widely used in signal processing, information theory, and related fields. Despite its practical robustness, the MMSE can be discontinuous under standard notions of stochastic convergence. To bridge this gap, we review classical counterexamples to the continuity of the MMSE and observe that they share a common pathology: along the approximating sequence, the observation is strictly more informative about the limit estimand than the limit observation is. Motivated by practical acquisition mechanisms, we study MMSE continuity under two natural constraints: (1) continuity of the second moment, and (2) a degradedness (Markov) restriction ensuring that each approximating observation is no more informative than the limit observation is about the limit estimand. Under these conditions, we establish continuity of the MMSE and of the MMSE estimator. We provide complementary semicontinuity results and continuity guarantees in related settings and establish continuity under linear estimation. We further extend the analysis to the families of Bregman divergences and continuous metric cost functions, including the Kullback-Leibler and Jensen-Shannon divergences as special cases.
arXiv:2505.03982v4 Announce Type: replace-cross
Abstract: We reformulate the problem of alternating projections between two affine subspaces of a Hilbert space as the minimization of a least-squares functional associated with a bounded linear operator. This viewpoint reveals that classical alternating projections coincide with the unit-step Landweber iteration and enables the introduction of a general residual-state iteration framework that encompasses Landweber, its steepest-descent variant, and the conjugate-gradient method. Within this framework, we establish abstract convergence principles based on residual extinction and translation equivariance, allowing convergence analyses to be carried out once at the level of least-squares optimization and then transferred directly to alternating projection algorithms. As applications, we obtain new variants of alternating projections accelerated by steepest descent and conjugate gradients, together with convergence guarantees in both the consistent and inconsistent settings. We also establish linear convergence results under closed-range assumptions and express the convergence rates explicitly in terms of the Friedrichs angle and the largest principal angle between the underlying subspaces.
arXiv:2505.23869v4 Announce Type: replace-cross
Abstract: A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a compression process. In building this connection, we use a performance of a learning task as a probe of compression, over series of compression cycles within a cascade. The Gibbs entropy at each cycle measures the degree of randomness. Consequently a lossy compression process can be seen as an equivalent to {\it directed randomness} that preserves information content under certain bounds of Gibbs entropy and the performance of the learning task. The term directed means we guide the compression process with set of mathematical rules on how to reduce the model size. We formulate this connection with a theorem using a $\delta$ and $\epsilon$ bounds, and demonstrated a logical proof via comonotonic relationship within a very small decrease in compression ratio and the performance. We have showcase the validity of this proposition with a canonical vision task in deep learning with three different model compression processes as {\it a baseline model}. We use the following, simpler to more complex model compression approaches: (1) random pruning, (2) magnitude pruning, and (3) a more complex compression by using dual tomographic compression, which utilizes compressed sensing in dual fashion. We use remaining weights of deep learning network as a measurement vector where we measure the Gibbs entropy. The proposition is supported with the experimental evidence, resulting in very high correlation between learning performance and the Gibbs entropy over compression ratios for all different compression processes. We show case the idea that there is an inherent computable connection between compression probed by performance degradation and randomness from an entropy measure on the learned model.
arXiv:2506.03549v2 Announce Type: replace-cross
Abstract: Quantum position verification (QPV) enables multiple verifiers to certify a prover's location using quantum communication and physical assumptions. With experimental demonstrations of QPV becoming increasingly feasible, enhancing the practicality and security of QPV protocols is more important than ever. In this work, we make three key contributions toward this goal. First, we introduce a robust QPV protocol in which the verifier's state preparation is independent of channel loss, improving reliability in real-world conditions. Second, we refine existing security analysis techniques to bolster protocol resilience against experimental imperfections. Third, we identify and address some implicit assumptions present in existing security analyses, providing a framework to eliminate such assumptions. Additionally, as an example of QPV application beyond location verification, we illustrate how QPV can be leveraged for authenticating classical communication in quantum key distribution.
arXiv:2507.03209v2 Announce Type: replace-cross
Abstract: The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a major bottleneck in RNA therapeutics development. Recent advances demonstrate the potential of machine learning (ML) models to predict transfection efficiency directly from lipid structure, enabling high-throughput virtual screening and accelerating lead identification. However, as new models for LNP transfection prediction continue to emerge, the lack of rigorous and standardized benchmarking poses a significant risk and may undermine confidence in their reliability for discovery. Here, we present a robust ML benchmarking framework for evaluating transfection prediction models based on ionizable lipid structures. This framework systematically benchmarks diverse molecular representations paired with a broad range of ML architectures spanning traditional models, feedforward neural networks, and state-of-the-art graph-based methods. In addition, the presented framework supports assessment of model generalization and evaluates prediction reliability beyond standard regression metrics. Using a curated dataset of 1,100 unique ionizable lipid structures derived from the HeLa transfection dataset originally reported by Xu et al., we show that within this framework, models leveraging explicit molecular substructure encoding consistently achieve the highest predictive accuracy and should serve as essential baselines for the development of new, more sophisticated models. In contrast, some current graph-based models, including AGILE, Chemprop, and KPGT, tend to show comparatively lower accuracy. The presented framework provides a standardized, transparent, and comprehensive benchmarking resource that enables meaningful comparison of emerging architectures and establishes strong baselines for future development of predictive models in lipid-based RNA delivery.
arXiv:2507.04268v2 Announce Type: replace-cross
Abstract: In this work, we present a compact analytical approximation for the quantum partition function of systems composed of quantum oscillators. The proposed formula is general and applicable to an arbitrary number of oscillators described by a rather general class of potential energy functions (not necessarily polynomials). Starting from the exact path integral expression of the partition function, we introduce a temperature-dependent Gaussian approximation for the high-temperature propagator and, then, invoke a principle of minimal sensitivity to minimize the error. This leads to a system of coupled nonlinear equations whose solution yields the optimal parameters of the Gaussian approximation. The resulting approximate partition function accurately reproduces thermodynamic quantities such as the free energy, average energy, and specific heat -- even at zero temperature -- with typical relative errors in the range of about 1\%--5\%. The accuracy deteriorates only moderately when the anharmonicity and coupling strengths are increased. We illustrate the performance of our analytical formula with numerical results for systems of up to ten coupled anharmonic oscillators. These results are compared to "exact" numerical results obtained via Hamiltonian diagonalization for small systems and Path Integral Monte Carlo simulations for larger ones.
arXiv:2507.13782v2 Announce Type: replace-cross
Abstract: Ultra-high resolution 7 tesla (7T) magnetic resonance imaging (MRI) provides detailed anatomical views, offering better signal-to-noise ratio, resolution and tissue contrast than 3T MRI, though at the cost of accessibility. We present an advanced deep learning model for synthesizing 7T brain MRI from 3T brain MRI. Paired 7T and 3T T1-weighted images were acquired from 172 participants (124 cognitively unimpaired, 48 impaired) from the Swedish BioFINDER-2 study. To synthesize 7T MRI from 3T images, we trained two models: a specialized U-Net, and a U-Net integrated with a generative adversarial network (GAN U-Net). Our models outperformed two previous state-of-the-art 3T-to-7T models in image-based evaluation metrics. Four blinded MRI professionals judged our synthetic 7T images as comparable in detail to real 7T images, and superior in subjective visual quality to 7T images, due to the reduction of artifacts. Using both SynthSeg and NextBrain, automated segmentations of the synthetic 7T images were more similar to real 7T segmentations than automated segmentations from the 3T images that were used to synthesize the 7T images. Finally, synthetic 7T images showed similar performance to real 3T images in downstream prediction of cognitive status using MRI derivatives (n=3,168). In all, we show that synthetic T1-weighted brain images approaching 7T quality can be generated from 3T images, which may improve image quality and segmentation, without compromising performance in downstream tasks. Future directions, possible clinical use cases, and limitations are discussed.
arXiv:2510.16657v3 Announce Type: replace-cross
Abstract: Synthetic data has been increasingly used to train frontier generative models. However, recent studies raise key concerns that iteratively retraining a generative model on its self-generated synthetic data may keep deteriorating model performance, a phenomenon often coined model collapse. In this paper, we investigate ways to modify the synthetic retraining process to avoid model collapse, and even possibly help reverse the trend from collapse to improvement. Our key finding is that by injecting information through an external synthetic data verifier, whether a human or a better model, synthetic retraining will not cause model collapse. Specifically, we situate our theoretical analysis in the fundamental linear regression setting, showing that verifier-guided retraining can yield near-term improvements, but ultimately drives the parameter estimate to the verifier's "knowledge center" in the long run. Our theory further predicts that, unless the verifier is perfectly reliable, these early gains will plateau and may even reverse. Indeed, our experiments across linear regression, Variational Autoencoders (VAEs) trained on MNIST, and fining-tuning SmolLM2-135M on the XSUM task confirm these theoretical insights.
arXiv:2510.25102v2 Announce Type: replace-cross
Abstract: We demonstrate single-shot laser-pulse-induced magnetization reversal in rare-earth-free CoFeB/MgO magnetic tunnel junctions (MTJs), a material system widely adopted in spin-transfer torque magnetic random-access memory (STT-MRAM). By tuning the Ru capping layer thickness, we modify the laser energy absorption profile and observe magnetization reversal from the parallel (P) to antiparallel (AP) state, with switching observed for $t_\text{Ru} \geq 2.0\,$ nm. Furthermore, we detect magnetization reversal in a micro-scale MTJ device via the tunnel magnetoresistance (TMR) effect. Our findings suggest that ultrafast spin transport, dipolar interactions, or a combination of both may contribute to the switching process, although the precise mechanism remains to be clarified. This work represents a significant step toward integrating ultrafast optical control with MTJ technology.