Significant shifts in the composition of consumer spending as a result of the COVID-19 crisis can complicate the interpretation of official inflation data, which are calculated by the Central Bureau of Statistics (CBS) based on a fixed basket of goods. We focus on Israel as a country that experienced three lockdowns, additional restrictions that significantly changed consumer behavior, and a successful vaccination campaign that has led to the lifting of most of these restrictions. We use credit card spending data to construct a consumption basket of goods representing the composition of household consumption during the COVID-19 period. We use this synthetic COVID-19 basket to calculate the adjusted inflation rate that should prevail during the pandemic period. We find that the differences between COVID-19-adjusted and CBS (unadjusted) inflation measures are transitory. Only the contribution of certain goods and services, particularly housing and transportation, to inflation changed significantly, especially during the first and second lockdowns. Although lockdowns and restrictions in developed countries created a significant bias in inflation weighting, the inflation bias remained unexpectedly small and transitory during the COVID-19 period in Israel.
Digital inclusion remains a challenge for marginalized communities, especially rural women in low-resource language regions like Bhojpuri. Voice-based access to agricultural services, financial transactions, government schemes, and healthcare is vital for their empowerment, yet existing ASR systems for this group remain largely untested. To address this gap, we create SRUTI ,a benchmark consisting of rural Bhojpuri women speakers. Evaluation of current ASR models on SRUTI shows poor performance due to data scarcity, which is difficult to overcome due to social and cultural barriers that hinder large-scale data collection. To overcome this, we propose generating synthetic speech using just 25-30 seconds of audio per speaker from approximately 100 rural women. Augmenting existing datasets with this synthetic data achieves an improvement of 4.7 WER, providing a scalable, minimally intrusive solution to enhance ASR and promote digital inclusion in low-resource language.
This paper studies the identification of the average treatment effect on the treated (ATT) under unconfoundedness when covariate overlap is partial. A formal diagnostic is proposed to characterize empirical support -- the subset of the covariate space where ATT is point-identified due to the presence of comparable untreated units. Where support is absent, standard estimators remain computable but cease to identify meaningful causal parameters. A general sensitivity framework is developed, indexing identified sets by curvature constraints on the selection mechanism. This yields a structural selection frontier tracing the trade-off between assumption strength and inferential precision. Two diagnostic statistics are introduced: the minimum assumption strength for sign identification (MAS-SI), and a fragility index that quantifies the minimal deviation from ignorability required to overturn qualitative conclusions. Applied to the LaLonde (1986) dataset, the framework reveals that nearly half the treated strata lack empirical support, rendering the ATT undefined in those regions. Simulations confirm that ATT estimates may be stable in magnitude yet fragile in epistemic content. These findings reframe overlap not as a regularity condition but as a prerequisite for identification, and recast sensitivity analysis as integral to empirical credibility rather than auxiliary robustness.
Double Machine Learning is commonly used to estimate causal effects in large observational datasets. The "residuals-on-residuals" regression estimator (RORR) is especially popular for its simplicity and computational tractability. However, when treatment effects are heterogeneous, the proper interpretation of RORR may not be well understood. We show that, for many-valued treatments with continuous dose-response functions, RORR converges to a conditional variance-weighted average of derivatives evaluated at points not in the observed dataset, which generally differs from the Average Causal Derivative (ACD). Hence, even if all units share the same dose-response function, RORR does not in general converge to an average treatment effect in the population represented by the sample. We propose an alternative estimator suitable for large datasets. We demonstrate the pitfalls of RORR and the favorable properties of the proposed estimator in both an illustrative numerical example and an application to real-world data from Netflix.
This paper presents a comprehensive formalization of the von Neumann-Morgenstern (vNM) expected utility theorem using the Lean 4 interactive theorem prover. We implement the classical axioms of preference-completeness, transitivity, continuity, and independence-enabling machine-verified proofs of both the existence and uniqueness of utility representations. Our formalization captures the mathematical structure of preference relations over lotteries, verifying that preferences satisfying the vNM axioms can be represented by expected utility maximization. Our contributions include a granular implementation of the independence axiom, formally verified proofs of fundamental claims about mixture lotteries, constructive demonstrations of utility existence, and computational experiments validating the results. We prove equivalence to classical presentations while offering greater precision at decision boundaries. This formalization provides a rigorous foundation for applications in economic modeling, AI alignment, and management decision systems, bridging the gap between theoretical decision theory and computational implementation.
Quantum circuits consist of gates applied to qubits. Current quantum hardware platforms impose connectivity restrictions on binary CX gates. Hence, Layout Synthesis is an important step to transpile quantum circuits before they can be executed. Since CX gates are noisy, it is important to reduce the CX count or CX depth of the mapped circuits. We provide a new and efficient encoding of Quantum-circuit Layout Synthesis in SAT. Previous SAT encodings focused on gate count and CX-gate count. Our encoding instead guarantees that we find mapped circuits with minimal circuit depth or minimal CX-gate depth. We use incremental SAT solving and parallel plans for an efficient encoding. This results in speedups of more than 10-100x compared to OLSQ2, which guarantees depth-optimality. But minimizing depth still takes more time than minimizing gate count with Q-Synth. We correlate the noise reduction achieved by simulating circuits after (CX)-count and (CX)-depth reduction. We find that minimizing for CX-count correlates better with reducing noise than minimizing for CX-depth. However, taking into account both CX-count and CX-depth provides the best noise reduction.
This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Efron's biased-coin design and stratified block randomization, where participants are first grouped into strata based on baseline covariates and assigned treatments within each stratum to ensure balance across groups. In practice, datasets often contain additional covariates beyond the strata indicators. We propose a flexible distribution regression framework that leverages off-the-shelf machine learning methods to incorporate these additional covariates, enhancing the precision of distributional treatment effect estimates. We establish the asymptotic distribution of the proposed estimator and introduce a valid inference procedure. Furthermore, we derive the semiparametric efficiency bound for distributional treatment effects under CAR and demonstrate that our regression-adjusted estimator attains this bound. Simulation studies and empirical analyses of microcredit programs highlight the practical advantages of our method.
Deepfake detection has gained significant attention across audio, text, and image modalities, with high accuracy in distinguishing real from fake. However, identifying the exact source--such as the system or model behind a deepfake--remains a less studied problem. In this paper, we take a significant step forward in audio deepfake model attribution or source tracing by proposing a training-free, green AI approach based entirely on k-Nearest Neighbors (kNN). Leveraging a pre-trained self-supervised learning (SSL) model, we show that grouping samples from the same generator is straightforward--we obtain an 0.93 F1-score across five deepfake datasets. The method also demonstrates strong out-of-domain (OOD) detection, effectively identifying samples from unseen models at an F1-score of 0.84. We further analyse these results in a multi-dimensional approach and provide additional insights. All code and data protocols used in this work are available in our open repository: https://github.com/adrianastan/tada/.
We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science. Motivated by the inherent relationship between the off-diagonal blocks of the Hamiltonian matrix and the SO(2) local frame, we propose a novel and efficient network, called QHNetV2, that achieves global SO(3) equivariance without the costly SO(3) Clebsch-Gordan tensor products. This is achieved by introducing a set of new efficient and powerful SO(2)-equivariant operations and performing all off-diagonal feature updates and message passing within SO(2) local frames, thereby eliminating the need of SO(3) tensor products. Moreover, a continuous SO(2) tensor product is performed within the SO(2) local frame at each node to fuse node features, mimicking the symmetric contraction operation. Extensive experiments on the large QH9 and MD17 datasets demonstrate that our model achieves superior performance across a wide range of molecular structures and trajectories, highlighting its strong generalization capability. The proposed SO(2) operations on SO(2) local frames offer a promising direction for scalable and symmetry-aware learning of electronic structures. Our code will be released as part of the AIRS library https://github.com/divelab/AIRS.
Physics-inspired inference often hinges on the ability to construct a likelihood, or the probability of observing a sequence of data given a model. These likelihoods can be directly maximized for parameter estimation, incorporated into Bayesian frameworks, or even used as loss functions in neural networks. Yet, many models, despite being conceptually simple, lack tractable likelihoods. A notable example arises in estimating protein production from snapshot measurements of actively dividing cells. Here, the challenge stems from cell divisions occurring at non-Exponentially distributed intervals with each division stochastically partitioning protein content between daughter cells, making protein counts in any given cell a function of its full division history. Such history dependence precludes a straightforward likelihood based on a (standard Markovian) master equation. Instead, we employ conditional normalizing flows (a class of neural network models designed to learn probability distributions) to approximate otherwise intractable likelihoods from simulated data. As a case study, we examine activation of the \emph{glc3} gene in yeast involved in glycogen synthesis and expressed under nutrient-limiting conditions. We monitor this activity using snapshot fluorescence measurements via flow cytometry, where GFP expression reflects \emph{glc3} promoter activity. A na\"ive analysis of flow cytometry data ignoring cell division suggests many cells are active with low expression. However, fluorescent proteins persist and can be inherited, so cells may appear active from retaining ancestral fluorescence. Explicitly accounting for the (non-Markovian) effects of cell division reveals \emph{glc3} is mostly inactive under stress, showing that while cells occasionally activate it, expression is brief and transient.
The decarbonization goals worldwide drive the energy transition of power distribution grids, which operate under increasingly volatile conditions and closer to their technical limits. In this context, localized operational data with high temporal and spatial resolution is essential for their effective planning and regulation. Nevertheless, information on grid-connected distributed energy resources, such as electric vehicles, photovoltaic systems, and heat pumps, is often fragmented, inconsistent, and unavailable. This work introduces a comprehensive database of distributed energy resources and non-controllable loads allocated in Switzerland's medium- and low-voltage distribution grid models, covering over 2 million points of connection. Remarkably, this data specifies the flexibility capabilities of the controllable devices, with a set of projections aligned with national forecasts for 2030, 2040, and 2050. The database supports studies on flexibility provision of distributed energy resources, distribution grid resilience, and national energy policy, among other topics. Importantly, its modular structure allows users to extract national- and local-scale information across medium- and low-voltage systems, enabling broad applicability across locations.
We present DEF (\textbf{\ul{D}}iffusion-augmented \textbf{\ul{E}}nsemble \textbf{\ul{F}}orecasting), a novel approach for generating initial condition perturbations. Modern approaches to initial condition perturbations are primarily designed for numerical weather prediction (NWP) solvers, limiting their applicability in the rapidly growing field of machine learning for weather prediction. Consequently, stochastic models in this domain are often developed on a case-by-case basis. We demonstrate that a simple conditional diffusion model can (1) generate meaningful structured perturbations, (2) be applied iteratively, and (3) utilize a guidance term to intuitivey control the level of perturbation. This method enables the transformation of any deterministic neural forecasting system into a stochastic one. With our stochastic extended systems, we show that the model accumulates less error over long-term forecasts while producing meaningful forecast distributions. We validate our approach on the 5.625$^\circ$ ERA5 reanalysis dataset, which comprises atmospheric and surface variables over a discretized global grid, spanning from the 1960s to the present. On this dataset, our method demonstrates improved predictive performance along with reasonable spread estimates.
Metamaterials are artificially engineered structures that manipulate electromagnetic waves, having optical properties absent in natural materials. Recently, machine learning for the inverse design of metamaterials has drawn attention. However, the highly nonlinear relationship between the metamaterial structures and optical behaviour, coupled with fabrication difficulties, poses challenges for using machine learning to design and manufacture complex metamaterials. Herein, we propose a general framework that implements customised spectrum-to-shape and size parameters to address one-to-many metamaterial inverse design problems using conditional diffusion models. Our method exhibits superior spectral prediction accuracy, generates a diverse range of patterns compared to other typical generative models, and offers valuable prior knowledge for manufacturing through the subsequent analysis of the diverse generated results, thereby facilitating the experimental fabrication of metamaterial designs. We demonstrate the efficacy of the proposed method by successfully designing and fabricating a free-form metamaterial with a tailored selective emission spectrum for thermal camouflage applications.
Pretrained molecular encoders have become indispensable in computational chemistry for tasks such as property prediction and molecular generation. However, the standard practice of relying solely on final-layer embeddings for downstream tasks may discard valuable information. In this work, we challenge this convention by conducting a comprehensive layer-wise analysis of five diverse molecular encoders across 22 ADMET property prediction tasks. Our results demonstrate that embeddings from intermediate layers consistently outperform final-layer representations. Specifically, using fixed embeddings from the optimal intermediate layers improved downstream performance by an average of 5.4%, reaching gains up to 28.6%. Furthermore, finetuning up to these intermediate layers yielded even greater average improvements of 8.5%, with performance increases as high as 40.8%, achieving new state-of-the-art results on several benchmarks. Additionally, a strong positive correlation between fixed embedding performance and finetuning outcomes supports an efficient evaluate-then-finetune approach, enabling identification of optimal layers with reduced computational cost. These findings highlight the importance of exploring the full representational depth of molecular encoders to achieve substantial performance improvements and computational efficiency. The code is made publicly available at https://github.com/luispintoc/Unlocking-Chemical-Insights.
Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, HI-SIGMA relies on generative ML models, rather than classifiers, to learn the signal and background distributions in the high-dimensional space. These ML models allow for efficient, interpretable inference while also incorporating model errors and other sources of systematic uncertainties. We showcase this methodology on a simplified version of a di-Higgs measurement in the $bb\gamma\gamma$ final state, where the di-photon resonance allows for efficient background interpolation from sidebands into the signal region. We demonstrate that HI-SIGMA provides improved sensitivity as compared to standard classifier-based methods, and that systematic uncertainties can be straightforwardly incorporated by extending methods which have been used for histogram based analyses.
Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences and engineering, where writing down a likelihood can be significantly more challenging than constructing a simulator. However, the performance of neural SBI can suffer when simulators are computationally expensive, thereby limiting the number of simulations that can be performed. In this paper, we propose a novel approach to neural SBI which leverages multilevel Monte Carlo techniques for settings where several simulators of varying cost and fidelity are available. We demonstrate through both theoretical analysis and extensive experiments that our method can significantly enhance the accuracy of SBI methods given a fixed computational budget.
We derive a single-cell level understanding of metabolism in an isogenic cyanobacterial population by integrating secondary ion mass spectrometry (SIMS) derived multi-isotope uptake measurements of Synechocystis sp. PCC6803 with a statistical inference protocol based on Liebig's law of the minimum, the maximum entropy principle, and constraint-based modeling. We find the population is structured in two metabolically distinct clusters: cells optimizing carbon yield while excessively turning over nitrogen, and cells which act reciprocally, optimizing nitrogen yield and excessively turning over carbon. This partition enables partial heterotrophy within the population via metabolic exchange, likely in the form of organic acids. Exchange increases the feasible metabolic space, and mixotrophic cells achieve the fastest growth rates. Metabolic flux analysis at the single-cell level reveals heterogeneity in carbon fixation rates, Rubisco specificity, and nitrogen assimilation. Our results provide a necessary foundation for understanding how population level phenotypes arise from the collective contributions of distinct individuals.
Physics has been transforming our view of nature for centuries. While combining physical knowledge with computational approaches has enabled detailed modeling of physical systems' evolution, understanding the emergence of patterns and structures remains limited. Correlations between quantities are the most reliable approach to describe relationships between different variables. However, for complex patterns, directly searching for correlations is often impractical, as complexity and spatial inhomogeneity can obscure correlations. We discovered that the key is to search for correlations in local regions and developed a new method, adjacent correlation analysis, to extract such correlations and represent them in phase space. When multiple observations are available, a useful way to study a system is to analyze distributions in phase space using the Probability Density Function (PDF). Adjacent correlation analysis evaluates vectors representing local correlations, which can be overlaid on the PDF plot to form the adjacent correlation plot. These correlation vectors often exhibit remarkably regular patterns and may lead to the discovery of new laws. The vectors we derive are equivalent to the vector field in dynamical systems on the attracting manifold. By efficiently representing spatial patterns as correlation vectors in phase space, our approach opens avenues for classification, prediction, parameter fitting, and forecasting.
The development of science has been transforming man's view towards nature for centuries. Observing structures and patterns in an effective approach to discover regularities from data is a key step toward theory-building. With increasingly complex data being obtained, revealing regularities systematically has become a challenge. Correlation is a most commonly-used and effective approach to describe regularities in data, yet for complex patterns, spatial inhomogeneity and complexity can often undermine the correlations. We present an algorithm to derive maps representing the type and degree of correlations, by taking the two-fold symmetry of the correlation vector into full account using the Stokes parameter. The method allows for a spatially resolved view of the nature and strength of correlations between physical quantities. In the correlation view, a region can often be separated into different subregions with different types of correlations. Subregions correspond to physical regimes for physical systems, or climate zones for climate maps. The simplicity of the method makes it widely applicable to a variety of data, where the correlation-based approach makes the map particularly useful in revealing regularities in physical systems and alike. As a new and efficient approach to represent data, the method should facilitate the development of new computational approaches to regularity discovery.
Population synthesis simulations of compact binary coalescences~(CBCs) play a crucial role in extracting astrophysical insights from an ensemble of gravitational wave~(GW) observations. However, realistic simulations are costly to implement for a dense grid of initial conditions. Normalizing flows can emulate the distribution functions of a simulated population of binary parameters and thereby enable empirical constraints on the astrophysical initial conditions and branching fractions of various formation channels given data from a catalog of GW observations. They can also be used for data amplification in sparse regions of the CBC parameter space to guide the development of phenomenological population models for rarely synthesizable systems with components in theorized mass gaps, without having to simulate a prohibitively large number of binaries. But flow predictions are wrought with uncertainties, especially for sparse training sets. In this work I develop a method for quantifying and marginalizing uncertainties in the emulators by introducing the Bayesian Normalizing flow, a conditional density estimator constructed from Bayesian neural networks. Using the exact likelihood function associated with density estimators I sample the posterior distribution of flow parameters with suitably chosen priors to quantify and marginalize over flow uncertainties. I demonstrate the accuracy, calibration, and data-amplification impacts of the estimated uncertainties for simulations of binary black hole populations formed through common envelope evolution. I outline applications of the methodology in simulation-based inference from growing GW catalogs and sketch other uses for general simulation-based approaches in GW astronomy.