arXiv subjects

Adam Smith

Publications and source records attributed to Adam Smith.

At least 19 recordsLinked to original sources

arXiv2026

Robust Statistical Estimators with Bounded Empirical Sensitivity

We introduce a new measure of robustness for statistical estimators, which we call \emph{empirical sensitivity}. An estimator $\hat \theta$ has bounded empirical sensitivity if, with high probability over a dataset $X = (X_1, \dots, X_n) \sim \mathcal{D}^{\otimes n}$, for any dataset $Y$ obtained by modifying at most $\eta n$ points in $X$, we have that $\hat \theta(Y)$ is close to $\hat \theta(X)$. We study bounds on this quantity for the prototypical problem of Gaussian mean estimation. We prove new lower bounds, showing that for any estimator $\hat \mu$ which achieves an optimal $\ell_2$-error bound of $O\left(\sqrt{d/n}\right)$, the empirical sensitivity is at least $\Omega\left(\eta + \sqrt{\eta d/n}\right)$. The two terms arise due to obstructions on the mean and variance (via an Efron-Stein argument) of such an estimator. We show that this bound is tight up to logarithmic factors, by employing recent results for robust empirical mean estimation.

math.ST↗

arXiv2026

Differentially Private Modeling of Disease Transmission within Human Contact Networks

Epidemiologic studies of infectious diseases often rely on models of contact networks to capture the complex interactions that govern disease spread, and ongoing projects aim to vastly increase the scale at which such data can be collected. However, contact networks may include sensitive information, such as sexual relationships or drug use behavior. Protecting individual privacy while maintaining the scientific usefulness of the data is crucial. We propose a privacy-preserving pipeline for disease spread simulation studies based on a sensitive network that integrates differential privacy (DP) with statistical network models such as stochastic block models (SBMs) and exponential random graph models (ERGMs). Our pipeline comprises three steps: (1) compute network summary statistics using \emph{node-level} DP (which corresponds to protecting individuals' contributions); (2) fit a statistical model, like an ERGM, using these summaries, which allows generating synthetic networks reflecting the structure of the original network; and (3) simulate disease spread on the synthetic networks using an agent-based model. We evaluate the effectiveness of our approach using a simple Susceptible-Infected-Susceptible (SIS) disease model under multiple configurations. We compare both numerical results, such as simulated disease incidence and prevalence, as well as qualitative conclusions such as intervention effect size, on networks generated with and without differential privacy constraints. Our experiments are based on egocentric sexual network data from the ARTNet study (a survey about HIV-related behaviors). Our results show that the noise added for privacy is small relative to other sources of error (sampling and model misspecification). This suggests that, in principle, curators of such sensitive data can provide valuable epidemiologic insights while protecting privacy.

cs.CR↗

arXiv2026

Ensemble of Small Classifiers For Imbalanced White Blood Cell Classification

Automating white blood cell classification for diagnosis of leukaemia is a promising alternative to time-consuming and resource-intensive examination of cells by expert pathologists. However, designing robust algorithms for classification of rare cell types remains challenging due to variations in staining, scanning and inter-patient heterogeneity. We propose a lightweight ensemble approach for classification of cells during Haematopoiesis, with a focus on the biology of Granulopoiesis, Monocytopoiesis and Lymphopoiesis. Through dataset expansion to alleviate some class imbalance, we demonstrate that a simple ensemble of lightweight pretrained SwinV2-Tiny, DinoBloom-Small and ConvNeXT-V2-Tiny models achieves excellent performance on this challenging dataset. We train 3 instantiations of each architecture in a stratified 3-fold cross-validation framework; for an input image, we forward-pass through all 9 models and aggregate through logit averaging. We further reason on the weaknesses of our model in confusing similar-looking myelocytes in granulopoiesis and lymphocytes in lymphopoiesis. Code: https://gitlab.com/siddharthsrivastava/wbc-bench-2026.

cs.CV↗

arXiv2026

Can QCD Axions Survive the Cosmological Constant Problem?

Mechanisms that dynamically relax the vacuum energy offer a concrete way to approach the cosmological constant problem, but because relaxation is not confined to the vacuum energy alone it can have consequences for the rest of low-energy physics. We explore this issue using the recently proposed 'yoga' relaxation models as an explicit framework and show how relaxation differentially suppresses 'slow' physics relative to a characteristic timescale set by the mass of the relaxon. It therefore need not alter e.g. Higgs & collider physics but can dramatically change how light scalar fields participate in cosmology. We revisit the QCD axion in this setting and show that the suppression of the axion's vacuum potential reshapes its behaviour on cosmological timescales while leaving fast, high-energy processes unaffected. The result is to alter the axion mass-coupling relation away from the standard QCD band, driving it into a regime already ruled out by observational constraints. In particular, suppression of the vacuum axion potential allows the QCD matter-induced potential to dominate even for matter densities relevant to cosmology and everyday matter, potentially driving the axion away from the CP-conserving minimum for QCD-motivated parameters. We conclude that conventional QCD axions are unlikely to remain viable in their standard form within vacuum-energy relaxation frameworks.

hep-ph↗

arXiv2026

Multi-Field Dilaton Screening Beyond the Thin-Shell Mechanism

We analyse screening in multi-field scalar-tensor theories, focusing on systems with a dilaton coupled to matter and an axion with a dilaton-dependent kinetic term, in the presence of both planetary and stellar density profiles. Using analytic arguments and fully coupled numerical solutions, we identify a regime in which full screening for a dark-energy-light, effectively unpinned string-dilaton, can occur without fine-tuning. The backreaction of the dilaton's partnered axion field can suppress the exterior scalar charge by selecting a minimum-energy configuration (the BBQ mechanism), yielding robust screening for generic axion gradients. In this regime screening is achieved by cancelling the dilaton's gradient rather than localising it. This reduces the exterior scalar charge and allows for gravity tests in the solar system to be passed. We then show that the more familiar thin-shell intuition need not apply in the multi-field setting. Axion surface gradients can drastically reshape the dilaton profile and drive a more localised transition without generically suppressing the fifth force. The exterior charge can remain essentially unchanged or even be enhanced as the shell is made thinner by a kinetically coupled field. Multi-field two-derivative dynamics therefore decouple localisation in thin shells from screening, evade single-field no-go arguments, and reopen viable parameter space for cosmologically light dilaton-like scalars with strong couplings to matter.

gr-qc↗

arXiv2026

Local Node Differential Privacy

We initiate an investigation of node differential privacy for graphs in the local model of private data analysis. In our model, dubbed LNDP*, each node sees its own edge list and releases the output of a local randomizer on this input. These outputs are aggregated by an untrusted server to obtain a final output. We develop a novel algorithmic framework for this setting that allows us to accurately answer arbitrary linear queries about the input graph's degree distribution. Our framework is based on a new object, called the blurry degree distribution, which closely approximates the degree distribution and has lower sensitivity. Instead of answering queries about the degree distribution directly, our algorithms answer queries about the blurry degree distribution. This framework yields accurate LNDP* algorithms for the edge count, PMF and CDF of the degree distribution, and other graph statistics. For some natural problems, our algorithms match the accuracy achievable with node privacy in the central model, where data are held and processed by a trusted server. We also prove lower bounds on the error required by LNDP* algorithms that imply the optimality of our framework for edge counting in sparse graphs and Erdos-Renyi parameter estimation. Our lower bounds apply even to interactive protocols with a constant number of rounds of interaction between the nodes and the server. Existing lower-bound techniques for related models either yield loose bounds or do not apply in our setting, as graph data results in inherently overlapping inputs to local randomizers. To prove our bounds, we develop a splicing argument that stitches together views from locally similar but globally different distributions on graphs to obtain hard instances. Finally, we prove structural results that reveal qualitative differences between local node privacy and the standard local model for tabular data.

cs.DS↗

arXiv2026

A Bridge Between Climate Science and Economics: OPTiMEM and the Heat Conjecture for Estimation of Social Cost of Greenhouse Gases

We present an entirely new physics founded approach to estimating the social cost of carbon (SCC). For this, we developed our Ocean-Heat-Content Physics and Time Macro Economic Model (OPTiMEM) to estimate future heat content. The heat conjecture assumes that weather damages curves are stochastically proportional to ocean heat increase. We model carbon combustion, validate to datasets for greenhouse gas (GHG), temperature, and ocean heat content (OHC). We show that the social cost of 4 GHGs: CO2, CH4, N2O and halogenated hydrocarbons, cannot be single values, but must be represented by a kind of economic phase space. We propose very long-term carbon bonds to implement real discounting. This obviates the Gordian knot of the descriptivist versus prescriptivist discount disagreement that is unsolvable. Implementing these bonds leads to a new monitoring metric: real-dollar spending and bond discount rates compared to SC-GHG cost with variation on the discount scale, where the discount has no relationship to the pure rate of time preference (PRTP). This heat conjecture is based on OPTiMEM. OPTiMEM initiates from a fossil fuel consumption function to produce CO2, with 18 scenarios implemented to provide the uncertainty range. We provide 1:N year loss risk models (1:10, 1:100, 1:1000) that government, engineers, and actuaries should find useful. A scenario implementing DICE family of models carbon and growth assumptions shows +18{\deg} C is breached by 2210 CE, and +110{\deg} C by 2300 CE -- both of which outcomes are obviously not compatible with the fairly rosy conclusions of DICE models. Concerns are raised about having enough low-cost fossil fuel for conversion to minimal CO$_2$ maximal energy return on energy invested (EROEI) power if nations wait too long, and low EROEI power is questioned because monetary value is dependent on energy.

econ.TH↗

arXiv2025

Social Cost of Greenhouse Gases -- OPTiMEM and the Heat Conjecture(s)

Despite well-meaning scenarios that propose global CO2 emissions will decline presented in every IPCC report since 1988, the trend of global CO2 increase continues without significant change. Even if any individual nation manages to flatten its emissions, what matters is the trajectory of the globe. Together the gulf between climate science and climate economics, plus the urgent need for alternative methods of estimation, provided the incentives for development of our Ocean-Heat-Content (OHC) Physics and Time Macro Economic Model (OPTiMEM) system. To link NOAA damages to climate required creating a carbon consumption model to drive a physics model of climate. How fast could carbon be burned and how much coal, oil and natural gas was reasonably available? A carbon model driving climate meant burning the carbon, and modelling how the earth heated up. We developed this using the most recent best greenhouse gas equations and production models for CO2, CH4, N2O, and halogenated gases. This developed an ocean heat content model for the globe. Each step is validated against Known carbon consumption, CO2, temperature, and ocean heat content. This allows a physics founded model of climate costs to be projected.

econ.TH↗

arXiv2025

Variational quantum eigensolver for chemical molecules

Solving interacting multi-particle systems is a central challenge in quantum chemistry and condensed matter physics. In this work, we investigate the computation of ground states and ground-state energies for the He-H+ and H2O molecules using quantum computing techniques. We employ the variational quantum eigensolver (VQE), implemented both on a quantum computer simulator and on an IBM quantum device. The resulting energies are benchmarked against exact ground-state energies obtained via classical methods. Simulations of the H2O molecule were performed on Nottingham's High Performance Computing (HPC) facilities.

quant-ph↗

arXiv2025

The Serendipitous Axiodilaton: A Self-Consistent Recombination-Era Solution to the Hubble Tension

Axio-dilaton cosmology provides a minimal benchmark model for both Dark Matter (DM) and Dark Energy (DE) that is well motivated by fundamental physics. The axion and dilaton arise as pseudo-Goldstone modes of symmetries that predict particle masses depend on the dilaton, and therefore to evolve cosmologically, leading to correlated modifications of recombination physics, the sound horizon, and late-time expansion and growth histories. We confront this model with Planck 2018 temperature, polarisation, and lensing data, SPT-3G high-$\ell$ measurements, DESI DR2 BAO, and Pantheon$+$ supernovae, assuming that the axion makes up all of the dark matter and that the dilaton plays the role of a dark energy field. We find that it fits the data somewhat better than $\Lambda$CDM cosmology, with the $\chi^2$ lowered by $\simeq 7$ for three additional parameters, and significantly raises the inferred Hubble constant to $H_0 \simeq 69.2\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$, reducing the Hubble tension to $\lesssim 3\sigma$ and thereby allowing a joint fit of CMB and SH0ES data. The model fits this enlarged data set as well as the $w_0w_a$ model with an electron mass modified by hand at recombination, though it does so with calculable dynamics. Axio-dilaton self-interactions robustly fake a phantom equation of state in DESI measurements. There is a sting: cosmology prefers dilaton-matter couplings $|\mathbf{g}|\sim 10^{-2}$-$10^{-1}$, which are large enough to have been detected in solar-system tests of General Relativity. These results show how axio-dilatons can provide a viable cosmology preferred by current data at surprisingly large couplings, within a framework that links dark energy, dark matter, and time-dependent particle masses in a coherent way. They suggest both new observable signals and new theoretical directions, aimed at resolving the apparent inconsistency with non-cosmological observations.

astro-ph.CO↗

arXiv2025

RSstitcher -- Merging 2D diffraction frames for Wide Range Reciprocal Space Maps with absorption correction and integration functions

Wide Range Reciprocal Space Mapping (WRRSM) is a technique that allows visualisation of the geometric relationships among multiple hkl spots in a whole reciprocal space map. However, commercial softwares for WRRSMs generation are associated with several issues or limitations, which are overcome by the currently reporting open-source python program RSstitcher (Reciprocal Space Stitcher). RSstitcher merges 2D scan frames formats supported by FabIO and enables WRRSM function on most laboratory X-ray diffractometers equipped with a goniometer cradle and a 2D detector of any sensor size. It is so far the only WRRSMs generation tool that applies diffraction intensity correction due to sample self-absorption, which enables quantitative analyses for WRRSMs including texture measurement and 1D data integration. The conversion equations used in the python program is explained geometrically, including novel WRRSM measurements in {\omega}-{\phi} compensated Side Inclination Grazing Incident Diffraction mode for thin film samples. The applications of RSstitcher for bulk and thin film samples are demonstrated using two common 2D X-ray diffraction systems.

physics.ins-det↗

arXiv2025

Dynamical Dark Energy Meets Varying Electron Mass: Implications for Phantom Crossing and the Hubble Constant

We investigate the interplay between varying electron mass ($m_e$) and dynamical dark energy by analysing the Chevallier-Polarski-Linder (CPL) parametrization and its non-crossing variants, both with and without a varying-$m_e$ component. Our aim is to assess whether the preference for late-time dynamics and phantom divide line (PDL) crossing persists when early-time physics is introduced, and whether these combined models improve the alleviation of the Hubble tension compared to the varying-$m_e$ extension alone. Using the latest CMB, BAO, and supernova datasets, we derive updated constraints on $\Lambda$CDM, CPL, and their extensions, and examine their impact on $H_0$ and the preference for late-time dynamics. We find that $\Lambda$CDM+$m_e$ yields the largest upward shift in $H_0$, while replacing $\Lambda$ with the CPL parametrization or its non-crossing variants provides modest improvements in the overall fit. The data consistently favour dynamical dark energy and a phantom divide line crossing at scale factors $a_{\rm c}\simeq0.6-0.9$, and these preferences remain robust, though somewhat weaker ($\gtrsim2\sigma$), when the electron mass is also allowed to vary. Among the late-time models, CPL performs better than its non-crossing variants, further reinforcing the evidence for a genuine phantom divide crossing. The alleviation of the $H_0$ tension in the varying-$m_e$ case arises from late-time data breaking the strong $\Omega_m$-$m_e$ degeneracy in the CMB, while the additional degrees of freedom in CPL models allow the late-time dynamics to absorb this impact, thereby weakening the degeneracy breaking and further lowering $H_0$ through their ability to yield a decreasing dark energy contribution.

astro-ph.CO↗

arXiv2025

High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares

The interplay between optimization and privacy has become a central theme in privacy-preserving machine learning. Noisy stochastic gradient descent (SGD) has emerged as a cornerstone algorithm, particularly in large-scale settings. These variants of gradient methods inject carefully calibrated noise into each update to achieve differential privacy, the gold standard notion of rigorous privacy guarantees. Prior work primarily provides various bounds on statistical risk and privacy loss for noisy SGD, yet the \textit{exact} behavior of the process remains unclear, particularly in high-dimensional settings. This work leverages a diffusion approach to analyze noisy SGD precisely, providing a continuous-time perspective that captures both statistical risk evolution and privacy loss dynamics in high dimensions. Moreover, we study a variant of noisy SGD that does not require explicit knowledge of gradient sensitivity, unlike existing work that assumes or enforces sensitivity through gradient clipping. Specifically, we focus on the least squares problem with $\ell_2$ regularization.

cs.LG↗

arXiv2025

The Sample Complexity of Membership Inference and Privacy Auditing

A membership-inference attack gets the output of a learning algorithm, and a target individual, and tries to determine whether this individual is a member of the training data or an independent sample from the same distribution. A successful membership-inference attack typically requires the attacker to have some knowledge about the distribution that the training data was sampled from, and this knowledge is often captured through a set of independent reference samples from that distribution. In this work we study how much information the attacker needs for membership inference by investigating the sample complexity-the minimum number of reference samples required-for a successful attack. We study this question in the fundamental setting of Gaussian mean estimation where the learning algorithm is given $n$ samples from a Gaussian distribution $\mathcal{N}(\mu,\Sigma)$ in $d$ dimensions, and tries to estimate $\hat\mu$ up to some error $\mathbb{E}[\|\hat \mu - \mu\|^2_{\Sigma}]\leq \rho^2 d$. Our result shows that for membership inference in this setting, $\Omega(n + n^2 \rho^2)$ samples can be necessary to carry out any attack that competes with a fully informed attacker. Our result is the first to show that the attacker sometimes needs many more samples than the training algorithm uses to train the model. This result has significant implications for practice, as all attacks used in practice have a restricted form that uses $O(n)$ samples and cannot benefit from $\omega(n)$ samples. Thus, these attacks may be underestimating the possibility of membership inference, and better attacks may be possible when information about the distribution is easy to obtain.

cs.LG↗

arXiv2025

HD 5501: A Rapidly Evolving Interacting Eclipsing Binary with a Variable Light Curve and H$\alpha$ Emission

HD~5501, a hitherto little studied eclipsing binary with an early A-type primary, has been caught in a short-lived, astrophysically interesting phase of its binary evolution. Recent photometric and spectroscopic observations, including photometric data from {\it TESS}, show it has a highly variable light curve as well as complex spectral variability, particularly in both the absorption and emission components at H~$\alpha$. Our current campaign, including both professional and amateur observers, has determined that the primary is evolving rapidly across the Hertzsprung gap and that, unusually in the case of mass transfer, the orbital period is declining with a characteristic time-scale $P/\dot{P} \approx$ 170,000 years. Significantly, the orbit is eccentric and it appears that mass transfer from the primary to the secondary occurs only near periastron. Modeling indicates the presumed B7 V secondary to be surrounded by an accretion torus, which likely has dynamically chaotic variations in size and shape. Our analysis further implies the presence of a circumbinary disc or shell supplied by mass loss through the Lagrange $L_3$ point. That mass loss appears to account for most of the emission at H$\alpha$. We describe how this astrophysically interesting system may yield valuable information about binary star evolution at the onset of Roche-lobe overflow, as well as insights into eccentricity-modifying mechanisms such as the Soker mechanism.

astro-ph.SR↗

arXiv2025

A Unified Framework for Adversary-Aware Differential Privacy Bounds

Differential Privacy (DP) bounds the privacy leakage of a mechanism against worst-case membership inference, but the precise tradeoff between complex adversarial models and DP protections remains poorly understood. In this paper, we present a unified framework that generalizes the patchwork of existing bounds across membership inference, attribute inference, and data reconstruction attacks. Crucially, our framework is the first to evaluate attacks that target multiple individuals simultaneously and measure success beyond exact matches under a single cohesive bound. Our bounds capture this broad family of previously unexplored attack settings by relying solely on the privacy parameters and the adversary's baseline success rate (i.e. its prior without access to the mechanism's output). To illustrate this, we compare our high-probability guarantees to empirical attacks in two novel settings: extracting multiple non-uniform secrets (passwords and PII) from DP-finetuned language models, and reconstructing tabular data from noisy marginals. Ultimately, this framework provides a rigorous theoretical foundation to investigate the risk landscape of DP algorithms in new adversarial settings.

cs.CR↗

arXiv2025

Black-Box Privacy Attacks on Shared Representations in Multitask Learning

Multitask learning (MTL) has emerged as a powerful paradigm that leverages similarities among multiple learning tasks, each with insufficient samples to train a standalone model, to solve them simultaneously while minimizing data sharing across users and organizations. MTL typically accomplishes this goal by learning a shared representation that captures common structure among the tasks by embedding data from all tasks into a common feature space. Despite being designed to be the smallest unit of shared information necessary to effectively learn patterns across multiple tasks, these shared representations can inadvertently leak sensitive information about the particular tasks they were trained on. In this work, we investigate what information is revealed by the shared representations through the lens of inference attacks. Towards this, we propose a novel, black-box task-inference threat model where the adversary, given the embedding vectors produced by querying the shared representation on samples from a particular task, aims to determine whether that task was present when training the shared representation. We develop efficient, purely black-box attacks on machine learning models that exploit the dependencies between embeddings from the same task without requiring shadow models or labeled reference data. We evaluate our attacks across vision and language domains for multiple use cases of MTL and demonstrate that even with access only to fresh task samples rather than training data, a black-box adversary can successfully infer a task's inclusion in training. To complement our experiments, we provide theoretical analysis of a simplified learning setting and show a strict separation between adversaries with training samples and fresh samples from the target task's distribution.

cs.LG↗

arXiv2025

Screened Axio-dilaton Cosmology: Novel Forms of Early Dark Energy

We study the cosmology of multi-field Dark Energy, using a well-motivated axio-dilaton model that contains the minimal number of fields to have the 2-derivative sigma-model interactions that power-counting arguments show naturally compete with General Relativity at low energies. Our analysis differs from earlier, related, studies by treating the case where the dilaton's couplings to matter are large enough to require screening to avoid unacceptable dilaton-mediated forces in the solar system. We use a recently proposed screening mechanism that exploits the interplay between stronger-than-gravitational axion-matter couplings with the 2-derivative axion-dilaton interactions to suppress the couplings of the dilaton to bulk matter. The required axion-matter couplings also modify cosmology, with the axion's background energy density turning out to resemble early dark energy. We compute the properties of the axion fluid describing the rapid oscillations of the axion field around the time-dependent minimum of its matter-dependent effective potential, extending the usual formalism to include nontrivial kinetic sigma-model interactions. We explore the implications of these models for the Cosmic Microwave Background and the growth of structure and find that for dilaton potentials of the Albrecht-Skordis form (itself well-motivated by UV physics), successful screening can be consistent with the early dark energy temporarily comprising as much as 10% of the total density in the past. We find that increasing the dilaton-matter coupling decreases the growth of structure due to enhanced Hubble friction, an effect that dominates the usual fifth-force effects that amplify structure growth.

hep-th↗