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Chris Miller

Publications and source records attributed to Chris Miller.

At least 19 recordsLinked to original sources
arXiv2026

Liquid nitrogen pre-cooling system for ELT instruments utilizing additively manufactured heat exchangers, integrated temperature and liquid level control

Using liquid Nitrogen as the cryogenic fluid for pre-cooling purposes is almost ubiquitous across modern observatories. That method tends to be inefficient since it relies on the evaporation of the fluid afforded by the available surface area within the exchanger without capturing any of its latent heat. An additively manufactured heat exchanger could address that inefficiency by providing an increased evaporation area as well as internal structures that can interact with Nitrogen in its gaseous phase. A miniature pre-cooling system utilizing an existing cryostat was developed so the performance of conventionally and additively manufactured heat exchangers would be compared by recording the time required to cool an instrumented mass. Our findings indicate that there is a clear cost and performance advantage of additively manufactured heat exchangers when compared to conventional units, that could lead to reducing the operating costs of observatories hosting large size instruments such as HARMONI.

astro-ph.IM↗
arXiv2026

Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models

Satellite observation operators play an essential role in atmospheric data assimilation by translating model state variables into observation space. Previous work has shown that deep-learned emulators can effectively predict the outputs of classic observation operators, like the Community Radiative Transfer Model (CRTM), with reduced inference time. This study expands previous work to show the potential for integrating observation operators into artificial intelligence (AI) weather forecasting models. Specifically, this study shows that (1) deep-learned models can effectively predict the innovations (or differences between the simulated and observed radiances) used by data assimilation models and (2) deep-learned observation models suffer only minor degradations in performance when the model state is represented with fewer vertical levels, as is commonly used by AI forecasting models. Experiments were performed using the Unified Forecast System (UFS) replay dataset, including Gridpoint Statistical Interpolation (GSI) observational data for the Advanced Technology Microwave Sounder (ATMS) sensor from 2022 and 2023. Code is available at https://github.com/mitre/deep-obs.

physics.ao-ph↗
arXiv2026

When Does Adaptation Win? Scaling Laws for Meta-Learning in Quantum Control

Quantum hardware suffers from intrinsic device heterogeneity and environmental drift, forcing practitioners to choose between suboptimal non-adaptive controllers or costly per-device recalibration. We derive a scaling law lower bound for meta-learning showing that the adaptation gain (expected fidelity improvement from task-specific gradient steps) saturates exponentially with gradient steps and scales linearly with task variance, providing a quantitative criterion for when adaptation justifies its overhead. Validation on quantum gate calibration shows negligible benefits for low-variance tasks but >40% fidelity gains on two-qubit gates under extreme out-of-distribution conditions (10$\times$ the training noise), with implications for reducing per-device calibration time on cloud quantum processors. Further validation on classical linear-quadratic control confirms these laws emerge from general optimization geometry rather than quantum-specific physics. We further introduce a few-shot pre-adaptation protocol that estimates the optimal adaptation budget from $N{=}3$-5 probe steps within 3-19% relative error across out-of-distribution regimes.

cs.LG↗
arXiv2024

CUBES, the Cassegrain U-Band Efficient Spectrograph: towards final design review

In the era of Extremely Large Telescopes, the current generation of 8-10m facilities are likely to remain competitive at ground-UV wavelengths for the foreseeable future. The Cassegrain U-Band Efficient Spectrograph (CUBES) has been designed to provide high instrumental efficiency ( $>$ 37\%) observations in the near UV (305-400 nm requirement, 300-420 nm goal) at a spectral resolving power of R $>$ 20, 000 (with a lower-resolution, sky-limited mode of R $\sim$ 7, 000). With the design focusing on maximizing the instrument throughput (ensuring a Signal to Noise Ratio -SNR- $\sim$ 20 per spectral resolution element at 313 nm for U $\sim$ 17.5 mag objects in 1h of observations), it will offer new possibilities in many fields of astrophysics: i) access to key lines of stellar spectra (e.g. lighter elements, in particular Beryllium), extragalactic studies (e.g. circumgalactic medium of distant galaxies, cosmic UV background) and follow-up of explosive transients. We present the CUBES instrument design, currently in Phase-C and approaching the final design review, summarizing the hardware architecture and interfaces between the different subsystems as well as the relevant technical requirements. We describe the optical, mechanical, electrical design of the different subsystems (from the telescope adapter and support structure, through the main opto-mechanical path, including calibration unit, detector devices and cryostat control, main control electronics), detailing peculiar instrument functions like the Active Flexure Compensation (AFC). Furthermore, we outline the AITV concept and the main instrument operations giving an overview of its software ecosystem. Installation at the VLT is planned for 2028-2029 and first science operations in late 2029.

astro-ph.IM↗
arXiv2024

Anticipatory Understanding of Resilient Agriculture to Climate

With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields.

cs.CV↗
arXiv2022

Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio

SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this process, Sleepio captures data about the sleep behaviour of the users that have consented to such data being processed. For neural networks, the scale of the data is an opportunity to train meaningful models translatable to actual clinical practice. In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401,174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner. We demonstrate that this neural network is more accurate than standard statistical methods in predicting the sleep quality of an individual based on his/her behaviour from the last 10 days. We compare model performance in a wide range of hyperparameter settings representing various scenarios. We further show that the neural network can be used to produce personalised recommendations of what sleep habits users should follow to maximise sleep quality, and show that these recommendations are substantially better than the ones generated by standard methods. We finally show that the neural network can explain the recommendation given to each participant and calculate confidence intervals for each prediction, all of which are essential for clinicians to be able to adopt such a tool in clinical practice.

cs.LG↗
arXiv2022

Gemini-LIGHTS: Herbig Ae/Be and massive T-Tauri protoplanetary disks imaged with Gemini Planet Imager

We present the complete sample of protoplanetary disks from the Gemini- Large Imaging with GPI Herbig/T-tauri Survey (Gemini-LIGHTS) which observed bright Herbig Ae/Be stars and T-Tauri stars in near-infrared polarized light to search for signatures of disk evolution and ongoing planet formation. The 44 targets were chosen based on their near- and mid-infrared colors, with roughly equal numbers of transitional, pre-transitional, and full disks. Our approach explicitly did not favor well-known, "famous" disks or those observed by ALMA, resulting in a less-biased sample suitable to probe the major stages of disk evolution during planet formation. Our optimized data reduction allowed polarized flux as low as 0.002% of the stellar light to be detected, and we report polarized scattered light around 80% of our targets. We detected point-like companions for 47% of the targets, including 3 brown dwarfs (2 confirmed, 1 new), and a new super-Jupiter mass candidate around V1295 Aql. We searched for correlations between the polarized flux and system parameters, finding a few clear trends: presence of a companion drastically reduces the polarized flux levels, far-IR excess correlates with polarized flux for non-binary systems, and systems hosting disks with ring structures have stellar masses $<$ 3 Msun. Our sample also included four hot, dusty "FS CMa" systems and we detected large-scale ($>100$ au) scattered light around each, signs of extreme youth for these enigmatic systems. Science-ready images are publicly available through multiple distribution channels using a new FITS file standard jointly developed with members of the VLT/SPHERE team.

astro-ph.EP↗
arXiv2022

Unsupervised Cross-Task Generalization via Retrieval Augmentation

Humans can perform unseen tasks by recalling relevant skills acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve this kind of cross-task generalization ability of massive multi-task language models, such as T0 and FLAN, in an unsupervised setting. We propose a retrieval-augmentation method named ReCross that takes a few unlabelled examples as queries to retrieve a small subset of upstream data and uses them to update the multi-task model for better generalization. ReCross is a straightforward yet effective retrieval method that combines both efficient dense retrieval and effective pair-wise reranking. Our results and analysis show that it significantly outperforms both non-retrieval methods and other baseline methods.

cs.CL↗
arXiv2022

Finding Trolls Under Bridges: Preliminary Work on a Motif Detector

Motifs are distinctive recurring elements found in folklore that have significance as communicative devices in news, literature, press releases, and propaganda. Motifs concisely imply a large constellation of culturally-relevant information, and their broad usage suggests their cognitive importance as touchstones of cultural knowledge, making their detection a worthy step toward culturally-aware natural language processing tasks. Until now, folklorists and others interested in motifs have only extracted motifs from narratives manually. We present a preliminary report on the development of a system for automatically detecting motifs. We briefly describe an annotation effort to produce data for training motif detection, which is on-going. We describe our in-progress architecture in detail, which aims to capture, in part, how people determine whether or not a motif candidate is being used in a motific way. This description includes a test of an off-the-shelf metaphor detector as a feature for motif detection, which achieves a F1 of 0.35 on motifs and a macro-average F1 of 0.21 across four categories which we assign to motif candidates.

cs.AI↗
arXiv2022

CUBES Phase A design overview -- The Cassegrain U-Band Efficient Spectrograph for the Very Large Telescope

We present the baseline conceptual design of the Cassegrain U-Band Efficient Spectrograph (CUBES) for the Very Large Telescope. CUBES will provide unprecedented sensitivity for spectroscopy on a 8 - 10 m class telescope in the ground ultraviolet (UV), spanning a bandwidth of > 100 nm that starts at 300 nm, the shortest wavelength accessible from the ground. The design has been optimized for end-to-end efficiency and provides a spectral resolving power of R > 20000, that will unlock a broad range of new topics across solar system, Galactic and extraglactic astronomy. The design also features a second, lower-resolution (R \sim 7000) mode and has the option of a fiberlink to the UVES instrument for simultaneous observations at longer wavelengths. Here we present the optical, mechanical and software design of the various subsystems of the instrument after the Phase A study of the project. We discuss the expected performances for the layout choices and highlight some of the performance trade-offs considered to best meet the instrument top-level requirements. We also introduce the model-based system engineering approach used to organize and manage the project activities and interfaces, in the context that it is increasingly necessary to integrate such tools in the development of complex astronomical projects.

astro-ph.IM↗
arXiv2020

Connectedness in structures on the real numbers: o-minimality and undecidability

We initiate an investigation of structures on the set of real numbers having the property that path components of definable sets are definable. All o\nobreakdash-\hspace{0pt}minimal structures on $(\mathbb{R},<)$ have the property, as do all expansions of $(\mathbb{R},+,\cdot,\mathbb{N})$. Our main analytic-geometric result is that any such expansion of $(\mathbb{R},<,+)$ by boolean combinations of open sets (of any arities) either is o\nobreakdash-\hspace{0pt}minimal or defines an isomorph of $(\mathbb N,+,\cdot\,)$. We also show that any given expansion of $(\mathbb{R}, <, +,\mathbb{N})$ by subsets of $\mathbb{N}^n$ ($n$ allowed to vary) has the property if and only if it defines all arithmetic sets. Variations arise by considering connected components or quasicomponents instead of path components.

math.LO↗
arXiv2020

Query-Free Adversarial Transfer via Undertrained Surrogates

Deep neural networks are vulnerable to adversarial examples -- minor perturbations added to a model's input which cause the model to output an incorrect prediction. We introduce a new method for improving the efficacy of adversarial attacks in a black-box setting by undertraining the surrogate model which the attacks are generated on. Using two datasets and five model architectures, we show that this method transfers well across architectures and outperforms state-of-the-art methods by a wide margin. We interpret the effectiveness of our approach as a function of reduced surrogate model loss function curvature and increased universal gradient characteristics, and show that our approach reduces the presence of local loss maxima which hinder transferability. Our results suggest that finding strong single surrogate models is a highly effective and simple method for generating transferable adversarial attacks, and that this method represents a valuable route for future study in this field.

cs.CV↗
arXiv2020

Emergent gravity fails to explain color-dependent galaxy-galaxy lensing signals from SDSS Dr7

We test the Emergent Gravity(EG) theory using the galaxy-galaxy lensing technique based on SDSS DR7 data. In the EG scenario, we do not expect color dependence of the galaxy sample in the 'apparent dark matter' predicted by EG, which is exerted only by the baryonic mass. If the baryonic mass is similar, then the predicted lensing profiles from the baryonic mass should be similar according to the EG, regardless of the color of the galaxy sample. We use the stellar mass of the galaxy as a proxy of its baryonic mass. We divide our galaxy sample into 5 stellar mass bins, and further classify them as red and blue subsamples in each stellar mass bin. If we set halo mass and concentration as free parameters, $\Lambda$CDM is favored by our data in terms of the reduced $\chi^2$ while EG fails to explain the color dependence of ESDs from the galaxy-galaxy lensing measurement.

astro-ph.GA↗
arXiv2019

Multiple spiral arms in the disk around intermediate-mass binary HD 34700A

We present the first images of the transition disk around the close binary system HD 34700A in polarized scattered light using the Gemini Planet Imager instrument on Gemini South. The J and H band images reveal multiple spiral-arm structures outside a large (R = 0.49" = 175 au) cavity along with a bluish spiral structure inside the cavity. The cavity wall shows a strong discontinuity and we clearly see significant non-azimuthal polarization Uphi consistent with multiple scattering within a disk at an inferred inclination ~42deg. Radiative transfer modeling along with a new Gaia distance suggest HD 37400A is a young (~5 Myr) system consisting of two intermediate-mass (~2Msun) stars surrounded by a transitional disk and not a solar-mass binary with a debris disk as previously classified. Conventional assumptions of the dust-to-gas ratio would rule out a gravitational instability origin to the spirals while hydrodynamical models using the known external companion or a hypothetical massive protoplanet in the cavity both have trouble reproducing the relatively large spiral arm pitch angles (~30deg) without fine tuning of gas temperature. We explore the possibility that material surrounding a massive protoplanet could explain the rim discontinuity after also considering effects of shadowing by an inner disk. Analysis of archival Hubble Space Telescope data suggests the disk is rotating counterclockwise as expected from the spiral arm structure and revealed a new low-mass companion at 6.45" separation. We include an appendix which sets out clear definitions of Q, U, Qphi, Uphi, correcting some confusion and errors in the literature.

astro-ph.EP↗
arXiv2018

Expansions of the real field by canonical products

We consider expansions of o-minimal structures on the real field by collections of restrictions to the positive real line of the canonical Weierstrass products associated to sequences such as $(-n^s)_{n>0}$ (for $s>0$) and $(-s^n)_{n>0}$ (for $s>1$), and also expansions by associated functions such as logarithmic derivatives. There are only three possible outcomes known for the resulting structures: (i)~o-minimality; (ii)~d-minimality (but not o-minimality); (iii)~definability of $\mathbb{Z}$.

math.LO↗
arXiv2017

Points of bounded height on oscillatory sets

We show that transcendental curves in $\mathbb R^n$ (not necessarily compact) have few rational points of bounded height provided that the curves are well behaved with respect to algebraic sets in a certain sense and can be parametrized by functions belonging to a specified algebra of infinitely differentiable functions. Examples of such curves include logarithmic spirals and solutions to Euler equations $x^2y''+xy'+cy=0$ with $c>0$.

math.AG↗