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We introduce a novel framework for design and optimization of 3D freeform metalenses that attains nearly linear scaling of computational cost with diameter, by breaking the lens into a sequence of radial ``zones'' with $n$-fold discrete axisymmetry, where $n$ increases with radius. This allows vastly more design freedom than imposing continuous axisymmetry, while avoiding the compromises of the locally periodic approximation (LPA) or scalar diffraction theory. Using a GPU-accelerated finite-difference time-domain (FDTD) solver in cylindrical coordinates, we perform full-wave simulation and topology optimization within each supra-wavelength zone. We validate our approach by designing millimeter and centimeter-scale, poly-achromatic, 3D freeform metalenses which outperform the state of the art. By demonstrating the scalability and resulting optical performance enabled by our ``zoned discrete axisymmetry'' (ZDA) and supra-wavelength domain decomposition, we highlight the potential of our framework to advance large-scale meta-optics and next-generation photonic technologies., Comment: 15 pages, 8 figures

We report on work using a newly developed code, SpheriCo.jl, that computes the gravitational collapse of a spherical scalar field, where the scalar can be either a classical field, or a quantum field operator. By utilising summation-by-parts methods for the numerical derivatives we are able to simulate the collapse longer than was possible previously due to enhanced numerical stability. We present a suite of tests for the code that tests its accuracy and stability, both for the classical and quantum fields. We are able to observe critical behavior of gravitational collapse for the classical setup, in agreement with expected results. The code is also used to compute two-point correlation functions, with results that hint at a non-trivial correlation across the horizon of Hawking quanta., Comment: 52 pages, 25 figures, code available at https://github.com/ThanasisGiannakopoulos/SpheriCo.jl, data available at https://zenodo.org/records/14365567, associated simulation videos at https://tinyurl.com/spherico

In the vast majority of the academic and scientific domains, LaTeX has established itself as the de facto standard for typesetting complex mathematical equations and formulae. However, LaTeX's complex syntax and code-like appearance present accessibility barriers for individuals with disabilities, as well as those unfamiliar with coding conventions. In this paper, we present a novel solution to this challenge through the development of a novel speech-to-LaTeX equations system specifically designed for the Greek language. We propose an end-to-end system that harnesses the power of Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) techniques to enable users to verbally dictate mathematical expressions and equations in natural language, which are subsequently converted into LaTeX format. We present the architecture and design principles of our system, highlighting key components such as the ASR engine, the LLM-based prompt-driven equations generation mechanism, as well as the application of a custom evaluation metric employed throughout the development process. We have made our system open source and available at https://github.com/magcil/greek-speech-to-math., Comment: 4 pages, 2 figures, SETN2024: 13th EETN Conference on Artificial Intelligence

This paper describes the design, construction and testing of a dual-band monopole microstrip patch antenna to operate in the GSM and DCS bands. For compactness the monopole antenna is mounted on top of a FR4 substrate and is designed as a planar structure which can be wrapped into a box like structure. The performance characteristics of the wrapped dual band monopole antenna are simulated on Ansoft HFSS before fabrication and testing on a Network Analyzer. The simulated HFSS return loss of the antenna shows broad agreement with experiment over the frequency range from 500MHz to 4GHz. Radiation patterns are generated in each band of operation of the antenna. The effect of feedline impedance matching on the performance of the monopole antenna is discussed., Comment: 6 pages, 10 figures

Interactive Voice Response (IVR) systems have undergone significant transformation in recent years, moving from traditional code-based development to more user-friendly approaches leveraging widgets and, most recently, harnessing the power of Artificial Intelligence (AI) for automated IVR flow creation. This paper explores the evolution of IVR building techniques, highlighting the industry's revolution and shaping the future of IVR systems. The authors delve into the historical context, current trends, and future prospects of IVR development, elucidating the impact of AI on simplifying IVR creation processes and enhancing customer experiences., Comment: 6 pages, 3 figures

Millimeter-wave refracting telescopes targeting the degree-scale structure of the cosmic microwave background (CMB) have recently grown to diffraction-limited apertures of over 0.5 meters. These instruments are entirely housed in vacuum cryostats to support their sub-kelvin bolometric detectors and to minimize radiative loading from thermal emission due to absorption loss in their transmissive optical elements. The large vacuum window is the only optical element in the system at ambient temperature, and therefore minimizing loss in the window is crucial for maximizing detector sensitivity. This motivates the use of low-loss polymer materials and a window as thin as practicable. However, the window must simultaneously meet the requirement to keep sufficient vacuum, and therefore must limit gas permeation and remain mechanically robust against catastrophic failure under pressure. We report on the development of extremely thin composite polyethylene window technology that meets these goals. Two windows have been deployed for two full observing seasons on the BICEP3 and BA150 CMB telescopes at the South Pole. On BICEP3, the window has demonstrated a 6% improvement in detector sensitivity., Comment: 20 pages, 12 figures, 4 tables

We use a custom-made calibrator to measure individual detectors' polarization angles of BICEP3, a small aperture telescope observing the cosmic microwave background (CMB) at 95GHz from the South Pole. We describe our calibration strategy and the statistical and systematic uncertainties associated with the measurement. We reach an unprecedented precision for such measurement on a CMB experiment, with a repeatability for each detector pair of $0.02\deg$. We show that the relative angles measured using this method are in excellent agreement with those extracted from CMB data. Because the absolute measurement is currently limited by a systematic uncertainty, we do not derive cosmic birefringence constraints from BICEP3 data in this work. Rather, we forecast the sensitivity of BICEP3 sky maps for such analysis. We investigate the relative contributions of instrument noise, lensing, and dust, as well as astrophysical and instrumental systematics. We also explore the constraining power of different angle estimators, depending on analysis choices. We establish that the BICEP3 2-year dataset (2017--2018) has an on-sky sensitivity to the cosmic birefringence angle of $\sigma = 0.078\deg$, which could be improved to $\sigma = 0.055\deg$ by adding all of the existing BICEP3 data (through 2023). Furthermore, we emphasize the possibility of using the BICEP3 sky patch as a polarization calibration source for CMB experiments, which with the present data could reach a precision of $0.035\deg$. Finally, in the context of inflation searches, we investigate the impact of detector-to-detector variations in polarization angles as they may bias the tensor-to-scalar ratio r. We show that while the effect is expected to remain subdominant to other sources of systematic uncertainty, it can be reliably calibrated using polarization angle measurements such as the ones we present in this paper., Comment: 29 Pages, 17 Figures, 6 Tables, as submitted to PRD

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

Measurements of B-mode polarization in the CMB sourced from primordial gravitational waves would provide information on the energy scale of inflation and its potential form. To achieve these goals, one must carefully characterize the Galactic foregrounds, which can be distinguished from the CMB by conducting measurements at multiple frequencies. BICEP Array is the latest-generation multi-frequency instrument of the BICEP/Keck program, which specifically targets degree-scale primordial B-modes in the CMB. In its final configuration, this telescope will consist of four small-aperture receivers, spanning frequency bands from 30 to 270 GHz. The 220/270 GHz receiver designed to characterize Galactic dust is currently undergoing commissioning at Stanford University and is scheduled to deploy to the South Pole during the 2024--2025 austral summer. Here, we will provide an overview of this high-frequency receiver and discuss the integration status and test results as it is being commissioned.

We study the classic single-item auction setting of Myerson, but under the assumption that the buyers' values for the item are distributed over finite supports. Using strong LP duality and polyhedral theory, we rederive various key results regarding the revenue-maximizing auction, including the characterization through virtual welfare maximization and the optimality of deterministic mechanisms, as well as a novel, generic equivalence between dominant-strategy and Bayesian incentive compatibility. Inspired by this, we abstract our approach to handle more general auction settings, where the feasibility space can be given by arbitrary convex constraints, and the objective is a convex combination of revenue and social welfare. We characterize the optimal auctions of such systems as generalized virtual welfare maximizers, by making use of their KKT conditions, and we present an analogue of Myerson's payment formula for general discrete single-parameter auction settings. Additionally, we prove that total unimodularity of the feasibility space is a sufficient condition to guarantee the optimality of auctions with integral allocation rules. Finally, we demonstrate this KKT approach by applying it to a setting where bidders are interested in buying feasible flows on trees with capacity constraints, and provide a combinatorial description of the (randomized, in general) optimal auction.

The BICEP/$\textit{Keck}$ (BK) series of cosmic microwave background (CMB) polarization experiments has, over the past decade and a half, produced a series of field-leading constraints on cosmic inflation via measurements of the "B-mode" polarization of the CMB. Primordial B modes are directly tied to the amplitude of primordial gravitational waves (PGW), their strength parameterized by the tensor-to-scalar ratio, $r$, and thus the energy scale of inflation. Having set the most sensitive constraints to-date on $r$, $\sigma(r)=0.009$ ($r_{0.05}<0.036, 95\%$ C.L.) using data through the 2018 observing season ("BK18"), the BICEP/$\textit{Keck}$ program has continued to improve its dataset in the years since. We give a brief overview of the BK program and the "BK18" result before discussing the program's ongoing efforts, including the deployment and performance of the $\textit{Keck Array}$'s successor instrument, BICEP Array, improvements to data processing and internal consistency testing, new techniques such as delensing, and how those will ultimately serve to allow BK reach $\sigma(r) \lesssim 0.003$ using data through the 2027 observing season., Comment: 9 pages, 5 figures. Contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

What do different contrastive learning (CL) losses actually optimize for? Although multiple CL methods have demonstrated remarkable representation learning capabilities, the differences in their inner workings remain largely opaque. In this work, we analyse several CL families and prove that, under certain conditions, they admit the same minimisers when optimizing either their batch-level objectives or their expectations asymptotically. In both cases, an intimate connection with the hyperspherical energy minimisation (HEM) problem resurfaces. Drawing inspiration from this, we introduce a novel CL objective, coined Decoupled Hyperspherical Energy Loss (DHEL). DHEL simplifies the problem by decoupling the target hyperspherical energy from the alignment of positive examples while preserving the same theoretical guarantees. Going one step further, we show the same results hold for another relevant CL family, namely kernel contrastive learning (KCL), with the additional advantage of the expected loss being independent of batch size, thus identifying the minimisers in the non-asymptotic regime. Empirical results demonstrate improved downstream performance and robustness across combinations of different batch sizes and hyperparameters and reduced dimensionality collapse, on several computer vision datasets., Comment: Accepted at ICML 2024. Code available at: https://github.com/pakoromilas/DHEL-KCL.git

We study the computational complexity of computing Bayes-Nash equilibria in first-price auctions with discrete value distributions and discrete bidding space, under general subjective beliefs. It is known that such auctions do not always have pure equilibria. In this paper, we prove that the problem of deciding their existence is NP-complete, even for approximate equilibria. On the other hand, it can be shown that mixed equilibria are guaranteed to exist; however, their computational complexity has not been studied before. We establish the PPAD-completeness of computing a mixed equilibrium and we complement this by an efficient algorithm for finding symmetric approximate equilibria in the special case of iid priors. En route to these results, we develop a computational equivalence framework between continuous and discrete first-price auctions, which can be of independent interest, and which allows us to transfer existing positive and negative results from one setting to the other. Finally, we show that correlated equilibria of the auction can be computed in polynomial time., Comment: 54 pages, 2 figures, Published in the 25th ACM Conference on Economics and Computation (EC'24)

Time-Division Multiplexing is the readout architecture of choice for many ground and space experiments, as it is a very mature technology with proven outstanding low-frequency noise stability, which represents a central challenge in multiplexing. Once fully populated, each of the two BICEP Array high frequency receivers, observing at 150GHz and 220/270GHz, will have 7776 TES detectors tiled on the focal plane. The constraints set by these two receivers required a redesign of the warm readout electronics. The new version of the standard Multi Channel Electronics, developed and built at the University of British Columbia, is presented here for the first time. BICEP Array operates Time Division Multiplexing readout technology to the limits of its capabilities in terms of multiplexing rate, noise and crosstalk, and applies them in rigorously demanding scientific application requiring extreme noise performance and systematic error control. Future experiments like CMB-S4 plan to use TES bolometers with Time Division/SQUID-based readout for an even larger number of detectors., Comment: 10 pages, 7 figures, Submitted to Journal of Low Temperature Physics

Observations of pulsar scintillation are among the few astrophysical probes of very small-scale ($\lesssim$ au) phenomena in the interstellar medium (ISM). In particular, characterization of scintillation arcs, including their curvature and intensity distributions, can be related to interstellar turbulence and potentially over-pressurized plasma in local ISM inhomogeneities, such as supernova remnants, HII regions, and bow shocks. Here we present a survey of eight pulsars conducted at the Five-hundred-meter Aperture Spherical Telescope (FAST), revealing a diverse range of scintillation arc characteristics at high sensitivity. These observations reveal more arcs than measured previously for our sample. At least nine arcs are observed toward B1929$+$10 at screen distances spanning $\sim 90\%$ of the pulsar's $361$ pc path-length to the observer. Four arcs are observed toward B0355$+$54, with one arc yielding a screen distance as close as $\sim10^5$ au ($<1$ pc) from either the pulsar or the observer. Several pulsars show highly truncated, low-curvature arcs that may be attributable to scattering near the pulsar. The scattering screen constraints are synthesized with continuum maps of the local ISM and other well-characterized pulsar scintillation arcs, yielding a three-dimensional view of the scattering media in context., Comment: 20 pages, 14 figures. Submitted to MNRAS and comments welcome. Interactive version of Figure 12 available at https://stella-ocker.github.io/scattering_ism3d_ocker2023

General relativity can describe various gravitational systems of astrophysical relevance, like black holes and neutron stars, or even strongly coupled systems through the holographic duality. The characteristic initial (boundary) value problem has numerous applications in general relativity involving numerical studies and is often formulated using Bondi-like coordinates. Well-posedness of the resulting systems of partial differential equations, however, remains an open question. The answer to this question affects the accuracy, and potentially the reliability of conclusions drawn from numerical studies based on such formulations. In the first part of this thesis, we expand our understanding of the hyperbolicity and well-posedness of Bondi-like free evolution systems. We show that several prototype Bondi-like formulations are only weakly hyperbolic and examine the root cause of this result. Consequently, the characteristic initial (boundary) value problem of general relativity in these gauges is rendered ill-posed in the simplest norms one would like to employ. We discuss the implications of this result in accurate gravitational waveform modeling methods and work towards the construction of alternative norms that might be more appropriate. We also present numerical tests that demonstrate weak hyperbolicity in practice and highlight important features to perform them effectively. In the second part, we turn our attention to applications of these formulations to the qualitative behavior of strongly coupled systems via holography., Comment: 179 pages, PhD Thesis, supplementary material at https://github.com/ThanasisGiannakopoulos/PhD-thesis-supp

Gravitational waves provide a powerful enhancement to our understanding of fundamental physics. To make the most of their detection we need to accurately model the entire process of their emission and propagation toward interferometers. Cauchy-characteristic extraction and matching are methods to compute gravitational waves at null infinity, a mathematical idealization of detector location, from numerical relativity simulations. Both methods can in principle contribute to modeling by providing highly accurate gravitational waveforms. An underappreciated subtlety in realizing this potential is posed by the (mere) weak hyperbolicity of the particular PDE systems solved in the characteristic formulation of the Einstein field equations. This shortcoming results from the popular choice of Bondi-like coordinates. So motivated, we construct toy models that capture that PDE structure and study Cauchy-characteristic extraction and matching with them. Where possible we provide energy estimates for their solutions and perform careful numerical norm convergence tests to demonstrate the effect of weak hyperbolicity on Cauchy-characteristic extraction and matching. Our findings strongly indicate that, as currently formulated, Cauchy-characteristic matching for the Einstein field equations would provide solutions that are, at best, convergent at an order lower than expected for the numerical method, and may be unstable. In contrast, under certain conditions, the extraction method can provide properly convergent solutions. Establishing however that these conditions hold for the aforementioned characteristic formulations is still an open problem., Comment: 20 pages, 11 figures, 2 tables, data and code can be found at http://dx.doi.org/10.5281/zenodo.7981429 and https://github.com/ThanasisGiannakopoulos/model_CCE_CCM_public, updated to match published version

We present a fully polynomial-time approximation scheme (FPTAS) for computing equilibria in congestion games, under smoothed running-time analysis. More precisely, we prove that if the resource costs of a congestion game are randomly perturbed by independent noises, whose density is at most $\phi$, then any sequence of $(1+\varepsilon)$-improving dynamics will reach an $(1+\varepsilon)$-approximate pure Nash equilibrium (PNE) after an expected number of steps which is strongly polynomial in $\frac{1}{\varepsilon}$, $\phi$, and the size of the game's description. Our results establish a sharp contrast to the traditional worst-case analysis setting, where it is known that better-response dynamics take exponentially long to converge to $\alpha$-approximate PNE, for any constant factor $\alpha\geq 1$. As a matter of fact, computing $\alpha$-approximate PNE in congestion games is PLS-hard. We demonstrate how our analysis can be applied to various different models of congestion games including general, step-function, and polynomial cost, as well as fair cost-sharing games (where the resource costs are decreasing). It is important to note that our bounds do not depend explicitly on the cardinality of the players' strategy sets, and thus the smoothed FPTAS is readily applicable to network congestion games as well., Comment: To appear at EC'24. Simplified analysis and improved bound in Lemma 1. Improved bound at Eq. (11). These result in improved smoothed running time bounds for all our congestion game models (i.e. Sections 3.2, 3.3.1, 3.3.2, and 3.3.3)

There is an imminent need for guidelines and standard test sets to allow direct and fair comparisons of speech emotion recognition (SER). While resources, such as the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, have emerged as widely-adopted reference corpora for researchers to develop and test models for SER, published work reveals a wide range of assumptions and variety in its use that challenge reproducibility and generalization. Based on a critical review of the latest advances in SER using IEMOCAP as the use case, our work aims at two contributions: First, using an analysis of the recent literature, including assumptions made and metrics used therein, we provide a set of SER evaluation guidelines. Second, using recent publications with open-sourced implementations, we focus on reproducibility assessment in SER., Comment: Accepted at ICASSP 2023

We propose Physics-Informed Fourier Networks for Electrical Properties (EP) Tomography (PIFON-EPT), a novel deep learning-based method for EP reconstruction using noisy and/or incomplete magnetic resonance (MR) measurements. Our approach leverages the Helmholtz equation to constrain two networks, responsible for the denoising and completion of the transmit fields, and the estimation of the object's EP, respectively. We embed a random Fourier features mapping into our networks to enable efficient learning of high-frequency details encoded in the transmit fields. We demonstrated the efficacy of PIFON-EPT through several simulated experiments at 3 and 7 tesla (T) MR imaging, and showed that our method can reconstruct physically consistent EP and transmit fields. Specifically, when only $20\%$ of the noisy measured fields were used as inputs, PIFON-EPT reconstructed the EP of a phantom with $\leq 5\%$ error, and denoised and completed the measurements with $\leq 1\%$ error. Additionally, we adapted PIFON-EPT to solve the generalized Helmholtz equation that accounts for gradients of EP between inhomogeneities. This yielded improved results at interfaces between different materials without explicit knowledge of boundary conditions. PIFON-EPT is the first method that can simultaneously reconstruct EP and transmit fields from incomplete noisy MR measurements, providing new opportunities for EPT research., Comment: 11 pages, accepted by IEEE JMMCT

Studying under-represented music traditions under the MIR scope is crucial, not only for developing novel analysis tools, but also for unveiling musical functions that might prove useful in studying world musics. This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre, among others. The content has been collected from a Greek documentary series that is available online, where academics present music traditions of Greece with live music and dance performance during the show, along with discussions about social, cultural and musicological aspects of the presented music. Therefore, this procedure has resulted in a significant wealth of descriptions regarding a variety of aspects, such as musical genre, places of origin and musical instruments. In addition, the audio recordings were performed under strict production-level specifications, in terms of recording equipment, leading to very clean and homogeneous audio content. In this work, apart from presenting the dataset in detail, we propose a baseline deep-learning classification approach to recognize the involved musicological attributes. The dataset, the baseline classification methods and the models are provided in public repositories. Future directions for further refining the dataset are also discussed.

We propose a unifying framework for smoothed analysis of combinatorial local optimization problems, and show how a diverse selection of problems within the complexity class PLS can be cast within this model. This abstraction allows us to identify key structural properties, and corresponding parameters, that determine the smoothed running time of local search dynamics. We formalize this via a black-box tool that provides concrete bounds on the expected maximum number of steps needed until local search reaches an exact local optimum. This bound is particularly strong, in the sense that it holds for any starting feasible solution, any choice of pivoting rule, and does not rely on the choice of specific noise distributions that are applied on the input, but it is parameterized by just a global upper bound $\phi$ on the probability density. The power of this tool can be demonstrated by instantiating it for various PLS-hard problems of interest to derive efficient smoothed running times (as a function of $\phi$ and the input size). Most notably, we focus on the important local optimization problem of finding pure Nash equilibria in Congestion Games, that has not been studied before from a smoothed analysis perspective. Specifically, we propose novel smoothed analysis models for general and Network Congestion Games, under various representations, including explicit, step-function, and polynomial resource latencies. We study PLS-hard instances of these problems and show that their standard local search algorithms run in polynomial smoothed time. Finally, we present further applications of our framework to a wide range of additional combinatorial problems, including local Max-Cut in weighted graphs, the Travelling Salesman problem (TSP) under the $k$-opt local heuristic, and finding pure equilibria in Network Coordination Games., Comment: An extended abstract of this paper was accepted to ICALP 2024

Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP can be potential biomarkers for pathology characterization, such as cancer, and improve therapeutic modalities, such radiofrequency hyperthermia and ablation. MR-based electrical properties tomography (MR-EPT) uses MR measurements to reconstruct the EP maps. Using the homogeneous Helmholtz equation, EP can be directly computed through calculations of second order spatial derivatives of the measured magnetic transmit or receive fields $(B_{1}^{+}, B_{1}^{-})$. However, the numerical approximation of derivatives leads to noise amplifications in the measurements and thus erroneous reconstructions. Recently, a noise-robust supervised learning-based method (DL-EPT) was introduced for EP reconstruction. However, the pattern-matching nature of such network does not allow it to generalize for new samples since the network's training is done on a limited number of simulated data. In this work, we leverage recent developments on physics-informed deep learning to solve the Helmholtz equation for the EP reconstruction. We develop deep neural network (NN) algorithms that are constrained by the Helmholtz equation to effectively de-noise the $B_{1}^{+}$ measurements and reconstruct EP directly at an arbitrarily high spatial resolution without requiring any known $B_{1}^{+}$ and EP distribution pairs., Comment: 2 pages, 4 figures, accepted by QMR Lucca workshop on MR Phase, Magnetic Susceptibility and Electrical Properties Mapping

We present estimates of line-of-sight distortion fields derived from the 95 GHz and 150 GHz data taken by BICEP2, BICEP3, and Keck Array up to the 2018 observing season, leading to cosmological constraints and a study of instrumental and astrophysical systematics. Cosmological constraints are derived from three of the distortion fields concerning gravitational lensing from large-scale structure, polarization rotation from magnetic fields or an axion-like field, and the screening effect of patchy reionization. We measure an amplitude of the lensing power spectrum $A_L^{\phi\phi}=0.95 \pm 0.20$. We constrain polarization rotation, expressed as the coupling constant of a Chern-Simons electromagnetic term $g_{a\gamma} \leq 2.6 \times 10^{-2}/H_I$, where $H_I$ is the inflationary Hubble parameter, and an amplitude of primordial magnetic fields smoothed over 1 Mpc $B_{1\text{Mpc}} \leq 6.6 \;\text{nG}$ at 95 GHz. We constrain the root mean square of optical-depth fluctuations in a simple "crinkly surface" model of patchy reionization, finding $A^\tau<0.19$ ($2\sigma$) for the coherence scale of $L_c=100$. We show that all of the distortion fields of the 95 GHz and 150 GHz polarization maps are consistent with simulations including lensed-$\Lambda$CDM, dust, and noise, with no evidence for instrumental systematics. In some cases, the EB and TB quadratic estimators presented here are more sensitive than our previous map-based null tests at identifying and rejecting spurious B-modes that might arise from instrumental effects. Finally, we verify that the standard deprojection filtering in the BICEP/Keck data processing is effective at removing temperature to polarization leakage., Comment: 34 pages, 19 figures, accepted for publication in The Astrophysical Journal

We characterize Galactic dust filaments by correlating BICEP/Keck and Planck data with polarization templates based on neutral hydrogen (H I) observations. Dust polarization is important for both our understanding of astrophysical processes in the interstellar medium (ISM) and the search for primordial gravitational waves in the cosmic microwave background (CMB). In the diffuse ISM, H I is strongly correlated with the dust and partly organized into filaments that are aligned with the local magnetic field. We analyze the deep BICEP/Keck data at 95, 150, and 220 GHz, over the low-column-density region of sky where BICEP/Keck has set the best limits on primordial gravitational waves. We separate the H I emission into distinct velocity components and detect dust polarization correlated with the local Galactic H I but not with the H I associated with Magellanic Stream I. We present a robust, multifrequency detection of polarized dust emission correlated with the filamentary H I morphology template down to 95 GHz. For assessing its utility for foreground cleaning, we report that the H I morphology template correlates in B modes at a $\sim$10-65$\%$ level over the multipole range $20 < \ell < 200$ with the BICEP/Keck maps, which contain contributions from dust, CMB, and noise components. We measure the spectral index of the filamentary dust component spectral energy distribution to be $\beta = 1.54 \pm 0.13$. We find no evidence for decorrelation in this region between the filaments and the rest of the dust field or from the inclusion of dust associated with the intermediate velocity H I. Finally, we explore the morphological parameter space in the H I-based filamentary model., Comment: 27 pages, 12 figures

Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution. Machine learning techniques have been recently proposed to learn CG particle interactions, i.e. develop CG force fields. Graph representations of molecules and supervised training of a graph convolutional neural network architecture are used to learn the potential of mean force through a force matching scheme. In this work, the force acting on each CG particle is correlated to a learned representation of its local environment that goes under the name of SchNet, constructed via continuous filter convolutions. We explore the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned., Comment: Proceedings of 12th Conference on Artificial Intelligence (SETN), 2022

Due to the wide range of timescales that are present in macromolecular systems, hierarchical multiscale strategies are necessary for their computational study. Coarse-graining (CG) allows to establish a link between different system resolutions and provides the backbone for the development of robust multiscale simulations and analyses. The CG mapping process is typically system- and application-specific, and it relies on chemical intuition. In this work, we explored the application of a Machine Learning strategy, based on Variational Autoencoders, for the development of suitable mapping schemes from the atomistic to the coarse-grained space of molecules with increasing chemical complexity. An extensive evaluation of the effect of the model hyperparameters on the training process and on the final output was performed, and an existing method was extended with the definition of different loss functions and the implementation of a selection criterion that ensures physical consistency of the output. The relationship between the input feature choice and the reconstruction accuracy was analyzed, supporting the need to introduce rotational invariance into the system. Strengths and limitations of the approach, both in the mapping and in the backmapping steps, are highlighted and critically discussed., Comment: Proceedings of 12th Conference on Artificial Intelligence (SETN), 2022

We apply post-processing to the class probability distribution outputs of audio event classification models and employ reinforcement learning to jointly discover the optimal parameters for various stages of a post-processing stack, such as the classification thresholds and the kernel sizes of median filtering algorithms used to smooth out model predictions. To achieve this we define a reinforcement learning environment where: 1) a state is the class probability distribution provided by the model for a given audio sample, 2) an action is the choice of a candidate optimal value for each parameter of the post-processing stack, 3) the reward is based on the classification accuracy metric we aim to optimize, which is the audio event-based macro F1-score in our case. We apply our post-processing to the class probability distribution outputs of two audio event classification models submitted to the DCASE Task4 2020 challenge. We find that by using reinforcement learning to discover the optimal per-class parameters for the post-processing stack that is applied to the outputs of audio event classification models, we can improve the audio event-based macro F1-score (the main metric used in the DCASE challenge to compare audio event classification accuracy) by 4-5% compared to using the same post-processing stack with manually tuned parameters., Comment: Published on IEEE Access journal, Volume 10, 2022

Observations of the Cosmic Microwave Background rely on cryogenic instrumentation with cold detectors, readout, and optics providing the low noise performance and instrumental stability required to make more sensitive measurements. It is therefore critical to optimize all aspects of the cryogenic design to achieve the necessary performance, with low temperature components and acceptable system cooling requirements. In particular, we will focus on our use of thermal filters and cold optics, which reduce the thermal load passed along to the cryogenic stages. To test their performance, we have made a series of in situ measurements while integrating the third receiver for the BICEP Array telescope. In addition to characterizing the behavior of this receiver, these measurements continue to refine the models that are being used to inform design choices being made for future instruments., Comment: 9 pages, 8 figures, Proceedings of SPIE 2022

Constraining the Galactic foregrounds with multi-frequency Cosmic Microwave Background (CMB) observations is an essential step towards ultimately reaching the sensitivity to measure primordial gravitational waves (PGWs), the sign of inflation after the Big-Bang that would be imprinted on the CMB. The BICEP Array telescope is a set of multi-frequency cameras designed to constrain the energy scale of inflation through CMB B-mode searches while also controlling the polarized galactic foregrounds. The lowest frequency BICEP Array receiver (BA1) has been observing from the South Pole since 2020 and provides 30 GHz and 40 GHz data to characterize the Galactic synchrotron in our CMB maps. In this paper, we present the design of the BA1 detectors and the full optical characterization of the camera including the on-sky performance at the South Pole. The paper also introduces the design challenges during the first observing season including the effect of out-of-band photons on detectors performance. It also describes the tests done to diagnose that effect and the new upgrade to minimize these photons, as well as installing more dichroic detectors during the 2022 deployment season to improve the BA1 sensitivity. We finally report background noise measurements of the detectors with the goal of having photon noise dominated detectors in both optical channels. BA1 achieves an improvement in mapping speed compared to the previous deployment season., Comment: Proceedings of SPIE Astronomical Telescopes + Instrumentation 2022 (AS22)

The BICEP3 Polarimeter is a small aperture, refracting telescope, dedicated to the observation of the Cosmic Microwave Background (CMB) at 95GHz. It is designed to target degree angular scale polarization patterns, in particular the very-much-sought-after primordial B-mode signal, which is a unique signature of cosmic inflation. The polarized signal from the sky is reconstructed by differencing co-localized, orthogonally polarized superconducting Transition Edge Sensor (TES) bolometers. In this work, we present absolute measurements of the polarization response of the detectors for more than $\sim 800$ functioning detector pairs of the BICEP3 experiment, out of a total of $\sim 1000$. We use a specifically designed Rotating Polarized Source (RPS) to measure the polarization response at multiple source and telescope boresight rotation angles, to fully map the response over 360 degrees. We present here polarization properties extracted from on-site calibration data taken in January 2022. A similar calibration campaign was performed in 2018, but we found that our constraint was dominated by systematics on the level of $\sim0.5^\circ$. After a number of improvements to the calibration set-up, we are now able to report a significantly lower level of systematic contamination. In the future, such precise measurements will be used to constrain physics beyond the standard cosmological model, namely cosmic birefringence., Comment: Submitted to: SPIE Astronomical Telescopes + Instrumentation (AS22)

Objective: We developed a hybrid volume surface integral equation (VSIE) method based on domain decomposition to perform fast and accurate magnetic resonance imaging (MRI) simulations that include both remote and local conductive elements. Methods: We separated the conductive surfaces present in MRI setups into two domains and optimized electromagnetic (EM) modeling for each case. Specifically, interactions between the body and EM waves originating from local radiofrequency (RF) coils were modeled with the precorrected fast Fourier transform, whereas the interactions with remote conductive surfaces (RF shield, scanner bore) were modeled with a novel cross tensor train-based algorithm. We compared the hybrid- VSIE with other VSIE methods for realistic MRI simulation setups. Results: The hybrid-VSIE was the only practical method for simulation using 1 mm voxel isotropic resolution (VIR). For 2 mm VIR, our method could be solved at least 23 times faster and required 760 times lower memory than traditional VSIE methods. Conclusion: The hybrid-VSIE demonstrated a marked improvement in terms of convergence times of the numerical EM simulation compared to traditional approaches in multiple realistic MRI scenarios. Significance: The efficiency of the novel hybrid-VSIE method could enable rapid simulations of complex and comprehensive MRI setups., Comment: 10 pages, 6 figures

For the past decade, the BICEP/Keck collaboration has been operating a series of telescopes at the Amundsen-Scott South Pole Station measuring degree-scale $B$-mode polarization imprinted in the Cosmic Microwave Background (CMB) by primordial gravitational waves (PGWs). These telescopes are compact refracting polarimeters mapping about 2% of the sky, observing at a broad range of frequencies to account for the polarized foreground from Galactic synchrotron and thermal dust emission. Our latest publication "BK18" utilizes the data collected up to the 2018 observing season, in conjunction with the publicly available WMAP and Planck data, to constrain the tensor-to-scalar ratio $r$. It particularly includes (1) the 3-year BICEP3 data which is the current deepest CMB polarization map at the foreground-minimum 95 GHz; and (2) the Keck 220 GHz map with a higher signal-to-noise ratio on the dust foreground than the Planck 353 GHz map. We fit the auto- and cross-spectra of these maps to a multicomponent likelihood model ($\Lambda$CDM+dust+synchrotron+noise+$r$) and find it to be an adequate description of the data at the current noise level. The likelihood analysis yields $\sigma(r)=0.009$. The inference of $r$ from our baseline model is tightened to $r_{0.05}=0.014^{+0.010}_{-0.011}$ and $r_{0.05}<0.036$ at 95% confidence, meaning that the BICEP/Keck $B$-mode data is the most powerful existing dataset for the constraint of PGWs. The up-coming BICEP Array telescope is projected to reach $\sigma(r) \lesssim 0.003$ using data up to 2027., Comment: 8 pages, 6 figures, contribution to the 2022 Cosmology session of the 56th Rencontres de Moriond