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The facility adaptive optics of the Subaru Telescope AO188 recently received some long-awaited upgrades: a new 3224-actuator deformable mirror (DM) from ALPAO (hence the name change to AO3000 or AO3k), an upgraded GPU-based real-time computer, a visible nonlinear curvature wavefront sensor and a near-infrared wavefront sensor (NIR WFS), closing the loop at up to 2~kHz. The wavefront sensors were added in 2023, while the DM will be installed at the beginning of 2024. With these new features, AO3k will provide extreme-AO level of correction to all the instruments on the IR Nasmyth platform: The NIR-MIR camera and spectrograph IRCS, the high-resolution Doppler spectrograph IRD, and the high-contrast instrument SCExAO. AO3k will also support laser tomography (LTAO), delivering high Strehl ratio imaging with large sky coverage. The high Strehl will especially benefit SCExAO for high-contrast imaging, both in infrared and visible. The second stage extreme AO will no longer have to chase large residual atmospheric turbulence, and will focus on truly high-contrast techniques to create and stabilize dark holes, as well as coherent differential imaging techniques. We will finally be able to leverage the several high performance coronagraphs tested in SCExAO, even in the visible. AO3k will answer crucial questions as a precursor for future adaptive optics systems for ELTs, especially as a technology demonstrator for the HCI Planetary Systems Imager on the Thirty Meter Telescope. A lot of questions are still unanswered on the on-sky behavior of high actuator counts DMs, NIR wavefront sensing, the effect of rolling shutters or persistence. We present here the first on-sky results of AO3k, before the system gets fully offered to the observers in the second half of 2024. These results give us some insight on the great scientific results we hope to achieve in the future., Comment: 15 pages, 11 figures, proceedings of the SPIE Astronomical Telescopes and Instrumentation 2024 (Yokohama, Japan)

Message passing is the dominant paradigm in Graph Neural Networks (GNNs). The efficiency of message passing, however, can be limited by the topology of the graph. This happens when information is lost during propagation due to being oversquashed when travelling through bottlenecks. To remedy this, recent efforts have focused on graph rewiring techniques, which disconnect the input graph originating from the data and the computational graph, on which message passing is performed. A prominent approach for this is to use discrete graph curvature measures, of which several variants have been proposed, to identify and rewire around bottlenecks, facilitating information propagation. While oversquashing has been demonstrated in synthetic datasets, in this work we reevaluate the performance gains that curvature-based rewiring brings to real-world datasets. We show that in these datasets, edges selected during the rewiring process are not in line with theoretical criteria identifying bottlenecks. This implies they do not necessarily oversquash information during message passing. Subsequently, we demonstrate that SOTA accuracies on these datasets are outliers originating from sweeps of hyperparameters -- both the ones for training and dedicated ones related to the rewiring algorithm -- instead of consistent performance gains. In conclusion, our analysis nuances the effectiveness of curvature-based rewiring in real-world datasets and brings a new perspective on the methods to evaluate GNN accuracy improvements., Comment: 19 pages, 10 figures

Convolutional layers with 1-D filters are often used as frontend to encode audio signals. Unlike fixed time-frequency representations, they can adapt to the local characteristics of input data. However, 1-D filters on raw audio are hard to train and often suffer from instabilities. In this paper, we address these problems with hybrid solutions, i.e., combining theory-driven and data-driven approaches. First, we preprocess the audio signals via a auditory filterbank, guaranteeing good frequency localization for the learned encoder. Second, we use results from frame theory to define an unsupervised learning objective that encourages energy conservation and perfect reconstruction. Third, we adapt mixed compressed spectral norms as learning objectives to the encoder coefficients. Using these solutions in a low-complexity encoder-mask-decoder model significantly improves the perceptual evaluation of speech quality (PESQ) in speech enhancement., Comment: Accepted at INTERSPEECH 2024

Currently, 6D pose estimation methods are benchmarked on datasets that consider, for their ground truth annotations, visual ambiguities as only related to global object symmetries. However, as previously observed [26], visual ambiguities can also happen depending on the viewpoint or the presence of occluding objects, when disambiguating parts become hidden. The visual ambiguities are therefore actually different across images. We thus first propose an automatic method to re-annotate those datasets with a 6D pose distribution specific to each image, taking into account the visibility of the object surface in the image to correctly determine the visual ambiguities. Given this improved ground truth, we re-evaluate the state-of-the-art methods and show this greatly modify the ranking of these methods. Our annotations also allow us to benchmark recent methods able to estimate a pose distribution on real images for the first time. We will make our annotations for the T-LESS dataset and our code publicly available.

We propose a new algorithm to identify a Wiener-Hammerstein system. This model represents a communication channel where two linear filters are separated by a non-linear function modelling an amplifier. The algorithm enables to recover each parameter of the model, namely the two linear filters and the non-linear function. This is to be opposed with estimation algorithms which identify the equivalent Volterra system. The algorithm is composed of three main steps and uses three distinct pilot sequences. The estimation of the parameters is done in the time domain via several instances of the least-square algorithm. However, arguments based on the spectral representation of the signals and filters are used to design the pilot sequences. We also provide an analysis of the proposed algorithm. We estimate, via the theory and simulations, the minimum required size of the pilot sequences to achieve a target mean squared error between the output of the true channel and the output of the estimated model. We obtain that the new method requires reduced-size pilot sequences: The sum of the length of the pilot sequences is approximately the one needed to estimate the convolutional product of the two linear filters with a back-off. A comparison with the Volterra approach is also provided., Comment: Submitted to IEEE Transactions on Communications

The rare electromagnetic $J/\psi \to \mu^+\mu^-\mu^+\mu^-$ decay is observed with a significance greatly exceeding the discovery threshold, using proton-proton collision data collected by the LHCb experiment during 2016-2018 at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of $5.4\,\text{fb}^{-1}$. The rate of this decay is measured relative to that of the $J/\psi \to \mu^+\mu^-$ mode. Using the QED model for the four-muon decay in the efficiency estimation, its branching fraction is determined to be \begin{equation*} {\mathcal{B}}(J/\psi \to \mu^+\mu^-\mu^+\mu^-) = (1.13\pm0.10\pm0.05\pm0.01)\times 10^{-6}, \end{equation*} where the uncertainties are statistical, systematic and due to the uncertainty on the branching fraction of the $J/\psi \to \mu^+\mu^-$ decay., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3453 (LHCb public pages)

This work provides evidence for the existence of supersymmetric and scale-separated AdS$_4$ vacua in M-theory of the Freund-Rubin type. The internal space has weak $G_2$-holonomy, which is obtained from the lift of AdS vacua in massless type IIA on a specific SU(3)-structure with O6-planes. Such lifts require a local treatment of the O6-planes, therefore going beyond the usual smeared approximation. The setup is analysed by solving the pure spinor equations and the Bianchi identities perturbatively in a small backreaction parameter, preserving supersymmetry manifestly and therefore extending on previous work. This approach is applicable to lifts of other type IIA vacua on half-flat SU(3)-structures, including those with D6-brane sources. The resulting 7d manifold presented here exhibits singularities originating from the O6-planes loci in type IIA theory. Additionally, scale separation in M-theory arises from a decoupling between the Ricci curvature and the first eigenvalue of the Laplacian of the proposed 7d manifold, thereby challenging certain conjectures in the swampland program., Comment: 22 pages + appendices

Large language models (LLMs), such as ChatGPT and Copilot, are transforming software development by automating code generation and, arguably, enable rapid prototyping, support education, and boost productivity. Therefore, correctness and quality of the generated code should be on par with manually written code. To assess the current state of LLMs in generating correct code of high quality, we conducted controlled experiments with ChatGPT and Copilot: we let the LLMs generate simple algorithms in Java and Python along with the corresponding unit tests and assessed the correctness and the quality (coverage) of the generated (test) codes. We observed significant differences between the LLMs, between the languages, between algorithm and test codes, and over time. The present paper reports these results together with the experimental methods allowing repeated and comparable assessments for more algorithms, languages, and LLMs over time.

PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption of the capability. This paper provides details of those enhancements as released in PyFR v2.0.3, explains efforts to grow an engaged developer and user community, and provides latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.

The numerical cost of variational methods suggests using perturbative approaches to determine the electronic structure of molecular systems. In this work, a sequential construction of effective Hamiltonians drives the definition of approximate model functions and energies in a multi-state Rayleigh-Schr\"odinger perturbative scheme. A second step takes advantage of an updated partitioning of the Hamiltonian to perform a state-specific Brillouin-Wigner energy correction based on a well-tempered perturbation expansion. The multi-step RSBW method is exemplified on model-Hamiltonians to stress its robustness, efficiency and applicability to spectroscopy determination.

Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility, but identifying the motility of individual cells is challenging. Previous work has established the importance of the average cell shape in predicting cell dynamics. Here, we aim to identify the importance of individual cell shape features, rather than collective features, to distinguish between high-motility (active) and low-motility (passive) cells in heterogeneous cell layers. Employing the Cellular Potts Model, we generate simulation snapshots and extract static features as inputs for a simple machine-learning model. Our results show that when the passive cells are non-motile, this machine-learning model can accurately predict whether a cell is passive or active using only single-cell shape features. Furthermore, we explore scenarios where passive cells also exhibit some degree of motility, albeit less than active cells. In such cases, our findings indicate that a neural network trained on shape features can accurately classify cell motility, particularly when the number of active cells is low, and the motility of active cells is significantly higher compared to passive cells. This work offers potential for physics-inspired predictions of single-cell properties with implications for inferring cell dynamics from static histological images.

This book is intended for teaching Signal Analysis methods and Inverse Problems theory. It is completely open access and will remain free. It is currently illustrated with examples that we have actually encountered in geophysics, but will eventually evolve to address problems in biology. The main idea is to develop this material over time (probably every six months) and throughout our careers. Therefore, it is not yet complete and some minor points need to be corrected in this English version. A French version is also available. The MATLAB codes we used can be found at the following address: https://www.dropbox.com/scl/fi/ndi2fs3yyup64i8xyzjqd/matlab_book.zip?rlkey=j112lo5kaxvinh362frc66bcl&st=enxxx1ps&dl=0, Comment: 334 pages, 97 figures

GeSn-based SWIR lasers featuring imaging, sensing, and communications has gained dynamic development recently. However, the existing SiGeSn/GeSn double heterostructure lacks adequate electron confinement and is insufficient for room temperature lasing. The recently demonstrated semiconductor grafting technique provides a viable approach towards AlGaAs/GeSn p-i-n heterojunctions with better electron confinement and high-quality interfaces, promising for room temperature electrically pumped GeSn laser devices. Therefore, understanding and quantitatively characterizing the band alignment in this grafted heterojunction is crucial. In this study, we explore the band alignment in the grafted monocrystalline Al0.3Ga0.7As /Ge0.853Sn0.147 p-i-n heterojunction. We determined the bandgap values of AlGaAs and GeSn to be 1.81 eV and 0.434 eV by photoluminescence measurements, respectively. We further conducted X-ray photoelectron spectroscopy measurements and extracted a valence band offset of 0.19 eV and a conduction band offset of 1.186 eV. A Type-I band alignment was confirmed which effectively confining electrons at the AlGaAs/GeSn interface. This study improves our understanding of the interfacial band structure in grafted AlGaAs/GeSn heterostructure, providing experimental evidence of the Type-I band alignment between AlGaAs and GeSn, and paving the way for their application in laser technologies., Comment: 18 pages, 4 figures

American income inequality, generally estimated with tax data, in the 20th century is widely recognized to have followed a U-curve, though debates persist over the extent of this curve, specifically regarding how high the peaks are and how deep the trough is. These debates focus on assumptions about defining income and handling deductions. However, the choice of interpolation methods for using tax authorities' tabular data to estimate the income of the richest centiles -- especially when no micro-files are available -- has not been discussed. This is crucial because tabular data were consistently used from 1917 to 1965. In this paper, we show that there is an alternative to the standard method of Pareto Interpolation (PI). We demonstrate that this alternative -- Maximum Entropy (ME) -- provides more accurate results and leads to significant revisions in the shape of the U-curve of income inequality.

Transiting planets around young stars are hard to find due to the enhanced stellar activity. Only a few transiting planets have been detected around stars younger than 100 Myr. We initially detected a transit-like signal in the K2 light curve of a very cool M dwarf star (EPIC 211101996) in the Pleiades open cluster, with an estimated age of about 100 Myr. Our detailed analysis of the per-pixel light curves, detrending with the W\={o}tan software and transit search with the Transit Least Squares algorithm showed that the source of the signal is a contaminant source (Gaia DR3 66767847894609792) 20" west of the target. The V-like shape of its phase-folded light curve and eclipse depth of ~15% suggest that it is a grazing eclipsing binary. The contaminant has hitherto been listed as a single star, which we now identify as an eclipsing stellar binary with a period of about 6 days., Comment: published in RNAAS, Aug. 2024, Vol. 8, No. 8, 3 pages, 1 col. Figure

The combination of a strong pump and a weak probe has been widely applied to investigate both optical and nanomechanical devices. Such pump-probe measurements allows for the exploration of nonlinear dynamics, driven by the large pump tone, by measuring the system response to a probe tone. In contrast, here we report on the dynamics of a mechanical Duffing resonator driven with a combination of two large tones at different frequencies. Our results indicate the presence of various distinct regimes with very different dynamics. We systematically investigate the impact of the relative strength and detuning between the two drives on the dynamical response. This provides an illustrative example of dynamical phase transitions in out-of-equilibrium systems., Comment: 6 pages, 4 figures

Over the last decade, experimental measurements of electrostatic screening lengths in concentrated electrolytes have exceeded theoretical predictions by orders of magnitude. This disagreement has led to a paradigm in which such screening lengths are referred to as 'anomalous underscreening', while others - predominantly those predicted by theory and molecular simulation - are referred to as 'normal underscreening'. Herein we use discrete Fourier analysis of the radial charge density obtained from molecular dynamics simulations to reveal the origin of anomalous underscreening in concentrated electrolytes. Normal underscreening above the Kirkwood point arises from low-frequency decay modes of the electrostatic potential, while anomalous underscreening arises from high-frequency decay modes that are observed only at high concentrations. The screening length associated with a particular decay mode is in turn determined by the degree of short-range interference between ion-ion correlation functions. The long-range decay associated with anomalous underscreening is thus ultimately determined by short range structure in the bulk electrolyte. These results reconcile the disagreement between experimental measurements and theoretical predictions of screening lengths in concentrated electrolytes., Comment: 16 pages, 3 figures

Fusion is a potentially transformational energy technology, which promises limitless clean energy. Yet, it requires continued scientific and technological development to realize its potential. The conditions necessary for fusion energy gain in terms of the product of plasma pressure $P$ and confinement time $\tau$ have been known for many decades. An underappreciated fact is that pulsed magnetic fusion has demonstrated $P \tau$ performance on par with laser-driven ICF and tokamaks despite receiving only a small fraction of investment relative to those concepts. In light of this demonstrated performance, well-established scaling relations, and opportunities for further innovations, here we advocate for pulsed magnetic fusion as the most attractive path towards commercialization of fusion energy.

We design the profit-maximizing mechanism to sell an excludable and non-rival good with network effects. Buyers have heterogeneous private values that depend on how many others also consume the good. We characterize an algorithm that implements the optimal allocation in dominant strategies. We apply our insights to digital content creation, and we are able to rationalize features seen in monetization schemes in this industry such as voluntary contributions, community subsidies, and exclusivity bids.

In this paper, we introduce a novel post-processing algorithm that is both model-agnostic and does not require the sensitive attribute at test time. In addition, our algorithm is explicitly designed to enforce minimal changes between biased and debiased predictions; a property that, while highly desirable, is rarely prioritized as an explicit objective in fairness literature. Our approach leverages a multiplicative factor applied to the logit value of probability scores produced by a black-box classifier. We demonstrate the efficacy of our method through empirical evaluations, comparing its performance against other four debiasing algorithms on two widely used datasets in fairness research.

The Earth Observation Satellite Planning (EOSP) is a difficult optimization problem with considerable practical interest. A set of requested observations must be scheduled on an agile Earth observation satellite while respecting constraints on their visibility window, as well as maneuver constraints that impose varying delays between successive observations. In addition, the problem is largely oversubscribed: there are much more candidate observations than what can possibly be achieved. Therefore, one must select the set of observations that will be performed while maximizing their weighted cumulative benefit, and propose a feasible schedule for these observations. As previous work mostly focused on heuristic and iterative search algorithms, this paper presents a new technique for selecting and scheduling observations based on Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL). GNNs are used to extract relevant information from the graphs representing instances of the EOSP, and DRL drives the search for optimal schedules. Our simulations show that it is able to learn on small problem instances and generalize to larger real-world instances, with very competitive performance compared to traditional approaches., Comment: Accepted at 17th European Workshop on Reinforcement Learning (EWRL 2024)

This article studies two particular algorithms, a Relaxation Least Squares (RLS) algorithm and a Relaxation Newton Iteration (RNI) scheme , for reconstructing unknown parameters in dissipative dynamical systems. Both algorithms are based on a continuous data assimilation (CDA) algorithm for state reconstruction of A. Azouani, E. Olson, and E.S. Titi \cite{Azouani_Olson_Titi_2014}. Due to the CDA origins of these parameter recovery algorithms, these schemes provide on-the-fly reconstruction, that is, as data is collected, of unknown state and parameters simultaneously. It is shown how both algorithms give way to a robust general framework for simultaneous state and parameter estimation. In particular, we develop a general theory, applicable to a large class of dissipative dynamical systems, which identifies structural and algorithmic conditions under which the proposed algorithms achieve reconstruction of the true parameters. The algorithms are implemented on a high-dimensional two-layer Lorenz 96 model, where the theoretical conditions of the general framework are explicitly verifiable. They are also implemented on the two-dimensional Rayleigh-B\'enard convection system to demonstrate the applicability of the algorithms beyond the finite-dimensional setting. In each case, systematic numerical experiments are carried out probing the efficacy of the proposed algorithms, in addition to the apparent benefits and drawbacks between them.

Multiferroic heterostructures have gained in recent years a renewed role in spintronic applications due to their possibility in controlling the magnetic properties via interfacial coupling by exploiting the ferroelectric response to various external stimuli. Whereas the main mechanisms ruling the converse magnetoelectric coupling are considered as established, the research on how to optimize the ferroelectric properties is an active field. In particular the complex phase diagram of [Pb(Mg1/3Nb2/3)O3]-x[PbTiO3] (PMN-xPT) single crystals, that present relaxor ferroelectric and photovoltaic properties, deserves further investigation. For instance, crystalline quality and thermal stability of the ferroelectric domains in heterostructures need assessment. Here we show how, by thermal annealing over the ferroelectric Curie temperature and then cooling PMN-0.4PT/Fe heterostructures in inert atmosphere the domain population is significantly modified, evolving from a highly disordered, mostly out-of-plane domain population, to improved crystallinity and prevalent in-plane oriented domains. Upon further annealing, the domain population switches back to prevalent out-of-plane orientation suggesting that intermediate annealing steps can freeze PMN-0.4PT domain population in a metastable configuration. The structural analysis was carried out by combining micro-Raman and X-ray diffraction (XRD) measurements. In the three states the magnetic properties of interfacial Fe thin film are affected by the ferroelectric configurations as a consequence of changing interfacial strain, evolving from an isotropic behavior to an anisotropic one and back., Comment: 6 figures

In the context of energy transition and decarbonization of the economy, several governments will ban the sale of new combustion vehicles by 2050. Thus, growing penetration of electric vehicles (EVs) in distribution networks (DN) is predicted. Impact analyzes must be performed to determine if mitigation means are needed to accommodate a large quantity of EVs in the DN. Furthermore, the habits of the local population resulting in different EVs charging patterns needs to be realistically considered. This article proposed an individual residential EVs multi-charging algorithm based on the observed charging behavior of 500 measured residential EVs located in a large North American utility. Probability functions are derived from the analysis of these charging patterns. These can be used to model daily charging profiles of individual EV to assess, in a quasi-static time-series perspective, their impact either on a single costumer or a whole DN. An impact evaluation study is also presented.

Biclustering is an unsupervised machine-learning approach aiming to cluster rows and columns simultaneously in a data matrix. Several biclustering algorithms have been proposed for handling numeric datasets. However, real-world data mining problems often involve heterogeneous datasets with mixed attributes. To address this challenge, we introduce a biclustering approach called HBIC, capable of discovering meaningful biclusters in complex heterogeneous data, including numeric, binary, and categorical data. The approach comprises two stages: bicluster generation and bicluster model selection. In the initial stage, several candidate biclusters are generated iteratively by adding and removing rows and columns based on the frequency of values in the original matrix. In the second stage, we introduce two approaches for selecting the most suitable biclusters by considering their size and homogeneity. Through a series of experiments, we investigated the suitability of our approach on a synthetic benchmark and in a biomedical application involving clinical data of systemic sclerosis patients. The evaluation comparing our method to existing approaches demonstrates its ability to discover high-quality biclusters from heterogeneous data. Our biclustering approach is a starting point for heterogeneous bicluster discovery, leading to a better understanding of complex underlying data structures., Comment: 11 pages, 5 figures

Phylogenetic networks are graphs that are used to represent evolutionary relationships between different taxa. They generalize phylogenetic trees since for example, unlike trees, they permit lineages to combine. Recently, there has been rising interest in semi-directed phylogenetic networks, which are mixed graphs in which certain lineage combination events are represented by directed edges coming together, whereas the remaining edges are left undirected. One reason to consider such networks is that it can be difficult to root a network using real data. In this paper, we consider the problem of when a semi-directed phylogenetic network is defined or encoded by the smaller networks that it induces on the $4$-leaf subsets of its leaf set. These smaller networks are called quarnets. We prove that semi-directed binary level-$2$ phylogenetic networks are encoded by their quarnets, but that this is not the case for level-$3$. In addition, we prove that the so-called blob tree of a semi-directed binary network, a tree that gives the coarse-grained structure of the network, is always encoded by the quarnets of the network.

\texttt{ml\_edm} is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data. The package is also modular, providing researchers an easy way to implement their own triggering strategy for classification, regression or any machine learning task. As of now, many Early Classification of Time Series (ECTS) state-of-the-art algorithms, are efficiently implemented in the library leveraging parallel computation. The syntax follows the one introduce in \texttt{scikit-learn}, making estimators and pipelines compatible with \texttt{ml\_edm}. This software is distributed over the BSD-3-Clause license, source code can be found at \url{https://github.com/ML-EDM/ml_edm}.

Continuous-variable quantum key distribution (CV-QKD) has been proposed as a method for securely exchanging keys to protect against the security concerns caused by potential advancements in quantum computing. In addition to optical fiber transmission, free-space optical (FSO) channel is an interesting channel for CV-QKD, as it is possible to share keys over this channel wirelessly. The instability of the FSO channel caused by turbulence-induced fading, however, can cause a degradation in the system's performance. One of the most important aspects of CV-QKD is the reconciliation step, which significantly impacts the performance of the CV-QKD system. Hence, rate-adaptive reconciliation is necessary for CV-QKD over FSO to combat the fluctuations in the channel and improve secret key rates (SKRs). Therefore, in this paper, we simulate the impact of discrete modulation on the reconciliation efficiency and consider the use of $d$-dimensional reconciliation with $d > 8$ to mitigate this impact, improving reconciliation efficiencies by up to 3.4%. We validate our results by experimentally demonstrating CV-QKD over a turbulent FSO link and demonstrate SKR gains by up to 165%. Furthermore, we optimise the reconciliation efficiency for FSO links, achieving additional SKR gains of up to 7.6%., Comment: Pre-print, submitted for journal review

We consider the problem of user equipment (UE) positioning based on radio signals via deep learning. As in most supervised-learning tasks, a critical aspect is the availability of a relevant dataset to train a model. However, in a cellular network, the data-collection step may induce a high communication overhead. As a result, to reduce the required size of the dataset, it may be interesting to carefully choose the positions to be labelled and to be used in the training. We therefore propose an active learning approach for efficient data collection. We first show that significant gains (both in terms of positioning accuracy and size of the required dataset) can be obtained for the considered positioning problem using a genie. This validates the interest of active learning for positioning. We then propose a \textcolor{blue}{practical} method to approximate this genie., Comment: Submitted to Electronics Letters

This document summarizes the presentation on Quantum-centric Supercomputing given at the 22nd International Workshop on Advanced Computing and Analysis Techniques in Physics Research, hosted at Stony Brook University., Comment: Accepted to IOPscience Journal of Physics Conference Series

Population protocols are a model of computation in which indistinguishable mobile agents interact in pairs to decide a property of their initial configuration. Originally introduced by Angluin et. al. in 2004 with a constant number of states, research nowadays focuses on protocols where the space usage depends on the number of agents. The expressive power of population protocols has so far however only been determined for protocols using $o(\log n)$ states, which compute only semilinear predicates, and for $\Omega(n)$ states. This leaves a significant gap, particularly concerning protocols with $\Theta(\log n)$ or $\Theta(\operatorname{polylog} n)$ states, which are the most common constructions in the literature. In this paper we close the gap and prove that for any $\varepsilon>0$ and $f\in\Omega(\log n)\cap\mathcal{O}(n^{1-\varepsilon})$, both uniform and non-uniform population protocols with $\Theta(f(n))$ states can decide exactly $\mathsf{NSPACE}(f(n) \log n)$.

Acoustic metamaterials are artificial structures, often lattice of resonators, with unusual properties. They can be engineered to stop wave propagation in specific frequency bands. Once manufactured, their dispersive qualities remain invariant in time and space, limiting their practical use. Actively tuned arrangements have received growing interest to address this issue. Here, we introduce a new class of active metamaterial made from dual-state unit cells, either vibration sources when powered or passive resonators when left disconnected. They possess self-tuning capabilities, enabling deep subwavelength band gaps to automatically match the carrier signal of powered cells, typically around 200Hz. Swift electronic commutations between both states establish the basis for real-time reconfiguration of waveguides and shaping of vibration patterns. A series of experiments highlight how these tailored acceleration fields can spatially encode information relevant to human touch. This novel metamaterial can readily be made using off-the-shelf smartphone vibration motors, paving the way for a widespread adoption of multi-touch tactile displays.

Second-order Raman scattering has been extensively studied in carbon-based nanomaterials, \emph{e.g.} nanotube and graphene, because it activates normally forbidden Raman modes that are sensitive to crystal disorder, such as defects, dopants, strain, etc. The sp$^2$-hybridized carbon systems are, however, the exception among most nanomaterials, where first-order Raman processes usually dominate. Here we report the identification of four second-order Raman modes, named $D_1$, $D_1'$, $D_2$ and $D_2'$, in exfoliated black phosphorus (P(black)), an elemental direct-gap semiconductor exhibiting strong mechanical and electronic anisotropies. Located in close proximity to the $A^1_g$ and $A^2_g$ modes, these new modes dominate at an excitation wavelength of 633 nm. Their evolutions as a function of sample thickness, excitation wavelength, and defect density indicate that they are defect-activated and involve high-momentum phonons in a doubly-resonant Raman process. \emph{Ab initio} simulations of a monolayer reveal that the $D'$ and $D$ modes occur through intravalley scatterings with split contributions in the armchair and zigzag directions, respectively. The high sensitivity of these $D$ modes to disorder helps explaining several discrepancies found in the literature., Comment: 18 pages, 5 figures, with supplementary file

This paper aims to develop the study of historical printed ornaments with modern unsupervised computer vision. We highlight three complex tasks that are of critical interest to book historians: clustering, element discovery, and unsupervised change localization. For each of these tasks, we introduce an evaluation benchmark, and we adapt and evaluate state-of-the-art models. Our Rey's Ornaments dataset is designed to be a representative example of a set of ornaments historians would be interested in. It focuses on an XVIIIth century bookseller, Marc-Michel Rey, providing a consistent set of ornaments with a wide diversity and representative challenges. Our results highlight the limitations of state-of-the-art models when faced with real data and show simple baselines such as k-means or congealing can outperform more sophisticated approaches on such data. Our dataset and code can be found at https://printed-ornaments.github.io/.

This study presents the fabrication and characterizations of an Al$_{0.3}$Ga$_{0.7}$As/Ge$_{0.87}$Sn$_{0.13}$/GeSn p-i-n double heterostructure (DHS) diode following the grafting approach for enhanced optoelectronic applications. By integrating ultra-thin Al$_2$O$_3$ as a quantum tunneling layer and enhancing interfacial double-side passivation, we achieved a heterostructure with a substantial 1.186 eV conduction band barrier between AlGaAs and GeSn, along with a low interfacial density of states. The diode demonstrated impressive electrical characteristics with high uniformity, including a mean ideality factor of 1.47 and a mean rectification ratio of 2.95E103 at +/-2 V across 326 devices, indicating high-quality device fabrication. Comprehensive electrical characterizations, including C-V and I-V profiling, affirm the diode's capability to provide robust electrical confinement and efficient carrier injection. These properties make the Al$_{0.3}$Ga$_{0.7}$As/Ge$_{0.87}$Sn$_{0.13}$/GeSn DHS a promising candidate for next-generation electrically pumped GeSn lasers, potentially operable at higher temperatures. Our results provide a viable pathway for further advancements in various GeSn-based devices., Comment: 5 pages, 4 figures

Reliable communication is a fundamental distributed communication abstraction that allows any two nodes of a network to communicate with each other. It is necessary for more powerful communication primitives, such as broadcast and consensus. Using different authentication models, two classical protocols implement reliable communication in unknown and sufficiently connected networks. In the first one, network links are authenticated, and processes rely on dissemination paths to authenticate messages. In the second one, processes generate digital signatures that are flooded in the network. This work considers the hybrid system model that combines authenticated links and authenticated processes. We additionally aim to leverage the possible presence of trusted nodes and trusted components in networks, which have been assumed in the scientific literature and in practice. We first extend the two classical reliable communication protocols to leverage trusted nodes. We then propose DualRC, a novel algorithm that enables reliable communication in the hybrid authentication model by manipulating both dissemination paths and digital signatures, and leverages the possible presence of trusted nodes (e.g., network gateways) and trusted components (e.g., Intel SGX enclaves). We provide correctness verification algorithms to assess whether our algorithms implement reliable communication for all nodes on a given network.

This paper characterizes the trade-offs between information and energy transmission over an additive white Gaussian noise channel in the finite block-length regime with finite channel input symbols. These trade-offs are characterized in the form of inequalities involving the information transmission rate, energy transmission rate, decoding error probability (DEP) and energy outage probability (EOP) for a given finite block-length code. The first set of results identify the set of necessary conditions that a given code must satisfy for simultaneous information and energy transmission. Following this, a novel method for constructing a family of codes that can satisfy a target information rate, energy rate, DEP and EOP is proposed. Finally, the achievability results identify the set of tuples of information rate, energy rate, DEP and EOP that can be simultaneously achieved by the constructed family of codes., Comment: arXiv admin note: substantial text overlap with arXiv:2211.05873

Diffusion generative models, famous for their performance in image generation, are popular in various cross-domain applications. However, their use in the communication community has been mostly limited to auxiliary tasks like data modeling and feature extraction. These models hold greater promise for fundamental problems in network optimization compared to traditional machine learning methods. Discriminative deep learning often falls short due to its single-step input-output mapping and lack of global awareness of the solution space, especially given the complexity of network optimization's objective functions. In contrast, diffusion generative models can consider a broader range of solutions and exhibit stronger generalization by learning parameters that describe the distribution of the underlying solution space, with higher probabilities assigned to better solutions. We propose a new framework Diffusion Model-based Solution Generation (DiffSG), which leverages the intrinsic distribution learning capabilities of diffusion generative models to learn high-quality solution distributions based on given inputs. The optimal solution within this distribution is highly probable, allowing it to be effectively reached through repeated sampling. We validate the performance of DiffSG on several typical network optimization problems, including mixed-integer non-linear programming, convex optimization, and hierarchical non-convex optimization. Our results show that DiffSG outperforms existing baselines. In summary, we demonstrate the potential of diffusion generative models in tackling complex network optimization problems and outline a promising path for their broader application in the communication community., Comment: 8 pages, 5 figures

Helium-burning stars, in particular Cepheids, are especially difficult to model, as the choice of free parameters can greatly impact the shape of the blue loops - the part of the evolutionary track at which instability strip is crossed. Contemporary one-dimensional stellar evolution codes, like MESA (Modules for Experiments in Stellar Astrophysics), come with a large number of free parameters that allow to model the physical processes in stellar interiors under many assumptions. The uncertainties that arise from this freedom are rarely discussed in the literature despite their impact on the evolution of the model. We calculate a grid of evolutionary models with MESA, varying several controls, like solar mixture of heavy elements, mixing length theory prescription, nuclear reaction rates, scheme to determine convective boundaries, atmosphere model, temporal and spatial resolution, and quantify their impact on age and location of the evolutionary track on the HR diagram from the main sequence till the end of core-helium burning. Our investigation was conducted for a full range of masses and metallicities expected for classical Cepheids. The uncertainties are significant, especially during core-helium burning, reaching or exceeding the observational uncertainties of log Teff and log L for detached eclipsing binary systems. For models below 9 solar masses, thin convective shells develop and evolve erratically, not allowing the models to converge. A careful inspection of Kippenhahn diagrams and convergence study is advised for a given mass and metallicity, to assess how severe this problem is and to what extent it may affect the evolution.

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

In phylogenetics, evolution is traditionally represented in a tree-like manner. However, phylogenetic networks can be more appropriate for representing evolutionary events such as hybridization, horizontal gene transfer, and others. In particular, the class of forest-based networks was recently introduced to represent introgression, in which genes are swapped between between species. A network is forest-based if it can be obtained by adding arcs to a collection of trees, so that the endpoints of the new arcs are in different trees. This contrasts with so-called tree-based networks, which are formed by adding arcs within a single tree. We are interested in the computational complexity of recognizing forest-based networks, which was recently left as an open problem by Huber et al. Forest-based networks coincide with directed acyclic graphs that can be partitioned into induced paths, each ending at a leaf of the original graph. Several types of path partitions have been studied in the graph theory literature, but to our knowledge this type of leaf induced path partition has not been considered before. The study of forest-based networks in terms of these partitions allows us to establish closer relationships between phylogenetics and algorithmic graph theory, and to provide answers to problems in both fields. We show that deciding whether a network is forest-based is NP-complete, even on input networks that are tree-based, binary, and have only three leaves. This shows that partitioning a directed acyclic graph into three induced paths is NP-complete, answering a recent question of Ferneau et al. We then show that the problem is polynomial-time solvable on binary networks with two leaves and on the class of orchards. Finally, for undirected graphs, we introduce unrooted forest-based networks and provide hardness results for this class as well.

The Santa Cruz Extreme AO Lab (SEAL) testbed is an optical bench meant to design and develop new wavefront control techniques for high-contrast imaging for segmented telescopes. These techniques allow for astronomical efficiency in exoplanet imaging and characterization. SEAL consists of several wavefront sensors (WFS) and deformable mirrors (DM) that are currently performing techniques like predictive control or non-linear reconstruction. In this paper, we present the implementation and characterization of a new coronagraphic branch on SEAL and assess the contrast limitations in the testbed. For our coronagraphic branch, we used a vector vortex coronagraph which has high contrast performance. The W. M. Keck Observatory also uses a vortex coronagraph, allowing us to compare the limitations with our own coronagraph. We relied on the testbed and simulations of the vortex coronagraph to compare performance with expected ones. To create a more reliable simulation, we also injected in our numerical model data collected by a Zernike Wavefront sensor (ZWFS) used to perform fine wavefront sensing on the bench. Now that the coronagraphic branch is aligned on SEAL, we will be able to use contrast as a metric for the performance of wavefront control methods on the bench., Comment: 10 pages, 14 figures, SPIE Astronomical Telescopes + Instrumentation: Adaptive Optics Systems IX (Yokohama 2024), Paper 13097-297

In this work, we describe the design and architecture of the open-source Quantum Engine Compiler (qe-compiler) currently used in production for IBM Quantum systems. The qe-compiler is built using LLVM's Multi-Level Intermediate Representation (MLIR) framework and includes definitions for several dialects to represent parameterized quantum computation at multiple levels of abstraction. The compiler also provides Python bindings and a diagnostic system. An open-source LALR lexer and parser built using Bison and Flex generates an Abstract Syntax Tree that is translated to a high-level MLIR dialect. An extensible hierarchical target system for modeling the heterogeneous nature of control systems at compilation time is included. Target-based and generic compilation passes are added using a pipeline interface to translate the input down to low-level intermediate representations (including LLVM IR) and can take advantage of LLVM backends and tooling to generate machine executable binaries. The qe-compiler is built to be extensible, maintainable, performant, and scalable to support the future of quantum computing., Comment: To be published in the proceedings of the IEEE International Conference on Quantum Computing and Engineering 2024 (QCE24)

We study a robust online convex optimization framework, where an adversary can introduce outliers by corrupting loss functions in an arbitrary number of rounds k, unknown to the learner. Our focus is on a novel setting allowing unbounded domains and large gradients for the losses without relying on a Lipschitz assumption. We introduce the Log Exponential Adjusted Robust and iNvex (LEARN) loss, a non-convex (invex) robust loss function to mitigate the effects of outliers and develop a robust variant of the online gradient descent algorithm by leveraging the LEARN loss. We establish tight regret guarantees (up to constants), in a dynamic setting, with respect to the uncorrupted rounds and conduct experiments to validate our theory. Furthermore, we present a unified analysis framework for developing online optimization algorithms for non-convex (invex) losses, utilizing it to provide regret bounds with respect to the LEARN loss, which may be of independent interest.

Humans observe only part of their environment at any moment but can still make complex, long-term decisions thanks to our long-term memory. To test how an AI can learn and utilize its long-term memory, we have developed a partially observable Markov decision processes (POMDP) environment, where the agent has to answer questions while navigating a maze. The environment is completely knowledge graph (KG) based, where the hidden states are dynamic KGs. A KG is both human- and machine-readable, making it easy to see what the agents remember and forget. We train and compare agents with different memory systems, to shed light on how human brains work when it comes to managing its own memory. By repurposing the given learning objective as learning a memory management policy, we were able to capture the most likely hidden state, which is not only interpretable but also reusable.

Grover Search is currently one of the main quantum algorithms leading to hybrid quantum-classical methods that reduce the worst-case time complexity for some combinatorial optimization problems. Specifically, the combination of Quantum Minimum Finding (obtained from Grover Search) with dynamic programming has proved particularly efficient in improving the complexity of NP-hard problems currently solved by classical dynamic programming. For these problems, the classical dynamic programming complexity in $\mathcal{O}^*(c^n)$, where $\mathcal{O}^*$ denotes that polynomial factors are ignored, can be reduced by a hybrid algorithm to $\mathcal{O}^*(c_{quant}^n)$, with $c_{quant} < c$. In this paper, we provide a bounded-error hybrid algorithm that achieves such an improvement for a broad class of NP-hard single-machine scheduling problems for which we give a generic description. Moreover, we extend this algorithm to tackle the 3-machine flowshop problem. Our algorithm reduces the exponential-part complexity compared to the best-known classical algorithm, sometimes at the cost of an additional pseudo-polynomial factor.

Our objective is to investigate the distribution of dust and associated large-scale structures of the Galaxy using optical linear polarization measurements of various open clusters located at different distances in the Galactic anticenter direction. We present R-band linear polarization observations of stars towards five open clusters: Kronberger~1, Berkeley~69, Berkeley~71, Berkeley~19, and King~8 in the anticenter direction. The polarization observations were carried out using AIMPOL instrument mounted on the 104 cm Sampurnanand telescope of ARIES, Nainital, making it the first study to target the polarization observations towards distant clusters ($\sim$6~kpc). We combined the observed polarization data with the distance information from the Gaia space telescope to infer the dust distribution along the line of sight. The variation in the degree of polarization and extinction with distance reveals the presence of multiple dust layers along each cluster direction. In addition, common foreground dust layers detected towards different cluster directions highlight the presence of global features such as spiral arms. Our results show that the dust clouds at 2~kpc towards Berkeley~69 and Berkeley~71 coincide with the Perseus arm, while the dust layer at $\sim$4~kpc towards distant clusters, Berkeley~19 and King~8, indicates the presence of the Outer arm. The large-scale dust distribution obtained by combining our polarization results with the previous polarization studies of nearby open clusters suggests that the anticenter direction is characterized by low extinction, homogeneous dust distribution with somewhat uniform orientation of the plane-of-sky component of the magnetic field along the line of sight. Our study demonstrates the utility of polarization as a tool to study the large-scale dust distribution., Comment: 22 Pages, 14 Figures, 7 Tables, Accepted for publication in Astronomy & Astrophysics Journal