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In a recent paper Rousseau-Rizzi and Emanuel (2019) presented a derivation of an upper limit on maximum hurricane velocity at the surface. This derivation was based on a consideration of an infinitely narrow (differential) Carnot cycle with the warmer isotherm at the point of the maximum wind velocity. Here we show that this derivation neglected a significant term describing the kinetic energy change in the outflow. Additionally, we highlight the importance of a proper accounting for the power needed to lift liquid water. Finally, we provide a revision to the formula for surface fluxes of heat and momentum showing that, if we accept the assumptions adopted by Rousseau-Rizzi and Emanuel (2019), the resulting velocity estimate does not depend on the flux of sensible heat., Comment: Our comments on paper "An evaluation of hurricane superintensity in axisymmetric numerical models" by Rapha\"el Rousseau-Rizzi and Kerry Emanuel published in J. Atmos. Sci. 76, 1697 (2019)

Manny Parzen passed away in February 2016, and this article is written partly as a memorial and appreciation. Manny made important contributions to several areas, but the two that influenced me most were his contributions to kernel density estimation and to Reproducing Kernel Hilbert Spaces, the two kernels of the title. Some fond memories of Manny as a PhD advisor begin this memorial, followed by a discussion of Manny's influence on density estimation and RKHS methods. A picture gallery of trips comes next, followed by the technical part of the article. Here our goal is to show how risk models can be built using RKHS penalized likelihood methods where subjects have personal (sample) densities which can be used as {\it attributes} in such models., Comment: 22 pages, 4 photos

Emanuel Haldeman-Julius was born in Philadelphia in 1889, the son of poor Russian immigrants. Emanuel did not achieve much at school and worked in various jobs until he found his niche as a reporter for a variety of socialist newspapers. He married a wealthy heiress, became a publisher and eventually achieved fame and wealth. The cornerstone of his success was producing little blue books selling at five cents each. Eventually he published 500 million copies of these books which the poor were able to buy because of their low price. It is argued that Haldeman-Julius's publications made a major contribution to the cultural background, both scientific and literary, of the period in the United States of America. His little blue books were bought by children as the cheapest source of information. They were also purchased by working men intent on self advancement. The booklets reflected his general view and that of his authors, being socialist in politics and atheist in religious outlook. They promoted sex education, evolutionary theory and a wide range of excellent literature. However even with Haldeman-Julius's knowledge of and enthusiasm for evolution, he failed to see the demise of the little blue books, which did not change sufficiently over time.

The rapid advancement of high-throughput sequencing and other assay technologies has resulted in the generation of large and complex multi-omics datasets, offering unprecedented opportunities for advancing precision medicine strategies. However, multi-omics data integration presents significant challenges due to the high dimensionality, heterogeneity, experimental gaps, and frequency of missing values across data types. Computational methods have been developed to address these issues, employing statistical and machine learning approaches to uncover complex biological patterns and provide deeper insights into our understanding of disease mechanisms. Here, we comprehensively review state-of-the-art multi-omics data integration methods with a focus on deep generative models, particularly variational autoencoders (VAEs) that have been widely used for data imputation and augmentation, joint embedding creation, and batch effect correction. We explore the technical aspects of loss functions and regularisation techniques including adversarial training, disentanglement and contrastive learning. Moreover, we discuss recent advancements in foundation models and the integration of emerging data modalities, while describing the current limitations and outlining future directions for enhancing multi-modal methodologies in biomedical research., Comment: 43 pages, 4 figures

We investigate the numerical stability of thermonuclear detonations in 1D accelerated reactive shocks and 2D binary collisions of equal mass, magnetized and unmagnetized white dwarf stars. To achieve high resolution at initiation sites, we devised geometric gridding and mesh velocity strategies specially adapted to the unique requirements of head-on collisional geometries, scenarios in which one expects maximum production of iron-group products. We study effects of grid resolution and the limiting of temperature, energy generation, and reactants for different stellar masses, separations, magnetic fields, initial compositions, detonation mechanisms, and limiter parameters across a range of cell sizes from 1 to 100 km. Our results set bounds on the parameter space of limiter amplitudes for which both temperature and energy limiting procedures yield consistent and monotonically convergent solutions. Within these bounds we find grid resolutions of 5 km or better are necessary for uncertainties in total released energy and iron-group products to drop below 10%. Intermediate mass products (e.g., calcium) exhibit similar convergence trends but with somewhat greater uncertainty. These conclusions apply equally to pure C/O WDs, multi-species compositions (including helium shells), magnetized and unmagnetized cores, and either single or multiple detonation scenarios.

We give a simple alternative proof of Royen's Gaussian Correlation inequality by using (a slightly generalized version of) Nakamura-Tsuji's symmetric inverse Brascamp-Lieb inequality for even log-concave functions. We explain that this inverse inequality is in a certain sense a dual counterpart to the forward inequality of Bennett-Carbery-Christ-Tao and Valdimarsson, and that the log-concavity assumption therein cannot be omitted in general., Comment: 10 pages

Cavitation, the formation of vapor bubbles in metastable liquids, is highly sensitive to nanoscale surface defects. Using molecular dynamics simulations and classical nucleation theory, we show that pure water confined within defect-free walls can withstand extreme negative pressures, far beyond those observed experimentally. Hydrophobic surfaces trigger heterogeneous cavitation and lower the cavitation pressure magnitude, but not to experimental levels. Notably, a single nanoscopic surface defect capable of hosting a vapor bubble drastically reduces the tensile strength of water. We find that defects as small as 1-2 nm can act as effective cavitation nuclei, a scale smaller than predicted by simple mechanical stability arguments. This discrepancy arises from stochastic fluctuations of the vapor bubble, which can overcome the kinetic free-energy barrier for cavitation. Our findings show that cavitation is predominantly determined by the largest surface defect rather than the overall defect density, emphasizing the importance of eliminating the largest surface imperfections to enhance stability against cavitation., Comment: 10 pages, 7 figures

In today's world, marked by social distancing and lockdowns, the development of digital financial services is becoming increasingly important, but there is little empirical work documenting the most important factors that contribute to the process of financial and digital inclusion. Because the speed with which states adapt to digital financial services is critical, we must ask how prepared states are for this transition and how far they have progressed in terms of financial and digital inclusion. In this context, the goal of this article is, on the one hand, to propose a financial responsibility process framework capable of raising awareness of the most important harmonized key levels of financial and digital inclusion process that, when properly managed, can lead to achieving an optimal level of financial responsibility, and, on the other hand, to assess the financial and digital inclusion process of two different age groups of individuals who are active in the financial environment (15-34 and 35-59 age groups). The Analytical Hierarchy Process AHP and Fuzzy AHP approaches are proposed as a framework for assessing the mechanism of financial and digital inclusion in five East Central European countries. The findings reflect differences between the analyzed countries in terms of the key levels of financial and digital inclusion (where digital and financial education are the most important levels), with Croatia, Czech Republic, and Poland being the most integrated and Romania being the least. According to the findings, as a country or region's level of financial and digital inclusion increases, so does its level of financial responsibility. This research can be a useful tool in raising awareness about the importance of directed behavior for financial responsibility, particularly for policymakers.

In this article, models for assessing national intangible resources are analysed through a lecture in the literature, and the best-known evaluation methods are categorized into academic models and models of international organizations, with the most important differences being identified. The European Innovation Scoreboard (EIS) and the World Economic Forum annual reports on Global Competitiveness were considered to assess Romania's position in the international context in terms of intangible assets. Despite the importance of intangible resources at national level and the fact that they are an important factor in determining economic growth in the current knowledge-based economy, this article concludes that Romania's position in the international context regarding intangible assets is very weak, with many weak points in research and innovation performance compared to other EU Member States. Therefore, there is a need in our country to re-evaluate the areas where all efforts need to be focused to stimulate innovation performance, to properly manage national intangible resources, a crucial process for improving the quality of life.

Strategic interactions can be represented more concisely, and analyzed and solved more efficiently, if we are aware of the symmetries within the multiagent system. Symmetries also have conceptual implications, for example for equilibrium selection. We study the computational complexity of identifying and using symmetries. Using the classical framework of normal-form games, we consider game symmetries that can be across some or all players and/or actions. We find a strong connection between game symmetries and graph automorphisms, yielding graph automorphism and graph isomorphism completeness results for characterizing the symmetries present in a game. On the other hand, we also show that the problem becomes polynomial-time solvable when we restrict the consideration of actions in one of two ways. Next, we investigate when exactly game symmetries can be successfully leveraged for Nash equilibrium computation. We show that finding a Nash equilibrium that respects a given set of symmetries is PPAD- and CLS-complete in general-sum and team games respectively -- that is, exactly as hard as Brouwer fixed point and gradient descent problems. Finally, we present polynomial-time methods for the special cases where we are aware of a vast number of symmetries, or where the game is two-player zero-sum and we do not even know the symmetries., Comment: Long and updated version to the published paper in the Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025). 24 pages, 2 figures, 1 table

Optimizing the free energy under a mass constraint may generate a convex crystal subject to assumptions on the potential $g(0)=0$, $g \ge 0$. The general problem classically attributed to Almgren is to infer if this is the case assuming the sub-level sets of g are convex. The theorem proven in the paper is that in one dimension the answer is positive.

This paper addresses the issue of intellectual property management in the knowledge-based economy. The starting point in carrying out the study is the presentation of some concepts regarding in the first phase, the intellectual capital. Arguments are made that the knowledge-based economy is a challenge for the current century. The subject of intellectual property is approached through the prism of a topical concept operationalized in the current global economic context. The main institutions that are directly related to this concept are mentioned. The topic of patents related to WOS indexed scientific papers is also debated, along with a series of statistics and studies on the state of patent protection worldwide in the top fields. The last part of the paper contains the conclusions and own points of view on the debated topic.

In recent years, several text-to-speech systems have been proposed to synthesize natural speech in zero-shot, few-shot, and low-resource scenarios. However, these methods typically require training with data from many different speakers. The speech quality across the speaker set typically is diverse and imposes an upper limit on the quality achievable for the low-resource speaker. In the current work, we achieve high-quality speech synthesis using as little as five minutes of speech from the desired speaker by augmenting the low-resource speaker data with noise and employing multiple sampling techniques during training. Our method requires only four high-quality, high-resource speakers, which are easy to obtain and use in practice. Our low-complexity method achieves improved speaker similarity compared to the state-of-the-art zero-shot method HierSpeech++ and the recent low-resource method AdapterMix while maintaining comparable naturalness. Our proposed approach can also reduce the data requirements for speech synthesis for new speakers and languages., Comment: Accepted for publication at the 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2025) to be held at Hyderabad, India

The CIELO project introduces a novel set of chemo-dynamical zoom-in simulations designed to simultaneously resolve galaxies and their nearby environments. The initial conditions include a diverse range of cosmic structures, such as local groups, filaments, voids, and walls, allowing for a detailed exploration of galaxies within the broader context of the cosmic web. This study presents the initial conditions and characterizes the global properties of CIELO galaxies and their environments. It focuses on galaxies with stellar masses ranging from log [8,11] solar masses and examines key scaling relations, including the mass-size relation, the Tully-Fisher relation, and the mass-metallicity relation for both stars and star-forming gas. The DisPerSe algorithm was used to determine the positions of CIELO galaxies within the cosmic web, with a specific focus on the Pehuen haloes. The selection of local group volumes was guided by criteria based on the relative positions and velocities of the two primary galaxies. The Pehuen regions were chosen to map walls, filaments, and voids. Synthetic images in the SDSS i, r, and g bands were generated using the SKIRT radiative transfer code. Additionally, a dynamical decomposition was performed to classify galaxy morphologies into bulge, disc, and stellar halo components (abridged)., Comment: 13 pages, 12 figures, submitted. Comments are welcomed

Hybrid pixel detectors have a well-established array of applications ranging from particle physics to life sciences. The small dimensions of Timepix3 as well as its relatively low energetic expenses make it an intriguing option additionally for ion detection in nuclear physics experiments, as it reveals simultaneously precise temporal, spatial and energetic properties of recorded events from nuclear reactions. Currently, a limiting factor is the electronics behavior at high input charge resulting in improper energy determination of incident heavier ions. While the low-energy per-pixel calibration of Timepix3 is normally performed with the use of photons of up to 60 keV, the characteristic linear range permits a correct extrapolation up to only 150 keV/pixel. We developed a global per-pixel energy correction method involving the use of short-ranged accelerated ions and spectroscopic alpha sources, to suitably extend the energy determination capability of Timepix3 for nuclear ion spectroscopy experiments, where spatial and temporal precision of recorded events are equally crucial. It was found that upon applying this correction, the reliable per-pixel energy range has been increased from the original 150 keV to at least 1.1 MeV, while maintaining the relative energy resolution to better than 2.5% for stopped protons of up to 1.9 MeV and better than 3.1% for alpha particles of 5.5 MeV. Furthermore, to demonstrate the spatial resolution of Timepix3 detectors with silicon sensors, we present alpha-radiography measurements from which we extract the modulation transfer function (MTF) and produce real-world biological sample images.

In recent years, toxic content and hate speech have become widespread phenomena on the internet. Moderators of online newspapers and forums are now required, partly due to legal regulations, to carefully review and, if necessary, delete reader comments. This is a labor-intensive process. Some providers of large language models already offer solutions for automated hate speech detection or the identification of toxic content. These include GPT-4o from OpenAI, Jigsaw's (Google) Perspective API, and OpenAI's Moderation API. Based on the selected German test dataset HOCON34k, which was specifically created for developing tools to detect hate speech in reader comments of online newspapers, these solutions are compared with each other and against the HOCON34k baseline. The test dataset contains 1,592 annotated text samples. For GPT-4o, three different promptings are used, employing a Zero-Shot, One-Shot, and Few-Shot approach. The results of the experiments demonstrate that GPT-4o outperforms both the Perspective API and the Moderation API, and exceeds the HOCON34k baseline by approximately 5 percentage points, as measured by a combined metric of MCC and F2-score.

Fock states of the quantum harmonic oscillator are fundamental to quantum sensing and information processing, serving as key resources for exploiting bosonic degrees of freedom. Here, we prepare high Fock states in a high-overtone bulk acoustic wave resonator (HBAR) by coupling it to a superconducting qubit and applying microwave pulses designed using quantum optimal control. We characterize the experimentally realized states by employing a criterion for genuine quantum non-Gaussianity (QNG) designed to reveal multiphonon contributions. Although energy relaxation and decoherence limit the achievable fidelities, we demonstrate genuine QNG features compatible with Fock state $\vert 6\rangle$, confirming that the prepared states cannot be generated through Gaussian operations on states with up to Fock state $\vert 5\rangle$ contributions. We further investigate the robustness of these QNG features to losses and their utility in sensing displacement amplitudes. In particular, we introduce a hierarchy based on the quantum Fisher information and show that, despite decoherence and measurement imperfections, the prepared states achieve a displacement sensitivity surpassing that of an ideal Fock state $\vert 3\rangle$. Our results have immediate applications in quantum sensing and simulations with HBAR devices.

Algebraic $kk$-theory, introduced by Corti\~nas and Thom, is a bivariant $K$-theory defined on the category $\mathrm{Alg}$ of algebras over a commutative unital ring $\ell$. It consists of a triangulated category $kk$ endowed with a functor from $\mathrm{Alg}$ to $kk$ that is the universal excisive, homotopy invariant and matrix-stable homology theory. Moreover, one can recover Weibel's homotopy $K$-theory $\mathrm{KH}$ from $kk$ since we have $kk(\ell,A)=\mathrm{KH}(A)$ for any algebra $A$. We prove that $\mathrm{Alg}$ with the split surjections as fibrations and the $kk$-equivalences as weak equivalences is a stable category of fibrant objects, whose homotopy category is $kk$. As a consecuence of this, we prove that the Dwyer-Kan localization $kk_\infty$ of the $\infty$-category of algebras at the set of $kk$-equivalences is a stable infinity category whose homotopy category is $kk$., Comment: Minor changes

Imperfect-recall games, in which players may forget previously acquired information, have found many practical applications, ranging from game abstractions to team games and testing AI agents. In this paper, we quantify the utility gain by endowing a player with perfect recall, which we call the value of recall (VoR). While VoR can be unbounded in general, we parameterize it in terms of various game properties, namely the structure of chance nodes and the degree of absentmindedness (the number of successive times a player enters the same information set). Further, we identify several pathologies that arise with VoR, and show how to circumvent them. We also study the complexity of computing VoR, and how to optimally apportion partial recall. Finally, we connect VoR to other previously studied concepts in game theory, including the price of anarchy. We use that connection in conjunction with the celebrated smoothness framework to characterize VoR in a broad class of games., Comment: 36 pages, 6 figures, main body to be published in Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), Philadelphia, Pennsylvania, USA, 2025

In this companion paper for the DAGECC (Domain Adaptation and GEneralization for Character Classification) competition organized within the frame of the ICPR 2024 conference, we present the general context of the tasks we proposed to the community, we introduce the data that were prepared for the competition and we provide a summary of the results along with a description of the top three winning entries. The competition was centered around domain adaptation and generalization, and our core aim is to foster interest and facilitate advancement on these topics by providing a high-quality, lightweight, real world dataset able to support fast prototyping and validation of novel ideas., Comment: Companion paper for the ICPR 2024 Competition on Domain Adaptation and GEneralization for Character Classification (DAGECC)

Optical dynamic nuclear polarization (DNP) offers an attractive approach to enhancing the sensitivity of nuclear magnetic resonance (NMR) spectroscopy. Efficient, optically-generated electron polarization can be leveraged to operate across a broad range of temperatures and magnetic fields, making it particularly appealing for applications requiring high DNP efficiency or spatial resolution. While a large class of systems hold promise for optical DNP, many candidates display both variable electron polarizability and electron and nuclear T1 relaxation times as functions of magnetic field and temperature. This necessitates tools capable of studying DNP under diverse experimental conditions. To address this, we introduce a cryogenic field cycling instrument that facilitates optical DNP studies across a wide range of magnetic fields (10mT to 9.4T) and temperatures (10K to 300K). Continuous cryogen replenishment enables sustained, long-term operation. Additionally, the system supports the ability to manipulate and probe hyperpolarized nuclear spins via pulse sequences involving millions of RF pulses. We describe innovations in the device design and demonstrate its operation on a model system of 13C nuclear spins in diamond polarized through optically pumped nitrogen vacancy (NV) centers. We anticipate the use of the instrument for a broad range of optical DNP systems and studies., Comment: 10 figures, 11 pages (including references)

Gaussian Splatting has enabled real-time 3D human avatars with unprecedented levels of visual quality. While previous methods require a desktop GPU for real-time inference of a single avatar, we aim to squeeze multiple Gaussian avatars onto a portable virtual reality headset with real-time drivable inference. We begin by training a previous work, Animatable Gaussians, on a high quality dataset captured with 512 cameras. The Gaussians are animated by controlling base set of Gaussians with linear blend skinning (LBS) motion and then further adjusting the Gaussians with a neural network decoder to correct their appearance. When deploying the model on a Meta Quest 3 VR headset, we find two major computational bottlenecks: the decoder and the rendering. To accelerate the decoder, we train the Gaussians in UV-space instead of pixel-space, and we distill the decoder to a single neural network layer. Further, we discover that neighborhoods of Gaussians can share a single corrective from the decoder, which provides an additional speedup. To accelerate the rendering, we develop a custom pipeline in Vulkan that runs on the mobile GPU. Putting it all together, we run 3 Gaussian avatars concurrently at 72 FPS on a VR headset. Demo videos are at https://forresti.github.io/squeezeme., Comment: v2

Stellar halos around galaxies contain key information about their formation and assembly history. Using simulations, we can trace the origins of different stellar populations in these halos, contributing to our understanding of galaxy evolution. We aim to investigate the assembly of stellar halos and their chemical abundances in 28 galaxies from CIELO project with logMgal[9 and 11]Msun. Stellar halos were identified using the AM E method, focusing on the outer regions between the 1.5 optical radius and the virial radius. We divided the stellar populations based on their formation channel: exsitu, endodebris, and insitu, and analyzed their chemical abundances, ages, and spatial distributions. Additionally, we explored correlations between halo mass, metallicity, and alpha element enrichment. CIELO simulations reveal that stellar halos are predominantly composed of accreted material (exsitu and endodebris stars), in agreement with previous works. The mass fraction of these populations is independent of stellar halo mass, though their metallicities scale linearly with it. Exsitu stars tend to dominate the outskirts and be more alpha rich and older, while endodebris stars are more prevalent at lower radii and tend to be less alpha rich and slightly younger. Massive stellar halos require a median of five additional satellites to build 90 percent of their mass, compared to lower mass halos, which typically need fewer (median of 2.5) and lower-mass satellites and are assembled earlier. The diversity of accreted satellite histories results in well defined stellar halo mass metallicity and [alpha/Fe] [Fe/H] relations, offering a detailed view of the chemical evolution and assembly history of stellar halos. We find that the [alpha/Fe] [Fe/H] is more sensitive to the characteristics and star formation history of the contributing satellites than the stellar halo mass metallicity relationship, Comment: 16 pages, 14 figures, accepted for publication in A&A

Maximal supergravities in ten and eleven dimensions admit consistent truncations on particular spheres to maximal supergravities in lower dimensions. Concurrently, the truncation to singlets under any subgroup of the sphere isometry group leads to consistent truncations with less or no supersymmetry. We review the relation between these truncations in the framework of exceptional field theory. As an application, we derive three new G$_2$-invariant solutions of $D=11$ supergravity. Their geometry is of the form AdS$_4\times \Sigma_7$ where $\Sigma_7$ is a deformed seven-sphere, preserving SO(7) isometries., Comment: 35 pages, 7 figures

In the wake of the recent resurgence of the Datalog language of databases, together with its extensions for ontological reasoning settings, this work aims to bridge the gap between the theoretical studies of DatalogMTL (Datalog extended with metric temporal logic) and the development of production-ready reasoning systems. In particular, we lay out the functional and architectural desiderata of a modern reasoner and propose our system, Temporal Vadalog. Leveraging the vast amount of experience from the database community, we go beyond the typical chase-based implementations of reasoners, and propose a set of novel techniques and a system that adopts a modern data pipeline architecture. We discuss crucial architectural choices, such as how to guarantee termination when infinitely many time intervals are possibly generated, how to merge intervals, and how to sustain a limited memory footprint. We discuss advanced features of the system, such as the support for time series, and present an extensive experimental evaluation. This paper is a substantially extended version of "The Temporal Vadalog System" as presented at RuleML+RR '22. Under consideration in Theory and Practice of Logic Programming (TPLP).

We present efficient algorithms for obtaining the Hamiltonian in Lightcone Conformal Truncation (LCT) for a 2d scalar field with a generic potential. We apply this method to the sine-Gordon and sinh-Gordon models in $1+1d$, and find precise agreement with integrability results when the scaling dimension $\Delta$ of the deforming cosine/cosinh potential is in the range $ \Delta \leq 1$. The agreement provides additional evidence for a recent conjecture for how to compute the effective lightcone Hamiltonian in this class of models. In addition, to high precision, we provide the first direct confirmation for the conjectured self-duality of the sinh-Gordon model ($\Delta < 0)$, which relates $\Delta \leftrightarrow 4/\Delta$. As the dimension approaches the upper limit $\Delta=1$ from below, we show analytically that the Hamiltonian matrix elements exactly reproduce those of a free Majorana fermion, demonstrating how bosonization is manifested in the LCT basis. We comment on the possible extension of the approach to $\Delta > 1$., Comment: 30 pages, 11 figures

Chaotic systems near black holes satisfy a universal bound, $\lambda \leq \kappa_H$ linking the Lyapunov coefficient $\lambda$ associated with unstable orbits to surface gravity $\kappa_H$ of the event horizon. A natural question is whether this bound is satisfied by unstable circular null geodesics in the vicinity of black holes. However, there are known cases where this bound is violated. It is intriguing to ask whether there exists an alternative universal bound that is valid in such situations. We show that for any spherically symmetric, static black hole that satisfies Einstein's equations and the dominant energy condition, there exist other universal bounds relating the Lyapunov coefficient to a generalized notion of surface gravity at the photon sphere, as well as to the Unruh temperature locally measured by static observers. As applications, we show how these bounds also constrain the imaginary part of quasinormal modes in the eikonal regime and how the Lyapunov coefficient relates to the shadow size and the entropy of the horizon., Comment: 8 pages. References added. Clarifications in the discussion of quasinormal modes and addition of a new bound derived from the relationship between the Lyapunov exponent and the Gaussian curvature of an associated optical metric

We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together with constraints and objectives on the final program. Advanced synthesis algorithms transform the model into a low-level quantum program that meets the user's specification and is directed at a stipulated hardware. This separation of description from implementation is essential for scale. The technology adapts electronic design automation methods to quantum computing, finding feasible implementations in a virtually unlimited functional space. The results show clear superiority over the compilation and transpilation methods used today. We expect that this technological approach will take over and prevail as quantum software become more demanding, complex, and essential.

Machine learning has been widely adopted in biomedical research, fueled by the increasing availability of data. However, integrating datasets across institutions is challenging due to legal restrictions and data governance complexities. Federated learning allows the direct, privacy preserving training of machine learning models using geographically distributed datasets, but faces the challenge of how to appropriately control for covariate effects. The naive implementation of conventional covariate control methods in federated learning scenarios is often impractical due to the substantial communication costs, particularly with high-dimensional data. To address this issue, we introduce dsLassoCov, a machine learning approach designed to control for covariate effects and allow an efficient training in federated learning. In biomedical analysis, this allow the biomarker selection against the confounding effects. Using simulated data, we demonstrate that dsLassoCov can efficiently and effectively manage confounding effects during model training. In our real-world data analysis, we replicated a large-scale Exposome analysis using data from six geographically distinct databases, achieving results consistent with previous studies. By resolving the challenge of covariate control, our proposed approach can accelerate the application of federated learning in large-scale biomedical studies., Comment: 17 pages, 5 figures

We report on a double moir\'e system consisting of four graphene layers, where the top and bottom pairs form small-twist-angle bilayer graphene, and the middle interface has a large rotational mismatch. This system shows clear signatures of two sets of spatially separated flat bands associated with the top and bottom twisted bilayer graphene (TBG) subsystems, each independently tunable. Thermodynamic analysis reveals weak correlations between layers that allow the chemical potential to be measured as a function of carrier density for each constituent TBG. We find that correlated insulating states at integer number of electrons per moir\'e unit cell are most robust near magic angle, whereas gapped states at neutrality are more robust at larger twist angles., Comment: 6 pages, 4 figures, includes supplemental material