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While there are many different types of education data, policymakers and education leaders often place heavy emphasis on data from large-scale quantitative measures, such as annual state assessments. But data from these sources alone do not provide a complete picture of learning and are often not well suited to informing improvements at the local level. This brief provides seven recommendations to help educators, administrators, policymakers, and others use a wider range of data to more effectively support equitable learning outcomes.

Differential Privacy (DP) can be applied to raw text by exploiting the spatial arrangement of words in an embedding space. We investigate the implications of such text privatization on Language Models (LMs) and their tendency towards stereotypical associations. Since previous studies documented that linguistic proficiency correlates with stereotypical bias, one could assume that techniques for text privatization, which are known to degrade language modeling capabilities, would cancel out undesirable biases. By testing BERT models trained on texts containing biased statements primed with varying degrees of privacy, our study reveals that while stereotypical bias generally diminishes when privacy is tightened, text privatization does not uniformly equate to diminishing bias across all social domains. This highlights the need for careful diagnosis of bias in LMs that undergo text privatization.

Artificial Intelligence (AI) faces persistent challenges in terms of transparency and accountability, which requires rigorous documentation. Through a literature review on documentation practices, we provide an overview of prevailing trends, persistent issues, and the multifaceted interplay of factors influencing the documentation. Our examination of key characteristics such as scope, target audiences, support for multimodality, and level of automation, highlights a dynamic evolution in documentation practices, underscored by a shift towards a more holistic, engaging, and automated documentation.

Fano resonances are ubiquitous phenomena appearing in many fields of physics, e.g. atomic or molecular photoionization, or electron transport in quantum dots. Recently, attosecond interferometric techniques have been used to measure the amplitude and phase of photoelectron wavepackets close to Fano resonances in argon and helium, allowing for the retrieval of the temporal dynamics of the photoionization process. In this work, we study the photoionization of argon atoms close to the $3s^13p^64p$ autoionizing state using an interferometric technique with high spectral resolution. The phase shows a monotonic $2{\pi}$ increase across the resonance or a sigmo\"idal less than ${\pi}$ variation depending on experimental conditions, e.g. the probe laser bandwidth. Using three different, state-of-the-art calculations, we show that the measured phase is influenced by the interaction between final states reached by two-photon transitions.

Enzymes are important proteins that catalyze chemical reactions. In recent years, machine learning methods have emerged to predict enzyme function from sequence; however, there are no standardized benchmarks to evaluate these methods. We introduce CARE, a benchmark and dataset suite for the Classification And Retrieval of Enzymes (CARE). CARE centers on two tasks: (1) classification of a protein sequence by its enzyme commission (EC) number and (2) retrieval of an EC number given a chemical reaction. For each task, we design train-test splits to evaluate different kinds of out-of-distribution generalization that are relevant to real use cases. For the classification task, we provide baselines for state-of-the-art methods. Because the retrieval task has not been previously formalized, we propose a method called Contrastive Reaction-EnzymE Pretraining (CREEP) as one of the first baselines for this task. CARE is available at https://github.com/jsunn-y/CARE/.

We consider the relativistic, spatially inhomogeneous Fokker-Planck equation with an external confining potential. We prove the exponential time decay of solutions towards the global equilibrium in weighted $L^2$ and Sobolov spaces. Our result holds for a wide class of external potentials and the estimates on the rate of convergence are explicit and constructive. Moreover, we prove that the associated semigroup of the equation has hypoelliptic regularizing properties and we obtain explicit rates on this regularization. The technique is based on the construction of suitable Lyapunov functionals.

The mammalian cell membrane is embedded with biomolecular condensates of protein and lipid clusters, which interact with an underlying viscoelastic cytoskeleton network to organize the cell surface and mechanically interact with the extracellular environment. However, the mechanical and thermodynamic interplay between the viscoelastic network and liquid-liquid phase separation of 2-dimensional (2D) lipid condensates remains poorly understood. Here, we engineer materials composed of 2D lipid membrane condensates embedded within a thin viscoelastic actin network. The network generates localized anisotropic stresses that deform lipid condensates into triangular morphologies with sharp edges and corners, shapes unseen in 3D composite gels. Kinetic coarsening of phase-separating lipid condensates accelerates the viscoelastic relaxation of the network, leading to an effectively softer composite material over intermediate timescales. We dynamically manipulate the membrane composition to control the elastic-to-viscous crossover of the network. Such viscoelastic composite membranes may enable the development of coatings, catalytic surfaces, separation membranes, and other interfaces with tunable spatial organization and plasticity mechanisms., Comment: There are 11 pages with four figures in the main text. Ancillary files include a supplemental appendix and three supplemental videos. The supplemental appendix contains three video legends, a supplemental note, and two supplemental figures

Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. Leveraging LCLMs' ability to natively ingest and process entire corpora of information offers numerous advantages. It enhances user-friendliness by eliminating the need for specialized knowledge of tools, provides robust end-to-end modeling that minimizes cascading errors in complex pipelines, and allows for the application of sophisticated prompting techniques across the entire system. To assess this paradigm shift, we introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks. However, LCLMs still face challenges in areas like compositional reasoning that are required in SQL-like tasks. Notably, prompting strategies significantly influence performance, emphasizing the need for continued research as context lengths grow. Overall, LOFT provides a rigorous testing ground for LCLMs, showcasing their potential to supplant existing paradigms and tackle novel tasks as model capabilities scale., Comment: 29 pages. Dataset available at https://github.com/google-deepmind/loft

Personalized medicine based on medical images, including predicting future individualized clinical disease progression and treatment response, would have an enormous impact on healthcare and drug development, particularly for diseases (e.g. multiple sclerosis (MS)) with long term, complex, heterogeneous evolutions and no cure. In this work, we present the first stochastic causal temporal framework to model the continuous temporal evolution of disease progression via Neural Stochastic Differential Equations (NSDE). The proposed causal inference model takes as input the patient's high dimensional images (MRI) and tabular data, and predicts both factual and counterfactual progression trajectories on different treatments in latent space. The NSDE permits the estimation of high-confidence personalized trajectories and treatment effects. Extensive experiments were performed on a large, multi-centre, proprietary dataset of patient 3D MRI and clinical data acquired during several randomized clinical trials for MS treatments. Our results present the first successful uncertainty-based causal Deep Learning (DL) model to: (a) accurately predict future patient MS disability evolution (e.g. EDSS) and treatment effects leveraging baseline MRI, and (b) permit the discovery of subgroups of patients for which the model has high confidence in their response to treatment even in clinical trials which did not reach their clinical endpoints.

We report the discovery of two new exoplanet systems around fully convective stars, found from the radial-velocity (RV) variations of their host stars measured with the nIR spectropolarimeter CFHT/SPIRou over multiple years. GJ 3378 b is a planet with minimum mass of $5.26^{+0.94}_{-0.97}$ Mearth in an eccentric 24.73-day orbit around an M4V star of 0.26 Msun. GJ 1289 b has a minimum mass of $6.27\pm1.25$ Mearth in a 111.74-day orbit, in a circular orbit around an M4.5V star of mass 0.21 Msun. Both stars are in the solar neighbourhood, at respectively 7.73 and 8.86 pc. The low-amplitude RV signals are detected after line-by-line post-processing treatment. These potential sub-Neptune class planets around cool stars may have temperate atmospheres and be interesting nearby systems for further studies. We also recovered the large-scale magnetic field of both stars, found to be mostly axisymmetric and dipolar, and with a polar strength of 20-30 G and 200-240 G for GJ 3378 (in 2019-21) and GJ 1289 (in 2022-23), respectively. The rotation periods measured with the magnetic field differ from the orbital periods, and in general, stellar activity is not seen in the studied nIR RV time series of both stars. GJ 3378 b detection is not confirmed by optical RVs and is therefore considered a candidate at this point., Comment: accepted in A&A

In this paper we present the European Low Frequency Survey (ELFS), a project that will enable foregrounds-free measurements of the primordial $B$-mode polarization and a detection of the tensor-to-scalar ratio, $r$, to a level $\sigma(r) = 0.001$ by measuring the Galactic and extra-galactic emissions in the 5--120\,GHz frequency window. Indeed, the main difficulty in measuring the B-mode polarization comes from the fact that many other processes in the Universe also emit polarized microwaves, which obscure the faint Cosmic Microwave Background (CMB) signal. The first stage of this project is being carried out in synergy with the Simons Array (SA) collaboration, installing a 5.5--11\,GHz (X-band) coherent receiver at the focus of one of the three 3.5\,m SA telescopes in Atacama, Chile, followed by the installation of the QUIJOTE-MFI2 in the 10--20 GHz range. We designate this initial iteration of the ELFS program as ELFS-SA. The receivers are equipped with a fully digital back-end that will provide a frequency resolution of 1\,MHz across the band, allowing us to clean the scientific signal from unwanted radio frequency interference, particularly from low-Earth orbit satellite mega constellations. This paper reviews the scientific motivation for ELFS and its instrumental characteristics, and provides an update on the development of ELFS-SA., Comment: SPIE conference on Astronomical Telescopes + Instrumentation, Yokohama, 16-22 June 2024. New version with correction in Eq. (3) arXiv admin note: text overlap with arXiv:2310.16509

Bose-Einstein condensates (BECs) have been proposed for many applications in atom interferometry, as their coherence over long evolution times promises unprecedented sensitivity. To date, BECs can be efficiently created in devices using atom chips, but these are still complex and place high demands on size, weight and power. To further simplify these setups, we equipped an atom chip with a nano-structured diffraction-grating to derive all beams for the magneto-optical trap (MOT) from a single laser beam. Moreover, using a 2D$^+$-MOT as an atomic source and a beam with uniform intensity for the grating illumination, we capture $1\times10^9$ atoms in one second, cool them to $14\,\mu$K, and demonstrate magnetic trapping using the atom chip. This is a major step towards the simplification of portable BEC devices for quantum sensing on earth and in space., Comment: 12 pages, 3 figures

Remote and van der Waals epitaxy are promising approaches for synthesizing single crystalline membranes for flexible electronics and discovery of new properties via extreme strain; however, a fundamental challenge is that most materials do not wet the graphene surface. We develop a cold seed approach for synthesizing smooth intermetallic films on graphene that can be exfoliated to form few nanometer thick single crystalline membranes. Our seeded GdAuGe films have narrow x-ray rocking curve widths of 9-24 arc seconds, which is two orders of magnitude lower than their counterparts grown by typical high temperature methods, and have atomically sharp interfaces observed by transmission electron microscopy. Upon exfoliation and rippling, strain gradients in GdAuGe membranes induce an antiferromagnetic to ferri/ferromagnetic transition. Our smooth, ultrathin membranes provide a clean platform for discovering new flexomagnetic effects in quantum materials.

We report observations of the atmospheric transmission spectrum of the sub-Neptune exoplanet GJ 3470 b taken using the Near-Infrared Camera (NIRCam) on JWST. Combined with two archival HST/WFC3 transit observations and fifteen archival Spitzer transit observations, we detect water, methane, sulfur dioxide, and carbon dioxide in the atmosphere of GJ 3470 b, each with a significance of >3-sigma. GJ 3470 b is the lowest mass -- and coldest -- exoplanet known to show a substantial sulfur dioxide feature in its spectrum, at $M_{p}$=11.2${\,{\rm M}_{\oplus}}$ and $T_{eq}$=600$\,$K. This indicates disequilibrium photochemistry drives sulfur dioxide production in exoplanet atmospheres over a wider range of masses and temperatures than has been reported or expected. The water, carbon dioxide, and sulfur dioxide abundances we measure indicate an atmospheric metallicity of approximately $100\times$ Solar. We see further evidence for disequilibrium chemistry in our inferred methane abundance, which is significantly lower than expected from equilibrium models consistent with our measured water and carbon dioxide abundances., Comment: 25 pages, 9 figures, 6 tables. Accepted in Astrophysical Journal Letters

Hyperspectral image segmentation is crucial for many fields such as agriculture, remote sensing, biomedical imaging, battlefield sensing and astronomy. However, the challenge of hyper and multi spectral imaging is its large data footprint. We propose both a novel camera design and a vision transformer-based (ViT) algorithm that alleviate both the captured data footprint and the computational load for hyperspectral segmentation. Our camera is able to adaptively sample image regions or patches at different resolutions, instead of capturing the entire hyperspectral cube at one high resolution. Our segmentation algorithm works in concert with the camera, applying ViT-based segmentation only to adaptively selected patches. We show results both in simulation and on a real hardware platform demonstrating both accurate segmentation results and reduced computational burden.

The celebrated Erd\H{o}s-Hajnal conjecture says that any graph without a fixed induced subgraph $H$ contains a very large homogeneous set. A direct analog of this conjecture is not true for hypergraphs. In this paper we present two natural variants of this problem which do hold for hypergraphs. We show that for every $r \geq 3$, $m \geq m_0(r)$ and $0 \leq f \leq \binom{m}{r}$, if an $r$-graph $G$ does not contain $m$ vertices spanning exactly $f$ edges, then $G$ contains much bigger homogeneous sets than what is guaranteed to exist in general $r$-graphs. We also prove that if a $3$-graph $G$ does not contain homogeneous sets of polynomial size, then for every $m \geq 3$ there are $\Omega(m^3)$ values of $f$ such that $G$ contains $m$ vertices spanning exactly $f$ edges. This makes progress on a conjecture of Axenovich, Brada\v{c}, Gishboliner, Mubayi and Weber.

Generative AI has received much attention in the image and language domains, with the transformer neural network continuing to dominate the state of the art. Application of these models to time series generation is less explored, however, and is of great utility to machine learning, privacy preservation, and explainability research. The present survey identifies this gap at the intersection of the transformer, generative AI, and time series data, and reviews works in this sparsely populated subdomain. The reviewed works show great variety in approach, and have not yet converged on a conclusive answer to the problems the domain poses. GANs, diffusion models, state space models, and autoencoders were all encountered alongside or surrounding the transformers which originally motivated the survey. While too open a domain to offer conclusive insights, the works surveyed are quite suggestive, and several recommendations for best practice, and suggestions of valuable future work, are provided.

Ultracold atoms are crucial for unlocking truly precise and accurate quantum metrology, and provide an essential platform for quantum computing, communication and memories. One of the largest ongoing challenges is the miniaturization of these quantum devices. Here, we show that the typically macroscopic optical lattice architecture at the heart of many ultra-precise quantum technologies can be realized with a single input laser beam on the same diffractive chip already used to create the ultracold atoms. Moreover, this inherently ultra-stable platform enables access to a plethora of new lattice dimensionalities and geometries, ideally suited for the design of high-accuracy, portable quantum devices., Comment: 7 pages, 2 figures

The MANTA (Modular Adjustable Negative Triangularity ARC-class) design study investigated how negative-triangularity (NT) may be leveraged in a compact, fusion pilot plant (FPP) to take a ``power-handling first" approach. The result is a pulsed, radiative, ELM-free tokamak that satisfies and exceeds the FPP requirements described in the 2021 National Academies of Sciences, Engineering, and Medicine report ``Bringing Fusion to the U.S. Grid". A self-consistent integrated modeling workflow predicts a fusion power of 450 MW and a plasma gain of 11.5 with only 23.5 MW of power to the scrape-off layer (SOL). This low $P_\text{SOL}$ together with impurity seeding and high density at the separatrix results in a peak heat flux of just 2.8 MW/m$^{2}$. MANTA's high aspect ratio provides space for a large central solenoid (CS), resulting in ${\sim}$15 minute inductive pulses. In spite of the high B fields on the CS and the other REBCO-based magnets, the electromagnetic stresses remain below structural and critical current density limits. Iterative optimization of neutron shielding and tritium breeding blanket yield tritium self-sufficiency with a breeding ratio of 1.15, a blanket power multiplication factor of 1.11, toroidal field coil lifetimes of $3100 \pm 400$ MW-yr, and poloidal field coil lifetimes of at least $890 \pm 40$ MW-yr. Following balance of plant modeling, MANTA is projected to generate 90 MW of net electricity at an electricity gain factor of ${\sim}2.4$. Systems-level economic analysis estimates an overnight cost of US\$3.4 billion, meeting the NASEM FPP requirement that this first-of-a-kind be less than US\$5 billion. The toroidal field coil cost and replacement time are the most critical upfront and lifetime cost drivers, respectively.

In a physical system, changing parameters such as temperature can induce a phase transition: an abrupt change from one state of matter to another. Analogous phenomena have recently been observed in large language models. Typically, the task of identifying phase transitions requires human analysis and some prior understanding of the system to narrow down which low-dimensional properties to monitor and analyze. Statistical methods for the automated detection of phase transitions from data have recently been proposed within the physics community. These methods are largely system agnostic and, as shown here, can be adapted to study the behavior of large language models. In particular, we quantify distributional changes in the generated output via statistical distances, which can be efficiently estimated with access to the probability distribution over next-tokens. This versatile approach is capable of discovering new phases of behavior and unexplored transitions -- an ability that is particularly exciting in light of the rapid development of language models and their emergent capabilities., Comment: 21 pages, 4 figures

Amyotrophic lateral sclerosis (ALS) severely impairs patients' ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis. The P300 speller brain-computer interface (BCI) offers an alternative communication method by interpreting a subject's EEG response to characters presented on a grid interface. This paper addresses the common speed limitations encountered in training efficient P300-based multi-subject classifiers by introducing innovative "across-subject" classifiers. We leverage a combination of the second-generation Generative Pre-Trained Transformer (GPT2) and Dijkstra's algorithm to optimize stimuli and suggest word completion choices based on typing history. Additionally, we employ a multi-layered smoothing technique to accommodate out-of-vocabulary (OOV) words. Through extensive simulations involving random sampling of EEG data from subjects, we demonstrate significant speed enhancements in typing passages containing rare and OOV words. These optimizations result in approximately 10% improvement in character-level typing speed and up to 40% improvement in multi-word prediction. We demonstrate that augmenting standard row/column highlighting techniques with layered word prediction yields close-to-optimal performance. Furthermore, we explore both "within-subject" and "across-subject" training techniques, showing that speed improvements are consistent across both approaches.

We present a novel, interferometric, two-color, high-order harmonic generation setup, based on a turn-key Ytterbium-doped femtosecond laser source and its second harmonic. Each interferometer arm contains a spatial light modulator, with individual capabilities to manipulate the spatial beam profiles and to stabilize the relative delay between the fundamental and the second harmonic. Additionally, separate control of the relative power and focusing geometries of the two color beams is implemented to conveniently perform automatized scans of multiple parameters. A live diagnostics system gives continuous information during ongoing measurements., Comment: 8 pages, 8 figures

Interactions between exoplanetary atmospheres and internal properties have long been hypothesized to be drivers of the inflation mechanisms of gaseous planets and apparent atmospheric chemical disequilibrium conditions. However, transmission spectra of exoplanets has been limited in its ability to observational confirm these theories due to the limited wavelength coverage of HST and inferences of single molecules, mostly H$_2$O. In this work, we present the panchromatic transmission spectrum of the approximately 750 K, low-density, Neptune-sized exoplanet WASP-107b using a combination of HST WFC3, JWST NIRCam and MIRI. From this spectrum, we detect spectroscopic features due to H$_2$O (21$\sigma$), CH$_4$ (5$\sigma$), CO (7$\sigma$), CO$_2$ (29$\sigma$), SO$_2$ (9$\sigma$), and NH$_3$ (6$\sigma$). The presence of these molecules enable constraints on the atmospheric metal enrichment (M/H is 10--18$\times$ Solar), vertical mixing strength (log$_{10}$K$_{zz}$=8.4--9.0 cm$^2$s$^{-1}$), and internal temperature ($>$345 K). The high internal temperature is suggestive of tidally-driven inflation acting upon a Neptune-like internal structure, which can naturally explain the planet's large radius and low density. These findings suggest that eccentricity driven tidal heating is a critical process governing atmospheric chemistry and interior structure inferences for a majority of the cool ($<$1,000K) super-Earth-to-Saturn mass exoplanet population., Comment: This preprint has not undergone any substantive post-submission improvements or corrections. The Version of Record of this article is published in Nature here: https://www.nature.com/articles/s41586-024-07514-w

In recent years considerable research in LiDAR semantic segmentation was conducted, introducing several new state of the art models. However, most research focuses on single-scan point clouds, limiting performance especially in long distance outdoor scenarios, by omitting time-sequential information. Moreover, varying-density and occlusions constitute significant challenges in single-scan approaches. In this paper we propose a LiDAR point cloud preprocessing and postprocessing method. This multi-stage approach, in conjunction with state of the art models in a multi-scan setting, aims to solve those challenges. We demonstrate the benefits of our method through quantitative evaluation with the given models in single-scan settings. In particular, we achieve significant improvements in mIoU performance of over 5 percentage point in medium range and over 10 percentage point in far range. This is essential for 3D semantic scene understanding in long distance as well as for applications where offline processing is permissible.

Traditional security mechanisms isolate resources from users who should not access them. We reflect the compositional nature of such security mechanisms back into the structure of LLMs to build a provably secure LLM; that we term SecureLLM. Other approaches to LLM safety attempt to protect against bad actors or bad outcomes, but can only do so to an extent making them inappropriate for sensitive data. SecureLLM blends access security with fine-tuning methods. Each data silo has associated with it a separate fine-tuning and a user has access only to the collection of fine-tunings that they have permission for. The model must then perform on compositional tasks at the intersection of those data silos with the combination of those individual fine-tunings. While applicable to any task like document QA or making API calls, in this work we concern ourselves with models that learn the layouts of new SQL databases to provide natural-language-to-SQL translation capabilities. Existing fine-tuning composition methods fail in this challenging environment, as they are not well-equipped for handling compositional tasks. Compositionality remains a challenge for LLMs. We contribute both a difficult new compositional natural-language-to-SQL translation task and a new perspective on LLM security that allows models to be deployed to secure environments today.

Trigger points are a concept introduced by Mau, Lux, and Westheuser (2023) to study qualitative focus group interviews and understand polarisation in Germany. When people communicate, trigger points represent moments when individuals feel that their understanding of what is fair, normal, or appropriate in society is questioned. In the original studies, individuals react affectively to such triggers and show strong and negative emotional responses. In this paper, we introduce the first systematic study of the large-scale effect of individual words as trigger points by analysing a large amount of social media posts. We examine online deliberations on Reddit between 2020 and 2022 and collect >100 million posts from subreddits related to a set of words identified as trigger points in UK politics. We find that such trigger words affect user engagement and have noticeable consequences on animosity in online discussions. We share empirical evidence of trigger words causing animosity, and how they provide incentives for hate speech, adversarial debates, and disagreements. Our work is the first to introduce trigger points to computational studies of online communication. Our findings are relevant to researchers interested in online harms and who examine how citizens debate politics and society in light of affective polarisation.

One of the main interests of 2D materials is their ability to be assembled with many degrees of freedom for tuning and manipulating excitonic properties. There is a need to understand how the structure of the interfaces between atomic layers influences exciton properties. Here we use cathodoluminescence (CL) and time-resolved CL experiments to study how excitons interact with the interface between two twisted hexagonal boron nitride (hBN) crystals with various angles. An efficient capture of free excitons by the interface is demonstrated, which leads to a population of long lived and interface-localized (2D) excitons. Temperature dependent experiments indicate that for high twist angles, these excitons localized at the interface further undergo a self-trapping. It consists in a distortion of the lattice around the exciton on which the exciton traps itself. Our results suggest that this exciton-interface interaction causes a broad optical emission of highly twisted hBN-hBN structures around 300 nm (4 eV). Exciton self-trapping is finally discussed as a common feature of sp2 hybridized boron nitride polytypes and nanostructures due to the ionic nature of the B-N bond and their compact excitons., Comment: 15 pages, 13 figures

When studying political communication, combining the information from text, audio, and video signals promises to reflect the richness of human communication more comprehensively than confining it to individual modalities alone. However, its heterogeneity, connectedness, and interaction are challenging to address when modeling such multimodal data. We argue that aligning the respective modalities can be an essential step in entirely using the potential of multimodal data because it informs the model with human understanding. Taking care of the data-generating process of multimodal data, our framework proposes four principles to organize alignment and, thus, address the challenges of multimodal data. We illustrate the utility of these principles by analyzing how German MPs address members of the far-right AfD in their speeches and predicting the tone of video advertising in the context of the 2020 US presidential race. Our paper offers important insights to all keen to analyze multimodal data effectively., Comment: Working Paper

Quantum computing has the potential for disruptive change in many sectors of industry, especially in materials science and optimization. In this paper, we describe how the Turbine Balancing Problem can be solved with quantum computing, which is the NP-hard optimization problem of analytically balancing rotor blades in a single plane as found in turbine assembly. Small yet relevant instances occur in industry, which makes the problem interesting for early quantum computing benchmarks. We model it as a Quadratic Unconstrained Binary Optimization problem and compare the performance of a classical rule-based heuristic and D-Wave Systems' Quantum Annealer Advantage_system4.1. In this case study, we use real-world as well as synthetic datasets and observe that the quantum hardware significantly improves an actively used heuristic's solution for small-scale problem instances with bare disk imbalance in terms of solution quality. Motivated by this performance gain, we subsequently design a quantum-inspired classical heuristic based on simulated annealing that achieves extremely good results on all given problem instances, essentially solving the optimization problem sufficiently well for all considered datasets, according to industrial requirements., Comment: 6 pages, 3 figure

The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment that includes small-aperture telescopes (SATs) observing from an altitude of 5,200 m in the Atacama Desert in Chile. The SO SATs will cover six spectral bands between 27 and 280 GHz to search for primordial B-modes to a sensitivity of $\sigma(r)=0.002$, with quantified systematic errors well below this value. Each SAT is a self-contained cryogenic telescope with a 35$^\circ$ field of view, 42 cm diameter optical aperture, 40 K half-wave plate, 1 K refractive optics, and $<0.1$ K focal plane that holds $>12,000$ TES detectors. We describe the nominal design of the SATs and present details about the integration and testing for one operating at 93 and 145 GHz.

The spatial-kinematic structure of 48 young star clusters and associations is investigated. Moran's $I$ statistic is used to quantify the degree of kinematic substructure in each region, and the results are compared to those expected assuming the hierarchical or monolithic models of star cluster formation. Of the observed regions, 39 are found to have significant kinematic substructure, such that they are compatible with the hierarchical model and incompatible with the monolithic model. This includes multiple regions whose $Q$ parameter shows the region to be centrally concentrated and clustered. The remaining nine are compatible with both models. From this it is concluded that the kinematic substructure of the observed star clusters represents strong evidence in favour the hierarchical model of star cluster formation over the monolithic model., Comment: Accepted for publication in MNRAS. 13 pages, 10 figures

In this paper we report near-infrared observations of the classical T Tauri star TW Hya with the SPIRou high-resolution spectropolarimeter and velocimeter at the 3.6-m Canada-France-Hawaii Telescope in 2019, 2020, 2021 and 2022. By applying Least-Squares Deconvolution (LSD) to our circularly polarized spectra, we derived longitudinal fields that vary from year to year from -200 to +100 G, and exhibit low-level modulation on the 3.6 d rotation period of TW Hya, despite the star being viewed almost pole-on. We then used Zeeman-Doppler Imaging to invert our sets of unpolarized and circularly-polarized LSD profiles into brightness and magnetic maps of TW Hya in all 4 seasons, and obtain that the large-scale field of this T Tauri star mainly consists of a 1.0-1.2 kG dipole tilted at about 20{\deg} to the rotation axis, whereas the small-scale field reaches strengths of up to 3-4 kG. We find that the large-scale field is strong enough to allow TW Hya to accrete material from the disc on the polar regions at the stellar surface in a more or less geometrically stable accretion pattern, but not to succeed in spinning down the star. We also report the discovery of a radial velocity signal of semi-amplitude $11.1^{+3.3}_{-2.6}$ m/s (detected at 4.3$\sigma$ at a period of 8.3 d in the spectrum of TW Hya, whose origin may be attributed to either a non-axisymmetric density structure in the inner accretion disc, or to a $0.55^{+0.17}_{-0.13}$ Jupiter mass candidate close-in planet (if orbiting in the disc plane), at an orbital distance of $0.075\pm0.001$ au., Comment: MNRAS, in press (23 pages, 16 figures, 6 tables)

Gene duplication is a fundamental evolutionary mechanism that contributes to biological complexity and diversity (Fortna et al., 2004). Traditionally, research has focused on the duplication of gene sequences (Zhang, 1914). However, evidence suggests that the duplication of regulatory elements may also play a significant role in the evolution of genomic functions (Teichmann and Babu, 2004; Hallin and Landry, 2019). In this work, the evolution of regulatory relationships belonging to gene-specific-substructures in a GRN are modeled. In the model, a network grows from an initial configuration by repeatedly choosing a random gene to duplicate. The likelihood that the regulatory relationships associated with the selected gene are retained through duplication is determined by a vector of probabilities. Occurrences of gene-family-specific substructures are counted under the gene duplication model. In this thesis, gene-family-specific substructures are referred to as subnetwork motifs. These subnetwork motifs are motivated by network motifs which are patterns of interconnections that recur more often in a specialized network than in a random network (Milo et al., 2002). Subnetwork motifs differ from network motifs in the way that subnetwork motifs are instances of gene-family-specific substructures while network motifs are isomorphic substructures. These subnetwork motifs are counted under Full and Partial Duplication, which differ in the way in which regulation relationships are inherited. Full duplication occurs when all regulatory links are inherited at each duplication step, and Partial Duplication occurs when regulation inheritance varies at each duplication step. Moments for the number of occurrences of subnetwork motifs are determined in each model. The results presented offer a method for discovering subnetwork motifs that are significant in a GRN under gene duplication.

Mueller matrices provide a complete description of a medium's response to excitation by polarized light, and their characterization is important across a broad range of applications from ellipsometry in material science to polarimetry in biochemistry, medicine and astronomy. Here we introduce single-shot Mueller matrix polarimetry based on generalized measurements performed with a Poincar\'e beam. We determine the Mueller matrix of a homogeneous medium with unknown optical activity by detecting its optical response to a Poincar\'e beam, which across its profile contains all polarization states, and analyze the resulting polarization pattern in terms of four generalized measurements, which are implemented as a path-displaced Sagnac interferometer. We illustrate the working of our Mueller matrix polarimetry on the example of tilted and rotated wave plates and find excellent agreement with predictions as well as alternative Stokes measurements. After initial calibration, the alignment of the device stays stable for up to 8 hours, promising suitability for the dynamic characterization of Mueller matrices that change in time.

In most prediction and estimation situations, scientists consider various statistical models for the same problem, and naturally want to select amongst the best. Hansen et al. (2011) provide a powerful solution to this problem by the so-called model confidence set, a subset of the original set of available models that contains the best models with a given level of confidence. Importantly, model confidence sets respect the underlying selection uncertainty by being flexible in size. However, they presuppose a fixed sample size which stands in contrast to the fact that model selection and forecast evaluation are inherently sequential tasks where we successively collect new data and where the decision to continue or conclude a study may depend on the previous outcomes. In this article, we extend model confidence sets sequentially over time by relying on sequential testing methods. Recently, e-processes and confidence sequences have been introduced as new, safe methods for assessing statistical evidence. Sequential model confidence sets allow to continuously monitor the models' performances and come with time-uniform, nonasymptotic coverage guarantees.

This study examines the extent to which U.S. federal agencies responded to and implemented the principles outlined in the White House's October 2022 "Blueprint for an AI Bill of Rights." The Blueprint provided a framework for the ethical governance of artificial intelligence systems, organized around five core principles: safety and effectiveness, protection against algorithmic discrimination, data privacy, notice and explanation about AI systems, and human alternatives and fallback. Through an analysis of publicly available records across 15 federal departments, the authors found limited evidence that the Blueprint directly influenced agency actions after its release. Only five departments explicitly mentioned the Blueprint, while 12 took steps aligned with one or more of its principles. However, much of this work appeared to have precedents predating the Blueprint or motivations disconnected from it, such as compliance with prior executive orders on trustworthy AI. Departments' activities often emphasized priorities like safety, accountability and transparency that overlapped with Blueprint principles, but did not necessarily stem from it. The authors conclude that the non-binding Blueprint seems to have had minimal impact on shaping the U.S. government's approach to ethical AI governance in its first year. Factors like public concerns after high-profile AI releases and obligations to follow direct executive orders likely carried more influence over federal agencies. More rigorous study would be needed to definitively assess the Blueprint's effects within the federal bureaucracy and broader society., Comment: 8 pages

Inside a medium, showers originating from a very high-energy particle may develop via medium-induced splitting processes such as hard bremsstrahlung or pair production. During shower development, two consecutive splittings sometimes overlap quantum mechanically, so that they cannot be treated independently. Some of these effects can be absorbed into an effective value of a medium parameter known as $\hat q$. Previous calculations (with certain simplifying assumptions) have found that, after adjusting the value of $\hat q$, the leftover effect of overlapping splittings is quite small for purely gluonic large-$N_c$ showers but is very much larger for large-$N_f$ QED showers, at comparable values of $N\alpha$. Those works did not quite make for apples-to-apples comparisons: the gluon shower work investigated energy deposition from a gluon-initiated shower, whereas the QED work investigated charge-deposition from an electron-initiated shower. As a first step to tighten up the comparison, this paper investigates energy deposition in the QED case. Along the way, we develop a framework that should be useful in the future to explore whether the very small effect of overlapping splitting in purely gluonic showers is an artifact of having ignored quarks., Comment: 48 pages, 14 figures

We review our progress in developing a frequency reference with singly ionized lutetium and give estimates of the levels of inaccuracy we expect to achieve in the near future with both the $^1S_0\leftrightarrow{}^3D_1$ and $^1S_0\leftrightarrow{}^3D_2$ transitions. Based on established experimental results, we show that inaccuracies at the low $10^{-19}$ level are readily achievable for the $^1S_0\leftrightarrow{}^3D_1$ transition, and the frequency ratio between the two transitions is limited almost entirely by the BBR shift. We argue that the frequency ratio measured within the one apparatus provides a well-defined metric to compare and establish the performance of remotely located systems. For the measurement of an in situ frequency ratio, relativistic shifts drop out and both transitions experience the same electromagnetic environment. Consequently, the uncertainty budget for the ratio is practically identical to the uncertainty budgets for the individual transitions. If the ratios for two or more systems disagree we can be certain at least one of the clock assessments is incorrect. If they agree, subsequent comparisons on one transition would only differ by relativistic effects. Since motional effects are easily assessed and typically small for a heavy ion, only the differential gravitational red-shift will significantly contribute and this can be confirmed by comparison on the second transition., Comment: 10 pages

In a wide range of quantum technology applications, ranging from atomic clocks to the creation of ultracold or quantum degenerate samples for atom interferometry, optimal laser sources are critical. In particular, two phase-locked laser sources with a precise difference frequency are needed for efficient coherent population trapping (CPT) clocks, gray molasses laser cooling, or driving Raman transitions. Here we show how a simple cost-effective laser diode can selectively amplify only one sideband of a fiber-electrooptically-modulated seed laser to produce moderate-power phase-locked light with sub-Hz relative linewidth and tunable difference frequencies up to $\approx 15\,$GHz. The architecture is readily scalable to multiple phase-locked lasers and could conceivably be used for future on-chip compact phase-locked laser systems for quantum technologies., Comment: 4 pages, 5 figures

Thermal imaging plays a crucial role in various applications, but the inherent low resolution of commonly available infrared (IR) cameras limits its effectiveness. Conventional super-resolution (SR) methods often struggle with thermal images due to their lack of high-frequency details. Guided SR leverages information from a high-resolution image, typically in the visible spectrum, to enhance the reconstruction of a high-res IR image from the low-res input. Inspired by SwinFusion, we propose SwinFuSR, a guided SR architecture based on Swin transformers. In real world scenarios, however, the guiding modality (e.g. RBG image) may be missing, so we propose a training method that improves the robustness of the model in this case. Our method has few parameters and outperforms state of the art models in terms of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM). In Track 2 of the PBVS 2024 Thermal Image Super-Resolution Challenge, it achieves 3rd place in the PSNR metric. Our code and pretained weights are available at https://github.com/VisionICLab/SwinFuSR., Comment: Accepted at 20th IEEE Workshop on Perception Beyond the Visible Spectrum, CVPR 2024

We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused solely on English texts and a multilingual track. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine. The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30). In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For all subtasks, the best systems used LLMs., Comment: 23 pages, 12 tables

Advanced biological intelligence learns efficiently from an information-rich stream of stimulus information, even when feedback on behaviour quality is sparse or absent. Such learning exploits implicit assumptions about task domains. We refer to such learning as Domain-Adapted Learning (DAL). In contrast, AI learning algorithms rely on explicit externally provided measures of behaviour quality to acquire fit behaviour. This imposes an information bottleneck that precludes learning from diverse non-reward stimulus information, limiting learning efficiency. We consider the question of how biological evolution circumvents this bottleneck to produce DAL. We propose that species first evolve the ability to learn from reward signals, providing inefficient (bottlenecked) but broad adaptivity. From there, integration of non-reward information into the learning process can proceed via gradual accumulation of biases induced by such information on specific task domains. This scenario provides a biologically plausible pathway towards bottleneck-free, domain-adapted learning. Focusing on the second phase of this scenario, we set up a population of NNs with reward-driven learning modelled as Reinforcement Learning (A2C), and allow evolution to improve learning efficiency by integrating non-reward information into the learning process using a neuromodulatory update mechanism. On a navigation task in continuous 2D space, evolved DAL agents show a 300-fold increase in learning speed compared to pure RL agents. Evolution is found to eliminate reliance on reward information altogether, allowing DAL agents to learn from non-reward information exclusively, using local neuromodulation-based connection weight updates only. Code available at github.com/aislab/dal., Comment: Camera ready version. 9 pages, 5 figures

Vector light beams, characterised by a spatially varying polarisation, can exhibit localised structures reminiscent of the Skyrmions familiar from the study of magnetic media. We present a theory of such Skyrmions within paraxial optics, exploiting mathematical analogies with the study of superfluids, especially the A phase of superfluid $\textrm{He}^3$. The key feature is the Skyrmion field which, together with the underlying Skyrmion vector potential, determines the properties of the Skyrmions and, more generally, the polarisation structure of every paraxial vector beam. In addition to structures with integer Skyrmion number we find polarisation patterns with non-integer Skyrmion number; these seem to have no analogue in other fields of physics.

In performance-based fire safety design, ensuring safe egress, e.g. by visibility of safety signs, is a crucial safety goal. Compliance with the building requirements is often demonstrated by simulations of smoke spread. Numerical models like the Fire Dynamics Simulator generally compute visibility as a local quantity using the light extinction coefficient, without the consideration of the actual light path to a safety sign. Here, visibility maps are introduced, providing an approach for post-processing fire simulation data. They indicate safe areas along egress routes, with respect to visibility. At each location, the available visibility is calculated using Jin's law, as an integrated value of the extinction coefficient along the line of sight to the closest exit sign. The required visibility results from the distance between those points. Additional parameters like view angle or visual obstructions are considered. The presented method allows for temporal visibility assessment, e.g. in an ASET-RSET analysis.

Dynamics between key neuroinflammatory components, detrimental M1 and beneficial M2 microglial cells, are not fully understood post-ischemic stroke. To discover, model, and predict these dynamics, we use a method based on sparse identification of nonlinear dynamics (SINDy). The resulting data-driven dynamical system involves constant and linear terms but does not include nonlinear interactions between cells. Results show M2 microglial cell dominance of four days. Forward predictions capture potential long-term dynamics of microglial cells and suggest a persistent inflammatory response., Comment: 4 pages, 5 figures. Accepted, 8th International Conference on Computational and Mathematical Biomedical Engineering (CMBE2024)