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The actions of a teacher are one of the strongest factors influencing students' academic achievement and future life success. However, some of the most common frameworks that are used to evaluate teachers and guide their development or improvement are not based on or informed by research about effective teaching methods. Other frameworks are difficult for schools, districts, teachers, and other relevant parties to access due to barriers such as cost. Considering the value of effective teaching, it is worthwhile for a teacher development and evaluation framework informed by research to be created and made easily available. This paper attempts to outline such a framework. This framework has never been researched in practice, but it is based on the available research regarding which teaching practices are generally most effective for students' learning and what sort of evaluation elements are most predictive and useful in assessing teachers. Given this, the framework may be a valuable tool to be used and/or adapted by schools, districts, teachers, and others involved in evaluating teacher effectiveness and guiding professional improvement for teachers.

With growing demand for online courses, it is important to assess the efficacy of quality online virtual instruction. This study investigates the impact of Edmentum's Courseware Algebra I on student achievement using data on 1,205 ninth-grade students from a large urban school district in the Midwest during the 2020-2021 school year. Utilizing a quasi-experimental design, the study compares the NWEA Measures of Academic Progress (MAP) Mathematics scores of students participating in online virtual instruction to those receiving traditional in-person instruction. The study findings reveal that students in the online Algebra I course demonstrated significantly higher mathematics achievement than their peers in in-person classes, with an effect size of 0.13. The positive outcomes associated with Courseware Algebra I highlight its potential as an effective option for high school students.

Edmentum offers a personalized learning platform called Exact Path. Over the past five years, about 56 schools and districts in Virginia have implemented the program. This quasiexperimental study, designed to meet ESSA Tier 2 evidence and What Works Clearinghouse standards with reservations, aimed to investigate the efficacy of Exact Path in Norfolk Public Schools. The study estimated the effects of the program for diverse groups of students in order to inform our understanding of the district context and guide instructional decisions. The study found that Exact Path was positively related to Math achievement for students in Grades 3 through 8. The findings suggest that Exact Path could be an effective tool for improving student success in Virginia and similar contexts. These results have implications for educators, policymakers, and researchers interested in improving academic outcomes through personalized learning.

Endometriosis, affecting about 10\% of individuals assigned female at birth, is challenging to diagnose and manage. Diagnosis typically involves the identification of various signs of the disease using either laparoscopic surgery or the analysis of T1/T2 MRI images, with the latter being quicker and cheaper but less accurate. A key diagnostic sign of endometriosis is the obliteration of the Pouch of Douglas (POD). However, even experienced clinicians struggle with accurately classifying POD obliteration from MRI images, which complicates the training of reliable AI models. In this paper, we introduce the \underline{H}uman-\underline{AI} \underline{Co}llaborative \underline{M}ulti-modal \underline{M}ulti-rater Learning (HAICOMM) methodology to address the challenge above. HAICOMM is the first method that explores three important aspects of this problem: 1) multi-rater learning to extract a cleaner label from the multiple ``noisy'' labels available per training sample; 2) multi-modal learning to leverage the presence of T1/T2 MRI images for training and testing; and 3) human-AI collaboration to build a system that leverages the predictions from clinicians and the AI model to provide more accurate classification than standalone clinicians and AI models. Presenting results on the multi-rater T1/T2 MRI endometriosis dataset that we collected to validate our methodology, the proposed HAICOMM model outperforms an ensemble of clinicians, noisy-label learning models, and multi-rater learning methods.

Short-period gas giant planets have been shown to be significantly rarer for host stars less massive than the Sun. We report the discovery of two transiting giant planets - TOI-2379 b and TOI-2384 b - with low-mass (early M) host stars. Both planets were detected using TESS photometry and for both the transit signal was validated using ground based photometric facilities. We confirm the planetary nature of these companions and measure their masses using radial velocity observations. We find that TOI-2379 b has an orbital period of 5.469 d and a mass and radius of $5.76\pm0.20$ M$_{J}$ and $1.046\pm0.023$ R$_{J}$ and TOI-2384 b has an orbital period of 2.136 d and a mass and radius of $1.966\pm0.059$ M$_{J}$ and $1.025\pm0.021$ R$_{J}$. TOI-2379 b and TOI-2384 b have the highest and third highest planet-to-star mass ratios respectively out of all transiting exoplanets with a low-mass host star, placing them uniquely among the population of known exoplanets and making them highly important pieces of the puzzle for understanding the extremes of giant planet formation., Comment: Accepted for publication in MNRAS. 15 pages, 12 figures

The MIRI Exoplanets Orbiting White dwarfs (MEOW) Survey is a cycle 2 JWST program to search for exoplanets around dozens of nearby white dwarfs via infrared excess and direct imaging. In this paper, we present the detection of mid-infrared excess at 18 and 21 microns towards the bright (V = 11.4) metal-polluted white dwarf WD 0310-688. The source of the IR excess is almost certainly within the system; the probability of background contamination is $<0.1\%$. While the IR excess could be due to an unprecedentedly small and cold debris disk, it is best explained by a $3.0^{+5.5}_{-1.9}$ M$_{\rm Jup}$ cold (248$^{+84}_{-61}$ K) giant planet orbiting the white dwarf within the forbidden zone (the region where planets are expected to be destroyed during the star's red giant phase). We constrain the source of the IR excess to an orbital separation of 0.1-2 AU, marking the first discovery of a white dwarf planet candidate within this range of separations. WD 0310-688 is a young remnant of an A or late B-type star, and at just 10.4 pc it is now the closest white dwarf with a known planet candidate. Future JWST observations could distinguish the two scenarios by either detecting or ruling out spectral features indicative of a planet atmosphere., Comment: Accepted for publication to ApJL

The stellar obliquity distribution of warm-Jupiter systems is crucial for constraining the dynamical history of Jovian exoplanets, as the warm Jupiters' tidal detachment likely preserves their primordial obliquity. However, the sample size of warm-Jupiter systems with measured stellar obliquities has historically been limited compared to that of hot Jupiters, particularly in hot-star systems. In this work, we present newly obtained sky-projected stellar obliquity measurements for warm-Jupiter systems, TOI-559, TOI-2025, TOI-2031, TOI-2485, TOI-2524, and TOI-3972, derived from the Rossiter-McLaughlin effect, and show that all six systems display alignment with a median measurement uncertainty of 13 degrees. Combining these new measurements with the set of previously reported stellar obliquity measurements, our analysis reveals that single-star warm-Jupiter systems tend to be aligned, even around hot stellar hosts. This alignment exhibits a 3.4-$\sigma$ deviation from the $T_{\rm eff}-\lambda$ dependency observed in hot-Jupiter systems, where planets around cool stars tend to be aligned, while those orbiting hot stars show considerable misalignment. The current distribution of spin-orbit measurements for Jovian exoplanets indicates that misalignments are neither universal nor primordial phenomena affecting all types of planets. The absence of misalignments in single-star warm-Jupiter systems further implies that many hot Jupiters, by contrast, have experienced a dynamically violent history., Comment: 18 pages, 2 figures, accepted for publication in ApJL

Development of methods to control the directional and spectral characteristics of thermal radiation from metallic surfaces is a critical factor enabling many important thermal management applications. In this paper, we study the thermal emission properties of functionalized aluminum surfaces produced through femtosecond laser surface processing (FLSP). These types of surfaces have recently been found to exhibit near-unity broadband omnidirectional emissivity. However, their ultrabroadband absorption response includes visible and near-infrared (IR) radiation, in addition to the mid-IR range, which limits their use as daytime passive radiative cooling devices. Here, we present ways to solve this problem by demonstrating a new design that uses a dielectric Bragg visible light reflector to accurately control the thermal emission spectra of the FLSP surface with the goal of achieving high performance daytime radiative cooling operation. In addition, we propose other designs based on dielectric multilayer structures to further tailor and control the spectra and thermal emission angles of the FLSP surfaces leading to narrowband and broadband directional thermal radiation. The presented photonic engineering approach combined with FLSP structures will be beneficial to various emerging applications, such as radiative cooling, thermal sensing, and thermophotovoltaics.

Recent years have seen increased awareness of the potential significant impacts of computing technologies, both positive and negative. This whitepaper explores how to address possible harmful consequences of computing technologies that might be difficult to anticipate, and thereby mitigate or address. It starts from the assumption that very few harms due to technology are intentional or deliberate; rather, the vast majority result from failure to recognize and respond to them prior to deployment. Nonetheless, there are concrete steps that can be taken to address the difficult problem of anticipating and responding to potential harms from new technologies.

Despite the recent surge of research efforts to make data visualizations accessible to people who are blind or have low vision (BLV), how to support BLV people's data analysis remains an important and challenging question. As refreshable tactile displays (RTDs) become cheaper and conversational agents continue to improve, their combination provides a promising approach to support BLV people's interactive data exploration and analysis. To understand how BLV people would use and react to a system combining an RTD with a conversational agent, we conducted a Wizard-of-Oz study with 11 BLV participants, where they interacted with line charts, bar charts, and isarithmic maps. Our analysis of participants' interactions led to the identification of nine distinct patterns. We also learned that the choice of modalities depended on the type of task and prior experience with tactile graphics, and that participants strongly preferred the combination of RTD and speech to a single modality. In addition, participants with more tactile experience described how tactile images facilitated a deeper engagement with the data and supported independent interpretation. Our findings will inform the design of interfaces for such interactive mixed-modality systems., Comment: Accepted to be presented at IEEE VIS 2024 (Honorable Mention Award) and published in IEEE TVCG

Growing demands in the semiconductor industry necessitate increasingly stringent requirements on throughput and positioning accuracy of lithographic equipment. Meeting these demands involves employing highly aggressive motion profiles, which introduce position-dependent flexible dynamics, thus compromising achievable position tracking performance. This paper introduces a control approach enabling active compensation of position-dependent flexible dynamics by extending the conventional rigid-body control structure to include active control of flexible dynamics. To facilitate real-time implementation of the control algorithm, appropriate position-dependent weighting functions are introduced, ensuring computationally efficient execution of the proposed approach. The efficacy of the proposed control design approach is demonstrated through experiments conducted on a state-of-the-art extreme ultraviolet (EUV) wafer stage., Comment: 7 pages, 7 Figures

Prime-Cam is a first-generation science instrument for the CCAT Observatory's six-meter aperture Fred Young Submillimeter Telescope (FYST). FYST's crossed-Dragone design provides high optical throughput to take advantage of its unique site at 5600 m on Cerro Chajnantor in Chile's Atacama Desert to reach mapping speeds over ten times greater than current and near-term submillimeter experiments. Housing up to seven independent instrument modules in its 1.8-meter diameter cryostat, Prime-Cam will combine broadband polarization-sensitive modules and spectrometer modules designed for observations in several frequency windows between 210 GHz and 850 GHz to study a wide range of astrophysical questions from Big Bang cosmology to the formation of stars and galaxies in the Epoch of Reionization and beyond. In order to cover this range of frequencies and observation modes, each of the modules contains a set of cold reimaging optics that is optimized for the science goals of that module. These optical setups include several filters, three or four anti-reflection-coated silicon lenses, and a Lyot stop to control the field of view and illumination of the primary mirror, satisfy a series of mechanical constraints, and maximize optical performance within each passband. We summarize the design considerations and trade-offs for the optics in these modules and provide a status update on the fabrication of the Prime-Cam receiver and the design of its 1 K and 100 mK thermal BUSs., Comment: 10 pages, 6 figures, presented at SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

Visual icons provide immediate recognition of features on print maps but do not translate well for touch reading by people who are blind or have low vision due to the low fidelity of tactile perception. We explored 3D printed icons as an equivalent to visual icons for tactile maps addressing these problems. We designed over 200 tactile icons (TactIcons) for street and park maps. These were touch tested by blind and sighted people, resulting in a corpus of 33 icons that can be recognised instantly and a further 34 icons that are easily learned. Importantly, this work has informed the creation of detailed guidelines for the design of TactIcons and a practical methodology for touch testing new TactIcons. It is hoped that this work will contribute to the creation of more inclusive, user-friendly tactile maps for people who are blind or have low vision., Comment: Published in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA

We report the spectroscopic confirmation and fundamental properties of TOI-757 b, a mini-Neptune on a 17.5-day orbit transiting a bright star ($V = 9.7$ mag) discovered by the TESS mission. We acquired high-precision radial velocity measurements with the HARPS, ESPRESSO, and PFS spectrographs to confirm the planet detection and determine its mass. We also acquired space-borne transit photometry with the CHEOPS space telescope to place stronger constraints on the planet radius, supported with ground-based LCOGT photometry. WASP and KELT photometry were used to help constrain the stellar rotation period. We also determined the fundamental parameters of the host star. We find that TOI-757 b has a radius of $R_{\mathrm{p}} = 2.5 \pm 0.1 R_{\oplus}$ and a mass of $M_{\mathrm{p}} = 10.5^{+2.2}_{-2.1} M_{\oplus}$, implying a bulk density of $\rho_{\text{p}} = 3.6 \pm 0.8$ g cm$^{-3}$. Our internal composition modeling was unable to constrain the composition of TOI-757 b, highlighting the importance of atmospheric observations for the system. We also find the planet to be highly eccentric with $e$ = 0.39$^{+0.08}_{-0.07}$, making it one of the very few highly eccentric planets among precisely characterized mini-Neptunes. Based on comparisons to other similar eccentric systems, we find a likely scenario for TOI-757 b's formation to be high eccentricity migration due to a distant outer companion. We additionally propose the possibility of a more intrinsic explanation for the high eccentricity due to star-star interactions during the earlier epoch of the Galactic disk formation, given the low metallicity and older age of TOI-757., Comment: Accepted for publication in MNRAS; 26 pages, 14 figures, 6 tables

This study presents estimates of the global expenditure on article processing charges (APCs) paid to six publishers for open access between 2019 and 2023. APCs are fees charged for publishing in some fully open access journals (gold) and in subscription journals to make individual articles open access (hybrid). There is currently no way to systematically track institutional, national or global expenses for open access publishing due to a lack of transparency in APC prices, what articles they are paid for, or who pays them. We therefore curated and used an open dataset of annual APC list prices from Elsevier, Frontiers, MDPI, PLOS, Springer Nature, and Wiley in combination with the number of open access articles from these publishers indexed by OpenAlex to estimate that, globally, a total of \$8.349 billion (\$8.968 billion in 2023 US dollars) were spent on APCs between 2019 and 2023. We estimate that in 2023 MDPI (\$681.6 million), Elsevier (\$582.8 million) and Springer Nature (\$546.6) generated the most revenue with APCs. After adjusting for inflation, we also show that annual spending almost tripled from \$910.3 million in 2019 to \$2.538 billion in 2023, that hybrid exceed gold fees, and that the median APCs paid are higher than the median listed fees for both gold and hybrid. Our approach addresses major limitations in previous efforts to estimate APCs paid and offers much needed insight into an otherwise opaque aspect of the business of scholarly publishing. We call upon publishers to be more transparent about OA fees., Comment: 21 pages, 6 figures, 4 tables

In a multi-valley electronic band structure, lifting of the valley degeneracy is associated with rotational symmetry breaking in the electronic fluid, and may emerge through spontaneous symmetry breaking order, or through a large response to a small external perturbation such as strain. In this work we use scanning tunneling microscopy to investigate an unexpected rotational symmetry breaking in Landau levels formed in the unusual floating surface band of ZrSiS. We visualize a ubiquitous splitting of Landau levels into valley-polarized sub-levels. We demonstrate methods to measure valley-selective Landau level spectroscopy, to infer unknown Landau level indices, and to precisely measure each valley's Berry phase in a way that is agnostic to the band structure and topology of the system. These techniques allow us to obtain each valley's dispersion curve and infer a rigid valley-dependent contribution to the band energies. Ruling out spontaneous symmetry breaking by establishing the sample-dependence of this valley splitting, we explain the effect in terms of residual strain. A quantitative estimate indicates that uniaxial strain can be measured to a precision of $ \lt 0.025 \% $. The extreme valley-polarization of the Landau levels results from as little as $ \sim 0.1 \% $ strain, and this suggests avenues for manipulation using deliberate strain engineering., Comment: 13 pages, 9 figures (plus 13 pages, 9 figures)

Bayesian inference provides a powerful tool for leveraging observational data to inform model predictions and uncertainties. However, when such data is limited, Bayesian inference may not adequately constrain uncertainty without the use of highly informative priors. Common approaches for constructing informative priors typically rely on either assumptions or knowledge of the underlying physics, which may not be available in all scenarios. In this work, we consider the scenario where data are available on a population of assets/individuals, which occurs in many problem domains such as biomedical or digital twin applications, and leverage this population-level data to systematically constrain the Bayesian prior and subsequently improve individualized inferences. The approach proposed in this paper is based upon a recently developed technique known as data-consistent inversion (DCI) for constructing a pullback probability measure. Succinctly, we utilize DCI to build population-informed priors for subsequent Bayesian inference on individuals. While the approach is general and applies to nonlinear maps and arbitrary priors, we prove that for linear inverse problems with Gaussian priors, the population-informed prior produces an increase in the information gain as measured by the determinant and trace of the inverse posterior covariance. We also demonstrate that the Kullback-Leibler divergence often improves with high probability. Numerical results, including linear-Gaussian examples and one inspired by digital twins for additively manufactured assets, indicate that there is significant value in using these population-informed priors., Comment: Corrected typos

Inference of Large Language Models (LLMs) across computer clusters has become a focal point of research in recent times, with many acceleration techniques taking inspiration from CPU speculative execution. These techniques reduce bottlenecks associated with memory bandwidth, but also increase end-to-end latency per inference run, requiring high speculation acceptance rates to improve performance. Combined with a variable rate of acceptance across tasks, speculative inference techniques can result in reduced performance. Additionally, pipeline-parallel designs require many user requests to maintain maximum utilization. As a remedy, we propose PipeInfer, a pipelined speculative acceleration technique to reduce inter-token latency and improve system utilization for single-request scenarios while also improving tolerance to low speculation acceptance rates and low-bandwidth interconnects. PipeInfer exhibits up to a 2.15$\times$ improvement in generation speed over standard speculative inference. PipeInfer achieves its improvement through Continuous Asynchronous Speculation and Early Inference Cancellation, the former improving latency and generation speed by running single-token inference simultaneously with several speculative runs, while the latter improves speed and latency by skipping the computation of invalidated runs, even in the middle of inference., Comment: 11 pages, submitted to SC24 conference

In perovskite solar cells, photovoltaic action is created by charge transport layers (CTLs) either side of the light-absorbing metal halide perovskite semiconductor. Hence, the rates for desirable charge extraction and unwanted interfacial recombination at the perovskite-CTL interfaces play a critical role for device efficiency. Here, the electrical properties of perovskite-CTL bilayer heterostructures are obtained using ultrafast THz and optical studies of the charge carrier dynamics after pulsed photoexcitation, combined with a physical model of charge carrier transport that includes the prominent Coulombic forces that arise after selective charge extraction into a CTL, and cross-interfacial recombination. The charge extraction velocity at the interface and the ambipolar diffusion coefficient within the perovskite are determined from the experimental decay profiles for heterostructures with three of the highest performing CTLs, namely C$_{60}$, PCBM and Spiro-OMeTAD. Definitive targets for the further improvement of devices are deduced: fullerenes deliver fast electron extraction, but suffer from a large rate constant for cross-interface recombination or hole extraction. Conversely, Spiro-OMeTAD exhibits slow hole extraction but does not increase the perovskite's surface recombination rate, likely contributing to its success in solar cell devices., Comment: 33 pages, 6 figures

We present a quantitative spectral analysis of the extreme nitrogen-enhanced supergiant HD 93840 (BN1 Ib) at an intermediate galactic latitude. Based on an optical high-resolution spectrum and complementary ultraviolet and infrared (spectro-)photometry, in addition to Gaia data, we carried out a full characterisation of the star's properties. We used both hydrostatic and unified (photosphere+wind) model atmospheres that account for deviations from local thermodynamic equilibrium. A highly unusual surface CNO-mixing signature and a marked stellar overluminosity compared to the mass imply a binary channel for the star's past evolution. The kinematics shows that it has reached its current position above the Galactic plane as a runaway star, likely ejected by the supernova explosion of its former companion star. Its current bulk composition, with a notably increased mean molecular weight due to core He- and progressed shell H-burning, suggests an advanced evolutionary stage. It is poised to yield a rare core-collapse supernova of a blue supergiant about ten OB star population scale heights above the Galactic disk relatively soon, contributing to the metal enrichment of the circumgalactic medium., Comment: 19 pages, 14 figures, published in Astronomy & Astrophysics

Submillimeter and millimeter wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fields, but there are still many open questions that cannot be answered with current facilities. In this report we summarise the science that is guiding the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST). We demonstrate how tranformational advances in topics including star formation in high redshift galaxies, the diffuse circumgalactic medium, Galactic ecology, cometary compositions and solar flares motivate the need for a 50m, single-dish telescope with a 1-2 degree field of view and a new generation of highly multiplexed continuum and spectral cameras. AtLAST will have the resolution to drastically lower the confusion limit compared to current single-dish facilities, whilst also being able to rapidly map large areas of the sky and detect extended, diffuse structures. Its high sensitivity and large field of view will open up the field of submillimeter transient science by increasing the probability of serendipitous detections. Finally, the science cases listed here motivate the need for a highly flexible operations model capable of short observations of individual targets, large surveys, monitoring programmes, target of opportunity observations and coordinated observations with other observatories. AtLAST aims to be a sustainable, upgradeable, multipurpose facility that will deliver orders of magnitude increases in sensitivity and mapping speeds over current and planned submillimeter observatories., Comment: 47 pages, 12 figures. For further details on AtLAST see https://atlast.uio.no

Edmentum offers a personalized learning platform called Exact Path. This quasi-experimental study, designed to meet ESSA Tier 2 evidence and What Works Clearinghouse standards with reservations, aimed to assess the efficacy of Exact Path in a district from the Midwestern United States. The goal was to provide specific recommendations to educators within the district and inform the broader community of policymakers and practitioners about the potential benefits of personalized learning for enhancing student academic achievement. The study found that the use of Exact Path was positively related to Math achievement in NWEA MAP tests, after controlling for students' prior test scores and their socioeconomic status. These findings suggest that Exact Path could be an effective tool for improving student success in this district and potentially other similar contexts. Therefore, these results may have important implications for educators, policymakers, and researchers interested in improving student outcomes through personalized learning.

Driven by advancements in sensing and computing, deep reinforcement learning (DRL)-based methods have demonstrated significant potential in effectively tackling distribution system restoration (DSR) challenges under uncertain operational scenarios. However, the data-intensive nature of DRL poses obstacles in achieving satisfactory DSR solutions for large-scale, complex distribution systems. Inspired by the transformative impact of emerging foundation models, including large language models (LLMs), across various domains, this paper explores an innovative approach harnessing LLMs' powerful computing capabilities to address scalability challenges inherent in conventional DRL methods for solving DSR. To our knowledge, this study represents the first exploration of foundation models, including LLMs, in revolutionizing conventional DRL applications in power system operations. Our contributions are twofold: 1) introducing a novel LLM-powered Physics-Informed Decision Transformer (PIDT) framework that leverages LLMs to transform conventional DRL methods for DSR operations, and 2) conducting comparative studies to assess the performance of the proposed LLM-powered PIDT framework at its initial development stage for solving DSR problems. While our primary focus in this paper is on DSR operations, the proposed PIDT framework can be generalized to optimize sequential decision-making across various power system operations.

Counterfactual reasoning allows us to explore hypothetical scenarios in order to explain the impacts of our decisions. However, addressing such inquires is impossible without establishing the appropriate mathematical framework. In this work, we introduce the problem of counterfactual reasoning in the context of vector autoregressive (VAR) processes. We also formulate the inference of a causal model as a joint regression task where for inference we use both data with and without interventions. After learning the model, we exploit linearity of the VAR model to make exact predictions about the effects of counterfactual interventions. Furthermore, we quantify the total causal effects of past counterfactual interventions. The source code for this project is freely available at https://github.com/KurtButler/counterfactual_interventions.

The large dimensionality of environments is the limiting factor in applying optimal control to open quantum systems beyond Markovian approximations. Multiple methods exist to simulate non-Markovian open systems which effectively reduce the environment to a number of active degrees of freedom. Here we show that several of these methods can be expressed in terms of a process tensor in the form of a matrix-product-operator, which serves as a unifying framework to show how they can be used in optimal control, and to compare their performance. The matrix-product-operator form provides a general scheme for computing gradients using back propagation, and allows the efficiency of the different methods to be compared via the bond dimensions of their respective process tensors., Comment: 14 pages, 9 figures

Non-Markovian dynamics arising from the strong coupling of a system to a structured environment is essential in many applications of quantum mechanics and emerging technologies. Deriving an accurate description of general quantum dynamics including memory effects is however a demanding task, prohibitive to standard analytical or direct numerical approaches. We present a major release of our open source software package, OQuPy (Open Quantum System in Python), which provides several recently developed numerical methods that address this challenging task. It utilizes the process tensor approach to open quantum systems in which a single map, the process tensor, captures all possible effects of an environment on the system. The representation of the process tensor in a tensor network form allows an exact yet highly efficient description of non-Markovian open quantum systems (NM-OQS). The OQuPy package provides methods to (1) compute the dynamics and multi-time correlations of quantum systems coupled to single and multiple environments, (2) optimize control protocols for NM-OQS, (3) simulate interacting chains of NM-OQS, and (4) compute the mean-field dynamics of an ensemble of NM-OQS coupled to a common central system. Our aim is to provide an easily accessible and extensible tool for researchers of open quantum systems in fields such as quantum chemistry, quantum sensing, and quantum information., Comment: 20 pages, 15 figures

Overcoming the challenge of limited data availability within materials science is crucial for the broad-based applicability of machine learning within materials science. One pathway to overcome this limited data availability is to use the framework of transfer learning (TL), where a pre-trained (PT) machine learning model (on a larger dataset) can be fine-tuned (FT) on a target (typically smaller) dataset. Our study systematically explores the effectiveness of various PT/FT strategies to learn and predict material properties with limited data. Specifically, we leverage graph neural networks (GNNs) to PT/FT on seven diverse curated materials datasets, encompassing sizes ranging from 941 to 132,752 datapoints. We consider datasets that cover a spectrum of material properties, ranging from band gaps (electronic) to formation energies (thermodynamic) and shear moduli (mechanical). We study the influence of PT and FT dataset sizes, strategies that can be employed for FT, and other hyperparameters on pair-wise TL among the datasets considered. We find our pair-wise PT-FT models to consistently outperform models trained from scratch on the target datasets. Importantly, we develop a GNN framework that is simultaneously PT on multiple properties (MPT), enabling the construction of generalized GNN models. Our MPT models outperform pair-wise PT-FT models on several datasets considered, and more significantly, on a 2D material band gap dataset that is completely out-of-distribution from the PT datasets. Finally, we expect our PT/FT and MPT frameworks to be generalizable to other GNNs and materials properties, which can accelerate materials design and discovery for various applications.

We report the discovery of two transiting planets detected by the Transiting Exoplanet Survey Satellite (TESS), TOI-2374 b and TOI-3071 b, orbiting a K5V and an F8V star, respectively, with periods of 4.31 and 1.27 days, respectively. We confirm and characterize these two planets with a variety of ground-based and follow-up observations, including photometry, precise radial velocity monitoring and high-resolution imaging. The planetary and orbital parameters were derived from a joint analysis of the radial velocities and photometric data. We found that the two planets have masses of $(57 \pm 4)$ $M_\oplus$ or $(0.18 \pm 0.01)$ $M_J$, and $(68 \pm 4)$ $M_\oplus$ or $(0.21 \pm 0.01)$ $M_J$, respectively, and they have radii of $(6.8 \pm 0.3)$ $R_\oplus$ or $(0.61 \pm 0.03)$ $R_J$ and $(7.2 \pm 0.5)$ $R_\oplus$ or $(0.64 \pm 0.05)$ $R_J$, respectively. These parameters correspond to sub-Saturns within the Neptunian desert, both planets being hot and highly irradiated, with $T_{\rm eq} \approx 745$ $K$ and $T_{\rm eq} \approx 1812$ $K$, respectively, assuming a Bond albedo of 0.5. TOI-3071 b has the hottest equilibrium temperature of all known planets with masses between $10$ and $300$ $M_\oplus$ and radii less than $1.5$ $R_J$. By applying gas giant evolution models we found that both planets, especially TOI-3071 b, are very metal-rich. This challenges standard formation models which generally predict lower heavy-element masses for planets with similar characteristics. We studied the evolution of the planets' atmospheres under photoevaporation and concluded that both are stable against evaporation due to their large masses and likely high metallicities in their gaseous envelopes., Comment: 24 pages, 22 figures, 10 tables, accepted for publication in MNRAS

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

Large vision-language models (VLMs) have demonstrated remarkable abilities in understanding everyday content. However, their performance in the domain of art, particularly culturally rich art forms, remains less explored. As a pearl of human wisdom and creativity, art encapsulates complex cultural narratives and symbolism. In this paper, we offer the Pun Rebus Art Dataset, a multimodal dataset for art understanding deeply rooted in traditional Chinese culture. We focus on three primary tasks: identifying salient visual elements, matching elements with their symbolic meanings, and explanations for the conveyed messages. Our evaluation reveals that state-of-the-art VLMs struggle with these tasks, often providing biased and hallucinated explanations and showing limited improvement through in-context learning. By releasing the Pun Rebus Art Dataset, we aim to facilitate the development of VLMs that can better understand and interpret culturally specific content, promoting greater inclusiveness beyond English-based corpora.

This paper introduces a dataset of article processing charges (APCs) produced from the price lists of six large scholarly publishers - Elsevier, Frontiers, PLOS, MDPI, Springer Nature and Wiley - between 2019 and 2023. APC price lists were downloaded from publisher websites each year as well as via Wayback Machine snapshots to retrieve fees per journal per year. The dataset includes journal metadata, APC collection method, and annual APC price list information in several currencies (USD, EUR, GBP, CHF, JPY, CAD) for 8,712 unique journals and 36,618 journal-year combinations. The dataset was generated to allow for more precise analysis of APCs and can support library collection development and scientometric analysis estimating APCs paid in gold and hybrid OA journals., Comment: 13 pages, 3 figures, 4 tables

Mechatronic systems are described by an interconnection of the electromagnetic part, i.e., a static position-dependent nonlinear relation between currents and forces, and the mechanical part, i.e., a dynamic relation from forces to position. Commutation inverts a model of the electromagnetic part of the system, and thereby removes the electromagnetic part from the position control problem. Typical commutation algorithms rely on simplified models derived from physics-based knowledge, which do not take into account position dependent parasitic effects. In turn, these commutation related model errors translate into position tracking errors, which limit the system performance. Therefore, in this work, we develop a data-driven approach to commutation using physics-guided neural networks (PGNNs). A novel PGNN model is proposed which structures neural networks (NNs) to learn specific motor dependent parasitic effects. The PGNN is used to identify a model of the electromagnetic part using force measurements, after which it is analytically inverted to obtain a PGNN-based commutation algorithm. Motivated by industrial applications, we develop an input transformation to deal with systems with fixed commutation, i.e., when the currents cannot be controlled. Real-life experiments on an industrial coreless linear motor (CLM) demonstrate a factor 10 improvement in the commutation error in driving direction and a factor 4 improvement in the position error with respect to classical commutation in terms of the mean--squared error (MSE).

Music has been identified as a promising medium to enhance the accessibility and experience of visual art for people who are blind or have low vision (BLV). However, composing music and designing soundscapes for visual art is a time-consuming, resource intensive process - limiting its scalability for large exhibitions. In this paper, we investigate the use of automated soundscapes to increase the accessibility of visual art. We built a prototype system and ran a qualitative study to evaluate the aesthetic experience provided by the automated soundscapes with 10 BLV participants. From the study, we identified a set of design considerations that reveal requirements from BLV people for the development of automated soundscape systems, setting new directions in which creative systems could enrich the aesthetic experience conveyed by these., Comment: Paper accepted into ICCC 24

Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this effort is a robust data management system paired with an interactive work platform. This platform should empower users to not only access others data but also integrate their analyses, paving the way for sophisticated data pipelines. To realize this vision, there is a need for an integrative collaboration platform, streamlined data sharing and analysis tools, and efficient communication channels. Such a collaborative mechanism should transcend geographical barriers, facilitating remote interaction and fostering a challenge-response dynamic. In this paper, we present our ongoing efforts in addressing the critical challenges related to an accelerated Materials Discovery Framework as a part of the High-Throughput Materials Discovery for Extreme Conditions Initiative. Our BIRDSHOT Center has successfully harnessed various tools and strategies, including the utilization of cloud-based storage, a standardized sample naming convention, a structured file system, the implementation of sample travelers, a robust sample tracking method, and the incorporation of knowledge graphs for efficient data management. Additionally, we present the development of a data collection platform, reinforcing seamless collaboration among our team members. In summary, this paper provides an illustration and insight into the various elements of an efficient and effective workflow within an accelerated materials discovery framework while highlighting the dynamic and adaptable nature of the data management tools and sharing platforms., Comment: 28 pages, 9 figures, 2 tables, with appendix that has 8 pages, accepted for publication at IMMI

In this study, we introduce a groundbreaking framework for materials discovery, we efficiently navigate a vast phase space of material compositions by leveraging Batch Bayesian statistics in order to achieve specific performance objectives. This approach addresses the challenge of identifying optimal materials from an untenably large array of possibilities in a reasonable timeframe with high confidence. Crucially, our batchwise methods align seamlessly with existing material processing infrastructure for synthesizing and characterizing materials. By applying this framework to a specific high entropy alloy system, we demonstrate its versatility and robustness in optimizing properties like strain hardening, hardness, and strain rate sensitivity. The fact that the Bayesian model is adept in refining and expanding the property Pareto front highlights its broad applicability across various materials, including steels, shape memory alloys, ceramics, and composites. This study advances the field of materials science and sets a new benchmark for material discovery methodologies. By proving the effectiveness of Bayesian optimization, we showcase its potential to redefine the landscape of materials discovery., Comment: 12 pages, 6 figures, with Supplementary Appendix that has 17 pages, 9 figures

We present a novel method for generating sequential parameter estimates and quantifying epistemic uncertainty in dynamical systems within a data-consistent (DC) framework. The DC framework differs from traditional Bayesian approaches due to the incorporation of the push-forward of an initial density, which performs selective regularization in parameter directions not informed by the data in the resulting updated density. This extends a previous study that included the linear Gaussian theory within the DC framework and introduced the maximal updated density (MUD) estimate as an alternative to both least squares and maximum a posterior (MAP) estimates. In this work, we introduce algorithms for operational settings of MUD estimation in real or near-real time where spatio-temporal datasets arrive in packets to provide updated estimates of parameters and identify potential parameter drift. Computational diagnostics within the DC framework prove critical for evaluating (1) the quality of the DC update and MUD estimate and (2) the detection of parameter value drift. The algorithms are applied to estimate (1) wind drag parameters in a high-fidelity storm surge model, (2) thermal diffusivity field for a heat conductivity problem, and (3) changing infection and incubation rates of an epidemiological model., Comment: 29 pages, 9 Figures, Code available at https://github.com/UT-CHG/pyDCI

Discovering transiting exoplanets with relatively long orbital periods ($>$10 days) is crucial to facilitate the study of cool exoplanet atmospheres ($T_{\rm eq} < 700 K$) and to understand exoplanet formation and inward migration further out than typical transiting exoplanets. In order to discover these longer period transiting exoplanets, long-term photometric and radial velocity campaigns are required. We report the discovery of TOI-2447 b ($=$ NGTS-29b), a Saturn-mass transiting exoplanet orbiting a bright (T=10.0) Solar-type star (T$_{\rm eff}$=5730 K). TOI-2447 b was identified as a transiting exoplanet candidate from a single transit event of 1.3% depth and 7.29 h duration in $TESS$ Sector 31 and a prior transit event from 2017 in NGTS data. Four further transit events were observed with NGTS photometry which revealed an orbital period of P=69.34 days. The transit events establish a radius for TOI-2447 b of $0.865 \pm 0.010\rm R_{\rm J}$, while radial velocity measurements give a mass of $0.386 \pm 0.025 \rm M_{\rm J}$. The equilibrium temperature of the planet is $414$ K, making it much cooler than the majority of $TESS$ planet discoveries. We also detect a transit signal in NGTS data not caused by TOI-2447 b, along with transit timing variations and evidence for a $\sim$150 day signal in radial velocity measurements. It is likely that the system hosts additional planets, but further photometry and radial velocity campaigns will be needed to determine their parameters with confidence. TOI-2447 b/NGTS-29b joins a small but growing population of cool giants that will provide crucial insights into giant planet composition and formation mechanisms., Comment: 16 pages, 12 figures. Accepted for publication in MNRAS

In this paper, a synergistic computational/experimental approach is presented for the rapid discovery and characterization of novel alloys within the compositionally complex (i.e., "medium/high entropy") refractory alloy space of Ti-V-Nb-Mo-Hf-Ta-W. This was demonstrated via a material design cycle aimed at simultaneously maximizing the objective properties of high specific hardness (hardness normalized by density) and high specific elastic modulus (elastic modulus normalized by density). This framework utilizes high-throughput computational thermodynamics and intelligent filtering to first reduce the untenably large alloy space to a feasible size, followed by an iterative design cycle comprised of high-throughput synthesis, processing, and characterization in batch sizes of 24 alloys. After the first iteration, Bayesian optimization was utilized to inform selection of the next batch of 24 alloys. This paper demonstrates the benefit of using batch Bayesian optimization (BBO) in material design, as significant gains in the objective properties were observed after only two iterations or "shots" of the design cycle without using any prior knowledge or physical models of how the objective properties relate to the design inputs (i.e., composition). Specifically, the hypervolume of the Pareto front increased by 54% between the first and second iterations. Furthermore, 10 of the 24 alloys in the second iteration dominated all alloys from the first iteration.

Titan's atmospheric composition and dynamical state have previously been studied over numerous epochs by both ground- and space-based facilities. However, stratospheric measurements remain sparse during Titan's northern summer and fall. The lack of seasonal symmetry in observations of Titan's temperature field and chemical abundances raises questions about the nature of the middle atmosphere's meridional circulation and evolution over Titan's 29-yr seasonal cycle that can only be answered through long-term monitoring campaigns. Here, we present maps of Titan's stratospheric temperature, acetonitrile (or methyl cyanide; CH$_3$CN), and monodeuterated methane (CH$_3$D) abundances following Titan's northern summer solstice obtained with Band 9 ($\sim0.43$ mm) ALMA observations. We find that increasing temperatures towards high-southern latitudes, currently in winter, resemble those observed during Titan's northern winter by the Cassini mission. Acetonitrile abundances have changed significantly since previous (sub)millimeter observations, and we find that the species is now highly concentrated at high-southern latitudes. The stratospheric CH$_3$D content is found to range between 4-8 ppm in these observations, and we infer the CH$_4$ abundance to vary between $\sim0.9-1.6\%$ through conversion with previously measured D/H values. A global value of CH$_4=1.15\%$ was retrieved, lending further evidence to the temporal and spatial variability of Titan's stratospheric methane when compared with previous measurements. Additional observations are required to determine the cause and magnitude of stratospheric enhancements in methane during these poorly understood seasons on Titan., Comment: Accepted for publication in the Planetary Science Journal. 9 Figures, 1 table

This work opens a new chapter in the 100,000 year-old concept of paint, by leveraging innovations in nano-technology in the sub-THz frequency range. More specifically, the groundbreaking concept of Internet of Paint (IoP) is introduced along with a comprehensive channel model and a capacity analysis for nano-scale radios embedded in paint and communicating through paint. Nano-network devices, integrated within a paint medium, communicate via a multipath strategy, encompassing direct waves, reflections from interfaces, and lateral wave propagation. The evaluation incorporates three distinct paint types to assess path losses, received powers, and channel capacity. Analysis of path loss indicates a slight non-linear increase with both frequency and Line of Sight (LoS) distance between transceivers. Notably, paints with high refractive indexes result in the highest path loss. Moreover, burying transceivers at similar depths near the Air-Paint interface showcases promising performance of lateral waves with increasing LoS distance. Increasing paint layer depth leads to amplified attenuation, while total received power exhibits promising results when in close proximity to the Air-Paint interface but steeply declines with burial depth. Additionally, a substantial reduction in channel capacity is observed with LoS distance and burial depth, so transceivers need to be close together and in proximity of the A-P interface to communicate effectively. Comparing paint and air mediums, IoP demonstrates approximately two orders of magnitude reduction in channel capacity compared to air-based communication channels. This paper provides valuable insights into the potential of IoP communication within paint mediums and offers a foundation for further advancements in this emerging field., Comment: 15 pages, 17 figures, accepted for publication in the IEEE Journal on Selected Areas in Communications Special Issue on Electromagnetic Nanonetworks

We present the largest optical photometry compilation of Gamma-Ray Bursts (GRBs) with redshifts ($z$). We include 64813 observations of 535 events (including upper limits) from 28 February 1997 up to 18 August 2023. We also present a user-friendly web tool \textit{grbLC} which allows users the visualization of photometry, coordinates, redshift, host galaxy extinction, and spectral indices for each event in our database. Furthermore, we have added a Gamma Ray Coordinate Network (GCN) scraper that can be used to collect data by gathering magnitudes from the GCNs. The web tool also includes a package for uniformly investigating colour evolution. We compute the optical spectral indices for 138 GRBs for which we have at least 4 filters at the same epoch in our sample and craft a procedure to distinguish between GRBs with and without colour evolution. By providing a uniform format and repository for the optical catalogue, this web-based archive is the first step towards unifying several community efforts to gather the photometric information for all GRBs with known redshifts. This catalogue will enable population studies by providing light curves (LCs) with better coverage since we have gathered data from different ground-based locations. Consequently, these LCs can be used to train future LC reconstructions for an extended inference of the redshift. The data gathering also allows us to fill some of the orbital gaps from Swift in crucial points of the LCs, e.g., at the end of the plateau emission or where a jet break is identified., Comment: 20 pages, 16 figures, 2 tables. Submitted to MNRAS, this version matches the third revision. The Online Materials and data will be available after the publication

We report on deep SCUBA-2 observations at 850$\mu$m and NOEMA spectroscopic measurements at 2 mm of the environment surrounding the luminous, massive ($M_{*} \approx 2 \times 10^{11}$ M$_{\odot}$) Herschel-selected source HerBS-70. This source was revealed by previous NOEMA observations to be a binary system of dusty star-forming galaxies at $z= 2.3$, with the East component (HerBS-70E) hosting an Active Galactic Nucleus (AGN). The SCUBA-2 observations detected, in addition to the binary system, twenty-one sources at $> 3.5 \sigma$ over an area of $\sim 25$ square comoving Mpc with a sensitivity of $\sigma_{850} = 0.75$ mJy. The surface density of continuum sources around HerBS-70 is three times higher than for field galaxies. The NOEMA spectroscopic measurements confirm the protocluster membership of three of the nine brightest sources through their CO(4 - 3) line emission, yielding a volume density 36 times higher than for field galaxies. All five confirmed sub-mm galaxies in the HerBS-70 system have relatively short gas depletion times ($80 - 500$ Myr), indicating the onset of quenching for this protocluster core due to the depletion of gas. The dark matter halo mass of the HerBS-70 system is estimated around $5 \times{} 10^{13}$ M$_{\odot}$, with a projected current-day mass of $10^{15}$ M$_{\odot}$, similar to the local Virgo and Coma clusters. These observations support the claim that DSFGs, in particular the ones with observed multiplicity, can trace cosmic overdensities., Comment: 19 pages, 13 figures, accepted for publication in MNRAS