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What does governance look like ‘from below’ – from the perspectives of poor and marginalised households? How do patterns of conflict affect that? These were the questions at the heart of the Governance at the Margins research project. Over three years from 2017-2020 we worked to explore this through in-depth study in conflict-affected areas of Mozambique, Myanmar, and Pakistan. Our research teams interviewed the same people regularly over that time, finding out how they resolved problems and interacted with authorities. In this paper we connect what we found to the realities and complexities of development practice, drawing on the input of 20 experienced practitioners working in bilateral and multilateral development agencies and international NGOs, who generously gave their time to help us think through the practical implications of our wealth of findings., This paper is also published on the Institute of Development Studies website.

The outbreak of the COVID-19 pandemic was an unprecedented event in the modern era. Earlier studies demonstrated how ‘critical junctures’, which comprise major historical events and emergencies, often play an essential role in social change. This paper seeks to explore whether the COVID-19 pandemic would prove to be a similar pivotal moment, and what lessons and insights we could gather for positive social change., The research set out to find key insights on how individuals, communities, and organizations in civil society were responding to the pandemic in low-income populations at the intersection of multiple inequalities. These lessons could inform how donors, governments and NGOs might reshape their efforts to reduce emerging or deepening inequalities, and how civil society organisations and community-based organisations could amplify their positive impacts., Over 18 months, from September 2020 to March 2022, the ‘Emergent Agency’ research convened a global conversation between activists, development practitioners, researchers, and academics to better understand the phenomena that were taking place in response to the pandemic. The research collected more than 200 case studies and held a series of webinars and conversations in thematic clusters to uncover what these responses could teach us. The research project was enabled with funding from The Atlantic Fellows for Social and Economic Equity programme (AFSEE) of the London School of Economics., The findings from the collaborative research have been summarised in two main documents, - this research report, which retraces the steps of the study, its methodology and main findings;, - and a brief, which summarizes key points that we believe are important for those working in international and national NGOs, and donor agencies.

The Wallace Foundation commissioned Research for Action and McClanahan Associates to study how out-of-school time intermediaries (OSTIs) responded to the COVID-19 pandemic and the role they could play in supporting communities moving forward. Citywide out-of-school time (OST) systems encompass a wide variety of afterschool and summer programs and associated provider organizations, youth participants, and their families. These systems were stretched during the COVID-19 pandemic and the national racial reckoning to respond to community needs in new and more equitable ways. Also impacted were OSTIs that manage OST systems within individual cities. OSTIs operate through various governance structures and perform a range of coordination functions intended to ensure quality, access, and equity in OST systems so that all youth have access to the unique out-of-school resources and opportunities they need to thrive.

Research and development work in artificial intelligence in education (AIED) is wide ranging and rapidly growing to support all areas of science, technology, engineering, and mathematics (STEM) teaching and learning. At the risk of hyperbole, this is potentially the most fundamentally game-changing technology for education to emerge since the internet. Building from decades of work on AI and AI-based learning and teaching technologies, the recent advances in AIED are pushing us to reimagine what is possible for STEM teaching and learning. AIED research initiatives are being speedily funded, and AIED advances are quickly becoming integrated into STEM education. It is transforming how teachers teach and how students learn. It is also transforming how education developers and researchers conduct their expansive work. There is excitement about the promise of AIED as well as growing concern that the breakthroughs in AIED are impacting everyday education practice in ways that may perpetuate long-standing biases and diminish the potential for positive outcomes. This brief is the first in a three-part series on AIED related to STEM research, teaching, and learning. The topics address ethical approaches to AI in STEM education research, AI for STEM teaching, and AI for STEM learning. This series is sponsored by the Community for Advancing Discovery Research in Education (CADRE), a National Science Foundation-funded network for STEM education researchers endeavoring to improve STEM teaching and learning through research, development, and various information-sharing and community-building mechanisms. Researchers in the CADRE network are part of a portfolio of projects funded through NSF's Discovery Research PreK-12 (DRK-12) program. The DRK-12 portfolio is wide-ranging, with a multitude of projects that focus on applied research and development to generate innovative research-informed and field-tested tools, products, and approaches that are intended to enhance STEM teaching and learning. Over the past several years, the portfolio has grown to include an increasing number of projects that leverage AIED to achieve their goals related to teaching or learning. It is expected to continue to grow. This series has been inspired by the question, "What are the essential considerations for researchers and developers who are designing, studying, and using AI in K-12 STEM?" Our hope is that the opportunities and challenges discussed in this series will generate reflection and rich discussion for the better and support the transformative use of AI to achieve positive and wide-reaching impact for all learners. In this first brief, "Toward Ethical and Just AI in Education Research," the authors are concerned with the ethical reasoning and decisions made in the development, study, and use of AIED technologies. Recognizing that AIED technologies reflect both the intended and unintended biases of the designers and the wider society, they advocate for the adoption of policies and practices that prioritize ethics, equity, and justice in research and development initiatives using AIED technologies in K-12 education. In an effort to provide guidance to researchers and developers, they lay the groundwork for responsible AI research and its implementation in educational settings. This foundation draws in part from the ethics rules for research with human subjects that have guided researchers for decades, but goes beyond this to frame a more all-encompassing stance rooted in justice and equity. The authors illustrate how ethical AI research can be strengthened by building from well-established ethical principles used in research and society at large. Taking into account these principles, they propose an ethical AIED framework and a set of tools that they have found to be supportive of continuous reflection, communication, and improvement toward inclusive and equitable AIED research and development. Their guidance is in the service of ensuring that the good intentions of researchers and developers will lead to positive design decisions and actions that create inclusive AIED technology products and systems. This is a valuable contribution that encourages a shift in focus to bring ethics, justice, and the values of communities of teachers, students, and families to the forefront of research and development practice.

The process of collecting blood from donors and making it available for transfusion requires a complex series of operations involving multiple actors and resources at each step. Ensuring hospitals receive adequate and safe blood for transfusion is a common challenge across low- and middle-income countries, but is rarely addressed from a system level. This paper presents the first use of discrete event simulation to study the blood system in Kenya and to explore the effect of variations and perturbations at different steps of the system on meeting patient blood demand. A process map of the Kenyan blood system was developed to capture critical steps from blood donation to transfusion using interviews with blood bank, hospital, and laboratory personnel at four public hospitals across three counties in Kenya. The blood system was simulated starting with blood collection, a blood bank where blood is tested and stored before it is issued, a major hospital attached to the blood bank, and several smaller hospitals served by the same blood bank. Values for supply-side parameters were based mainly on expert opinion; demand-side parameters were based on data from blood requisitions made in hospital wards, and dispatch of blood from the hospital laboratory. Illustrative examples demonstrate how the model can be used to explore the impacts of changes in blood collection (e.g., prioritising different donor types), blood demand (e.g., differing clinical case mix), and blood distribution (e.g., restocking strategies) on meeting demand at patient level. The model can reveal potential process impediments in the blood system and aid in choosing strategies for improving blood collection, distribution or use. Such a systems approach allows for interventions at different steps in the blood continuum to be tested on blood availability for different patients presenting at diverse hospitals across the country., Comment: 38 pages, 8 figures

Combining Atacama Large Millimeter/sub-millimeter Array CO(2-1) mapping and JWST near- and mid-infrared imaging, we characterize the relationship between CO(2-1) and polycyclic aromatic hydrocarbon (PAH) emission at ~100 pc resolution in 66 nearby star-forming galaxies, expanding the sample size from previous ~100 pc resolution studies by more than an order of magnitude. Focusing on regions of galaxies where most of the gas is likely to be molecular, we find strong correlations between CO(2-1) and 3.3 micron, 7.7 micron, and 11.3 micron PAH emission, estimated from JWST's F335M, F770W, and F1130W filters. We derive power law relations between CO(2-1) and PAH emission, which have indices in the range 0.8-1.2, implying relatively weak variations in the observed CO-to-PAH ratios across the regions that we study. We find that CO-to-PAH ratios and scaling relationships near HII regions are similar to those in diffuse sight lines. The main difference between the two types of regions is that sight lines near HII regions show higher intensities in all tracers. Galaxy centers, on the other hand, show higher overall intensities and enhanced CO-to-PAH ratios compared to galaxy disks. Individual galaxies show 0.19 dex scatter in the normalization of CO at fixed I_PAH and this normalization anti-correlates with specific star formation rate (SFR/M*) and correlates with stellar mass. We provide a prescription that accounts for these galaxy-to-galaxy variations and represents our best current empirical predictor to estimate CO(2-1) intensity from PAH emission, which allows one to take advantage of JWST's excellent sensitivity and resolution to trace cold gas., Comment: 21 pages, 4 figures, 3 tables. Submitted to ApJ

Quantum error correction (QEC) will be essential to achieve the accuracy needed for quantum computers to realise their full potential. The field has seen promising progress with demonstrations of early QEC and real-time decoded experiments. As quantum computers advance towards demonstrating a universal fault-tolerant logical gate set, implementing scalable and low-latency real-time decoding will be crucial to prevent the backlog problem, avoiding an exponential slowdown and maintaining a fast logical clock rate. Here, we demonstrate low-latency feedback with a scalable FPGA decoder integrated into the control system of a superconducting quantum processor. We perform an 8-qubit stability experiment with up to $25$ decoding rounds and a mean decoding time per round below $1$ ${\mu}s$, showing that we avoid the backlog problem even on superconducting hardware with the strictest speed requirements. We observe logical error suppression as the number of decoding rounds is increased. We also implement and time a fast-feedback experiment demonstrating a decoding response time of $9.6$ ${\mu}s$ for a total of $9$ measurement rounds. The decoder throughput and latency developed in this work, combined with continued device improvements, unlock the next generation of experiments that go beyond purely keeping logical qubits alive and into demonstrating building blocks of fault-tolerant computation, such as lattice surgery and magic state teleportation., Comment: 11 pages, 4 figures, Supplementary Information

Effective patient-provider communication is crucial in clinical care, directly impacting patient outcomes and quality of life. Traditional evaluation methods, such as human ratings, patient feedback, and provider self-assessments, are often limited by high costs and scalability issues. Although existing natural language processing (NLP) techniques show promise, they struggle with the nuances of clinical communication and require sensitive clinical data for training, reducing their effectiveness in real-world applications. Emerging large language models (LLMs) offer a new approach to assessing complex communication metrics, with the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities. Specifically, using simulated scripts crafted and labeled by healthcare professionals, we test proprietary models (e.g., GPT-4) and fine-tune open-source LLMs (e.g., LLaMA2) with a synthetic dataset generated by GPT-4 to evaluate clinical conversations, to identify key metrics such as `understanding' and `empathy'. Our findings demonstrated LLMs' superior performance in evaluating clinical communication, providing actionable feedback with reasoning, and demonstrating the feasibility and practical viability of developing in-house LLMs. This research highlights LLMs' potential to enhance patient-provider interactions and lays the groundwork for downstream steps in developing LLM-empowered clinical health systems., Comment: Accepted by ACM Transactions on Computing for Healthcare, Special Issue on Large Language Models, Conversational Systems, and Generative AI in Health, pending minor revisions

While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, particularly for underwater acoustic signals. The methods by which audio signals are converted into time-frequency representations and the subsequent handling of these spectrograms can significantly impact performance. This work demonstrates the performance impact of using different combinations of time-frequency features in a histogram layer time delay neural network. An optimal set of features is identified with results indicating that specific feature combinations outperform single data features., Comment: 5 pages, 14 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

The Milky Way's Central Molecular Zone (CMZ) differs dramatically from our local solar neighbourhood, both in the extreme interstellar medium conditions it exhibits (e.g. high gas, stellar, and feedback density) and in the strong dynamics at play (e.g. due to shear and gas influx along the bar). Consequently, it is likely that there are large-scale physical structures within the CMZ that cannot form elsewhere in the Milky Way. In this paper, we present new results from the Atacama Large Millimeter/submillimeter Array (ALMA) large programme ACES (ALMA CMZ Exploration Survey) and conduct a multi-wavelength and kinematic analysis to determine the origin of the M0.8$-$0.2 ring, a molecular cloud with a distinct ring-like morphology. We estimate the projected inner and outer radii of the M0.8$-$0.2 ring to be 79" and 154", respectively (3.1 pc and 6.1 pc at an assumed Galactic Centre distance of 8.2 kpc) and calculate a mean gas density $> 10^{4}$ cm$^{-3}$, a mass of $\sim$ $10^6$ M$_\odot$, and an expansion speed of $\sim$ 20 km s$^{-1}$, resulting in a high estimated kinetic energy ($> 10^{51}$ erg) and momentum ($> 10^7$ M$_\odot$ km s$^{-1}$). We discuss several possible causes for the existence and expansion of the structure, including stellar feedback and large-scale dynamics. We propose that the most likely cause of the M0.8$-$0.2 ring is a single high-energy hypernova explosion. To viably explain the observed morphology and kinematics, such an explosion would need to have taken place inside a dense, very massive molecular cloud, the remnants of which we now see as the M0.8$-$0.2 ring. In this case, the structure provides an extreme example of how supernovae can affect molecular clouds., Comment: 17 pages, 13 figures, and 2 tables. Accepted for publication in Astronomy & Astrophysics

The radial velocity (RV) method of exoplanet detection requires mitigation of nuisance signals arising from stellar activity. Using analytic cool and facular spot models, we explore the use of central line moments (CLMs) for recovering and monitoring rotation induced RV variability. Different spot distribution patterns, photosphere-spot contrast ratios and the presence or absence of the convective blueshift lead to differences in CLM signals between M dwarfs and G dwarfs. Harmonics of the rotation period are often recovered with the highest power in standard periodogram analyses. By contrast, we show the true stellar rotation may be more reliably recovered with string length minimisation. For solar minimum activity levels, recovery of the stellar rotation signal from CLMs is found to require unfeasibly high signal-to-noise observations. The stellar rotation period can be recovered at solar maximum activity levels from CLMs for reasonable cross-correlation function (CCF) signal-to-noise ratios $> 1000 - 5000$. The CLMs can be used to recover and monitor stellar activity through their mutual correlations and correlations with RV and bisector inverse span. The skewness of a CCF, a measure of asymmetry, is described by the third CLM, $M_3$. Our noise-free simulations indicate the linear RV vs $M_3$ correlation is up to 10 per cent higher than the RV vs bisector inverse span correlation. We find a corresponding $\sim 5$ per cent increase in linear correlation for CARMENES observations of the M star, AU Mic. We also assess the effectiveness of the time derivative of the second CLM, $M_2$, for monitoring stellar activity., Comment: 24 pages, 18 fgures

We introduce a novel method for updating 3D geospatial models, specifically targeting occlusion removal in large-scale maritime environments. Traditional 3D reconstruction techniques often face problems with dynamic objects, like cars or vessels, that obscure the true environment, leading to inaccurate models or requiring extensive manual editing. Our approach leverages deep learning techniques, including instance segmentation and generative inpainting, to directly modify both the texture and geometry of 3D meshes without the need for costly reprocessing. By selectively targeting occluding objects and preserving static elements, the method enhances both geometric and visual accuracy. This approach not only preserves structural and textural details of map data but also maintains compatibility with current geospatial standards, ensuring robust performance across diverse datasets. The results demonstrate significant improvements in 3D model fidelity, making this method highly applicable for maritime situational awareness and the dynamic display of auxiliary information., Comment: Preprint of SPIE Sensor + Imaging 2024 conference paper

The ideal magnetohydrodynamic torus instability can drive the eruption of coronal mass ejections. The critical threshold of magnetic field strength decay for the onset of the torus instability occurs at different heights in different solar active regions, and understanding this variation could therefore improve space weather prediction. In this work, we aim to find out how the critical torus instability height evolves throughout the solar activity cycle. We study a significant subset of HMI and MDI Space-Weather HMI Active Region Patches (SHARPs and SMARPs) from 1996-2023, totalling 21584 magnetograms from 4436 unique active region patches. For each magnetogram, we compute the critical height averaged across the main polarity inversion line, the total unsigned magnetic flux and the separation between the positive and negative magnetic polarities. We find the critical height in active regions varies with the solar cycle, with higher (lower) average critical heights observed around solar maximum (minimum). We conclude this is because the critical height is proportional to the separation between opposite magnetic polarities, which in turn is proportional to the total magnetic flux in a region, and more magnetic regions with larger fluxes and larger sizes are observed at solar maximum. This result is noteworthy because, despite the higher critical heights, more CMEs are observed around solar maximum than at solar minimum., Comment: Accepted to ApJ. 14 pages, 6 Figures, 1 Table

Strongly driven ion-scale turbulence in tokamak plasmas is shown to be regulated by a new propagating zonal flow mode, the toroidal secondary, which is nonlinearly supported by the turbulence. The mode grows and propagates due to the combined effects of zonal flow shearing and advection by the magnetic drift. Above a threshold in the turbulence level, small-scale toroidal secondary modes become unstable and shear apart turbulent eddies, forcing the turbulence level to remain near the threshold. By including the new zonal flow physics into a theory of turbulence saturation based on the critical balance conjecture, scaling laws for the turbulent heat flux, fluctuation spectra, and zonal flow amplitude are derived and shown to be satisfied in nonlinear gyrokinetic simulations.

The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback (RLHF). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant paradigm. However, the broader field of imitation learning can more effectively utilize the sequential structure underlying autoregressive generation. We focus on investigating the inverse reinforcement learning (IRL) perspective to imitation, extracting rewards and directly optimizing sequences instead of individual token likelihoods and evaluate its benefits for fine-tuning large language models. We provide a new angle, reformulating inverse soft-Q-learning as a temporal difference regularized extension of MLE. This creates a principled connection between MLE and IRL and allows trading off added complexity with increased performance and diversity of generations in the supervised fine-tuning (SFT) setting. We find clear advantages for IRL-based imitation, in particular for retaining diversity while maximizing task performance, rendering IRL a strong alternative on fixed SFT datasets even without online data generation. Our analysis of IRL-extracted reward functions further indicates benefits for more robust reward functions via tighter integration of supervised and preference-based LLM post-training.

Aims. Using HARPS spectroscopic data obtained by the RedDots campaign, as well as archival data from HARPS and CARMENES, supplemented with ASH2 and T90 photometry, we aim to search for additional planets around the three M dwarfs GJ 832, GJ 674, and Ross 128. We also aim to determine limits on possible undetected, habitable planets. We investigate (i) the reliability of the recovered orbital eccentricities and (ii) the reliability of Bayesian evidence as a diagnostic for selecting the best model. Methods. We employed Markov-chain Monte Carlo, nested sampling, and Gaussian process (GP) analyses to fit a total of 20 different models. We used the residuals to create grids for injection-recovery simulations to obtain detection limits on potentially undiscovered planets. Results. Our refined orbital elements for GJ 832 b, GJ 674 b, and Ross 128 b confirm (GJ 832, GJ 674) or increase (Ross 128) prior eccentricity determinations. No additional planets were found in any of the systems. The detection limits obtained for all three systems are between 30 and 50 cm/s for orbital periods in the range of 1 to 10 000 days. Using N-body simulations, we find that undiscovered secondary planets are unlikely (Ross 128) or incapable (GJ 674) of having caused the observed eccentricities of the known planets. We find that the eccentricity of GJ 832 b is not significantly different from zero. Conclusions. GJ 832 b, GJ 674 b, and Ross 128 b retain their status as hosting lonely and (for the latter two) eccentric planets. Finally, our results show that Bayesian evidence, when used in conjunction with GP, is not a robust diagnostic for selecting the best model in cases of low-activity stars. In such cases, we advise an inspection of the shapes of the posterior distributions and to ensure that relevant simulations are performed to assess the validity of the perceived best model., Comment: Abstract shortened to fit the character limit; 25 pages, 14 Figures, 6 Tables; Appendix available at Zenodo; Accepted for publication by A&A

Any two reduced expressions for the same Coxeter group element are related by a sequence of commutation and braid moves. Two reduced expressions are said to be braid equivalent if they are related via a sequence of braid moves. Braid equivalence is an equivalence relation and the corresponding equivalence classes are called braid classes. Each braid class can be encoded in terms of a braid graph in a natural way. In a recent paper, Awik et al.~proved that when a Coxeter system is simply laced and triangle free (i.e., the corresponding Coxeter graph has no three-cycles), the braid graph for a reduced expression is a partial cube (i.e., isometric to a subgraph of a hypercube). In this paper, we will provide an alternate proof of this fact, as well as determine the minimal dimension hypercube into which a braid graph can be isometrically embedded, which addresses an open question posed by Awik et al. For our main result, we prove that braid graphs in simply-laced triangle-free Coxeter systems are median, which is a strengthening of previous results., Comment: 24 pages, 19 figures

Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of ML tools. To address data limitations, we compiled multi-modal data: both experimental and computational results for several properties. We find that multi-task neural networks can learn from multi-modal data and outperform single-task models trained for specific properties. As expected, the improvement is more significant for data-scarce properties. These models are trained using descriptors built from simple molecular information and can be readily applied for large-scale materials screening to explore multiple properties simultaneously. This approach is widely applicable to fields outside energetic materials., Comment: 16 pages, 4 figures, 2 tables

We present new Australia Telescope Compact Array (ATCA) radio observations toward N49, one of the brightest extragalactic Supernova remnants (SNRs) located in the Large Magellanic Cloud. Our new and archival ATCA radio observations were analysed along with $Chandra$ X-ray data. These observations show a prominent `bullet' shaped feature beyond the southwestern boundary of the SNR. Both X-ray morphology and radio polarisation analysis support a physical connection of this feature to the SNR. The 'bullet' feature's apparent velocity is estimated at $\sim$1300 km s$^{-1}$, based on its distance ($\sim$10 pc) from the remnant's geometric centre and estimated age ($\sim$7600 yrs). we estimated the radio spectral index, $\alpha= -0.55 \pm 0.03$ which is typical of middle-age SNRs. Polarisation maps created for N49 show low to moderate levels of mean fractional polarisation estimated at 7$\pm$1% and 10$\pm$1% for 5.5 and 9 GHz, respectively. These values are noticeably larger than found in previous studies. Moreover, the mean value for the Faraday rotation of SNR N49 from combining CABB data is 212$\pm$65 rad m$^{-2}$ and the maximum value of RM is 591$\pm$103 rad m$^{-2}$., Comment: 16 pages, accepted for publication in Publications of the Astronomical Society of Australia

Image-with-text memes combine text with imagery to achieve comedy, but in today's world, they also play a pivotal role in online communication, influencing politics, marketing, and social norms. A "meme template" is a preexisting layout or format that is used to create memes. It typically includes specific visual elements, characters, or scenes with blank spaces or captions that can be customized, allowing users to easily create their versions of popular meme templates by adding personal or contextually relevant content. Despite extensive research on meme virality, the task of automatically identifying meme templates remains a challenge. This paper presents a comprehensive comparison and evaluation of existing meme template identification methods, including both established approaches from the literature and novel techniques. We introduce a rigorous evaluation framework that not only assesses the ability of various methods to correctly identify meme templates but also tests their capacity to reject non-memes without false assignments. Our study involves extensive data collection from sites that provide meme annotations (Imgflip) and various social media platforms (Reddit, X, and Facebook) to ensure a diverse and representative dataset. We compare meme template identification methods, highlighting their strengths and limitations. These include supervised and unsupervised approaches, such as convolutional neural networks, distance-based classification, and density-based clustering. Our analysis helps researchers and practitioners choose suitable methods and points to future research directions in this evolving field., Comment: 10 pages, 3 figures

Standing as one of the most significant barriers to reaching quantum advantage, state-preparation fidelities on noisy intermediate-scale quantum processors suffer from quantum-gate errors, which accumulate over time. A potential remedy is pulse-based state preparation. We numerically investigate the minimal evolution times (METs) attainable by optimizing (microwave and exchange) pulses on silicon hardware. We investigate two state preparation tasks. First, we consider the preparation of molecular ground states and find the METs for H$_2$, HeH$^+$, and LiH to be 2.4 ns, 4.4 ns, and 27.2 ns, respectively. Second, we consider transitions between arbitrary states and find the METs for transitions between arbitrary four-qubit states to be below 50 ns. For comparison, connecting arbitrary two-qubit states via one- and two-qubit gates on the same silicon processor requires approximately 200 ns. This comparison indicates that pulse-based state preparation is likely to utilize the coherence times of silicon hardware more efficiently than gate-based state preparation. Finally, we quantify the effect of silicon device parameters on the MET. We show that increasing the maximal exchange amplitude from 10 MHz to 1 GHz accelerates the METs, e.g., for H$_2$ from 84.3 ns to 2.4 ns. This demonstrates the importance of fast exchange. We also show that increasing the maximal amplitude of the microwave drive from 884 kHz to 56.6 MHz shortens state transitions, e.g., for two-qubit states from 1000 ns to 25 ns. Our results bound both the state-preparation times for general quantum algorithms and the execution times of variational quantum algorithms with silicon spin qubits., Comment: 9 + (7) pages, 6 figs, comments are welcomed