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This study analyzed Pennsylvania School Climate Survey data from students and staff in the 2021/22 school year to assess the validity and reliability of the elementary school student version of the survey; approaches to scoring the survey in individual schools at all grade levels; and perceptions of school climate across student, staff, and school groups. The survey encourages data-informed efforts in participating Pennsylvania schools to foster supportive learning environments that promote social and emotional wellness for students and staff. The study validated the elementary school student survey but found that one domain--safe and respectful school climate--did not meet the reliability threshold and thus suggests that revisions are needed. At all grade levels noninstructional staff had the most positive perceptions of school climate, followed by classroom teachers then students. The study found that different approaches to combining the school climate scores of students, teachers, and noninstructional staff within schools yielded slightly different distributions of school climate summary index scores. It also found that different performance category thresholds resulted in similar distributions of schools across categories. Scores calculated using simple averages were strongly and positively correlated with scores calculated using a more complex approach (Rasch models), suggesting that both approaches deliver similar information. School climate scores varied across student groups (defined by race/ethnicity, gender, and grade level) within schools and across school groups. Larger schools and schools with higher percentages of Black students tended to have lower school climate scores than other schools. The findings can inform the Pennsylvania Department of Education's decisionmaking on revisions to the elementary school student survey, approaches to scoring and reporting survey results, and efforts to increase participation in future survey administrations.

Background: Verbal deception detection research relies on narratives and commonly assumes statements as truthful or deceptive. A more realistic perspective acknowledges that the veracity of statements exists on a continuum with truthful and deceptive parts being embedded within the same statement. However, research on embedded lies has been lagging behind. Methods: We collected a novel dataset of 2,088 truthful and deceptive statements with annotated embedded lies. Using a within-subjects design, participants provided a truthful account of an autobiographical event. They then rewrote their statement in a deceptive manner by including embedded lies, which they highlighted afterwards and judged on lie centrality, deceptiveness, and source. Results: We show that a fined-tuned language model (Llama-3-8B) can classify truthful statements and those containing embedded lies with 64% accuracy. Individual differences, linguistic properties and explainability analysis suggest that the challenge of moving the dial towards embedded lies stems from their resemblance to truthful statements. Typical deceptive statements consisted of 2/3 truthful information and 1/3 embedded lies, largely derived from past personal experiences and with minimal linguistic differences with their truthful counterparts. Conclusion: We present this dataset as a novel resource to address this challenge and foster research on embedded lies in verbal deception detection.

Recent advancements in AI models are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources. To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE's unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications.

Background: Deception detection through analysing language is a promising avenue using both human judgments and automated machine learning judgments. For both forms of credibility assessment, automated adversarial attacks that rewrite deceptive statements to appear truthful pose a serious threat. Methods: We used a dataset of 243 truthful and 262 fabricated autobiographical stories in a deception detection task for humans and machine learning models. A large language model was tasked to rewrite deceptive statements so that they appear truthful. In Study 1, humans who made a deception judgment or used the detailedness heuristic and two machine learning models (a fine-tuned language model and a simple n-gram model) judged original or adversarial modifications of deceptive statements. In Study 2, we manipulated the target alignment of the modifications, i.e. tailoring the attack to whether the statements would be assessed by humans or computer models. Results: When adversarial modifications were aligned with their target, human (d=-0.07 and d=-0.04) and machine judgments (51% accuracy) dropped to the chance level. When the attack was not aligned with the target, both human heuristics judgments (d=0.30 and d=0.36) and machine learning predictions (63-78%) were significantly better than chance. Conclusions: Easily accessible language models can effectively help anyone fake deception detection efforts both by humans and machine learning models. Robustness against adversarial modifications for humans and machines depends on that target alignment. We close with suggestions on advancing deception research with adversarial attack designs., Comment: preprint

We measure the radio continuum fluxes at the locations of all Gaia${-}$confirmed members of Taurus${-}$Auriga using Karl G. Jansky Very Large Array Sky Survey data (VLASS; 2${-}$4 GHz, $\sigma_{\rm{VLASS}}{\sim}110{-}140 \mu$Jy, $2.5''$ resolution) spanning 3 VLASS epochs (2019, 2021, and 2023). We present 35 detections coincident with young Taurus${-}$Auriga stars (29 in individual VLASS images, 6 via stacking). We find a strong dependence on spectral type, wherein the fractional detection rate of radio emission coincident with early-type young stellar objects (YSOs) is systematically higher than late-type YSOs, ranging from 25% $\pm$ 13% for B${-}$F YSOs, 21% $\pm$ 11% for G YSOs, 18.4% $\pm$ 6.3% for K0${-}$K4 YSOs, 15.5% $\pm$ 5.4% for K5${-}$K9 YSOs, 7.0% $\pm$ 2.7% for M0${-}$M2 YSOs, 2.3% $\pm$ 0.9% for M3${-}$M6 YSOs, and 1.9% $\pm$ 1.9% for YSOs with SpTs later than M7. We present cumulative density distributions of radio luminosity densities that demonstrate a significant luminosity enhancement for early- versus late-type YSOs. We find 25% of the detected sources to be significantly variable. We discuss possible interpretations of this dependence, which may reflect stellar magnetic activity, binary interactions, or stellar flaring. We find that mid-infrared YSO class is a strong indicator of radio detectability consistent with higher frequency Taurus-Auriga VLA surveys, with class III stars detected at a rate of 8.8% $\pm$ 1.6%, class IIs at 2.0% $\pm$ 1.2%, and combined class 0s, Is and Fs at 8.0% $\pm$ 5.4%., Comment: Accepted for publication in ApJ. 26 pages, inc. 12 figures, 3 tables

Recent work has demonstrated that Bayesian neural networks (BNN's) trained with mean field variational inference (MFVI) can be implemented in analog hardware, promising orders of magnitude energy savings compared to the standard digital implementations. However, while Gaussians are typically used as the variational distribution in MFVI, it is difficult to precisely control the shape of the noise distributions produced by sampling analog devices. This paper introduces a method for MFVI training using real device noise as the variational distribution. Furthermore, we demonstrate empirically that the predictive distributions from BNN's with the same weight means and variances converge to the same distribution, regardless of the shape of the variational distribution. This result suggests that analog device designers do not need to consider the shape of the device noise distribution when hardware-implementing BNNs performing MFVI., Comment: Presented at the NeurIPS 2024 Workshop on Machine Learning with New Compute Paradigms, https://openreview.net/forum?id=soS5qgU7Yb

Background: Healthcare has many manual processes that can benefit from automation and augmentation with Generative Artificial Intelligence (AI), the medical billing and coding process. However, current foundational Large Language Models (LLMs) perform poorly when tasked with generating accurate International Classification of Diseases, 10th edition, Clinical Modification (ICD-10-CM) and Current Procedural Terminology (CPT) codes. Additionally, there are many security and financial challenges in the application of generative AI to healthcare. We present a strategy for developing generative AI tools in healthcare, specifically for medical billing and coding, that balances accuracy, accessibility, and patient privacy. Methods: We fine tune the PHI-3 Mini and PHI-3 Medium LLMs using institutional data and compare the results against the PHI-3 base model, a PHI-3 RAG application, and GPT-4o. We use the post operative surgical report as input and the patients billing claim the associated ICD-10, CPT, and Modifier codes as the target result. Performance is measured by accuracy of code generation, proportion of invalid codes, and the fidelity of the billing claim format. Results: Both fine-tuned models performed better or as well as GPT-4o. The Phi-3 Medium fine-tuned model showed the best performance (ICD-10 Recall and Precision: 72%, 72%; CPT Recall and Precision: 77%, 79%; Modifier Recall and Precision: 63%, 64%). The Phi-3 Medium fine-tuned model only fabricated 1% of ICD-10 codes and 0.6% of CPT codes generated. Conclusions: Our study shows that a small model that is fine-tuned on domain-specific data for specific tasks using a simple set of open-source tools and minimal technological and monetary requirements performs as well as the larger contemporary consumer models., Comment: 21 pages, 3 figures, 2 tables

We examined the anomalies in the light curves of the lensing events MOA-2022-BLG-033, KMT-2023-BLG-0119, and KMT-2023-BLG-1896. We conducted detailed modeling of the light curves to uncover the nature of the anomalies. This modeling revealed that all signals originated from planetary companions to the primary lens. The planet-to-host mass ratios are very low: $q\sim 7.5\times 10^{-5}$ for MOA-2022-BLG-033, $q\sim 3.6\times 10^{-4}$ for KMT-2023-BLG-0119, and $q\sim 6.9\times 10^{-5}$ for KMT-2023-BLG-1896. The anomalies occurred as the source passed through the negative deviation region behind the central caustic along the planet-host axis. The solutions are subject to a common inner-outer degeneracy, resulting in variations in estimating the projected planet-host separation. For KMT-2023-BLG-1896, although the planetary scenario provides the best explanation of the anomaly, the binary companion scenario is marginally possible. We estimate the physical parameters of the planetary systems through Bayesian analyses based on the lensing observables. The analysis identifies MOA-2022-BLG-033L as a planetary system with an ice giant, approximately 12 times the mass of Earth, orbiting an early M dwarf star. The companion of KMT-2023-BLG-1896L is also an ice giant, with a mass around 16 Earth masses, orbiting a mid-K-type main-sequence star. The companion of KMT-2023-BLG-0119L, which has a mass about the mass of Saturn, orbits a mid-K-type dwarf star. The lens for MOA-2022-BLG-033 is highly likely to be located in the disk, whereas for the other events, the probabilities of the lens being in the disk or the bulge are roughly comparable., Comment: 9 pages, 10 figures, 6 tables

We present a measurement of the branching fraction and fraction of longitudinal polarization of $B^0 \to \rho^+ \rho^-$ decays, which have two $\pi^0$'s in the final state. We also measure time-dependent $CP$ violation parameters for decays into longitudinally polarized $\rho^+ \rho^-$ pairs. This analysis is based on a data sample containing $(387\pm6) \times 10^6$ \BBbar pairs collected with the Belle~II detector at the SuperKEKB asymmetric-energy $e^+e^-$ collider in 2019-2022. We obtain ${B}(B^0\to\rho^+\rho^-) = (2.88 ^{+0.23}_{-0.22} {}^{+0.29}_{-0.27}) \times 10^{-5}, f_{L} = 0.921 ^{+0.024}_{-0.025} {}^{+0.017}_{-0.015}$, $S = -0.26\pm0.19\pm0.08$, and $C = -0.02\pm0.12^{+0.06}_{-0.05}$, where the first uncertainties are statistical and the second are systematic. We use these results to perform an isospin analysis to constrain the CKM angle $\phi_2$ and obtain two solutions; the result consistent with other Standard Model constraints is $\phi_2 = (92.6^{+4.5}_{-4.8})^\circ$.

Mean kurtosis in iron-rich grey matter has values similar to that seen in white matter. We suspect these elevated values may be related to iron. Multi-shell diffusion and multi-echo gradient echo acquisitions were used to derive mean kurtosis and R2*, respectively. Mean kurtosis and R2* were measured in subcortical grey matter nuclei and white matter tracts in 93 older adults and 62 younger adults. Grey matter regions exhibited higher mean kurtosis and R2* in the older adult group whereas white matter regions had reduced mean kurtosis in the older adult group. Grey matter mean kurtosis was significantly correlated with R2* iron-rich grey matter nuclei in both groups. Our findings indicate that higher mean kurtosis in iron-rich grey matter structures may be due to either increased tissue complexity or to decreases in signal-to-noise ratios from iron deposition, Comment: 7 pages, 5 figures, 2 Tables

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

We present the first search for the lepton flavor-violating decay modes $B^0 \rightarrow K_S^0 \tau^\pm \ell^\mp~(\ell=\mu, e)$ using the 711 fb$^{-1}$ and 365 fb$^{-1}$ data samples recorded by the Belle and Belle II detectors, respectively. We use a hadronic $B$-tagging technique, and search for the signal decay in the system recoiling against the fully reconstructed $B$ meson. We find no evidence for $B^0 \rightarrow K_S^0 \tau^\pm \ell^\mp$ decays and set 90\% confidence level upper limits on the branching fractions in the range of $[0.8,\,3.6]\times10^{-5}$.

Suppose $G$ is a graph and $L$ is a list assignment for $G$. A request of $L$ is a function $r$ with nonempty domain $D\subseteq V(G)$ such that $r(v) \in L(v)$ for each $v \in D$. The triple $(G,L,r)$ is $\epsilon$-satisfiable if there exists a proper $L$-coloring $f$ of $G$ such that $f(v) = r(v)$ for at least $\epsilon|D|$ vertices in $D$. We say $G$ is $(k, \epsilon)$-flexible if $(G,L',r')$ is $\epsilon$-satisfiable whenever $L'$ is a $k$-assignment for $G$ and $r'$ is a request of $L'$. It is known that a graph $G$ is not $(k, \epsilon)$-flexible for any $k$ if and only if $\epsilon > 1/ \rho(G)$ where $\rho(G)$ is the Hall ratio of $G$. The list flexibility number of a graph $G$, denoted $\chi_{\ell flex}(G)$, is the smallest $k$ such that $G$ is $(k,1/ \rho(G))$-flexible. A fundamental open question on list flexibility numbers asks: Is there a graph with list flexibility number greater than its coloring number? In this paper, we show that the list flexibility number of any complete multipartite graph $G$ is at most the coloring number of $G$. With this as a starting point, we study list epsilon flexibility functions of complete bipartite graphs which was first suggested by Kaul, Mathew, Mudrock, and Pelsmajer in 2024. Specifically, we completely determine the list epsilon flexibility function of $K_{m,n}$ when $m \in \{1,2\}$ and establish additional bounds. Our proofs reveal a connection to list coloring complete bipartite graphs with asymmetric list sizes which is a topic that was explored by Alon, Cambie, and Kang in 2021., Comment: 18 pages

We measure the branching fraction and $\it CP$-violating flavor-dependent rate asymmetry of $B^{0} \to \pi^{0} \pi^{0}$ decays reconstructed using the Belle II detector in an electron-positron collision sample containing $387 \times 10^{6}$ $B\overline{B}$ pairs. Using an optimized event selection, we find $126\pm 20$ signal decays in a fit to background-discriminating and flavor-sensitive distributions. The resulting branching fraction is $(1.25 \pm 0.23)\times 10^{-6}$ and the $\it CP$-violating asymmetry is $0.03 \pm 0.30$.

We measure the branching fraction of the decay $B^0 \to J/\psi \omega$ using data collected with the Belle II detector at the SuperKEKB collider. The data contain $(387 \pm 6) \times 10^6$ $B\overline{B}$ meson pairs produced in energy-asymmetric $e^+e^-$ collisions at the $\Upsilon (4S)$ resonance. The measured branching fraction $\mathcal{B}(B^0 \to J/\psi \omega) = \left( 2.16 \pm 0.30 \pm 0.14 \right) \times 10^{-5}$, where the first uncertainty is statistical and the second is systematic, is more precise than previous results and constitutes the first observation of the decay with a significance of $6.5$ standard deviations.

We establish a structure theorem for the Brascamp--Lieb constant formulated in the general setting of locally compact abelian groups. This extends and unifies the finiteness characterisations previously known for euclidean spaces and for finitely generated groups and their duals. We place particular emphasis on Fourier invariance throughout, reflecting the fundamental Fourier invariance of Brascamp--Lieb multilinear forms in this context., Comment: 26 pages, one typo corrected and a reference added

Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus anchor their judgments on an objective benchmark. The system also aggregates human and machine forecasts weighting both for propinquity and based on assessed skill while adjusting for overconfidence. We present results from the Hybrid Forecasting Competition (HFC) - larger than comparable forecasting tournaments - including 1085 users forecasting 398 real-world forecasting problems over eight months. Our main result is that the hybrid system generated more accurate forecasts compared to a human-only baseline which had no machine generated predictions. We found that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data. We also demonstrated the inclusion of machine-generated forecasts in our aggregation algorithms improved performance, both in terms of accuracy and scalability. This suggests that hybrid forecasting systems, which potentially require fewer human resources, can be a viable approach for maintaining a competitive level of accuracy over a larger number of forecasting questions., Comment: 20 pages, 6 figures, 4 tables

Using data samples of 983.0~$\rm fb^{-1}$ and 427.9~$\rm fb^{-1}$ accumulated with the Belle and Belle~II detectors operating at the KEKB and SuperKEKB asymmetric-energy $e^+e^-$ colliders, singly Cabibbo-suppressed decays $\Xi_c^{+} \to pK_{S}^{0}$, $\Xi_c^+ \to \Lambda \pi^+$, and $\Xi_c^+ \to \Sigma^{0} \pi^+$ are observed for the first time. The ratios of branching fractions of $\Xi_{c}^{+}\to p K_{S}^{0}$, $\Xi_{c}^{+}\to \Lambda \pi^{+}$, and $\Xi_{c}^{+}\to \Sigma^{0} \pi^{+}$ relative to that of $\Xi_c^+ \to \Xi^- \pi^{+} \pi^{+}$ are measured to be \begin{equation} \frac{{\cal B}(\Xi_c^+ \to pK_S^0)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (2.47 \pm 0.16 \pm 0.07)\% \notag, \end{equation} \begin{equation} \frac{{\cal B}(\Xi_c^+ \to \Lambda \pi^+)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (1.56 \pm 0.14 \pm 0.09)\% \notag, \end{equation} \begin{equation} \frac{{\cal B}(\Xi_c^+ \to \Sigma^0 \pi^+)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (4.13 \pm 0.26 \pm 0.22)\% \notag. \end{equation} Multiplying these values by the branching fraction of the normalization channel, ${\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+\pi^+) = (2.9 \pm 1.3)\%$, the absolute branching fractions are determined to be \begin{equation} {\cal B}(\Xi_c^{+} \to p K_{S}^{0}) = (7.16 \pm 0.46 \pm 0.20 \pm 3.21) \times 10^{-4} \notag, \end{equation} \begin{equation} {\cal B}(\Xi_c^{+} \to \Lambda \pi^+) = (4.52 \pm 0.41 \pm 0.26 \pm 2.03) \times 10^{-4} \notag, \end{equation} \begin{equation} {\cal B}(\Xi_c^{+} \to \Sigma^0 \pi^+) = (1.20 \pm 0.08 \pm 0.07 \pm 0.54) \times 10^{-3} \notag. \end{equation} The first and second uncertainties above are statistical and systematic, respectively, while the third ones arise from the uncertainty in ${\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^{+} \pi^{+})$., Comment: 21 pages, 5 pages

We present a comprehensive analysis of a planetary microlensing event OGLE-2015-BLG-1609. The planetary anomaly was detected by two survey telescopes, OGLE and MOA. Each of these surveys collected enough data over the planetary anomaly to allow for an unambiguous planet detection. Such survey detections of planetary anomalies are needed to build a robust sample of planets that could improve studies on the microlensing planetary occurrence rate by reducing biases and statistical uncertainties. In this work, we examined different methods for modeling microlensing events using individual datasets, particularly we incorporated a Galactic model prior to better constrain poorly defined microlensing parallax. Ultimately, we fitted a comprehensive model to all available data, identifying three potential typologies, with two showing comparably high Bayesian evidence. Our analysis indicates that the host of the planet is a brown dwarf with a probability of 34%, or a low-mass stellar object (M-dwarf) with the probability of 66%., Comment: 12 pages, 7 figures

We present high angular resolution imaging from the {\sl Hubble Space Telescope} combined with adaptive optics imaging results from the {\sl Keck}-II telescope to determine the mass of the OGLE-2012-BLG-0563L host star and planet to be $M_{\rm host} = 0.801\pm 0.033M_\odot$ and $M_{\rm planet} = 1.116 \pm 0.087 M_{\rm Jupiter}$, respectively, located at a distance of $D_L = 5.46\pm 0.56\,$kpc. There is a close-wide degeneracy in the light curve models that indicates star-planet projected separation of $1.50\pm 0.16\,$AU for the close model and $8.41\pm 0.87\,$AU for the wide model. We used the image-constrained modeling method to analyze the light curve data with constraints from this high angular resolution image analysis. This revealed systematic errors in some of the ground-based light curve photometry that led to an estimate of the angular Einstein Radius, $\theta_E$, that was too large by a factor of $\sim 2$. The host star mass is a factor of 2.4 larger than the value presented in the \citet{fukui15} discovery paper. Although most systematic photometry errors seen in ground-based microlensing light curve photometry will not be repeated in data from the {\sl Roman Space Telescope}'s Galactic Bulge Time Domain Survey, we argue that image constrained modeling will be a valuable method to identify possible systematic errors in {\sl Roman} photometry., Comment: 30 pages, including 8 figures. Submitted to the Astronomical Journal

We present a general-purpose algorithm for automatic production of a structure that induces a desired Casimir-Polder force. As a demonstration of the capability and wide applicability of the method, we use it to develop a geometry that leads to a repulsive Casimir-Polder force on a ground-state atom. The results turn out to be reminiscent of the ring-like geometries previously used to induce repulsion, but with some new features and -- importantly -- discovered completely independently of any input from the user. This represents a powerful new paradigm in the study of atom-surface forces -- instead of the user testing various geometries against a desired figure of merit, the goal can be specified and then an appropriate geometry created automatically., Comment: 5 pages, 4 figures

We report the results of an extensive numerical study of the $Sp(4)$ lattice gauge theory with three (Dirac) flavors of fermion in the two-index antisymmetric representation. In the presence of (degenerate) fermion masses, the theory has an enhanced global $SU(6)$ symmetry, broken explicitly and spontaneously to its $SO(6)$ subgroup. This symmetry breaking pattern makes the theory interesting for applications in the context of composite Higgs models, as well as for the implementation of top partial compositeness. It can also provide a dynamical realisation of the strongly interacting massive particle paradigm for the origin of dark matter. We adopt the standard plaquette gauge action with the Wilson-Dirac formulation for the fermions and apply the (rational) hybrid Monte Carlo algorithm in our ensemble generation process. We monitor the autocorrelation and topology of the ensembles. We explore the bare parameter space, and identify the weak and strong coupling regimes separated by a line of first-order bulk phase transitions. We measure two-point correlation functions between meson operators that transform as non-trivial representations of $SO(6)$, and extract the ground-state masses and the decay constants, in all accessible spin and parity channels. In addition, we measure the mass of the first excited state for the vector meson by solving a generalised eigenvalue problem. Spectral quantities show a mass dependence that is compatible with the expectation that, at long distances, the theory undergoes confinement, accompanied by the spontaneous breaking of the approximate global symmetries acting on the matter fields. Finally, we discuss the continuum and massless extrapolations, after setting the physical scale using the gradient flow method, and compare the results to those of existing studies in the quenched approximation, as well as to the literature on closely related theories., Comment: 43 pages, 20 figures, 11 tables

Challenging neutron-capture cross-section measurements of small cross sections and samples with a very limited number of atoms require high-flux time-of-flight facilities. In turn, such facilities need innovative detection setups that are fast, have low sensitivity to neutrons, can quickly recover from the so-called $\gamma$-flash, and offer the highest possible detection sensitivity. In this paper, we present several steps toward such advanced systems. Specifically, we describe the performance of a high-sensitivity experimental setup at CERN n\_TOF EAR2. It consists of nine sTED detector modules in a compact cylindrical configuration, two conventional used large-volume C$_{6}$D$_{6}$ detectors, and one LaCl$_{3}$(Ce) detector. The performance of these detection systems is compared using $^{93}$Nb($n$,$\gamma$) data. We also developed a detailed \textsc{Geant4} Monte Carlo model of the experimental EAR2 setup, which allows for a better understanding of the detector features, including their efficiency determination. This Monte Carlo model has been used for further optimization, thus leading to a new conceptual design of a $\gamma$ detector array, STAR, based on a deuterated-stilbene crystal array. Finally, the suitability of deuterated-stilbene crystals for the future STAR array is investigaged experimentally utilizing a small stilbene-d12 prototype. The results suggest a similar or superior performance of STAR with respect to other setups based on liquid-scintillators, and allow for additional features such as neutron-gamma discrimination and a higher level of customization capability.

We report progress on our lattice calculations for the mass spectra of low-lying composite states in the Sp(4) gauge theory coupled to two and three flavors of Dirac fermions transforming in the fundamental and the two-index antisymmetric representations, respectively. This theory provides an ultraviolet completion to the composite Higgs model with Goldstone modes in the SU(4)/Sp(4) coset and with partial compositeness for generating the top-quark mass. We measure the meson and chimera baryon masses. These masses are crucial for constructing the composite Higgs model. In particular, the chimera baryon masses are important inputs for implementing top partial compositeness. We employ Wilson fermions and the Wilson plaquette action in our simulations. Techniques such as APE and Wuppertal smearing, as well as the procedure of generalised eigenvalue problem, are implemented in our analysis., Comment: 12 pages, 8 figures, 1 tables, Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024), July 28th - August 3rd, 2024, University of Liverpool, UK

Twistronics has received much attention as a new method to manipulate the properties of 2D van der Waals structures by introducing moir\'e patterns through a relative rotation between two layers. Here we begin a theoretical exploration of twistronics beyond the realm of van der Waals materials by developing a first-principles description of the electronic structure and interlayer interactions of ultrathin perovskite bilayers. We construct both an ab initio tight-binding model as well as a minimal 3-band effective model for the valence bands of monolayers and bilayers of oxides derived from the Ruddlesden-Popper phase of perovskites, which is amenable to thin-layer formation. We illustrate the approach with the specific example of Sr$_2$TiO$_4$ layers but also provide model parameters for Ca$_2$TiO$_4$ and Ba$_2$TiO$_4$ ., Comment: 14 pages, 7 figures, supplementary material

A $q$-ary code $C$ of length $n$ is a set of $n$-dimensional vectors (code words) with entries in $\{0, \ldots, q-1\}$. We say $C$ has constant weight $w$ if each code word has exactly $w$ nonzero entries. We say $C$ has minimum distance $d$ if any two distinct code words in $C$ differ in at least $d$ entries. We let $A_q(n, d, w)$ be the largest possible cardinality of any $q$-ary code of length $n$ with constant weight $w$ and minimum distance $d$. Very recently, Liu and Shangguan gave an asymptotically sharp estimate for $A_q(n, d, w)$ where $q, d, w$ are fixed, $d$ is odd and $n \rightarrow \infty$. In this note we answer a question of Liu and Shangguan by obtaining such an estimate in the case where $d$ is even., Comment: 6 pages

We report measurements of the absolute branching fractions $\mathcal{B}(B_s^0 \to D_s^{\pm} X)$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X)$, and $\mathcal{B}(B_s^0 \to D^{\pm} X)$, where the latter is measured for the first time. The results are based on a 121.4\,fb$^{-1}$ data sample collected at the $\Upsilon(10860)$ resonance by the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. We reconstruct one $B_s^0$ meson in $e^+e^- \to \Upsilon(10860) \to B_s^{*} \bar{B}_s^{*}$ events and measure yields of $D_s^+$, $D^0$, and $D^+$ mesons in the rest of the event. We obtain $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (68.6 \pm 7.2 \pm 4.0)\%$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (21.5 \pm 6.1 \pm 1.8)\%$, and $\mathcal{B}(B_s^0 \to D^{\pm} X) = (12.6 \pm 4.6 \pm 1.3)\%$, where the first uncertainty is statistical and the second is systematic. Averaging with previous Belle measurements gives $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (63.4 \pm 4.5 \pm 2.2)\%$ and $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (23.9 \pm 4.1 \pm 1.8)\%$. For the $B_s^0$ production fraction at the $\Upsilon(10860)$, we find $f_s = (21.4^{+1.5}_{-1.7})\%$., Comment: 23 pages, 9 figures, submitted to JHEP

Understanding of the formation and evolution of the Solar System requires understanding key and common materials found on and in planetary bodies. Mineral mixing and its implications on planetary body formation is a topic of high interest to the planetary science community. Previous work establishes a case for the use of Optical PhotoThermal InfraRed (O-PTIR) in planetary science and introduces and demonstrates the technique's capability to study planetary materials. In this paper, we performed a measurement campaign on granular materials relevant to planetary science, such as minerals found in lunar and martian soils. These laboratory measurements serve to start a database of O-PTIR measurements. We also present FTIR absorption measurements of the materials we observed in O-PTIR for comparison purposes. We find that the O-PTIR technique suffers from granular orientation effects similar to other IR techniques, but in most cases, is is directly comparable to commonly used absorption spectroscopy techniques. We conclude that O-PTIR would be an excellent tool for the purpose of planetary material identification during in-situ investigations on regolith and bedrock surfaces., Comment: This paper follows, "Photothermal Spectroscopy for Planetary Sciences: Mid-IR Absorption Made Easy," (arXiv:2409.11626) and is part 2 of a series. This work catalogues O-PTIR Mid-IR measurements of various planetary materials. This is a pre-print

First-order phase transitions in the early universe have rich phenomenological implications, such as the production of a potentially detectable signal of stochastic relic background gravitational waves. The hypothesis that new, strongly coupled dynamics, hiding in a new dark sector, could be detected in this way, via the telltale signs of its confinement/deconfinement phase transition, provides a fascinating opportunity for interdisciplinary synergy between lattice field theory and astro-particle physics. But its viability relies on completing the challenging task of providing accurate theoretical predictions for the parameters characterising the strongly coupled theory. Density of states methods, and in particular the linear logarithmic relaxation (LLR) method, can be used to address the intrinsic numerical difficulties that arise due the meta-stable dynamics in the vicinity of the critical point. For example, it allows one to obtain accurate determinations of thermodynamic observables that are otherwise inaccessible, such as the free energy. In this contribution, we present an update on results of the analysis of the finite temperature deconfinement phase transition in a pure gauge theory with a symplectic gauge group, $Sp(4)$, by using the LLR method. We present a first analysis of the properties of the transition in the thermodynamic limit, and provide a road map for future work, including a brief preliminary discussion that will inform future publications., Comment: 13 pages, 3 figures. Contribution to Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024), July 28th - August 3rd, 2024, University of Liverpool, UK

Single-photon detectors are ``blind" after the detection of a photon, and thereafter display a characteristic recovery in efficiency, during which the number of undetected photons depends on the statistics of the incident light. We show how the efficiency-recovery, photon statistics and intensity have an interdependent relationship which suppresses a detector's ability to count photons and measure correlations. We also demonstrate this effect with an experiment using $n$ such detectors to determine the $n^{\mathrm{th}}$ order correlation function with pseudothermal light., Comment: 8 pages, 6 figures including appendix

We present the in-lab and on-sky performance for the upgraded 90 GHz focal plane of the Cosmology Large Angular Scale Surveyor (CLASS), which had four of its seven detector wafers updated during the austral winter of 2022. The update aimed to improve the transition-edge-sensor (TES) stability and bias range and to realize the high optical efficiency of the sensor design. Modifications included revised circuit terminations, electrical contact between the TES superconductor and the normal metal providing the bulk of the bolometer's heat capacity, and additional filtering on the TES bias lines. The upgrade was successful: 94% of detectors are stable down to 15% of the normal resistance, providing a wide overlapping range of bias voltages for all TESs on a wafer. The median telescope efficiency improved from $0.42^{+0.15}_{-0.22}$ to $0.60^{+0.10}_{-0.32}$ (68% quantiles). For the four upgraded wafers alone, median telescope efficiency increased to $0.65^{+0.06}_{-0.06}$. Given our efficiency estimate for the receiver optics, this telescope efficiency implies a detector efficiency exceeding $0.90$. The overall noise-equivalent temperature of the 90 GHz focal plane improved from 19 $\mu$K$\sqrt{s}$ to 11.3 $\mu$K$\sqrt{s}$., Comment: 18 pages, 10 figures.Submitted to ApJS

First-principles density functional theory (DFT) codes which employ a localized basis offer advantages over those which use plane-wave bases, such as better scaling with system size and better suitability to low-dimensional systems. The trade-off is that care must be taken in order to generate a good localized basis set which is efficient and accurate in a variety of environments. Here we develop and make freely available optimized local basis sets for two common two-dimensional (2D) materials, graphene and hexagonal boron nitride, for the \siesta DFT code. Each basis set is benchmarked against the \abinit plane-wave code, using the same pseudopotentials and exchange-correlation functionals. We find that a significant improvement is obtained by including the $l+2$ polarization orbitals ($4f$) to the basis set, which greatly improves angular flexibility. The optimized basis sets yield much better agreement with plane-wave calculations for key features of the physical system, including total energy, lattice constant and cohesive energy. The optimized basis sets also result in a speedup of the calculations with respect to the non-optimized, native choices.

In this paper, we extend recent work of the third author and Ziegler on triples of integers $(a,b,c)$, with the property that each of $(a,b,c)$, $(a+1,b+1,c+1)$ and $(a+2,b+2,c+2)$ is multiplicatively dependent, completely classifying such triples in case $a=2$. Our techniques include a variety of elementary arguments together with more involved machinery from Diophantine approximation., Comment: 31 pages. Just added funding acknowledgement

We report measurements of production cross sections for $\rho^+$, $\rho^0$, $\omega$, $K^{*+}$, $K^{*0}$, $\phi$, $\eta$, $K_S^0$, $f_0(980)$, $D^+$, $D^0$, $D_s^+$, $D^{*+}$, $D^{*0}$, and $D^{*+}_s$ in $e^+e^-$ collisions at a center-of-mass energy near 10.58 GeV. The data were recorded by the Belle experiment, consisting of 571 fb$^{-1}$ at 10.58 GeV and 74 fb$^{-1}$ at 10.52 GeV. Production cross sections are extracted as a function of the fractional hadron momentum $x_p$ . The measurements are compared to {\sc pythia} Monte Carlo generator predictions with various fragmentation settings, including those that have increased fragmentation into vector mesons over pseudo-scalar mesons. The cross sections measured for light hadrons are consistent with no additional increase of vector over pseudo-scalar mesons. The charmed-meson cross sections are compared to earlier measurements -- when available -- including older Belle results, which they supersede. They are in agreement before application of an improved initial-state radiation correction procedure that causes slight changes in their \xp shapes., Comment: 21 pages, 18 figures, submitted to Phys. Rev. D

We present images of IRAS~08235--4316 with the Dark Energy Camera Plane Survey (DECaPS; spanning 0.398--1.034$\,\mu$m, at ${\sim}1''$ resolution) and the Submillimeter Array (SMA; at 1.38\,mm/217\,GHz, at ${\sim}1.9''\times1.2''$ resolution), a YSO located in the Vela constellation near to the Puppis boundary, detected in a systematic search for new large/extended emission sources. The DECaPS data show an asymmetric bi--polar morphology with a large angular extent of ${\sim}7.1''$ separated by a dark lane, characteristic of highly--inclined protoplanetary disks and less-evolved YSOs with outflows. The SMA data show an extended continuum structure along the optical dark lane with a smaller angular extent of ${\sim}4.6''$. The detected $^{12}$CO J=2--1 emission tentatively shows a velocity gradient along the position angle of the dark lane/millimeter continuum, that may trace rotating gas. Additional $^{12}$CO emission is present which could trace infalling/outflowing gas, and/or a nearby gas cloud. We estimate a distance to IRAS~08235--4316 of at least ${\sim}191\,$pc. Supported by additional SED modelling, we infer IRAS~08235--4316 to be a newly discovered class~I YSO with an outflow, host to an embedded protoplanetary disk, with a large millimeter radius of ${\sim}440$\,au and dust mass ${\gtrsim}11\,M_\oplus$., Comment: Accepted for publication in AJ 14 pages (10 page paper), 6 figures

Informal carers provide the majority of care for people living with challenges related to older age, long-term illness, or disability. However, the care they provide often results in a significant income penalty for carers, a factor largely overlooked in the economics literature and policy discourse. Leveraging data from the UK Household Longitudinal Study, this paper provides the first robust causal estimates of the caring income penalty using a novel individual synthetic control based method that accounts for unit-level heterogeneity in post-treatment trajectories over time. Our baseline estimates identify an average relative income gap of up to 45%, with an average decrease of {\pounds}162 in monthly income, peaking at {\pounds}192 per month after 4 years, based on the difference between informal carers providing the highest-intensity of care and their synthetic counterparts. We find that the income penalty is more pronounced for women than for men, and varies by ethnicity and age.

We present measurements of $B \to K{}^{*}(892)\gamma$ decays using $365\,{\rm fb}^{-1}$ of data collected from 2019 to 2022 by the Belle~II experiment at the SuperKEKB asymmetric-energy $e^+e^-$ collider. The data sample contains $(387 \pm 6) \times 10^6$ $B\overline{B}$ events. We measure branching fractions ($\mathcal{B}$) and $C\!P$ asymmetries ($\mathcal{A}_{C\!P}$) for both $B^{0}\to K{}^{*0}\gamma$ and $B^{+}\to K{}^{*+}\gamma$ decays. The difference in $C\!P$ asymmetries ($\Delta \mathcal{A}_{C\!P}$) and the isospin asymmetry ($\Delta_{0+}$) between these neutral and charged channels are also measured. We obtain the following branching fractions and $C\!P$ asymmetries: $\mathcal{B} (B^{0} \to K{}^{*0}\gamma) = (4.14 \pm 0.10 \pm 0.11 ) \times 10^{-5}$, $\mathcal{B} (B^{+} \to K{}^{*+}\gamma) = (4.02 \pm 0.13 \pm 0.13 )\times 10^{-5}$, $\mathcal{A}_{C\!P} (B^{0} \to K{}^{*0}\gamma) = (-3.3 \pm 2.3 \pm 0.4 )\%$, and $\mathcal{A}_{C\!P} (B^{+} \to K{}^{*+}\gamma) = (-0.7 \pm 2.9 \pm 0.6 )\%$. The measured difference in $C\!P$ asymmetries is $\Delta \mathcal{A}_{C\!P} = (+2.6 \pm 3.8 \pm 0.7 )\%$, and the measured isospin asymmetry is $\Delta_{0+} = (+5.0 \pm 2.0 \pm 1.5 )\%$. The first uncertainties listed are statistical and the second are systematic. These results are consistent with world-average values and theory predictions.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024/019

We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlensing events KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly complex patterns with three or more anomaly features. These features cannot be adequately explained by a binary-lens (2L1S) model alone. However, the 2L1S model can effectively describe certain segments of the light curve. By incorporating an additional source into the modeling, we identified a comprehensive model that accounts for all the observed anomaly features. Bayesian analysis, based on constraints provided by lensing observables, indicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary systems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an early K-type main-sequence star with an M dwarf companion. The lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge, whereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In all events, the binary stars of the sources have similar magnitudes due to a detection bias favoring binary source events with a relatively bright secondary source star, which increases detection efficiency., Comment: 9 pages, 9 figures

Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such as spherical and stereographic spherical sliced Wasserstein distances, have recently been developed to address this need. These methods reduce the computational burden of optimal transport by slicing hyperspheres into one-dimensional projections, i.e., lines or circles. Concurrently, linear optimal transport has been proposed to embed distributions into \( L^2 \) spaces, where the \( L^2 \) distance approximates the optimal transport distance, thereby simplifying comparisons across multiple distributions. In this work, we introduce the Linear Spherical Sliced Optimal Transport (LSSOT) framework, which utilizes slicing to embed spherical distributions into \( L^2 \) spaces while preserving their intrinsic geometry, offering a computationally efficient metric for spherical probability measures. We establish the metricity of LSSOT and demonstrate its superior computational efficiency in applications such as cortical surface registration, 3D point cloud interpolation via gradient flow, and shape embedding. Our results demonstrate the significant computational benefits and high accuracy of LSSOT in these applications.

We study the intersection of a random geometric graph with an Erd\H{o}s-R\'enyi graph. Specifically, we generate the random geometric graph $G(n, r)$ by choosing $n$ points uniformly at random from $D=[0, 1]^2$ and joining any two points whose Euclidean distance is at most $r$. We let $G(n, p)$ be the classical Erd\H{o}s-R\'enyi graph, i.e. it has $n$ vertices and every pair of vertices is adjacent with probability $p$ independently. In this note we study $G(n, r, p):=G(n, r) \cap G(n, p)$. One way to think of this graph is that we take $G(n, r)$ and then randomly delete edges with probability $1-p$ independently. We consider the clique number, independence number, connectivity, Hamiltonicity, chromatic number, and diameter of this graph where both $p(n)\to 0$ and $r(n)\to 0$; the same model was studied by Kahle, Tian and Wang (2023) for $r(n)\to 0$ but $p$ fixed.

Evolved cool stars have three distinct evolutionary status: shell Hydrogen-burning (RGB), core Helium and shell Hydrogen burning (RC), and double shell burning (AGB). Asteroseismology can distinguish between the RC and the other status, but distinguishing RGB and AGB has been difficult seismically and spectroscopically. The precise boundaries of different status in the HR diagram have also been difficult to establish. In this article, we present a comprehensive catalog of asteroseismic evolutionary status, RGB and RC, for evolved red giants in the Kepler field. We carefully examine boundary cases to define the lower edge of the RC phase in radius and surface gravity. We also test different published asteroseisemic methods claiming to distinguish AGB and RGB stars against a sample where AGB candidates were selected using a spectrocopic identification method. We used six different seismic techniques to distinguish RC and RGB stars, and tested two proposed methods for distinguishing AGB and RGB stars. These status were compared with those inferred from spectroscopy. We present consensus evolutionary status for 18,784 stars out of the 30,337 red giants present in the Kepler data, including 11,516 stars with APOGEE spectra available. The agreement between seismic and spectroscopic classification is excellent for distinguishing RC stars, agreeing at the 94% level. Most disagreements can be traced to uncertainties in spectroscopic parameters, but some are caused by blends with background stars. We find a sharp lower boundary in surface gravity at log(g) = 2.99+/-0.01 for the RC and discuss the implications. We demonstrate that asteroseismic tools for distinguishing AGB and RGB stars are consistent with spectroscopic evolutionary status at near the RC but that the agreement between the different methods decreases rapidly as the star evolves., Comment: Article submitted to Astronomy&Astrophysics, 16 pages, 11 figures

Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search (GitHub link) to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over $10^{14}$ molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources.

A crucial question in active patient care is determining if a treatment is having the desired effect, especially when changes are subtle over short periods. We propose using inter-patient data to train models that can learn to detect these fine-grained changes within a single patient. Specifically, can a model trained on multi-patient scans predict subtle changes in an individual patient's scans? Recent years have seen increasing use of deep learning (DL) in predicting diseases using biomedical imaging, such as predicting COVID-19 severity using lung ultrasound (LUS) data. While extensive literature exists on successful applications of DL systems when well-annotated large-scale datasets are available, it is quite difficult to collect a large corpus of personalized datasets for an individual. In this work, we investigate the ability of recent computer vision models to learn fine-grained differences while being trained on data showing larger differences. We evaluate on an in-house LUS dataset and a public ADNI brain MRI dataset. We find that models pre-trained on clips from multiple patients can better predict fine-grained differences in scans from a single patient by employing contrastive learning., Comment: Under review at ISBI 2025 conference

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that minimizes the model's use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information minimized, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two state-of-the-art T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD). Approximately 4 years before MCI diagnosis, dMRI-based brain age yields better performance than T1w MRI-based brain ages in predicting transition from CN to MCI.

Proper quality control (QC) is time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorly trained machine learning models. Most efforts to reduce data QC time rely on outlier detection, which cannot capture every instance of algorithm failure. Thus, there is a need to visually inspect every output of data processing pipelines in a scalable manner. We design a QC pipeline that allows for low time cost and effort across a team setting for a large database of diffusion weighted and structural magnetic resonance images. Our proposed method satisfies the following design criteria: 1.) a consistent way to perform and manage quality control across a team of researchers, 2.) quick visualization of preprocessed data that minimizes the effort and time spent on the QC process without compromising the condition or caliber of the QC, and 3.) a way to aggregate QC results across pipelines and datasets that can be easily shared. In addition to meeting these design criteria, we also provide information on what a successful output should be and common occurrences of algorithm failures for various processing pipelines. Our method reduces the time spent on QC by a factor of over 20 when compared to naively opening outputs in an image viewer and demonstrate how it can facilitate aggregation and sharing of QC results within a team. While researchers must spend time on robust visual QC of data, there are mechanisms by which the process can be streamlined and efficient., Comment: 22 pages, 12 figures, 1 table, 6 supplemental figures