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Monitoring the Future (MTF) is designed to give such attention to substance use among the nation's youth and adults. It is an investigator-initiated study that originated with, and is conducted by, a team of research professors at the University of Michigan's Institute for Social Research. Since its onset in 1975, MTF has been funded continuously by the National Institute on Drug Abuse--one of the National Institutes of Health--under a series of peer reviewed, competitive research grants. This 2023 survey is the 49th consecutive national survey of 12th grade students and the 33rd national survey of 8th and 10th grade students (who were added to the study in 1991). MTF contains ongoing national surveys of both adolescents and adults in the United States. It provides the nation with a vital window into the important but often hidden problem behaviors of use of illegal drugs, alcohol, tobacco, and psychotherapeutic drugs used without a doctor's orders. Two of the major topics included in the present monograph are: (1) the prevalence and frequency of use of a great many substances, both licit and illicit, among U.S. secondary school students in 8th, 10th, and 12th grades; and (2) historical trends in use by students in those grades. Distinctions are made among important demographic subgroups in these populations based on gender, college plans, region of the country, population density, parent education, and race/ethnicity. MTF has demonstrated that key attitudes and beliefs about drug use are important determinants of usage trends, in particular the amount of risk to the user perceived to be associated with the various drugs and disapproval of using them; thus, those measures also are tracked over time, as are students' perceptions of certain relevant aspects of the social environment--in particular, perceived availability of each drug, peer norms about their use, and use by friends. Data on grade of first use, noncontinuation of use, trends in use in lower grades (based on retrospective reports), and intensity of use are also reported. [For "Monitoring the Future National Survey Results on Drug Use, 1975-2022: Secondary School Students," see ED627366.]

We present the most detailed spectrum of the intracluster light (ICL) in an individual cluster to date, the relaxed system RX J2129.7+0005, at $z\sim 0.234$. Using 15 broad-band, deep images observed with HST and JWST in the optical and the infrared, plus deep integral field spectroscopy from MUSE, we computed a total of 3696 ICL maps spanning the spectral range $\sim 0.4-5$ $\mu$m with our algorithm CICLE, a method that is extremely well suited to analyzing large samples of data in a fully automated way. We used both parametric and non-parametric approaches to fit the spectral energy distribution of the ICL and infer its physical properties, yielding a stellar mass $log_{10}(M_*/M_{\odot})$ between $11.5-12.7$ and an average age between $9.7-10.5$ Gyr, from CIGALE and Prospector results. This implies that the ICL in RX J2129.7+0005 is, on average, older than that of disturbed clusters, suggesting that the contribution from different stellar populations to the ICL are at play depending on the cluster's dynamical state. Coupled with X-ray observations of the hot gas distribution, we confirm the relaxed state of RX J2129.7+0005, showing clear signs of sloshing after a last major merger with a high-mass ratio satellite that could have happened $\sim 6.6$ Gyr ago in a relatively radial orbit. The presence of substructure in the ICL, such as shells, clouds with different densities and a certain degree of boxyness, and a clump, supports this scenario., Comment: 21 pages, 12 figures, 3 tables, accepted for publication in ApJ

The CHIPS R&D project focuses on development of low-cost water Cherenkov neutrino detectors through novel design strategies and resourceful engineering. This work presents an end-to-end DAQ solution intended for a recent 5 kt CHIPS prototype, which is largely based on affordable mass-produced components. Much like the detector itself, the presented instrumentation is composed of modular arrays that can be scaled up and easily serviced. A single such array can carry up to 30 photomultiplier tubes (PMTs) accompanied by electronics that generate high voltage in-situ and deliver time resolution of up to 0.69 ns. In addition, the technology is compatible with the White Rabbit timing system, which can synchronize its elements to within 100 ps. While deployment issues did not permit the presented DAQ system to operate beyond initial evaluation, the presented hardware and software successfully passed numerous commissioning tests that demonstrated their viability for use in a large-scale neutrino detector, instrumented with thousands of PMTs., Comment: 30 pages, 28 figures, submitted to MDPI Applied Sciences, Special Issue: Advanced Neutrino Detector Development and Application

Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer a non-negligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine learning classifier developed by Cabero et al. (2020), demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO-Virgo-KAGRA's fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LIGO-Virgo-KAGRA's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on a LIGO-Virgo-KAGRA mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals correctly identified.

Collective perception has received considerable attention as a promising approach to overcome occlusions and limited sensing ranges of vehicle-local perception in autonomous driving. In order to develop and test novel collective perception technologies, appropriate datasets are required. These datasets must include not only different environmental conditions, as they strongly influence the perception capabilities, but also a wide range of scenarios with different road users as well as realistic sensor models. Therefore, we propose the Synthetic COllective PErception (SCOPE) dataset. SCOPE is the first synthetic multi-modal dataset that incorporates realistic camera and LiDAR models as well as parameterized and physically accurate weather simulations for both sensor types. The dataset contains 17,600 frames from over 40 diverse scenarios with up to 24 collaborative agents, infrastructure sensors, and passive traffic, including cyclists and pedestrians. In addition, recordings from two novel digital-twin maps from Karlsruhe and T\"ubingen are included. The dataset is available at https://ekut-es.github.io/scope

We demonstrate a qubit-readout architecture where the dispersive coupling is entirely mediated by a kinetic inductance. This allows us to engineer the dispersive shift of the readout resonator independent of the qubit and resonator capacitances. We validate the pure kinetic coupling concept and demonstrate various generalized flux qubit regimes from plasmon to fluxon, with dispersive shifts ranging from 60 kHz to 2 MHz at the half-flux quantum sweet spot. We achieve readout performances comparable to conventional architectures with quantum state preparation fidelities of 99.7 % and 92.7 % for the ground and excited states, respectively, and below 0.1 % leakage to non-computational states., Comment: 20 pages, 10 figures

The COVID-19 pandemic disrupted nearly every aspect of the social and economic lives of individuals, families, and communities. It also highlighted and deepened existing racial/ethnic and socioeconomic disparities. Education, too, was negatively impacted by the pandemic. Research has provided ample evidence about the challenges related to learning loss, access, and mental health among students in elementary and secondary schools. Students' achievement, attendance, and grade-level progression declined substantially more than expected during the pandemic (Fuller et al. 2023; Strunk et al. 2023). And students of color, low-income students, and those who attended high-poverty schools experienced substantially slower growth in their test scores during the pandemic as compared to their pre-COVID growth (Peters et al. 2023). This report builds on this emerging evidence, focusing on trends in Rhode Island and bringing granular, longitudinal, student-level administrative data to bear on the following questions: (1) How has postsecondary enrollment and persistence changed during the COVID-19 pandemic? (2) Did students change the kinds of institutions in which they enrolled? (3) How do trends in enrollment and persistence compare across student subgroups (for instance, by race and socioeconomic status) and by institution type? and (4) How do students' high school experiences shape their postsecondary pathways?

The American Institutes for Research® (AIR®) partnered with Girls Who Code (GWC) to conduct an independent study evaluating the effectiveness of two GWC summer virtual programs, the Summer Immersion Program (SIP) and the Self-Paced Program (SPP), on promoting the pursuit of Computer Science (CS)-related postsecondary education for high school female and non-binary students. Employing a quasi-experimental design with inverse propensity score weighting technique, this study compared SIP and SPP participants with similar students who were waitlisted on the likelihood of majoring in a CS-related field. By analyzing publicly available National Student Clearinghouse data and GWC program records for the years 2020--2022, the study yielded two key findings. First, on average, both SIP and SPP participants were significantly more likely to major in a CS-related field (by 13.2 percentage points and 11.5 percentage points, respectively) than comparison students. Second, both SIP and SPP consistently demonstrated positive effects on majoring in a CS-related field across most of the student groups examined, including White, Black or African American, and Hispanic or Latinx students; students who are historically underrepresented in computing; and students with little to no prior CS knowledge. To enhance program outcomes, the study proposed to recommendations: (a) exploring supplementary feedback mechanisms to gather insights from participants, including program exit interviews, focus groups and long-term alumni surveys; and (b) establishing a comprehensive evaluation system to track the program's impact on various outcomes by including additional mid-term outcomes, such as enrollment in CS-related Advanced Placement courses and successful completion of these courses. This paper includes appendices detailing the technical aspects of the study and additional findings from In-Person SIP summer programs and a pilot program previously implemented by GWC before the introduction of the virtual programs.

The CAEL (Council for Adult and Experiential Learning) and the Western Interstate Commission for Higher Education (WICHE) announce the inaugural winners of the Credit for Prior Learning (CPL) Equity Awards, recognizing strategies that have been proven to put CPL in reach of key adult learner populations and/or increase their CPL credit-earning. Equity means different things in different contexts. For the purposes of the 2024 CPL Equity Awards, CAEL and WICHE identified the following populations that are of particular interest: (1) Black or African American adult learners; (2) Native American or Alaska Native adult learners; (3) Latino/a adult learners (of any race); (4) Low-income adult learners; and (5) Adult learners at community colleges. The diverse range of nominations submitted offered a range of CPL approaches designed to serve one or more of these important student populations. A panel of experts reviewed the nominations and selected a CPL Equity Champion, two CPL Equity Rising Stars, and four Honorable Mentions. (All received national recognition and a cash prize.) The report that follows distills key takeaways from all nominees that can be used across contexts and highlights the unique approaches of the award-winning institutions.

Quantum error mitigation (QEM) can recover accurate expectation values from a noisy quantum computer by trading off bias for variance, such that an averaged result is more accurate but takes longer to converge. Probabilistic error cancellation (PEC) stands out among QEM methods as an especially robust means of controllably eliminating bias. However, PEC often exhibits a much larger variance than other methods, inhibiting application to large problems for a given error rate. Recent analyses have shown that the variance of PEC can be reduced by not mitigating errors lying outside the causal lightcone of the desired observable. Here, we improve the lightcone approach by classically computing tighter bounds on how much each error channel in the circuit can bias the final result. This set of bounds, which we refer to as a "shaded lightcone," enables a more targeted application of PEC, improving the tradespace of bias and variance, while illuminating how the structure of a circuit determines the difficulty of error-mitigated computation. Although a tight shaded lightcone is exponentially hard to compute, we present an algorithm providing a practical benefit for some problems even with modest classical resources, leveraging the ease of evolving an error instead of the state or the observable. The algorithm reduces the runtime that would be needed to apply PEC for a target accuracy in an example 127-qubit Trotter circuit by approximately two orders of magnitude compared to standard lightcone-PEC, expanding the domain of problems that can be computed via direct application of PEC on noisy hardware.

Quantum signal processing (QSP) provides a systematic framework for implementing a polynomial transformation of a linear operator, and unifies nearly all known quantum algorithms. In parallel, recent works have developed randomized compiling, a technique that promotes a unitary gate to a quantum channel and enables a quadratic suppression of error (i.e., $\epsilon \rightarrow O(\epsilon^2)$) at little to no overhead. Here we integrate randomized compiling into QSP through Stochastic Quantum Signal Processing. Our algorithm implements a probabilistic mixture of polynomials, strategically chosen so that the average evolution converges to that of a target function, with an error quadratically smaller than that of an equivalent individual polynomial. Because nearly all QSP-based algorithms exhibit query complexities scaling as $O(\log(1/\epsilon))$ -- stemming from a result in functional analysis -- this error suppression reduces their query complexity by a factor that asymptotically approaches $1/2$. By the unifying capabilities of QSP, this reduction extends broadly to quantum algorithms, which we demonstrate on algorithms for real and imaginary time evolution, phase estimation, ground state preparation, and matrix inversion.

Sub-picosecond optical laser processing of metals is actively utilized for modification of a heated surface layer. But for deeper modification of different materials a laser in the hard x-ray range is required. Here, we demonstrate that a single 9-keV x-ray pulse from a free-electron laser can form a um-diameter cylindrical cavity with length of ~1 mm in LiF surrounded by shock-transformed material. The plasma-generated shock wave with TPa-level pressure results in damage, melting and polymorphic transformations of any material, including transparent and non-transparent to conventional optical lasers. Moreover, cylindrical shocks can be utilized to obtain a considerable amount of exotic high-pressure polymorphs. Pressure wave propagation in LiF, radial material flow, formation of cracks and voids are analyzed via continuum and atomistic simulations revealing a sequence of processes leading to the final structure with the long cavity. Similar results can be produced with semiconductors and ceramics, which opens a new pathway for development of laser material processing with hard x-ray pulses.

Decision making under uncertainty is challenging since the data-generating process (DGP) is often unknown. Bayesian inference proceeds by estimating the DGP through posterior beliefs about the model's parameters. However, minimising the expected risk under these posterior beliefs can lead to sub-optimal decisions due to model uncertainty or limited, noisy observations. To address this, we introduce Distributionally Robust Optimisation with Bayesian Ambiguity Sets (DRO-BAS) which hedges against uncertainty in the model by optimising the worst-case risk over a posterior-informed ambiguity set. We show that our method admits a closed-form dual representation for many exponential family members and showcase its improved out-of-sample robustness against existing Bayesian DRO methodology in the Newsvendor problem., Comment: 13 pages, 3 figures. Under review

Despite advancements in splicing detection, practitioners still struggle to fully leverage forensic tools from the literature due to a critical issue: deep learning-based detectors are extremely sensitive to their trained instances. Simple post-processing applied to evaluation images can easily decrease their performances, leading to a lack of confidence in splicing detectors for operational contexts. In this study, we show that a deep splicing detector behaves differently against unknown post-processes for different learned weights, even if it achieves similar performances on a test set from the same distribution as its training one. We connect this observation to the fact that different learnings create different latent spaces separating training samples differently. Our experiments reveal a strong correlation between the distributions of latent margins and the ability of the detector to generalize to post-processed images. We thus provide to the practitioner a way to build deep detectors that are more robust than others against post-processing operations, suggesting to train their architecture under different conditions and picking the one maximizing the latent space margin.

This letter reports the first complete observation of magnetized collisionless shock precursors formed through the compression of Biermann-battery magnetic fields in laser produced plasmas. At OMEGA, lasers produce a supersonic CH plasma flow which is magnetized with Biermann-battery magnetic fields. The plasma flow collides with an unmagnetized hydrogen gas jet plasma to create a magnetized shock precursor. The situation where the flowing plasma carries the magnetic field is similar to the Venusian bow shock. Imaging 2$\omega$ Thomson scattering confirms that the interaction is collisionless and shows density and temperature jumps. Proton radiographs have regions of strong deflections and FLASH magnetohydrodynamic (MHD) simulations show the presence of Biermann fields in the Thomson scattering region. Electrons are accelerated to energies of up to 100 keV in a power-law spectrum. OSIRIS particle-in-cell (PIC) simulations, initialized with measured parameters, show the formation of a magnetized shock precursor and corroborate the experimental observables., Comment: 6 pages, 5 figures

The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a balloon-borne telescope designed to survey star formation over cosmological time scales using intensity mapping in the 420 - 540 GHz frequency range. EXCLAIM uses a fully cryogenic telescope coupled to six on-chip spectrometers featuring kinetic inductance detectors (KIDs) to achieve high sensitivity, allowing for fast integration in dark atmospheric windows. The telescope receiver is cooled to $\approx$ 1.7 K by immersion in a superfluid helium bath and enclosed in a superfluid-tight shell with a meta-material anti-reflection coated silicon window. In addition to the optics and the spectrometer package, the receiver contains the magnetic shielding, the cryogenic segment of the spectrometer readout, and the sub-Kelvin cooling system. A three-stage continuous adiabatic demagnetization refrigerator (CADR) keeps the detectors at 100 mK while a $^4$He sorption cooler provides a 900 mK thermal intercept for mechanical suspensions and coaxial cables. We present the design of the EXCLAIM receiver and report on the flight-like testing of major receiver components, including the superfluid-tight receiver window and the sub-Kelvin coolers., Comment: 13 pages, 7 figures, 1 table

$^{180}$Hg is experimentally found to fission asymmetrically. This result was not expected as a naive fragment shell effects study would support the symmetric mode to be the most probable. In the present study we investigate both symmetric and asymmetric $^{180}$Hg fission modes at the mean field level using various multipole moment constraints. Potential energy surfaces are analysed in terms of shell effects that shape their topographies and connections to fragment shell effects are made. The non-occurrence of low energy symmetric fission is interpreted in terms of $^{90}$Zr fragment properties. Throughout this study a comparison with $^{264}$Fm and its symmetric doubly magic $^{132}$Sn fission fragments is done., Comment: 20 pages,7 figures

Domain adaptation is a widely used method in natural language processing (NLP) to improve the performance of a language model within a specific domain. This method is particularly common in the biomedical domain, which sees regular publication of numerous scientific articles. PubMed, a significant corpus of text, is frequently used in the biomedical domain. The primary objective of this study is to explore whether refining a pre-training dataset using specific quality metrics for scientific papers can enhance the performance of the resulting model. To accomplish this, we employ two straightforward journal impact metrics and conduct experiments by continually pre-training BERT on various subsets of the complete PubMed training set, we then evaluate the resulting models on biomedical language understanding tasks from the BLURB benchmark. Our results show that pruning using journal impact metrics is not efficient. But we also show that pre-training using fewer abstracts (but with the same number of training steps) does not necessarily decrease the resulting model's performance.

Grain boundaries are dominant imperfections in nanocrystalline materials that form a complex 3-dimensional (3D) network. Solute segregation to grain boundaries is strongly coupled to the grain boundary character, which governs the stability and macroscopic properties of nanostructured materials. Here, we develop a 3-dimensional transmission electron microscopy and atom probe tomography correlation framework to retrieve the grain boundary character and composition at the highest spatial resolution and chemical sensitivity by correlating four-dimensional scanning precession electron diffraction tomography (4D-SPED) and atom probe tomography (APT) on the same sample. We obtain the 3D grain boundary habit plane network and explore the preferential segregation of Cu and Si in a nanocrystalline Ni-W alloy. The correlation of structural and compositional information reveals that Cu segregates predominantly along high angle grain boundaries and incoherent twin boundaries, whereas Si segregation to low angle and incommensurate grain boundaries is observed. The novel full 3D correlative approach employed in this work opens up new possibilities to explore the 3D crystallographic and compositional nature of nanomaterials. This lays the foundation for both probing the true 3D structure-chemistry at the sub-nanometer scale and, consequentially, tailoring the macroscopic properties of advanced nanomaterials.

Outlier detection is a crucial analytical tool in various fields. In critical systems like manufacturing, malfunctioning outlier detection can be costly and safety-critical. Therefore, there is a significant need for explainable artificial intelligence (XAI) when deploying opaque models in such environments. This study focuses on manufacturing time series data from a German automotive supply industry. We utilize autoencoders to compress the entire time series and then apply anomaly detection techniques to its latent features. For outlier interpretation, we (i) adopt widely used XAI techniques to the autoencoder's encoder. Additionally, (ii) we propose AEE, Aggregated Explanatory Ensemble, a novel approach that fuses explanations of multiple XAI techniques into a single, more expressive interpretation. For evaluation of explanations, (iii) we propose a technique to measure the quality of encoder explanations quantitatively. Furthermore, we qualitatively assess the effectiveness of outlier explanations with domain expertise., Comment: 14 pages, 8 figures, accepted at TempXAI @ ECML-PKDD

A grand challenge in biology is to discover evolutionary traits - features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as phylogenetic tree). With the growing availability of image repositories in biology, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes. However, current prototype-based methods are mostly designed to operate over a flat structure of classes and face several challenges in discovering hierarchical prototypes, including the issue of learning over-specific features at internal nodes. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net). We empirically show that HComP-Net learns prototypes that are accurate, semantically consistent, and generalizable to unseen species in comparison to baselines on birds, butterflies, and fishes datasets. The code and datasets are available at https://github.com/Imageomics/HComPNet., Comment: 34 pages, 27 figures

By performing an ensemble of molecular dynamics simulations, the model-dependent ionisation state is computed for strongly interacting systems self-consistently. This is accomplished through a free energy minimisation framework based on the technique of thermodynamic integration. To illustrate the method, two simple models applicable to partially ionised hydrogen plasma are presented in which pair potentials are employed between ions and neutral particles. Within the models, electrons are either bound in the hydrogen ground state or distributed in a uniform charge-neutralising background. Particular attention is given to the transition between atomic gas and ionised plasma, where the effect of neutral interactions is explored beyond commonly used models in the chemical picture. Furthermore, pressure ionisation is observed when short range repulsion effects are included between neutrals. The developed technique is general, and we discuss the applicability to a variety of molecular dynamics models for partially ionised warm dense matter., Comment: 14 pages, 8 figures

The analysis of experimental results with Python often requires writing many code scripts which all need access to the same set of functions. In a common field of research, this set will be nearly the same for many users. The qspec Python package was developed to provide functions for physical formulas, simulations and data analysis routines widely used in laser spectroscopy and related fields. Most functions are compatible with numpy arrays, enabling fast calculations with large samples of data. A multidimensional linear regression algorithm enables a King plot analyses over multiple atomic transitions. A modular framework for constructing lineshape models can be used to fit large sets of spectroscopy data. A simulation module within the package provides user-friendly methods to simulate the coherent time-evolution of atoms in electro-magnetic fields without the need to explicitly derive a Hamiltonian., Comment: 10 pages, 4 figures

In this work we present the non-relativistic regime of the Hietarinta gravity theory and its extension to supergravity. At the bosonic level, we derive the non-relativistic version of the Hietarinta model by employing a contraction process and addressing the non-degeneracy of the invariant metric. To incorporate supersymmetry, we apply the Lie algebra expansion method to obtain the non-relativistic formulation of $\mathcal{N}=2$ Hietarinta supergravity. Our results reveal that the non-relativistic Hietarinta theory encompasses the extended Bargmann (super)gravity as a special case, yet it differs significantly from other existing non-relativistic (super)gravity models. Furthermore, we generalize our analysis to include a cosmological constant term in the non-relativistic Hietarinta (super)gravity action and examine its effects on the torsion structure., Comment: 30 pages

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed; and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery; during live surgery; and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision; and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://doi.org/10.5522/04/26531686.

Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poor prognosis. Molecular tumor boards accelerate access to effective therapies by tailoring treatment based on biomarkers, beyond cancer type. Unstructured data that requires manual curation hinders efficient use of biomarker profiling for therapy matching. This study explores the use of large language models (LLMs) to construct digital twins for precision medicine in RGTs. Our proof-of-concept digital twin system integrates clinical and biomarker data from institutional and published cases (n=21) and literature-derived data (n=655 publications with n=404,265 patients) to create tailored treatment plans for metastatic uterine carcinosarcoma, identifying options potentially missed by traditional, single-source analysis. LLM-enabled digital twins efficiently model individual patient trajectories. Shifting to a biology-based rather than organ-based tumor definition enables personalized care that could advance RGT management and thus enhance patient outcomes., Comment: 20 pages, 2 figures, 3 tables, supplements, original article

We derive and analyze a broad class of finite element methods for numerically simulating the stationary, low Reynolds number flow of concentrated mixtures of several distinct chemical species in a common thermodynamic phase. The underlying partial differential equations that we discretize are the Stokes$\unicode{x2013}$Onsager$\unicode{x2013}$Stefan$\unicode{x2013}$Maxwell (SOSM) equations, which model bulk momentum transport and multicomponent diffusion within ideal and non-ideal mixtures. Unlike previous approaches, the methods are straightforward to implement in two and three spatial dimensions, and allow for high-order finite element spaces to be employed. The key idea in deriving the discretization is to suitably reformulate the SOSM equations in terms of the species mass fluxes and chemical potentials, and discretize these unknown fields using stable $H(\textrm{div}) \unicode{x2013} L^2$ finite element pairs. We prove that the methods are convergent and yield a symmetric linear system for a Picard linearization of the SOSM equations, which staggers the updates for concentrations and chemical potentials. We also discuss how the proposed approach can be extended to the Newton linearization of the SOSM equations, which requires the simultaneous solution of mole fractions, chemical potentials, and other variables. Our theoretical results are supported by numerical experiments and we present an example of a physical application involving the microfluidic non-ideal mixing of hydrocarbons.

We present measurements on Fe$_2$O$_3$ amorphization and melt under laser-driven shock compression up to 209(10) GPa via time-resolved in situ x-ray diffraction. At 122(3) GPa, a diffuse signal is observed indicating the presence of a non-crystalline phase. Structure factors have been extracted up to 182(6) GPa showing the presence of two well-defined peaks. A rapid change in the intensity ratio of the two peaks is identified between 145(10) and 151(10) GPa, indicative of a phase change. Present DFT+$U$ calculations of temperatures along Fe$_2$O$_3$ Hugoniot are in agreement with SESAME 7440 and indicate relatively low temperatures, below 2000 K, up to 150 GPa. The non-crystalline diffuse scattering is thus consistent with the - as yet unreported - shock amorphization of Fe$_2$O$_3$ between 122(3) and 145(10) GPa, followed by an amorphous-to-liquid transition above 151(10) GPa. Upon release, a non-crystalline phase is observed alongside crystalline $\alpha$-Fe$_2$O$_3$. The extracted structure factor and pair distribution function of this release phase resemble those reported for Fe$_2$O$_3$ melt at ambient pressure., Comment: 11 pages, 4 figures, under review

The Necklace Splitting problem is a classical problem in combinatorics that has been intensively studied both from a combinatorial and a computational point of view. It is well-known that the Necklace Splitting problem reduces to the discrete Ham Sandwich problem. This reduction was crucial in the proof of PPA-completeness of the Ham Sandwich problem. Recently, Borzechowski, Schnider and Weber [ISAAC'23] introduced a variant of Necklace Splitting that similarly reduces to the $\alpha$-Ham Sandwich problem, which lies in the complexity class UEOPL but is not known to be complete. To make this reduction work, the input necklace is guaranteed to be n-separable. They showed that these necklaces can be fairly split in polynomial time and thus this subproblem cannot be used to prove UEOPL-hardness for $\alpha$-Ham Sandwich. We consider the more general unfair necklace splitting problem on n-separable necklaces, i.e., the problem of splitting these necklaces such that each thief gets a desired fraction of each type of jewels. This more general problem is the natural necklace-splitting-type version of $\alpha$-Ham Sandwich, and its complexity status is one of the main open questions posed by Borzechowski, Schnider and Weber. We show that the unfair splitting problem is also polynomial-time solvable, and can thus also not be used to show UEOPL-hardness for $\alpha$-Ham Sandwich., Comment: 34 pages, 14 figures

Given a JPEG pipeline (compression or decompression), this paper shows how to find the antecedent of a 8 x 8 block. If it exists, the block is compatible with the pipeline. For unaltered images, all blocks are always compatible with the original pipeline; however, for manipulated images, this is not always the case. This article demonstrates the potential of compatibility concepts for JPEG image forensics. It presents a solution to the main challenge of finding a block antecedent in a high-dimensional space. This solution relies on a local search algorithm with restrictions on the search space. We show that inpainting, copy-move, or splicing applied after a JPEG compression can be turned into three different mismatch problems and be detected. In particular, when the image is re-compressed after the modification, we can detect the manipulation if the quality factor of the second compression is higher than the first one. Our method can pinpoint forgeries down to the JPEG block with great detection power and without False Positive. We compare our method with two state-of-the-art models on localizing inpainted forgeries after a simple or a double compression. We show that under our working assumptions, it outperforms those models for most experiments.

Research into tonal music examines the structural relationships among sounds and how they align with our auditory perception. The exploration of integrating tonal cognition into sonic interaction design, particularly for practitioners lacking extensive musical knowledge, and developing an accessible software tool, remains limited. We report on a study of designers to understand the sound creation practices of industry experts and explore how infusing tonal music principles into a sound design tool can better support their craft and enhance the sonic experiences they create. Our study collected qualitative data through semi-structured individual and focus group interviews with six participants. We developed a low-fidelity prototype sound design tool that involves practical methods of functional harmony and interaction design discussed in focus groups. We identified four themes through reflexive thematic analysis: decision-making, domain knowledge and terminology, collaboration, and contexts in sound creation. Finally, we discussed design considerations for an accessible sonic interaction design tool that aligns auditory experience more closely with tonal cognition., Comment: To be published in: Proceedings of the 19th Audio Mostly Conference: A Conference on Explorations in Sonic Cultures, Milan, Italy, 2024

The problem of reliably detecting and geolocating objects of different classes in soft real-time is essential in many application areas, such as Search and Rescue performed using Unmanned Aerial Vehicles (UAVs). This research addresses the complementary problems of system contextual vision-based detector selection, allocation, and execution, in addition to the fusion of detection results from teams of UAVs for the purpose of accurately and reliably geolocating objects of interest in a timely manner. In an offline step, an application-independent evaluation of vision-based detectors from a system perspective is first performed. Based on this evaluation, the most appropriate algorithms for online object detection for each platform are selected automatically before a mission, taking into account a number of practical system considerations, such as the available communication links, video compression used, and the available computational resources. The detection results are fused using a method for building maps of salient locations which takes advantage of a novel sensor model for vision-based detections for both positive and negative observations. A number of simulated and real flight experiments are also presented, validating the proposed method., Comment: 42 pages, 19 figures

A charge current flowing through a three-dimensional topological insulator (3D-TI) can inject a spin current into a ferromagnet placed on the surface of the 3D-TI. Here, we report leveraging this mechanism to implement a nano-antenna that radiates an electromagnetic wave (1-10 GHz) into the surrounding medium efficiently despite being orders of magnitude smaller than the radiated free space wavelength. An alternating charge current of 1-10 GHz frequency is injected into a thin film of the 3D-TI Bi2Se3, resulting in the injection of an alternating spin current (of the same frequency) into a periodic array of cobalt nanomagnets deposited on the surface of the 3D-TI. This causes the magnetizations of the nanomagnets to oscillate in time and radiate electromagnetic waves in space, thereby implementing a nano-antenna. Because it is so much smaller than the free space wavelength, the nano-antenna is effectively a "point source" and yet it radiates anisotropically because of internal anisotropy. One can change the anisotropic radiation pattern by changing the direction of the injected alternating charge current, which implements beam steering. This would normally not have been possible in a conventional extreme sub-wavelength antenna.

The ultrafast control of magnetisation states in magnetically ordered systems is a key technological challenge for developing memory devices operable at picosecond timescales or terahertz (THz) frequencies. Despite significant efforts in ultrafast magnetic switching, convenient ultrafast readout of magnetic states remains under investigation. Currently, many experiments exploit magneto-optical effects for detecting magnetisation states, necessitating laser sources and optical components. However, energy-efficient and cost-effective electrical detection is preferred for practical applications. Unidirectional spin-Hall magnetoresistance (USMR) was proposed as a simple two-terminal geometry for the electrical detection of the magnetisation state in magnetic heterostructures. Here, we demonstrate that USMR is active at THz frequencies for picosecond time readouts, and can be initiated with light fields. We detect ultrafast USMR in various types of ferromagnet/heavy metal thin film heterostructures through THz second harmonic generation. Our studies, combined with temperature-dependent measurements of USMR, reveal a significant contribution from electron-magnon spin-flip scattering. This suggests possibilities for all-electrical detection of THz magnon modes., Comment: 20 pages, 5 figures

Twisted bilayer graphene (TBG) has elements in common with two paradigmatic examples of strongly correlated physics: quantum Hall physics and Hubbard physics. On one hand, TBG hosts flat topological Landau-level-like bands which exhibits quantum anomalous Hall effects. On the other hand, these bands have concentrated charge density and show signs of extensive entropy resembling local moments. The combination of these features leads to a question: can decoupled moments emerge in an isolated topological band, despite the lack of exponentially localized Wannier states? In this work, we answer the question affirmatively by proposing a minimal model for these bands in TBG that combines topology and charge concentration at the AA sites, leading to analytic wavefunctions that closely approximate those of the BM model with realistic parameters. Importantly, charge concentration also leads to Berry curvature concentration at $\Gamma$, generating a small parameter $s$ that yields analytic tractability. We show that, rather surprisingly, the model hosts nearly decoupled flavor moments without any extra degrees of freedom. These moments are non-local due to topology-enforced power-law tails, yet have parametrically small overlap. We further develop a diagrammatic expansion in which the self energy can be computed exactly to leading order in $s^2$ in the fluctuating moment regime. At charge neutrality, we find a "Mott semimetal", with large flavor entropy and a Mott gap everywhere in the BZ except for the vicinity of the $\Gamma$ point. Away from neutrality, the Mott semimetal gaps out in a spectrally imbalanced manner, with one Mott band having zero $Z_k$ at the $\Gamma$ point. The model accurately reproduces results from finite temperature thermodynamic measurements, leads to new experimental predictions, and resolves the problem of the emergence of Hubbard physics in isolated topological bands., Comment: 22 pages

Epigenetics plays a crucial role in understanding the underlying molecular processes of several types of cancer as well as the determination of innovative therapeutic tools. To investigate the complex interplay between genetics and environment, we develop a novel procedure to identify differentially methylated probes (DMPs) among cases and controls. Statistically, this translates to an ultra high-dimensional testing problem with sparse signals and an inherent grouping structure. When the total number of variables being tested is massive and typically exhibits some degree of dependence, existing group-wise multiple comparisons adjustment methods lead to inflated false discoveries. We propose a class of Correlation-Adjusted Simultaneous Testing (CAST) procedures incorporating the general dependence among probes within and between genes to control the false discovery rate (FDR). Simulations demonstrate that CASTs have superior empirical power while maintaining the FDR compared to the benchmark group-wise. Moreover, while the benchmark fails to control FDR for small-sized grouped correlated data, CAST exhibits robustness in controlling FDR across varying group sizes. In bladder cancer data, the proposed CAST method confirms some existing differentially methylated probes implicated with the disease (Langevin, et. al., 2014). However, CAST was able to detect novel DMPs that the previous study (Langevin, et. al., 2014) failed to identify. The CAST method can accurately identify significant potential biomarkers and facilitates informed decision-making aligned with precision medicine in the context of complex data analysis., Comment: 25 pages, 4 figures, 2 tables

A localized Zeeman field, intensified at heterostructure interfaces, could play a crucial role in a broad area including spintronics and unconventional superconductors. Conventionally, the generation of a local Zeeman field is achieved through magnetic exchange coupling with a magnetic material. However, magnetic elements often introduce defects, which could weaken or destroy superconductivity. Alternatively, the coupling between a superconductor with strong spin-orbit coupling and a non-magnetic chiral material could serve as a promising approach to generate a spin active interface. In this study, we leverage an interface superconductor, namely induced superconductivity in noble metal surface states, to probe the spin active interface. Our results unveil an enhanced interface Zeeman field, which selectively closes the surface superconducting gap while preserving the bulk superconducting pairing. The chiral material, i.e. trigonal tellurium, also induces Andreev bound states (ABS) exhibiting spin polarization. The field dependence of ABS manifests a substantially enhanced interface Land\'e g-factor (g_eff ~ 12), thereby corroborating the enhanced interface Zeeman energy., Comment: 27 pages, 11 figures