Miracle-dz/UniFAR-FTUs huggingface dataset:
"You don't know what you don't know": A qualitative study of informational needs of patients, family members, and living donors to inform transplant system metrics.

INTRODUCTION Informational needs and potential use of transplant metrics, especially among patients, remain understudied and a critical component of the transplant community's commitment to patient-centered care. We sought to understand the perspectives and needs of patients, family members/caregivers, living donors, and deceased donor family members. METHODS We examined decision-making experiences and perspectives on the needs of these stakeholder groups for data about the national transplant system among 58 participants of 14 focus groups and 6 interviews. RESULTS Three major themes emerged: 1) informational priorities and unmet needs (transplantation system processes, long-term outcomes data, prelisting data, patient-centered outcomes, and ability to compare centers and regions); 2) challenges obtaining relevant and trustworthy information (patient burden and effort, challenges with medical jargon, and difficulty finding trustworthy information); and 3) burden of facing the unknown (stress and anxiety leading to difficulty processing information, challenges facing the transplant journey when you "don't know what you don't know"). CONCLUSION Patient, family member, and living donor participation in shared decision-making has been limited by inadequate access to patient-centered information. New metrics and patient-facing data presentations should address these content gaps using best practices to improve understanding and support shared decision-making.
La Plata Radiocarbon Laboratory Liquid Scintillation Counting and Inter-Laboratory Check Samples

The development and improvement of benzene synthesis for radiocarbon dating by liquid scintillation counting was started at LATYR in 1981. The basic technique employed was described by Polach, Gower & Frazer (1972). The line of benzene synthesis is similar to that used by Coleman et al (1972). The pretreatment of the samples and transformation to CO2 has been previously described (Figini et al, 1984). The conditions of measurement were partially determined according to Pearson (1979). Results of measurements of interlaboratory check samples using the technique described are given.
Asteroid bombardment and the core of Theia as possible sources for the Earth's late veneer component

The silicate Earth contains Pt‐group elements in roughly chondritic relative ratios, but with absolute concentrations <1% chondrite. This veneer implies addition of chondrite‐like material with 0.3–0.7% mass of the Earth's mantle or an equivalent planet‐wide thickness of 5–20 km. The veneer thickness, 200–300 m, within the lunar crust and mantle is much less. One hypothesis is that the terrestrial veneer arrived after the moon‐forming impact within a few large asteroids that happened to miss the smaller Moon. Alternatively, most of terrestrial veneer came from the core of the moon‐forming impactor, Theia. The Moon then likely contains iron from Theia's core. Mass balances lend plausibility. The lunar core mass is ∼1.6 × 1021 kg and the excess FeO component in the lunar mantle is 1.3–3.5 × 1021 kg as Fe, totaling 3–5 × 1021 kg or a few percent of Theia's core. This mass is comparable to the excess Fe of 2.3–10 × 1021 kg in the Earth's mantle inferred from the veneer component. Chemically in this hypothesis, Fe metal from Theia's core entered the Moon‐forming disk. H2O and Fe2O3 in the disk oxidized part of the Fe, leaving the lunar mantle near a Fe‐FeO buffer. The remaining iron metal condensed, gathered Pt‐group elements eventually into the lunar core. The silicate Moon is strongly depleted in Pt‐group elements. In contrast, the Earth's mantle contained excess oxidants, H2O and Fe2O3, which quantitatively oxidized the admixed Fe from Theia's core, retaining Pt‐group elements. In this hypothesis, asteroid impacts were relatively benign with ∼1 terrestrial event that left only thermophile survivors.
Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances

Score-based generative modeling with probability flow ordinary differential equations (ODEs) has achieved remarkable success in a variety of applications. While various fast ODE-based samplers have been proposed in the literature and employed in practice, the theoretical understandings about convergence properties of the probability flow ODE are still quite limited. In this paper, we provide the first non-asymptotic convergence analysis for a general class of probability flow ODE samplers in 2-Wasserstein distance, assuming accurate score estimates. We then consider various examples and establish results on the iteration complexity of the corresponding ODE-based samplers.
MelNet: A Real-Time Deep Learning Algorithm for Object Detection

In this study, a novel deep learning algorithm for object detection, named MelNet, was introduced. MelNet underwent training utilizing the KITTI dataset for object detection. Following 300 training epochs, MelNet attained an mAP (mean average precision) score of 0.732. Additionally, three alternative models -YOLOv5, EfficientDet, and Faster-RCNN-MobileNetv3- were trained on the KITTI dataset and juxtaposed with MelNet for object detection. The outcomes underscore the efficacy of employing transfer learning in certain instances. Notably, preexisting models trained on prominent datasets (e.g., ImageNet, COCO, and Pascal VOC) yield superior results. Another finding underscores the viability of creating a new model tailored to a specific scenario and training it on a specific dataset. This investigation demonstrates that training MelNet exclusively on the KITTI dataset also surpasses EfficientDet after 150 epochs. Consequently, post-training, MelNet's performance closely aligns with that of other pre-trained models.
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?

There is increasing interest in employing large language models (LLMs) as cognitive models. For such purposes, it is central to understand which cognitive properties are well-modeled by LLMs, and which are not. In this work, we study the biases of LLMs in relation to those known in children when solving arithmetic word problems. Surveying the learning science literature, we posit that the problem-solving process can be split into three distinct steps: text comprehension, solution planning and solution execution. We construct tests for each one in order to understand which parts of this process can be faithfully modeled by current state-of-the-art LLMs. We generate a novel set of word problems for each of these tests, using a neuro-symbolic method that enables fine-grained control over the problem features. We find evidence that LLMs, with and without instruction-tuning, exhibit human-like biases in both the text-comprehension and the solution-planning steps of the solving process, but not during the final step which relies on the problem's arithmetic expressions (solution execution).
Reduction for $(n+2)$-rigid subcategories in extriangulated categories

In this work we study how to extend the concept of \emph{"reduction,"} given for rigid and functorially finite subcategories in an extriangulated category $\mathcal{C}$, to $(n+2)$-rigid ones. We define the reduction of such subcategories as the intersection of orthogonal complements when certain orthogonal condition is satisfied and we prove that this reduction depends mainly on the subcategory itself beyond the type of extriangulated category for which belongs to. Specifically, we show that some results proven for Frobenius extriangulated categories can be carried to extriangulated categories in general. We also study its properties among we can mention: weakly idempotent completeness, existence of enough $\mathbb{E}$-projectives and $\mathbb{E}$-injectives, and conditions to be Frobenius. That generalization covers the usual case and for the general case (for $n\geq 0$), we provide several examples. Finally, we extend a well-known result given for the reduction in stably $2$-Calabi Yau Frobenius extriangulated categories related with $2$-cluster tilting subcategories.
Heavy flavor production under a strong magnetic field

The magnetic field created in high energy nuclear collisions will affect the dynamical processes in the QCD medium, especially the heavy quark production that happens in the initial stage of the collisions. We calculate in a strong magnetic field the heavy quark production cross section for the elementary process gg {\rightarrow} Q{\bar Q} at leading order and the corresponding transverse momentum distribution in nucleus-nucleus collisions. In comparison to the QED process, the heavy quark production is dominated by the unique QCD channel with gluon self-interaction. Due to the dimension reduction of quark phase space in a strong magnetic field, the production is concentrated in a very narrow energy region above the threshold. Since the translation invariance is broken, the production becomes anisotropic in magnetic field.
A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification

Large language models (LLMs) have demonstrated rapid progress across a wide array of domains. Owing to the very large number of parameters and training data in LLMs, these models inherently encompass an expansive and comprehensive materials knowledge database, far exceeding the capabilities of individual researcher. Nonetheless, devising methods to harness the knowledge embedded within LLMs for the design and discovery of novel materials remains a formidable challenge. We introduce a general approach for addressing materials classification problems, which incorporates LLMs, prompt engineering, and deep learning. Utilizing a dataset of metallic glasses as a case study, our methodology achieved an improvement of up to 463% in prediction accuracy compared to conventional classification models. These findings underscore the potential of leveraging textual knowledge generated by LLMs for materials especially in the common situation where datasets are sparse, thereby promoting innovation in materials discovery and design.
Molecular Bubble and Outflow in S Mon Revealed by Multiband Data Sets

We identify a molecular bubble, and study the star formation and its feedback in the S Mon region, using multiple molecular lines, young stellar objects (YSOs), and infrared data. We revisit the distance to S Mon, ∼722 ± 9 pc, using Gaia Data Release 3 parallaxes of the associated Class II YSOs. The bubble may be mainly driven by a massive binary system (namely 15 Mon), the primary of which is an O7V-type star. An outflow is detected in the shell of the bubble, suggesting ongoing star formation activities in the vicinity of the bubble. The total wind energy of the massive binary star is 3 orders of magnitude higher than the sum of the observed turbulent energy in the molecular gas and the kinetic energy of the bubble, indicating that stellar winds help to maintain the turbulence in the S Mon region and drive the bubble. We conclude that the stellar winds of massive stars have an impact on their surrounding environment.
Laboratory Setup for Testing Low-Frequency Disturbances of Power Quality

Low-frequency disturbances of power quality are one of the most common disturbances in the power grid. These disturbances are most often the result of the impact of power electronic and energy-saving devices, the number of which is increasing significantly in the power grid. Due to the simultaneous operation of various types of loads in the power grid, various types of simultaneous disturbances of power quality occur, such as voltage fluctuations and distortions. Therefore, there is a need to analyze this type of simultaneous interaction. For this purpose, a special and complementary laboratory setup has been prepared, which allows for the examination of actual states occurring in modern power networks. Selected research results are presented for this laboratory setup, which determine its basic properties. Possible applications and possibilities of the laboratory setup are presented from the point of view of current challenges.
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition

Continual learning (CL) is the research field that aims to build machine learning models that can accumulate knowledge continuously over different tasks without retraining from scratch. Previous studies have shown that pre-training graph neural networks (GNN) may lead to negative transfer (Hu et al., 2020) after fine-tuning, a setting which is closely related to CL. Thus, we focus on studying GNN in the continual graph learning (CGL) setting. We propose the first continual graph learning benchmark for spatio-temporal graphs and use it to benchmark well-known CGL methods in this novel setting. The benchmark is based on the N-UCLA and NTU-RGB+D datasets for skeleton-based action recognition. Beyond benchmarking for standard performance metrics, we study the class and task-order sensitivity of CGL methods, i.e., the impact of learning order on each class/task's performance, and the architectural sensitivity of CGL methods with backbone GNN at various widths and depths. We reveal that task-order robust methods can still be class-order sensitive and observe results that contradict previous empirical observations on architectural sensitivity in CL.
Optimising O-to-U Band Transmission Using Fast ISRS Gaussian Noise Numerical Integral Model

We model the transmission of ultrawideband (UWB) signals, including wavelength-dependent fibre parameters: dispersion, nonlinear coefficient and effective fibre core area. To that end, the inter-channel stimulated Raman scattering Gaussian noise (ISRS GN) integral model is extended to include these parameters. The integrals involved in this frequency-domain model are numerically solved in hyperbolic coordinates using a Riemann sum. The model implementation is designed to work on parallel graphics processing units (GPUs) and is optimised for fast computational time. The model is valid for Gaussian-distributed signals and is compared with the split-step Fourier method (SSFM), for transmission over standard single-mode fibre (SSMF) in the O-band (wavelengths around the zero-dispersion wavelength), showing reasonable agreement. Further, we demonstrated SNR evaluation over an 80 km SSFM single-span transmission using 589$\times$96 GBaud channels, corresponding to almost 59 THz optical bandwidth, fully populating the O, E, S, C, L and U bands (1260$-$1675 nm). The SNR evaluation is completed in just 3.6 seconds using four Nvidia V100 16GB PCIe GPUs. Finally, we used this model to find the optimum launch power profile for this system achieving 747 Tbps of potential throughput over 80 km fibre and demonstrating its suitability for UWB optimisation routines.
SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization

Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations. Traditional language models often overlook the distinct features of these dialogues by treating them as regular text. In this paper, we propose a speaker-enhanced pre-training method for long dialogue summarization, which leverages the inherent structure of multiple-turn dialogues. To support our study, we curate a diverse dataset that includes transcripts from real-world scenarios, movie or TV show transcripts, and dialogues generated by a Large Language Model. We then perform a pre-training, which encompasses the detection of speaker changes, and masked utterance generation. Experimental results of fine-tuned models demonstrate that our model achieves state-of-the-art performance on downstream benchmarks with long context, surpassing baseline models and highlighting the effectiveness of our approach. Our findings highlight the importance of curating pre-training datasets that exhibit diversity and variations in length distribution to ensure effective alignment with downstream datasets.
EchoWrist: Continuous Hand Pose Tracking and Hand-Object Interaction Recognition Using Low-Power Active Acoustic Sensing On a Wristband

Our hands serve as a fundamental means of interaction with the world around us. Therefore, understanding hand poses and interaction context is critical for human-computer interaction. We present EchoWrist, a low-power wristband that continuously estimates 3D hand pose and recognizes hand-object interactions using active acoustic sensing. EchoWrist is equipped with two speakers emitting inaudible sound waves toward the hand. These sound waves interact with the hand and its surroundings through reflections and diffractions, carrying rich information about the hand's shape and the objects it interacts with. The information captured by the two microphones goes through a deep learning inference system that recovers hand poses and identifies various everyday hand activities. Results from the two 12-participant user studies show that EchoWrist is effective and efficient at tracking 3D hand poses and recognizing hand-object interactions. Operating at 57.9mW, EchoWrist is able to continuously reconstruct 20 3D hand joints with MJEDE of 4.81mm and recognize 12 naturalistic hand-object interactions with 97.6% accuracy.
Robustly overfitting latents for flexible neural image compression

Neural image compression has made a great deal of progress. State-of-the-art models are based on variational autoencoders and are outperforming classical models. Neural compression models learn to encode an image into a quantized latent representation that can be efficiently sent to the decoder, which decodes the quantized latent into a reconstructed image. While these models have proven successful in practice, they lead to sub-optimal results due to imperfect optimization and limitations in the encoder and decoder capacity. Recent work shows how to use stochastic Gumbel annealing (SGA) to refine the latents of pre-trained neural image compression models. We extend this idea by introducing SGA+, which contains three different methods that build upon SGA. Further, we give a detailed analysis of our proposed methods, show how they improve performance, and show that they are less sensitive to hyperparameter choices. Besides, we show how each method can be extended to three- instead of two-class rounding. Finally, we show how refinement of the latents with our best-performing method improves the compression performance on the Tecnick dataset and how it can be deployed to partly move along the rate-distortion curve.
The Metallicity–Electron Temperature Relationship in H ii Regions

H ii region heavy-element abundances throughout the Galactic disk provide important constraints to theories of the formation and evolution of the Milky Way. In LTE, radio recombination line (RRL) emission and free–free continuum emission are accurate extinction-free tracers of the H ii region electron temperature. Since metals act as coolants in H ii regions via the emission of collisionally excited lines, the electron temperature is a proxy for metallicity. Shaver et al. found a linear relationship between metallicity and electron temperature with little scatter. Here we use CLOUDY H ii region simulations to (1) investigate the accuracy of using RRLs to measure the electron temperature and (2) explore the metallicity–electron temperature relationship. We model 135 H ii regions with different ionizing radiation fields, densities, and metallicities. We find that electron temperatures derived under the assumption of LTE are about 20% systematically higher owing to non-LTE effects, but overall LTE is a good assumption for centimeter-wavelength RRLs. Our CLOUDY simulations are consistent with the Shaver et al. metallicity–electron temperature relationship, but there is significant scatter since earlier spectral types or higher electron densities yield higher electron temperatures. Using RRLs to derive electron temperatures assuming LTE yields errors in the predicted metallicity as large as 10%. We derive correction factors for log(O/H) + 12 in each CLOUDY simulation. For lower metallicities the correction factor depends primarily on the spectral type of the ionizing star and ranges from 0.95 to 1.10, whereas for higher metallicities the correction factor depends on the density and is between 0.97 and 1.05.
Multi-zone trapped-ion qubit control in an integrated photonics QCCD device

Multiplexed operations and extended coherent control over multiple trapping sites are fundamental requirements for a trapped-ion processor in a large scale architecture. Here we demonstrate these building blocks using a surface electrode trap with integrated photonic components which are scalable to larger numbers of zones. We implement a Ramsey sequence using the integrated light in two zones, separated by 375 $\mu$m, performing transport of the ion from one zone to the other in 200 $\mu$s between pulses. In order to achieve low motional excitation during transport we developed techniques to measure and mitigate the effect of the exposed dielectric surfaces used to deliver the integrated light to the ion. We also demonstrate simultaneous control of two ions in separate zones with low optical crosstalk, and use this to perform simultaneous spectroscopy to correlate field noise between the two sites. Our work demonstrates the first transport and coherent multi-zone operations in integrated photonic ion trap systems, forming the basis for further scaling in the trapped-ion QCCD architecture.
Floquet-Bloch Theory for Nonperturbative Response to a Static Drive

We develop the Floquet-Bloch theory of noninteracting fermions on a periodic lattice in the presence of a constant electric field. As long as the field lies along a reciprocal lattice vector, time periodicity of the Bloch Hamiltonian is inherited from the evolution of momentum in the Brillouin zone. The corresponding Floquet quasienergies yield the Wannier-Stark ladder with interband couplings included to all orders. These results are compared to perturbative results where the lowest-order interband correction gives the field-induced polarization shift in terms of the electric susceptibility. Additionally, we investigate electronic transport by coupling the system to a bath within the Floquet-Keldysh formalism. We then study the breakdown of the band-projected theory from the onset of interband contributions and Zener resonances in the band-resolved currents. In particular, we consider the transverse quantum-geometric response in two spatial dimensions due to the Berry curvature. In the strong-field regime, the semiclassical theory predicts a plateau of the geometric response as a function of field strength. Here, we scrutinize the conditions under which the semiclassical results continue to hold in the quantum theory.
On Fourier transforms of fractal measures on the parabola

Let $s \in [0,1]$ and $t \in [0,\min\{3s,s + 1\})$. Let $\sigma$ be a Borel measure supported on the parabola $\mathbb{P} = \{(x,x^{2}) : x \in [-1,1]\}$ satisfying the $s$-dimensional Frostman condition $\sigma(B(x,r)) \leq r^{s}$. Answering a question of the first author, we show that there exists an exponent $p = p(s,t) \geq 1$ such that $$\|\hat{\sigma}\|_{L^{p}(B(R))} \leq C_{s,t}R^{(2 - t)/p}, \qquad R \geq 1. $$ Moreover, when $s \geq 2/3$ and $t \in [0,s + 1)$, the previous inequality is true for $p \geq 6$. We also obtain the following fractal geometric counterpart of the previous results. If $K \subset \mathbb{P}$ is a Borel set with $\dim_{\mathrm{H}} K = s \in [0,1]$, and $n \geq 1$ is an integer, then $$ \dim_{\mathrm{H}}(nK) \geq \min\{3s - s \cdot 2^{-(n - 2)},s + 1\}. $$