Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinations of the 12C/13C Ratio for the Secondary Stars of AE Aquarii, SS Cygni, and RU Pegasi

We present new moderate-resolution near-infrared spectroscopy of three CVs obtained using GNIRS on Gemini-North. These spectra covered three 13CO bandheads found in the K-band, allowing us to derive the isotopic abundance ratios for carbon. We find small 12C/13C ratios for all three donor stars. In addition, these three objects show carbon deficits, with AE Aqr being the most extreme ([C/Fe] = −1.4). This result confirms the conjecture that the donor stars in some long-period CVs have undergone considerable nuclear evolution prior to becoming semi-contact binaries. In addition to the results for carbon, we find that the abundance of sodium is enhanced in these three objects, and the secondary stars in both RU Peg and SS Cyg suffer magnesium deficits. Explaining such anomalies appears to require higher mass progenitors than commonly assumed for the donor stars of CVs.
A review on photo-mediated ultrasound therapy

Photo-mediated ultrasound therapy (PUT) is a novel therapeutic technique based on the combination of ultrasound and laser. The underlying mechanism of PUT is the enhanced cavitation effect inside blood vessels. The enhanced cavitation activity can result in bio-effects such as reduced perfusion in microvessels. The reduced perfusion effect in microvessels in the eye has the potential to control the progression of eye diseases such as diabetic retinopathy and age-related macular degeneration. Several in vivo studies have demonstrated the feasibility of PUT in removing microvasculature in the eye using rabbit eye model and vasculature in the skin using rabbit ear model. Numerical studies using a bubble dynamics model found that cavitation is enhanced during PUT due to the dramatic increase in size of air/vapor nuclei in blood. In addition, the study conducted to model cavitation dynamics inside a blood vessel during PUT found stresses induced on the vessel wall during PUT are higher than that at normal physiological levels, which may be responsible for bio-effects. The concentration of vasodilators such as nitric oxide and prostacyclin were also found to be affected during PUT in an in vitro study, which may limit blood perfusion in vessels. The main advantage of PUT over conventional techniques is non-invasive, precise, and selective removal of microvessels with high efficiency at relatively low energy levels of ultrasound and laser, without affecting the nearby structures. However, the main limitation of vessel rupture/hemorrhage needs to be overcome through the development of real-time monitoring of treatment effects during PUT. In addition to the application in removing microvessels, PUT-based techniques were also explored in treating other diseases. Studies have found a combination of ultrasound and laser to be effective in removing blood clots inside veins, which has the potential to treat deep-vein thrombosis. The disruption of atherosclerotic plaque using combined ultrasound and laser was also tested, and the feasibility was demonstrated.
Development of multisensory processing in ferret parietal cortex

It is well known that the nervous system adjusts itself to its environment during development. Although a great deal of effort has been directed towards understanding the developmental processes of the individual sensory systems (e.g., vision, hearing, etc.), only one major study has examined the maturation of multisensory processing in cortical neurons. Therefore, the present investigation sought to evaluate multisensory development in a different cortical region and species. Using multiple single‐unit recordings in anaesthetised ferrets (n = 18) of different ages (from postnatal day 80 to 300), we studied the responses of neurons from the rostral posterior parietal (PPr) area to presentations of visual, tactile and combined visual‐tactile stimulation. The results showed that multisensory neurons were infrequent at the youngest ages (pre‐pubertal) and progressively increased through the later ages. Significant response changes that result from multisensory stimulation (defined as multisensory integration [MSI]) were observed in post‐pubertal adolescent animals, and the magnitude of these integrated responses also increased across this age group. Furthermore, non‐significant multisensory response changes were progressively increased in adolescent animals. Collectively, at the population level, MSI was observed to shift from primarily suppressive levels in infants to increasingly higher levels in later stages. These data indicate that, like the unisensory systems from which it is derived, multisensory processing shows developmental changes whose specific time course may be regionally and species‐dependent.
Preliminary efficacy and predictors of response to a remotely-delivered symptom self-management program for persistent symptoms after concussion

ABSTRACT Background More than a quarter of adults with concussion endure prolonged symptoms of >3 months. We developed the Concussion Education Self-Management program to help people manage persisting symptoms. Here, we assess feasibility, preliminary efficacy, and correlates of response. Methods N = 80 adults participated in the program; ages ranged from 18 to 65 years and time post-injury ranged from 6 months to 18 years. Weekly sessions, delivered remotely and in groups, comprised education and strategies for management of cognitive, emotional, and physical symptoms. Primary outcome: Confidence to self-manage symptoms. Secondary outcomes: Quality of life; mood/anxiety/stress. Predictors of response: Self-reported cognitive, emotional and physical symptoms at intake. Results Pre- to post-program improvements were observed in confidence to self-manage, p < 0.03; quality of life, p < 0.001; depression, p < 0.001; anxiety, p < 0.001; and stress, p < 0.001. Considering confidence to self-manage, those with fewer cognitive and physical symptoms benefitted more (p’s < 0.0005 and p < 0.01, respectively). Discussion This program shows promise for improving self-management of prolonged symptoms. Those with high symptom burden may need extra sessions to benefit. This is a cost-effective and scalable program that can reach people regardless of geographic location or impediments to travel.
Two ripples in a pond: The subtleties of mapping observations in localizing premature ventricular complex sites

We read with interest the study by Arps et al. 1 and technique using ripple mapping that can be used to differentiate successful premature ventricular complex (PVC) sites with high accuracy. Ripple mapping (CARTO ‐ 3 © ; Biosense Webster Inc) is a visual dynamic display of activation and voltage that does not require local activation time annotation or a window of interest as it displays the entire electrogram (EGM) relative to a fiduciary stable time or reference point. In this study, the earliest ripple signal (ERS) that was concordant to the earliest activation (EA) chamber predicted ablation success, compared to discordance. EA was defined using unipolar − dV/dT maximal . We congratulate the authors for highlighting this technique and suggest annotating all local activation times to first bipolar deflection along with assessment of the earliest sites with unipolar − dV/dT relative to the timing of the QRS. Based on the study by Higuchi et al., 2 we have also found that when the first bipolar deflection correlates with unipolar dV/dT, it predicts success from an endocardial exit site (median of 3.5 ms). However, intramural sites are characterized by a delay from the first bipolar deflection to the unipolar − dV/dT (median 25.5 ms). 2 Finally and importantly, this delay was also observed in failed cases (median 12.5 ms), however, the discriminating factor was that the first bipolar deflection was late to surface QRS in these cases as opposed to successful intramural cases where the first bipolar deflection was early. We wish to comment on two nuanced points regarding this concept. The first specific nuance is related to the earliest local bipolar EGM ‐ unipolar − dV/dT discrepancy, but yet success, relating to specific anatomical scenarios such as the coronary cusps compared to typical close and concordant timing of bipolar and unipolar
Diagnostic management of acute pulmonary embolism: a prediction model based on a patient data meta-analysis

Abstract Aims Risk stratification is used for decisions regarding need for imaging in patients with clinically suspected acute pulmonary embolism (PE). The aim was to develop a clinical prediction model that provides an individualized, accurate probability estimate for the presence of acute PE in patients with suspected disease based on readily available clinical items and D-dimer concentrations. Methods and results An individual patient data meta-analysis was performed based on sixteen cross-sectional or prospective studies with data from 28 305 adult patients with clinically suspected PE from various clinical settings, including primary care, emergency care, hospitalized and nursing home patients. A multilevel logistic regression model was built and validated including ten a priori defined objective candidate predictors to predict objectively confirmed PE at baseline or venous thromboembolism (VTE) during follow-up of 30 to 90 days. Multiple imputation was used for missing data. Backward elimination was performed with a P-value <0.10. Discrimination (c-statistic with 95% confidence intervals [CI] and prediction intervals [PI]) and calibration (outcome:expected [O:E] ratio and calibration plot) were evaluated based on internal-external cross-validation. The accuracy of the model was subsequently compared with algorithms based on the Wells score and D-dimer testing. The final model included age (in years), sex, previous VTE, recent surgery or immobilization, haemoptysis, cancer, clinical signs of deep vein thrombosis, inpatient status, D-dimer (in µg/L), and an interaction term between age and D-dimer. The pooled c-statistic was 0.87 (95% CI, 0.85–0.89; 95% PI, 0.77–0.93) and overall calibration was very good (pooled O:E ratio, 0.99; 95% CI, 0.87–1.14; 95% PI, 0.55–1.79). The model slightly overestimated VTE probability in the lower range of estimated probabilities. Discrimination of the current model in the validation data sets was better than that of the Wells score combined with a D-dimer threshold based on age (c-statistic 0.73; 95% CI, 0.70–0.75) or structured clinical pretest probability (c-statistic 0.79; 95% CI, 0.76–0.81). Conclusion The present model provides an absolute, individualized probability of PE presence in a broad population of patients with suspected PE, with very good discrimination and calibration. Its clinical utility needs to be evaluated in a prospective management or impact study. Registration PROSPERO ID 89366.
$\Phi$-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation

Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from data-space to model-space is unknown. To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder ($\Phi$-DVAE) to embed diverse data streams into time-evolving physical systems described by differential equations. Our approach combines a standard, possibly nonlinear, filter for the latent state-space model and a VAE, to assimilate the unstructured data into the latent dynamical system. Unstructured data, in our example systems, comes in the form of video data and velocity field measurements, however the methodology is suitably generic to allow for arbitrary unknown observation operators. A variational Bayesian framework is used for the joint estimation of the encoding, latent states, and unknown system parameters. To demonstrate the method, we provide case studies with the Lorenz-63 ordinary differential equation, and the advection and Korteweg-de Vries partial differential equations. Our results, with synthetic data, show that $\Phi$-DVAE provides a data efficient dynamics encoding methodology which is competitive with standard approaches. Unknown parameters are recovered with uncertainty quantification, and unseen data are accurately predicted.
CodeQueries: A Dataset of Semantic Queries over Code

Developers often have questions about semantic aspects of code they are working on, e.g.,"Is there a class whose parent classes declare a conflicting attribute? ". Answering them requires understanding code semantics such as attributes and inheritance relation of classes. An answer to such a question should identify code spans constituting the answer (e.g., the declaration of the subclass) as well as supporting facts (e.g., the definitions of the conflicting attributes). The existing work on question-answering over code has considered yes/no questions or method-level context. We contribute a labeled dataset, called CodeQueries, of semantic queries over Python code. Compared to the existing datasets, in CodeQueries, the queries are about code semantics, the context is file level and the answers are code spans. We curate the dataset based on queries supported by a widely-used static analysis tool, CodeQL, and include both positive and negative examples, and queries requiring single-hop and multi-hop reasoning. To assess the value of our dataset, we evaluate baseline neural approaches. We study a large language model (GPT3.5-Turbo) in zero-shot and few-shot settings on a subset of CodeQueries. We also evaluate a BERT style model (CuBERT) with fine-tuning. We find that these models achieve limited success on CodeQueries. CodeQueries is thus a challenging dataset to test the ability of neural models, to understand code semantics, in the extractive question-answering setting.
Free and Forced Vibration of Laminated Composite Beams

A new higher-order zig-zag theory is proposed for the free and forced vibration analysis of laminated composite and sandwich beams with different thickness at each ply. In-plane displacements in the proposed model nonlinearly distribute through the thickness and exhibit an abrupt discontinuity of slope at interfaces, which coincide with three-dimensional analysis. Differing from previous approaches, the transverse shear stress satisfies the continuity condition at interface by integrating the three-dimensional equilibrium equations, whereas the higher-order derivatives of displacement parameters have been eliminated using the three-field Hu-Washizu (HW) mixed variational principle. Moreover, the accurate transverse shear stresses are introduced in the equations of motion by using Hamilton’s principle, which actively impact the accurate prediction of natural frequencies of the laminated composite beams with arbitrary layouts. The performance of the proposed model is tested with different examples. It is ob.
Parallelising Glauber dynamics

For distributions over discrete product spaces $\prod_{i=1}^n \Omega_i'$, Glauber dynamics is a Markov chain that at each step, resamples a random coordinate conditioned on the other coordinates. We show that $k$-Glauber dynamics, which resamples a random subset of $k$ coordinates, mixes $k$ times faster in $\chi^2$-divergence, and assuming approximate tensorization of entropy, mixes $k$ times faster in KL-divergence. We apply this to obtain parallel algorithms in two settings: (1) For the Ising model $\mu_{J,h}(x)\propto \exp(\frac1 2\left\langle x,Jx \right\rangle + \langle h,x\rangle)$ with $\|J\|<1-c$ (the regime where fast mixing is known), we show that we can implement each step of $\widetilde \Theta(n/\|J\|_F)$-Glauber dynamics efficiently with a parallel algorithm, resulting in a parallel algorithm with running time $\widetilde O(\|J\|_F) = \widetilde O(\sqrt n)$. (2) For the mixed $p$-spin model at high enough temperature, we show that with high probability we show that we can implement each step of $\widetilde \Theta(\sqrt n)$-Glauber dynamics efficiently and obtain running time $\widetilde O(\sqrt n)$.
AnyStar: Domain randomized universal star-convex 3D instance segmentation

Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires substantial and often impractical manual annotation effort. Further, significant reengineering or finetuning is needed when presented with new datasets and imaging modalities due to changes in contrast, shape, orientation, resolution, and density. We present AnyStar, a domain-randomized generative model that simulates synthetic training data of blob-like objects with randomized appearance, environments, and imaging physics to train general-purpose star-convex instance segmentation networks. As a result, networks trained using our generative model do not require annotated images from unseen datasets. A single network trained on our synthesized data accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM, and placental cotyledons in human fetal MRI, all without any retraining, finetuning, transfer learning, or domain adaptation. Code is available at https://github.com/neel-dey/AnyStar.
Temperature dependent temporal coherence of metallic-nanoparticle-induced single-photon emitters in a WSe2 monolayer

In recent years, much research has been undertaken to investigate the suitability of two-dimensional materials to act as single-photon sources with high optical and quantum optical quality. Amongst them, transition-metal dichalcogenides, especially WSe2, have been one of the subjects of intensive studies. Yet, their single-photon purity and photon indistinguishability remain the most significant challenges to compete with mature semiconducting systems such as self-assembled InGaAs quantum dots. In this work, we explore the emission properties of quantum emitters in a WSe2 monolayer which are induced by metallic nanoparticles. Under quasi-resonant pulsed excitation, we verify clean single-photon emission with a g (2)(0) = 0.036 ± 0.004. Furthermore, we determine the temperature dependent coherence time via Michelson interferometry, where a value of (13.5 ± 1.0) ps is extracted for the zero-phonon line at 4 K, which reduces to (9 ± 2) ps at 8 K. Associated time-resolved photoluminescence experiments reveal a decrease of the decay time from (2.4 ± 0.1) ns to (0.42 ± 0.05) ns. This change in decay time is explained by a model which considers a Förster-type resonant energy transfer process which yields a strong temperature induced energy loss from the single-photon emitters to the nearby Ag nanoparticle.
Adaptive Coding and Modulation-Aided Mobile Relaying for Millimeter-Wave Flying Ad Hoc Networks

The emerging drone swarms are capable of carrying out sophisticated tasks in support of demanding Internet of Things (IoT) applications by synergistically working together. However, the target area may be out of the coverage of the ground station (GS) and it may be impractical to deploy a large number of drones in the target area due to cost, electromagnetic interference, and flight-safety regulations. By exploiting the innate agility and mobility of unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone swarm network architecture, which is capable of extending the coverage of the GS and enhancing the effective end-to-end throughput. Explicitly, a swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing and collecting data with the aid of their mounted cameras and/or sensors, and a powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the DCDS and a GS. Given a time period, in order to maximize the data delivered while minimizing the delay imposed, we harness an $\epsilon $ -multiple-objective genetic algorithm ( $\epsilon $ -MOGA)-assisted Pareto-optimization scheme. Our simulation results demonstrate that the proposed mobile relaying is capable of delivering more data. As specific examples investigated in our simulations, our mobile relaying-assisted drone swarm network is capable of delivering 45.38% more data than the benchmark solutions, when a stationary relay is available, and it is capable of delivering 26.86% more data than the benchmark solutions when no stationary relay is available.
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis

Zero-shot text-to-speech (TTS) aims to synthesize voices with unseen speech prompts, which significantly reduces the data and computation requirements for voice cloning by skipping the fine-tuning process. However, the prompting mechanisms of zero-shot TTS still face challenges in the following aspects: 1) previous works of zero-shot TTS are typically trained with single-sentence prompts, which significantly restricts their performance when the data is relatively sufficient during the inference stage. 2) The prosodic information in prompts is highly coupled with timbre, making it untransferable to each other. This paper introduces Mega-TTS 2, a generic prompting mechanism for zero-shot TTS, to tackle the aforementioned challenges. Specifically, we design a powerful acoustic autoencoder that separately encodes the prosody and timbre information into the compressed latent space while providing high-quality reconstructions. Then, we propose a multi-reference timbre encoder and a prosody latent language model (P-LLM) to extract useful information from multi-sentence prompts. We further leverage the probabilities derived from multiple P-LLM outputs to produce transferable and controllable prosody. Experimental results demonstrate that Mega-TTS 2 could not only synthesize identity-preserving speech with a short prompt of an unseen speaker from arbitrary sources but consistently outperform the fine-tuning method when the volume of data ranges from 10 seconds to 5 minutes. Furthermore, our method enables to transfer various speaking styles to the target timbre in a fine-grained and controlled manner. Audio samples can be found in https://boostprompt.github. io/boostprompt/.
Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs

In any system that uses structured knowledge graph (KG) data as its underlying knowledge representation, KG-to-text generation is a useful tool for turning parts of the graph data into text that can be understood by humans. Recent work has shown that models that make use of pretraining on large amounts of text data can perform well on the KG-to-text task, even with relatively little training data on the specific graph-to-text task. In this paper, we build on this concept by using large language models to perform zero-shot generation based on nothing but the model’s understanding of the triple structure from what it can read. We show that ChatGPT achieves near state-of-the-art performance on some measures of the WebNLG 2020 challenge, but falls behind on others. Additionally, we compare factual, counter-factual and fictional statements, and show that there is a significant connection between what the LLM already knows about the data it is parsing and the quality of the output text.