Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence for the Preferential Disruption of Moderately Massive Stars by Supermassive Black Holes

Tidal disruption events (TDEs) provide a unique opportunity to probe the stellar populations around supermassive black holes (SMBHs). By combining light-curve modeling with spectral line information and knowledge about the stellar populations in the host galaxies, we are able to constrain the properties of the disrupted star for three TDEs. The TDEs in our sample have UV spectra, and measurements of the UV N iii to C iii line ratios enabled estimates of the nitrogen-to-carbon abundance ratios for these events. We show that the measured nitrogen line widths are consistent with originating from the disrupted stellar material dispersed by the central SMBH. We find that these nitrogen-to-carbon abundance ratios necessitate the disruption of moderately massive stars (≳1–2 M ⊙). We determine that these moderately massive disruptions are overrepresented by a factor of ≳102 when compared to the overall stellar population of the post-starburst galaxy hosts. This implies that SMBHs are preferentially disrupting higher mass stars, possibly due to ongoing top-heavy star formation in nuclear star clusters or to dynamical mechanisms that preferentially transport higher mass stars to their tidal radii.
Efficient and Private Federated Learning with Partially Trainable Networks

Federated learning is used for decentralized training of machine learning models on a large number (millions) of edge mobile devices. It is challenging because mobile devices often have limited communication bandwidth and local computation resources. Therefore, improving the efficiency of federated learning is critical for scalability and usability. In this paper, we propose to leverage partially trainable neural networks, which freeze a portion of the model parameters during the entire training process, to reduce the communication cost with little implications on model performance. Through extensive experiments, we empirically show that Federated learning of Partially Trainable neural networks (FedPT) can result in superior communication-accuracy trade-offs, with up to $46\times$ reduction in communication cost, at a small accuracy cost. Our approach also enables faster training, with a smaller memory footprint, and better utility for strong differential privacy guarantees. The proposed FedPT method can be particularly interesting for pushing the limitations of over-parameterization in on-device learning.
Hydrogenated Amorphous Silicon Carbide: A Low-Loss Deposited Dielectric for Microwave to Submillimeter-Wave Superconducting Circuits

Low-loss deposited dielectrics will beneﬁt superconducting devices such as integrated superconducting spectrometers, superconducting qubits and kinetic inductance parametric ampliﬁers. Compared with planar structures, multi-layer structures such as microstrips are more compact and elim-inate radiation loss at high frequencies. Multi-layer structures are most easily fabricated with deposited dielectrics, which typically exhibit higher dielectric loss than crystalline dielectrics. We measured the sub-kelvin and low-power microwave and mm-submm wave dielectric loss of hydrogenated amorphous silicon carbide (a-SiC:H), using a superconducting chip with NbTiN/a-SiC:H/NbTiN microstrip resonators. We deposited the a-SiC:H by plasma-enhanced chemical vapor deposition at a substrate temperature of 400 ° C. The a-SiC:H has a mm-submm loss tangent ranging from 0 . 80 ± 0 . 01 × 10 − 4 to 1 . 43 ± 0 . 04 × 10 − 4 in the range of 270–385 GHz. The microwave loss tangent is 3 . 2 ± 0 . 2 × 10 − 5 . These are the lowest low-power sub-kelvin loss tangents that have been reported for microstrip resonators at mm-submm and microwave frequencies. We observe that the loss tangent increases
Federated Learning via Plurality Vote

Federated learning allows collaborative clients to solve a machine-learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learning, but the joint optimization of communication overhead, learning reliability, and deployment efficiency is still an open problem. To this end, we propose a new scheme named federated learning via plurality vote (FedVote). In each communication round of FedVote, clients transmit binary or ternary weights to the server with low communication overhead. The model parameters are aggregated via weighted voting to enhance the resilience against Byzantine attacks. When deployed for inference, the model with binary or ternary weights is resource-friendly to edge devices. Our results demonstrate that the proposed method can reduce quantization error and converges faster compared to the methods directly quantizing the model updates.
A Systematic Study of the Escape of LyC and Lyα Photons from Star-forming, Magnetized Turbulent Clouds

Understanding the escape of Lyman continuum (LyC) and Lyα photons from giant molecular clouds (GMCs) is crucial if we are to study the reionization of the universe and to interpret spectra of observed galaxies at high redshift. To this end, we perform high-resolution, radiation-magnetohydrodynamic simulations of GMCs with self-consistent star formation and stellar feedback. We find that a significant fraction (15%–70%) of ionizing radiation escapes from the simulated GMCs with different masses (105 and 106 M ⊙), as the clouds are dispersed within about 2–5 Myr from the onset of star formation. The fraction of LyC photons leaked is larger when the GMCs are less massive, metal poor, less turbulent, and less dense. The most efficient leakage of LyC radiation occurs when the total star formation efficiency of a GMC is about 20%. The escape of Lyα shows a trend similar to that of LyC photons, except that the fraction of Lyα photons escaping from the GMCs is larger ( fLyα≈f9000.27 ) and that a GMC with strong turbulence shows larger f Lyα . The simulated GMCs show a characteristic velocity separation of Δv ≈ 120 km s−1 in the time-averaged emergent Lyα spectra, suggesting that Lyα could be useful to infer the kinematics of the interstellar and circumgalactic medium. We show that Lyα luminosities are a useful indicator of the LyC escape, provided the number of LyC photons can be deduced through stellar population modeling. Finally, we find that the correlations between the escape fractions of Lyα, ultraviolet photons at 1500 Å, and the Balmer α line are weak.
Frame Averaging for Invariant and Equivariant Network Design

Many machine learning tasks involve learning functions that are known to be invariant or equivariant to certain symmetries of the input data. However, it is often challenging to design neural network architectures that respect these symmetries while being expressive and computationally efficient. For example, Euclidean motion invariant/equivariant graph or point cloud neural networks. We introduce Frame Averaging (FA), a general purpose and systematic framework for adapting known (backbone) architectures to become invariant or equivariant to new symmetry types. Our framework builds on the well known group averaging operator that guarantees invariance or equivariance but is intractable. In contrast, we observe that for many important classes of symmetries, this operator can be replaced with an averaging operator over a small subset of the group elements, called a frame. We show that averaging over a frame guarantees exact invariance or equivariance while often being much simpler to compute than averaging over the entire group. Furthermore, we prove that FA-based models have maximal expressive power in a broad setting and in general preserve the expressive power of their backbone architectures. Using frame averaging, we propose a new class of universal Graph Neural Networks (GNNs), universal Euclidean motion invariant point cloud networks, and Euclidean motion invariant Message Passing (MP) GNNs. We demonstrate the practical effectiveness of FA on several applications including point cloud normal estimation, beyond $2$-WL graph separation, and $n$-body dynamics prediction, achieving state-of-the-art results in all of these benchmarks.
Gradient Step Denoiser for convergent Plug-and-Play

Plug-and-Play methods constitute a class of iterative algorithms for imaging problems where regularization is performed by an off-the-shelf denoiser. Although Plug-and-Play methods can lead to tremendous visual performance for various image problems, the few existing convergence guarantees are based on unrealistic (or suboptimal) hypotheses on the denoiser, or limited to strongly convex data terms. In this work, we propose a new type of Plug-and-Play methods, based on half-quadratic splitting, for which the denoiser is realized as a gradient descent step on a functional parameterized by a deep neural network. Exploiting convergence results for proximal gradient descent algorithms in the non-convex setting, we show that the proposed Plug-and-Play algorithm is a convergent iterative scheme that targets stationary points of an explicit global functional. Besides, experiments show that it is possible to learn such a deep denoiser while not compromising the performance in comparison to other state-of-the-art deep denoisers used in Plug-and-Play schemes. We apply our proximal gradient algorithm to various ill-posed inverse problems, e.g. deblurring, super-resolution and inpainting. For all these applications, numerical results empirically confirm the convergence results. Experiments also show that this new algorithm reaches state-of-the-art performance, both quantitatively and qualitatively.
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective

In Federated Learning (FL), hyper-parameters significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. The current practice of manually selecting FL hyper-parameters puts a high burden on FL practitioners since various applications have different training preferences. In this paper, we propose FedTune, an automatic hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training. FedTune is lightweight and flexible, achieving 8.48%-26.75% improvement for different datasets compared to using fixed FL hyper-parameters.
Molecular and multiscale modeling of the dissolution of active pharmaceutical ingredients

This thesis evaluates the accessibility of predicting the dissolution properties of crystals of active pharmaceutical ingredients (API) from their molecular structure on the example of aspirin. Starting from the atomistic description of the molecular interactions, a multiscale approach is followed to bridge the gap from molecular to macroscopic simulation techniques. The techniques applied are molecular dynamics, kinetic Monte Carlo simulations with molecular resolution, and approaches based on continuously formulated mass transfer equations. The successful prediction of face-specific dissolution velocities is validated by comparison with literature data and experimental assessment of aspirin dissolution using an interferometer.
Quantum Measurement Theory for Systems with Finite Dimensional State Spaces

In this paper, we present a general theory of finite quantum measurements, for which we assume that the state space of the measured system is a finite dimensional Hilbert space and that the possible outcomes of a measurement is a finite set of real numbers. We develop the theory in a deductive manner from the basic postulates for quantum mechanics and a few plausible axioms for general quantum measurements. We derive an axiomatic characterization of all the physically realizable finite quantum measurements. Mathematical tools necessary to describe measurement statistics, such as POVMs and quantum instruments, are not assumed at the outset, but we introduce them as natural consequences of our axioms. Our objective is to show that those mathematical tools can be naturally derived from obvious theoretical requirements.
A Model Selection Approach for Corruption Robust Reinforcement Learning

We develop a model selection approach to tackle reinforcement learning with adversarial corruption in both transition and reward. For finite-horizon tabular MDPs, without prior knowledge on the total amount of corruption, our algorithm achieves a regret bound of Õ ( min{ 1 ∆ , √ T} + C ) where T is the number of episodes, C is the total amount of corruption, and ∆ is the reward gap between the best and the second-best policy. This is the first worst-case optimal bound achieved without knowledge of C, improving previous results of Lykouris et al. (2021); Chen et al. (2021b); Wu et al. (2021). For finite-horizon linear MDPs, we develop a computationally efficient algorithm with a regret bound of Õ( √ (1 + C)T ), and another computationally inefficient one with Õ( √ T + C), improving the result of Lykouris et al. (2021) and answering an open question by Zhang et al. (2021b). Finally, our model selection framework can be easily applied to other settings including linear bandits, linear contextual bandits, and MDPs with general function approximation, leading to several improved or new results.
InfinityGAN: Towards Infinite-Pixel Image Synthesis

We present a novel framework, InfinityGAN, for arbitrary-sized image generation. The task is associated with several key challenges. First, scaling existing models to an arbitrarily large image size is resource-constrained, in terms of both computation and availability of large-field-of-view training data. InfinityGAN trains and infers in a seamless patch-by-patch manner with low computational resources. Second, large images should be locally and globally consistent, avoid repetitive patterns, and look realistic. To address these, InfinityGAN disentangles global appearances, local structures, and textures. With this formulation, we can generate images with spatial size and level of details not attainable before. Experimental evaluation validates that InfinityGAN generates images with superior realism compared to baselines and features parallelizable inference. Finally, we show several applications unlocked by our approach, such as spatial style fusion, multi-modal outpainting, and image inbetweening. All applications can be operated with arbitrary input and output sizes. Please find the full version of the paper at https://openreview.net/forum? id=ufGMqIM0a4b .
Self-Supervised Inference in State-Space Models

We perform approximate inference in state-space models with nonlinear state transitions. Without parameterizing a generative model, we apply Bayesian update formulas using a local linearity approximation parameterized by neural networks. This comes accompanied by a maximum likelihood objective that requires no supervision via uncorrupt observations or ground truth latent states. The optimization backpropagates through a recursion similar to the classical Kalman filter and smoother. Additionally, using an approximate conditional independence, we can perform smoothing without having to parameterize a separate model. In scientific applications, domain knowledge can give a linear approximation of the latent transition maps, which we can easily incorporate into our model. Usage of such domain knowledge is reflected in excellent results (despite our model's simplicity) on the chaotic Lorenz system compared to fully supervised and variational inference methods. Finally, we show competitive results on an audio denoising experiment.
Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

In recent decades, heart disease threatens people's health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.
Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children

Objective The study focused on the features of the convolutional neural networks- (CNN-) processed magnetic resonance imaging (MRI) images for plastic bronchitis (PB) in children. Methods 30 PB children were selected as subjects, including 19 boys and 11 girls. They all received the MRI examination for the chest. Then, a CNN-based algorithm was constructed and compared with Active Appearance Model (AAM) algorithm for segmentation effects of MRI images in 30 PB children, factoring into occurring simultaneously than (OST), Dice, and Jaccard coefficient. Results The maximum Dice coefficient of CNN algorithm reached 0.946, while that of active AAM was 0.843, and the Jaccard coefficient of CNN algorithm was also higher (0.894 vs. 0.758, P < 0.05). The MRI images showed pulmonary inflammation in all subjects. Of 30 patients, 14 (46.66%) had complicated pulmonary atelectasis, 9 (30%) had the complicated pleural effusion, 3 (10%) had pneumothorax, 2 (6.67%) had complicated mediastinal emphysema, and 2 (6.67%) had complicated pneumopericardium. Also, of 30 patients, 19 (63.33%) had lung consolidation and atelectasis in a single lung lobe and 11 (36.67%) in both two lung lobes. Conclusion The algorithm based on CNN can significantly improve the segmentation accuracy of MRI images for plastic bronchitis in children. The pleural effusion was a dangerous factor for the occurrence and development of PB.
Assessment Methods of Body Fat in Recreational Marathon Runners: Bioelectrical Impedance Analysis versus Skinfold Thickness

The aim of the present study was to examine (a) the relationship of body fat (BF) assessed by bioimpedance analysis (BIA) and skinfold thickness (SKF) and (b) the variation of BF by age depending on the assessment method. Participants were 32 women and 134 men recreational marathon runners, who were tested for BF using both assessment methods (BIA and SKF). Rc between BIA and SKF assessment methods was 0.803 (95% CI; 0.640, 0.897) in women and 0.568 (95% CI; 0.481, 0.644) in men. A large main effect of the assessment method on BF was observed (p < 0.001, η2 = 0.156) with SKF presenting higher BF than BIA by 2.9%. The difference between SKF and BIA was 3.9 ± 2.7% (95% confidence intervals, CI; 3.4; 4.3, p < 0.001) in men, whereas no difference was found in women (−0.9 ± 2.9%; 95% CI; -1.9; -0.2, p = 0.101). BF correlated with age with small magnitude (BIA, r = 0.18, p = 0.036; SKF, r = 0.23, p = 0.007) in men, i.e., the older the age, the higher the BF. A similar trend of moderate magnitude was observed in women for BIA (r = 0.45, p = 0.011), but not for SKF (r = 0.33, p = 0.067). In conclusion, practitioners involved in the training of recreational runners would be advised to consider that BIA elicits a lower BF value than the SKF method in men.
No effect of ‘watching eyes’: An attempted replication and extension investigating individual differences

Some evidence suggests that people behave more cooperatively and generously when observed or in the presence of images of eyes (termed the ‘watching eyes’ effect). Eye images are thought to trigger feelings of observation, which in turn motivate people to behave more cooperatively to earn a good reputation. However, several recent studies have failed to find evidence of the eyes effect. One possibility is that inconsistent evidence in support of the eyes effect is a product of individual differences in sensitivity or susceptibility to the cue. In fact, some evidence suggests that people who are generally more prosocial are less susceptible to situation-specific reputation-based cues of observation. In this paper, we sought to (1) replicate the eyes effect, (2) replicate the past finding that people who are dispositionally less prosocial are more responsive to observation than people who are more dispositionally more prosocial, and (3) determine if this effect extends to the watching eyes effect. Results from a pre-registered study showed that people did not give more money in a dictator game when decisions were made public or in the presence of eye images, even though participants felt more observed when decisions were public. That is, we failed to replicate the eyes effect and observation effect. An initial, but underpowered, interaction model suggests that egoists give less than prosocials in private, but not public, conditions. This suggests a direction for future research investigating if and how individual differences in prosociality influence observation effects.
Assessing the double burden of malnutrition among Bangladeshi reproductive‐aged women: A comparison between unconditional and conditional quantile regression

The coexistence of undernutrition and overnutrition creates a double burden of malnutrition (DBM) among women in Bangladesh. This study aimed to assess heterogeneous effects of sociodemographic factors on women's nutritional status using quantile regression (QR) models and to investigate the differences between the results of unconditional QR (UQR) and conditional QR (CQR) models.
Joint Analysis of Genome-Wide Association Data Reveals No Genetic Correlations Between Low Back Pain and Neurodegenerative Diseases

Background: We aimed to explore the genetic correlation and bidirectional causal relationships between low back pain (LBP) and three neurodegenerative diseases, Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Methods: Summary-level statistics were obtained from genome-wide association studies of LBP (n = 177,860), AD (n = 63,926), PD (n = 482,730), and ALS (n = 80,610). We implemented linkage disequilibrium score regression to calculate heritability estimates and genetic correlations. To investigate possible causal associations between LBP and three neurodegenerative diseases, we also conducted a bidirectional two-sample Mendelian randomization (MR) study. Inverse variance-weighted MR was employed as the primary method to generate overall estimates, whereas complementary approaches and sensitivity analyses were conducted to confirm the consistency and robustness of the findings. Results: There was no evidence of genetic correlations between LBP and AD (Rg = −0.033, p = 0.766). MR analyses did not support the causal effect of LBP on AD (OR = 1.031; 95% CI, 0.924–1.150; p = 0.590) or the effect of AD on LBP (OR = 0.963; 95% CI, 0.923–1.006; p = 0.090). Likewise, this study failed to identify genetic correlations between LBP and two other neurodegenerative diseases. MR results of the associations of LBP with PD and ALS, and the reverse associations, did not reach Bonferroni-corrected significance. Conclusion: The study did not support genetic correlations or causations between LBP and three common neurodegenerative diseases, AD, PD, and ALS in the European population.