Miracle-dz/UniFAR-FTUs huggingface dataset:
Reverse Algols and hydrogen-rich Wolf-Rayet stars from very massive binaries

Massive star feedback affects the evolution of galaxies, where the most massive stars may have the largest impact. The majority of massive stars are born as members of close binary systems. Here, we investigate detailed evolutionary models of very massive binaries (30$\dots$90$M_{\odot}$) with Large Magellanic Cloud (LMC) metallicity. We identify four effects defying the conventional knowledge of binary evolution, which are all related to the proximity of the models to the Eddington limit. We find that the majority of systems undergo mass transfer during core hydrogen burning. During the ensuing nuclear timescale evolution, many mass donors remain more massive than their companions (``reverse Algols''), and nuclear timescale mass transfer may be interrupted or absent altogether. Furthermore, due to the elevated luminosity-to-mass ratio, many of the core-hydrogen burning donors may develop Wolf-Rayet-type winds, at luminosities where single stars would not. We identify observational counterparts of very massive reverse Algol binaries in the LMC, and discuss their contribution to the observed hydrogen-rich Wolf-Rayet stars. We argue that an understanding of very massive Algol systems is key to predicting the advanced evolution of very massive binaries, including their ability to evolve into observable gravitational wave sources.
Jensen-Shannon Divergence Based Novel Loss Functions for Bayesian Neural Networks

The Kullback-Leibler (KL) divergence is widely used in state-of-the-art Bayesian Neural Networks (BNNs) to approximate the posterior distribution of weights. However, the KL divergence is unbounded and asymmetric, which may lead to instabilities during optimization or may yield poor generalizations. To overcome these limitations, we examine the Jensen-Shannon (JS) divergence that is bounded, symmetric, and more general. Towards this, we propose two novel loss functions for BNNs. The first loss function uses the geometric JS divergence (JS-G) that is symmetric, unbounded, and offers an analytical expression for Gaussian priors. The second loss function uses the generalized JS divergence (JS-A) that is symmetric and bounded. We show that the conventional KL divergence-based loss function is a special case of the two loss functions presented in this work. To evaluate the divergence part of the loss we use analytical expressions for JS-G and use Monte Carlo methods for JS-A. We provide algorithms to optimize the loss function using both these methods. The proposed loss functions offer additional parameters that can be tuned to control the degree of regularisation. The regularization performance of the JS divergences is analyzed to demonstrate their superiority over the state-of-the-art. Further, we derive the conditions for better regularization by the proposed JS-G divergence-based loss function than the KL divergence-based loss function. Bayesian convolutional neural networks (BCNN) based on the proposed JS divergences perform better than the state-of-the-art BCNN, which is shown for the classification of the CIFAR data set having various degrees of noise and a histopathology data set having a high bias.
Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments

Deriving robust control policies for realistic urban navigation scenarios is not a trivial task. In an end-to-end approach, these policies must map high-dimensional images from the vehicle's cameras to low-level actions such as steering and throttle. While pure Reinforcement Learning (RL) approaches are based exclusively on rewards,Generative Adversarial Imitation Learning (GAIL) agents learn from expert demonstrations while interacting with the environment, which favors GAIL on tasks for which a reward signal is difficult to derive. In this work, the hGAIL architecture was proposed to solve the autonomous navigation of a vehicle in an end-to-end approach, mapping sensory perceptions directly to low-level actions, while simultaneously learning mid-level input representations of the agent's environment. The proposed hGAIL consists of an hierarchical Adversarial Imitation Learning architecture composed of two main modules: the GAN (Generative Adversarial Nets) which generates the Bird's-Eye View (BEV) representation mainly from the images of three frontal cameras of the vehicle, and the GAIL which learns to control the vehicle based mainly on the BEV predictions from the GAN as input.Our experiments have shown that GAIL exclusively from cameras (without BEV) fails to even learn the task, while hGAIL, after training, was able to autonomously navigate successfully in all intersections of the city.
JADES: Probing interstellar medium conditions at z~5.5-9.5 with ultra-deep JWST/NIRSpec spectroscopy

We present emission line ratios from a sample of 26 Lyman break galaxies from $z\sim5.5-9.5$ with $-17.0<M_{1500}<-20.4$, measured from ultra-deep JWST/NIRSpec MSA spectroscopy from JADES. We use 28 hour deep PRISM/CLEAR and 7 hour deep G395M/F290LP observations to measure, or place strong constraints on, ratios of widely studied rest-frame optical emission lines including H$\alpha$, H$\beta$, [OII] $\lambda\lambda$3726,3729, [NeIII] $\lambda$3869, [OIII] $\lambda$4959, [OIII] $\lambda$5007, [OI] $\lambda$6300, [NII] $\lambda$6583, and [SII] $\lambda\lambda$6716,6731 in individual $z>5.5$ spectra. We find that the emission line ratios exhibited by these $z\sim5.5-9.5$ galaxies occupy clearly distinct regions of line-ratio space compared to typical z~0-3 galaxies, instead being more consistent with extreme populations of lower-redshift galaxies. This is best illustrated by the [OIII]/[OII] ratio, tracing interstellar medium (ISM) ionisation, in which we observe more than half of our sample to have [OIII]/[OII]>10. Our high signal-to-noise spectra reveal more than an order of magnitude of scatter in line ratios such as [OII]/H$\beta$ and [OIII]/[OII], indicating significant diversity in the ISM conditions within the sample. We find no convincing detections of [NII] in our sample, either in individual galaxies, or a stack of all G395M/F290LP spectra. The emission line ratios observed in our sample are generally consistent with galaxies with extremely high ionisation parameters (log $U\sim-1.5$), and a range of metallicities spanning from $\sim0.1\times Z_\odot$ to higher than $\sim0.3\times Z_\odot$, suggesting we are probing low-metallicity systems undergoing periods of rapid star-formation, driving strong radiation fields. These results highlight the value of deep observations in constraining the properties of individual galaxies, and hence probing diversity within galaxy population.
Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.
Adding and multiplying in the tile assembly model

The tile assembly model, a formal model of crystal growth, is of special interest to computer scientists and mathematicians because it is universal [1]. Therefore, tile assembly model systems can compute all the functions that computers compute. In this paper, I formally define what it means for a system to compute a function deterministically and present systems that add and multiply. While the proof that the tile assembly model is universal [2] implies the construction of such systems, those systems are in some sense “large” and “slow. ” The systems presented here all use Θ(1) different tiles (8 to add and 28 to multiply) and compute in time linear in the input size.
COVID-19 Susceptibility, Mortality and Length of Hospitalization based on Age-Sex Composition: Insights for Intervention and Stratification

The coronavirus disease (COVID-19) has spread worldwide with an unprecedented impact on society. In the Philippines, several interventions such as mobility restrictions for different age groups and vaccination prioritization programs have been implemented to reduce the risks of infections and mortality. This study aimed to identify age-sex composition with greater susceptibility, longer hospitalization and higher fatality. The COVID-19 cases from March 2020 to April 2021 provided by the Department of Health Davao Region in the Philippines were analyzed. A Chi-square test was used to determine the difference in proportions of COVID-19 cases among age-sex compositions. A correlation plot of χ2 test residual was employed to investigate the differences in susceptibility. Boxplots and Kruskal-Wallis tests were utilized to compare the length of hospitalizations. The study found a significant difference in the COVID-19 susceptibility among age-sex compositions (p < 0.01). Male children and female senior citizens were the most susceptible age-sex compositions. Furthermore, senior citizens had the longest hospital days wherein the median and IQR days were 19 (15-27) for men and 18 (16-29) for women. Male senior citizen was the subgroup with the highest case fatality (21.4%, p < 0.01). It is recommended that the number of cases among senior citizens be used as an input in the planning and allocation of medical resources at the provincial and regional levels. The local government unit executives in the region can also take advantage of the availability of age-sex composition data in stratifying localities, planning, allocating COVID-19-related resources and imposing mobility restrictions.
The Effects of Acoustic Nonlinearities in Acousto-Optic Signal Processing Systems

Acousto-optic signal processing systems are adversely affected by acoustic nonlinearities in Bragg cells. These nonlinearities produce acoustic power depletion and the generation of intermodulation products which can limit the achievable dynamic range. This paper presents a mathematical model which describes these effects. Longitudinal acoustic modes commonly used in Bragg cells are analyzed using this model.
DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks

With ever increasing depth and width in deep neural networks to achieve state-of-the-art performance, deep learning computation has significantly grown, and dot-products remain dominant in overall computation time. Most prior works are built on conventional dot-product where weighted input summation is used to represent the neuron operation. However, another implementation of dot-product based on the notion of angles and magnitudes in the Euclidean space has attracted limited attention. This paper proposes DeepCAM, an inference accelerator built on two critical innovations to alleviate the computation time bottleneck of convolutional neural networks. The first innovation is an approximate dot-product built on computations in the Euclidean space that can replace addition and multiplication with simple bit-wise operations. The second innovation is a dynamic size content addressable memory-based (CAM-based) accelerator to perform bit-wise operations and accelerate the CNNs with a lower computation time. Our experiments on benchmark image recognition datasets demonstrate that DeepCAM is up to $523\times$ and $3498\times$ faster than Eyeriss and traditional CPUs like Intel Skylake, respectively. Furthermore, the energy consumed by our DeepCAM approach is $2.16\times$ to $109\times$ less compared to Eyeriss.
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes

Autoencoders and their variants are among the most widely used models in representation learning and generative modeling. However, autoencoder-based models usually assume that the learned representations are i.i. d. and fail to capture the correlations between the data samples. To address this issue, we propose a novel Sparse Gaussian Process Bayesian Autoencoder (SGPBAE) model in which we impose fully Bayesian sparse Gaussian Process priors on the latent space of a Bayesian Autoencoder. We perform posterior estimation for this model via stochastic gradient Hamiltonian Monte Carlo. We evaluate our approach qualitatively and quantitatively on a wide range of representation learning and generative modeling tasks and show that our approach consistently outperforms multiple alternatives relying on Variational Autoencoders.
Angle dependence of acoustic phonon-assisted tunneling in a weakly coupled superlattice: Evidence for terahertz phonon amplification

We investigate nonequilibrium acoustic phonon-assisted tunneling in a weakly coupled GaAs∕AlAs superlattice (SL). Previously it had been predicted that, in such structures, the conditions for terahertz acoustic phonon amplification can be achieved [B. A. Glavin, V. A. Kochelap, and T. L. Linnik, Appl. Phys. Lett. 74, 3525 (1999)]. We present experimental measurements of the change in current ΔI due to an incident nonequilibrium phonon pulse generated by thermalizing a laser pulse in a metal film. In particular, we measure the dependence on the angle of incidence θ of the phonons relative to the SL growth direction. The results are compared with numerical calculations made using the two-well model. It is found that good agreement between the measurements and calculations can be obtained for all θ if we assume that amplification is occurring for phonons propagating in a direction near θ=0. Due to the amplification of phonons with energy ℏωq≲Δ, where Δ is the energy drop per period of the SL, the nonequilibr.
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion

Graph neural networks are widely used tools for graph prediction tasks. Motivated by their empirical performance, prior works have developed generalization bounds for graph neural networks, which scale with graph structures in terms of the maximum degree. In this paper, we present generalization bounds that instead scale with the largest singular value of the graph neural network's feature diffusion matrix. These bounds are numerically much smaller than prior bounds for real-world graphs. We also construct a lower bound of the generalization gap that matches our upper bound asymptotically. To achieve these results, we analyze a unified model that includes prior works' settings (i.e., convolutional and message-passing networks) and new settings (i.e., graph isomorphism networks). Our key idea is to measure the stability of graph neural networks against noise perturbations using Hessians. Empirically, we find that Hessian-based measurements correlate with the observed generalization gaps of graph neural networks accurately. Optimizing noise stability properties for fine-tuning pretrained graph neural networks also improves test performance on several graph-level classification tasks.
Designing workflows for materials characterization

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research.
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks

We provide a new efficient version of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware.
Single-Shot Laser-Induced Switching of an Exchange Biased Antiferromagnet.

Ultrafast manipulation of magnetic order has challenged our understanding the fundamental and dynamic properties of magnetic materials. So far single shot magnetic switching has been limited to ferrimagnetic alloys, multilayers and designed ferromagnetic heterostructures. In ferromagnetic (FM)/antiferromagnetic (AFM) bilayers, exchange bias (He ) arises from the interfacial exchange coupling between the two layers and reflects the microscopic orientation of the antiferromagnet. Here we demonstrate the possibility of single shot switching of the antiferromagnet (change of the sign and amplitude of He ) with a single femtosecond laser pulse in IrMn/CoGd bilayers. We demonstrate the manipulation in a wide range of fluences for different layer thicknesses and compositions. Atomistic simulations predict ultrafast switching and recovery of the AFM magnetization on a timescale of 2 ps. Our results provide the fastest and the most energy-efficient method to set the exchange bias and pave the way to potential applications for ultrafast spintronic devices. This article is protected by copyright. All rights reserved.
RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols

Reinforcement Learning (RL) has gained significant momentum in the development of network protocols. However, RL-based protocols are still in their infancy, and substantial research is required to build deployable solutions. Developing a protocol based on RL is a complex and challenging process that involves several model design decisions and requires significant training and evaluation in real and simulated network topologies. Network simulators offer an efficient training environment for RL-based protocols, because they are deterministic and can run in parallel. In this paper, we introduce RayNet , a scalable and adaptable simulation platform for the development of RL-based network protocols. RayNet integrates OMNeT++, a fully programmable network simulator, with Ray/RLlib, a scalable training platform for distributed RL. RayNet facilitates the methodical development of RL-based network protocols so that researchers can focus on the problem at hand and not on implementation details of the learning aspect of their research. We developed a simple RL-based congestion control approach as a proof of concept showcasing that RayNet can be a valuable platform for RL-based research in computer networks, enabling scalable training and evaluation. We compared RayNet with ns3-gym , a platform with similar objectives to RayNet, and showed that RayNet performs better in terms of how fast agents can collect experience in RL environments.
Detecting Contextomized Quotes in News Headlines by Contrastive Learning

Quotes are critical for establishing credibility in news articles. A direct quote enclosed in quotation marks has a strong visual appeal and is a sign of a reliable citation. Unfortunately, this journalistic practice is not strictly followed, and a quote in the headline is often “contextomized. ” Such a quote uses words out of context in a way that alters the speaker’s intention so that there is no semantically matching quote in the body text. We present QuoteCSE, a contrastive learning framework that represents the embedding of news quotes based on domain-driven positive and negative samples to identify such an editorial strategy. The dataset and code are available at https://github.com/ssu-humane/contextomized-quote-contrastive.
Practical Guidance for Bayesian Inference in Astronomy

In the last two decades, Bayesian inference has become commonplace in astronomy. At the same time, the choice of algorithms, terminology, notation, and interpretation of Bayesian inference varies from one sub-field of astronomy to the next, which can lead to confusion to both those learning and those familiar with Bayesian statistics. Moreover, the choice varies between the astronomy and statistics literature, too. In this paper, our goal is two-fold: (1) provide a reference that consolidates and clarifies terminology and notation across disciplines, and (2) outline practical guidance for Bayesian inference in astronomy. Highlighting both the astronomy and statistics literature, we cover topics such as notation, specification of the likelihood and prior distributions, inference using the posterior distribution, and posterior predictive checking. It is not our intention to introduce the entire field of Bayesian data analysis -- rather, we present a series of useful practices for astronomers who already have an understanding of the Bayesian"nuts and bolts"and wish to increase their expertise and extend their knowledge. Moreover, as the field of astrostatistics and astroinformatics continues to grow, we hope this paper will serve as both a helpful reference and as a jumping off point for deeper dives into the statistics and astrostatistics literature.