Miracle-dz/UniFAR-FTUs huggingface dataset:
FAST Reveals New Evidence for M94 as a Merger

We report the first high-sensitivity H i observation toward the spiral galaxy M94 with the Five-hundred-meter Aperture Spherical radio Telescope. From these observations, we discovered that M94 has a very extended H i disk, twice larger than that observed by THINGS, which is accompanied by a H i filament and seven high velocity clouds (HVCs) at different distances. The projected distances of these clouds and filaments are less than 50 kpc from the galactic center. We measured a total integrated flux (including all clouds/filament) of 127.3 ( ±1) Jy km s−1, corresponding to a H i mass of (6.51 ± 0.06)×108 M ⊙ , which is 63.0% more than that observed by THINGS. By comparing numerical simulations with the H i maps and the optical morphology of M94, we suggest that M94 is likely a remnant of a major merger of two galaxies, and the HVCs and H i filament could be the tidal features that originated from the first collision of the merger, which happened about 5 Gyr ago. Furthermore, we found a seemingly isolated H i cloud at a projection distance of 109 kpc without any optical counterpart detected. We discuss the possibilities of the origin of this cloud, such as dark dwarf galaxy and RELHIC (REionization-Limited H i Cloud). Our results demonstrate that high-sensitivity and wide-field H i imaging is important in revealing diffuse cold gas structures and tidal debris, which is crucial to understand the dynamical evolution of galaxies.
Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders

The posterior collapse phenomenon in variational autoencoder (VAE), where the variational posterior distribution closely matches the prior distribution, can hinder the quality of the learned latent variables. As a consequence of posterior collapse, the latent variables extracted by the encoder in VAE preserve less information from the input data and thus fail to produce meaningful representations as input to the reconstruction process in the decoder. While this phenomenon has been an actively addressed topic related to VAE performance, the theory for posterior collapse remains underdeveloped, especially beyond the standard VAE. In this work, we advance the theoretical understanding of posterior collapse to two important and prevalent yet less studied classes of VAE: conditional VAE and hierarchical VAE. Specifically, via a non-trivial theoretical analysis of linear conditional VAE and hierarchical VAE with two levels of latent, we prove that the cause of posterior collapses in these models includes the correlation between the input and output of the conditional VAE and the effect of learnable encoder variance in the hierarchical VAE. We empirically validate our theoretical findings for linear conditional and hierarchical VAE and demonstrate that these results are also predictive for non-linear cases with extensive experiments.
Improving Language Model Integration for Neural Machine Translation

The integration of language models for neural machine translation has been extensively studied in the past. It has been shown that an external language model, trained on additional target-side monolingual data, can help improve translation quality. However, there has always been the assumption that the translation model also learns an implicit target-side language model during training, which interferes with the external language model at decoding time. Recently, some works on automatic speech recognition have demonstrated that, if the implicit language model is neutralized in decoding, further improvements can be gained when integrating an external language model. In this work, we transfer this concept to the task of machine translation and compare with the most prominent way of including additional monolingual data - namely back-translation. We find that accounting for the implicit language model significantly boosts the performance of language model fusion, although this approach is still outperformed by back-translation.
How to Incorporate Systematic Effects into Parameter Determination

We describe two different approaches for incorporating systematics into analyses for parameter determination in the physical sciences. We refer to these as the Pragmatic and the Full methods, with the latter coming in two variants: Full Likelihood and Fully Bayesian. By the use of a simple and readily understood example, we point out the advantage of using the Full Likelihood and Fully Bayesian approaches; a more realistic example from Astrophysics is also presented. This could be relevant for data analyses in a wide range of scientific fields, for situations where systematic effects need to be incorporated in the analysis procedure. This note is an extension of part of the talk by van Dyk at the PHYSTAT-Systematics meeting.
Carbon–oxygen ultra-massive white dwarfs in general relativity

We employ the La Plata stellar evolution code, lpcode, to compute the first set of constant rest-mass carbon–oxygen ultra-massive white dwarf evolutionary sequences for masses higher than 1.29 M⊙ that fully take into account the effects of general relativity on their structural and evolutionary properties. In addition, we employ the lp-pul pulsation code to compute adiabatic g-mode Newtonian pulsations on our fully relativistic equilibrium white dwarf models. We find that carbon–oxygen white dwarfs more massive than 1.382 M⊙ become gravitationally unstable with respect to general relativity effects, being this limit higher than the 1.369 M⊙ we found for oxygen–neon white dwarfs. As the stellar mass approaches the limiting mass value, the stellar radius becomes substantially smaller compared with the Newtonian models. Also, the thermo-mechanical and evolutionary properties of the most massive white dwarfs are strongly affected by general relativity effects. We also provide magnitudes for our cooling sequences in different passbands. Finally, we explore for the first time the pulsational properties of relativistic ultra-massive white dwarfs and find that the period spacings and oscillation kinetic energies are strongly affected in the case of most massive white dwarfs. We conclude that the general relativity effects should be taken into account for an accurate assessment of the structural, evolutionary, and pulsational properties of white dwarfs with masses above ∼1.30 M⊙.
Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming

The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (referred to as"pAIr programming") where an AI system collaborates with a human programmer. While traditional pair programming between humans has been extensively studied, it remains uncertain whether its findings can be applied to human-AI pair programming. We compare human-human and human-AI pair programming, exploring their similarities and differences in interaction, measures, benefits, and challenges. We find that the effectiveness of both approaches is mixed in the literature (though the measures used for pAIr programming are not as comprehensive). We summarize moderating factors on the success of human-human pair programming, which provides opportunities for pAIr programming research. For example, mismatched expertise makes pair programming less productive, therefore well-designed AI programming assistants may adapt to differences in expertise levels.
Quantum theory of three-dimensional de Sitter space

We sketch the construction of a quantum model of 3 dimensional de Sitter space, based on the Covariant Entropy Principle and the observation that semi-classical physics suggests the possibility of a consistent theory of a finite number of unstable massive particles with purely gravitational interactions. Our model is holographic, finite, unitary, causal, plausibly exhibits fast scrambling, and qualitatively reproduces features of semi-classical de Sitter physics. In an appendix we outline some calculations that might lead to further tests of the model.
Zero-sum stopper vs. singular-controller games with constrained control directions

We consider a class of zero-sum stopper vs. singular-controller games in which the controller can only act on a subset $d_0<d$ of the $d$ coordinates of a controlled diffusion. Due to the constraint on the control directions these games fall outside the framework of recently studied variational methods. In this paper we develop an approximation procedure, based on $L^1$-stability estimates for the controlled diffusion process and almost sure convergence of suitable stopping times. That allows us to prove existence of the game's value and to obtain an optimal strategy for the stopper, under continuity and growth conditions on the payoff functions. This class of games is a natural extension of (single-agent) singular control problems, studied in the literature, with similar constraints on the admissible controls.
Early Hard X-Rays from the Nearby Core-collapse Supernova SN 2023ixf

We present NuSTAR observations of the nearby SN 2023ixf in M101 (d = 6.9 Mpc) that provide the earliest hard X-ray detection of a nonrelativistic stellar explosion to date at δ t ≈ 4 days and δ t ≈ 11 days. The spectra are well described by a hot thermal bremsstrahlung continuum with T > 25 keV shining through a thick neutral medium with a neutral hydrogen column that decreases with time (initial N Hint = 2.6 × 1023 cm−2). A prominent neutral Fe Kα emission line is clearly detected, similar to other strongly interacting supernovae (SNe) such as SN 2010jl. The rapidly decreasing intrinsic absorption with time suggests the presence of a dense but confined circumstellar medium (CSM). The absorbed broadband X-ray luminosity (0.3–79 keV) is L X ≈ 2.5 × 1040 erg s−1 during both epochs, with the increase in overall X-ray flux related to the decrease in the absorbing column. Interpreting these observations in the context of thermal bremsstrahlung radiation originating from the interaction of the SN shock with a dense medium we infer large particle densities in excess of n CSM ≈ 4 × 108 cm−3 at r < 1015 cm, corresponding to an enhanced progenitor mass-loss rate of Ṁ≈3×10−4 M ⊙ yr−1 for an assumed wind velocity of v w = 50 km s−1.
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings

It is essential for users to understand what their AI systems can and can't do in order to use them safely. However, the problem of enabling users to assess AI systems with sequential decision-making (SDM) capabilities is relatively understudied. This paper presents a new approach for modeling the capabilities of black-box AI systems that can plan and act, along with the possible effects and requirements for executing those capabilities in stochastic settings. We present an active-learning approach that can effectively interact with a black-box SDM system and learn an interpretable probabilistic model describing its capabilities. Theoretical analysis of the approach identifies the conditions under which the learning process is guaranteed to converge to the correct model of the agent; empirical evaluations on different agents and simulated scenarios show that this approach is few-shot generalizable and can effectively describe the capabilities of arbitrary black-box SDM agents in a sample-efficient manner.
Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction

In the domain of remote sensing image interpretation, road extraction from high-resolution aerial imagery has already been a hot research topic. Although deep CNNs have presented excellent results for semantic segmentation, the efficiency and capabilities of vision transformers (ViTs) are yet to be fully researched. As such, for accurate road extraction, a deep semantic segmentation neural network that utilizes the abilities of residual learning, heterogeneous convolutions (HetConvs), UNet, and ViTs, which is called ResUNetFormer, is proposed in this letter. The developed ResUNetFormer is evaluated on various cutting-edge deep learning-based road extraction techniques on the public Massachusetts road dataset. Statistical and visual results demonstrate the superiority of the ResUNetFormer over the state-of-the-art CNNs and ViTs for segmentation. The code will be made available publicly at https://github.com/aj1365/ResUNetFormer.
Using Earth to search for long-range spin-velocity interactions

Precision measurements of the possible coupling of spin to other scalars, vectors and pseudovectors has proven to be a sensitive way to search for new particle physics beyond the standard model. Indeed, in addition to searching for exotic spin-spin interactions, studies have been undertaken to look for couplings of spin to gravity, the relative velocity between particles, and preferred directions. Several laboratory experiments have established upper bounds on the energy associated with various fermion spin-orientations relative to Earth. Here, we combine these results with a model of Earth in order to investigate the possible long-range spin-velocity interactions associated with the exchange of ultralight ($m_{z'}<1$ neV) or massless scalar or vector bosons. We establish stringent bounds on the strength of these couplings between electrons, neutrons, protons and nucleons.
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates

Distributed and federated learning algorithms and techniques associated primarily with minimization problems. However, with the increase of minimax optimization and variational inequality problems in machine learning, the necessity of designing efficient distributed/federated learning approaches for these problems is becoming more apparent. In this paper, we provide a unified convergence analysis of communication-efficient local training methods for distributed variational inequality problems (VIPs). Our approach is based on a general key assumption on the stochastic estimates that allows us to propose and analyze several novel local training algorithms under a single framework for solving a class of structured non-monotone VIPs. We present the first local gradient descent-accent algorithms with provable improved communication complexity for solving distributed variational inequalities on heterogeneous data. The general algorithmic framework recovers state-of-the-art algorithms and their sharp convergence guarantees when the setting is specialized to minimization or minimax optimization problems. Finally, we demonstrate the strong performance of the proposed algorithms compared to state-of-the-art methods when solving federated minimax optimization problems.
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rendering them infeasible when label information is not available. A straightforward direction is to employ the widely used Infomax technique from typical Unsupervised Graph Representation Learning (UGRL) to learn robust unsupervised representations. Nonetheless, directly transplanting the Infomax technique from typical UGRL to robust UGRL may involve a biased assumption. In light of the limitation of Infomax, we propose a novel unbiased robust UGRL method called Robust Graph Information Bottleneck (RGIB), which is grounded in the Information Bottleneck (IB) principle. Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph. There are mainly two challenges to optimize RGIB: 1) high complexity of adversarial attack to perturb node features and graph structure jointly in the training procedure; 2) mutual information estimation upon adversarially attacked graphs. To tackle these problems, we further propose an efficient adversarial training strategy with only feature perturbations and an effective mutual information estimator with subgraph-level summary. Moreover, we theoretically establish a connection between our proposed RGIB and the robustness of downstream classifiers, revealing that RGIB can provide a lower bound on the adversarial risk of downstream classifiers. Extensive experiments over several benchmarks and downstream tasks demonstrate the effectiveness and superiority of our proposed method.
ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field

Physical simulations produce excellent predictions of weather effects. Neural radiance fields produce SOTA scene models. We describe a novel NeRF-editing procedure that can fuse physical simulations with NeRF models of scenes, producing realistic movies of physical phenomena in those scenes. Our application – Climate NeRF – allows people to visualize what climate change outcomes will do to them.ClimateNeRF allows us to render realistic weather effects, including smog, snow, and flood. Results can be controlled with physically meaningful variables like water level. Qualitative and quantitative studies show that our simulated results are significantly more realistic than those from SOTA 2D image editing and SOTA 3D NeRF stylization.
Practical quantum simulation of small-scale non-Hermitian dynamics

Non-Hermitian quantum systems have recently attracted considerable attention due to their exotic properties. Though many experimental realizations of non-Hermitian systems have been reported, the non-Hermiticity usually resorts to the hard-to-control environments and cannot last for too long times. An alternative approach is to use quantum simulation with the closed system, whereas how to simulate non-Hermitian Hamiltonian dynamics remains a great challenge. To tackle this problem, we propose a protocol which combines a dilation method with the variational quantum algorithm. The dilation method is used to transform a non-Hermitian Hamiltonian into a Hermitian one through an exquisite quantum circuit, while the variational quantum algorithm is for efficiently approximating the complex entangled gates in this circuit. As a demonstration, we apply our protocol to simulate the dynamics of an Ising chain with nonlocal non-Hermitian perturbations, which is an important model to study quantum phase transition at nonzero temperatures. The numerical simulation results are highly consistent with the theoretical predictions, revealing the effectiveness of our protocol. The presented protocol paves the way for practically simulating small-scale non-Hermitian dynamics.
Schr\"odinger as a Quantum Programmer: Estimating Entanglement via Steering

Quantifying entanglement is an important task by which the resourcefulness of a quantum state can be measured. Here we develop a quantum algorithm that tests for and quantifies the separability of a general bipartite state, by making use of the quantum steering effect, the latter originally discovered by Schr\"odinger. Our separability test consists of a distributed quantum computation involving two parties: a computationally limited client, who prepares a purification of the state of interest, and a computationally unbounded server, who tries to steer the reduced systems to a probabilistic ensemble of pure product states. To design a practical algorithm, we replace the role of the server by a combination of parameterized unitary circuits and classical optimization techniques to perform the necessary computation. The result is a variational quantum steering algorithm (VQSA), which is a modified separability test that is better suited for the capabilities of quantum computers available today. We then simulate our VQSA on noisy quantum simulators and find favorable convergence properties on the examples tested. We also develop semidefinite programs, executable on classical computers, that benchmark the results obtained from our VQSA. Our findings here thus provide a meaningful connection between steering, entanglement, quantum algorithms, and quantum computational complexity theory. They also demonstrate the value of a parameterized mid-circuit measurement in a VQSA and represent a first-of-its-kind application for a distributed VQA.
Deep learning for pneumothorax diagnosis: a systematic review and meta-analysis

Background Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed. Methods A search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysis via a hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Assessment Tool. Results In 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96–0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79–89%) for DL and 85% (95% CI 73–92%) for physicians and the pooled specificity was 96% (95% CI 94–98%) for DL and 98% (95% CI 95–99%) for physicians. More than half of the original studies (57%) had a high risk of bias. Conclusions Our review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed. In this, the first systematic review and meta-analysis of pneumothorax diagnostic AIs, physicians and AI models showed comparable performance in diagnosing pneumothorax from chest radiographs. https://bit.ly/3JZeGN4
LIM-kinase 1 effects on memory abilities and male courtship song in Drosophila depend on the neuronal type

The signal pathway of actin remodeling, including LIM-kinase 1 (LIMK1) and its substrate cofilin, regulates multiple processes in neurons of vertebrates and invertebrates. Drosophila melanogaster is widely used as a model object for studying mechanisms of memory formation, storage, retrieval and forgetting. Previously, active forgetting in Drosophila was investigated in the standard Pavlovian olfactory conditioning paradigm. The role of specific dopaminergic neurons (DAN) and components of the actin remodeling pathway in different forms of forgetting was shown. In our research, we investigated the role of LIMK1 in Drosophila memory and forgetting in the conditioned courtship suppression paradigm (CCSP). In the Drosophila brain, LIMK1 and p-cofilin levels appeared to be low in specific neuropil structures, including the mushroom body (MB) lobes and the central complex. At the same time, LIMK1 was observed in cell bodies, such as DAN clusters regulating memory formation in CCSP. We applied GAL4 × UAS binary system to induce limk1 RNA interference in different types of neurons. The hybrid strain with limk1 interference in MB lobes and glia showed an increase in 3-h short-term memory (STM), without significant effects on long-term memory. limk1 interference in cholinergic neurons (CHN) impaired STM, while its interference in DAN and serotoninergic neurons (SRN) also dramatically impaired the flies’ learning ability. By contrast, limk1 interference in fruitless neurons (FRN) resulted in increased 15–60 min STM, indicating a possible LIMK1 role in active forgetting. Males with limk1 interference in CHN and FRN also showed the opposite trends of courtship song parameters changes. Thus, LIMK1 effects on the Drosophila male memory and courtship song appeared to depend on the neuronal type or brain structure.