Miracle-dz/UniFAR-FTUs huggingface dataset:
Solar Flare 1/f Fluctuations from Amplitude-Modulated Five-Minute Oscillation

We first report that the solar flare time sequence exhibits a fluctuation characterized by its power spectral density being inversely proportional to the signal frequency. This is the 1/f fluctuation, or pink noise, observed ubiquitously in nature. Using GOES16 data, we found that low-energy flares (E≤Emean) display 1/f fluctuations, whereas high-energy flares (E>Emean) show a flat spectrum. Furthermore, we found that the timing sequence of the flares reveals clearer 1/f fluctuations. These observations suggest that the solar flare 1/f fluctuations are associated with low-energy phenomena. We investigated the origin of these 1/f fluctuations based on our recent hypothesis: 1/f fluctuations arise from amplitude modulation and demodulation. We propose that this amplitude modulation is encoded by the resonance with the solar five-minute oscillation (SFO) and demodulated by magnetic reconnections. We partially demonstrate this scenario by analyzing the SFO eigenmodes resolving the frequency degeneration in the azimuthal order number m using the solar rotation and resonance. Given the robust nature of 1/f fluctuations, we speculated that the solar flare 1/f fluctuations may be inherited by the various phenomena around the Sun, such as the sunspot numbers and cosmic rays. In addition, we draw parallels between solar flares and earthquakes, both exhibiting 1/f fluctuations. Interestingly, the analysis applied to solar flares can also be adapted to earthquakes if we read the SFO as Earth’s free oscillation and magnetic reconnections as fault ruptures. Moreover, we point out the possibility that the same analysis also applies to the activity of a black hole/disk system if we read the SFO as the quasi-periodic oscillation of a black hole.
Interesting clues to detect hidden Tidal Disruption Events in Active Galactic Nuclei

In the manuscript, effects of Tidal Disruption Events (TDEs) are estimated on long-term AGN variability, to provide interesting clues to detect probable hidden TDEs in normal broad line AGN with apparent intrinsic variability which overwhelm the TDEs expected variability features, after considering the unique TDEs expected variability patterns. Based on theoretical TDEs expected variability plus AGN intrinsic variability randomly simulated by Continuous AutoRegressive process, long-term variability properties with and without TDEs contributions are well analyzed in AGN. Then, interesting effects of TDEs can be determined on long-term observed variability of AGN. First, more massive BHs, especially masses larger than 107 M⊙, can lead to more sensitive and positive dependence of τTN on RTN, with τTN as variability timescale ratio of light curves with TDEs contributions to intrinsic light curves without TDEs contributions, and RTN as ratio of peak intensity of TDEs expected variability to the mean intensity of intrinsic AGN variability without TDEs contributions. Second, stronger TDEs contributions RTN can lead to τTN quite larger than 5. Third, for intrinsic AGN variability having longer variability timescales, TDEs contributions will lead τTN to be increased more slowly. The results actually provide an interesting forward-looking method to detect probable hidden TDEs in normal broad line AGN, due to quite different variability properties, especially different DRW/CAR process expected variability timescales, in different epochs, especially in normal broad line AGN with shorter intrinsic variability timescales and with BH masses larger than 107 M⊙.
DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation

Direct speech-to-speech translation (S2ST) translates speech from one language into another using a single model. However, due to the presence of linguistic and acoustic diversity, the target speech follows a complex multimodal distribution, posing challenges to achieving both high-quality translations and fast decoding speeds for S2ST models. In this paper, we propose DASpeech, a non-autoregressive direct S2ST model which realizes both fast and high-quality S2ST. To better capture the complex distribution of the target speech, DASpeech adopts the two-pass architecture to decompose the generation process into two steps, where a linguistic decoder first generates the target text, and an acoustic decoder then generates the target speech based on the hidden states of the linguistic decoder. Specifically, we use the decoder of DA-Transformer as the linguistic decoder, and use FastSpeech 2 as the acoustic decoder. DA-Transformer models translations with a directed acyclic graph (DAG). To consider all potential paths in the DAG during training, we calculate the expected hidden states for each target token via dynamic programming, and feed them into the acoustic decoder to predict the target mel-spectrogram. During inference, we select the most probable path and take hidden states on that path as input to the acoustic decoder. Experiments on the CVSS Fr-En benchmark demonstrate that DASpeech can achieve comparable or even better performance than the state-of-the-art S2ST model Translatotron 2, while preserving up to 18.53x speedup compared to the autoregressive baseline. Compared with the previous non-autoregressive S2ST model, DASpeech does not rely on knowledge distillation and iterative decoding, achieving significant improvements in both translation quality and decoding speed. Furthermore, DASpeech shows the ability to preserve the speaker's voice of the source speech during translation.
Adversarial Optimization Leads to Over-Optimistic Security-Constrained Dispatch, but Sampling Can Help

To ensure safe, reliable operation of the electrical grid, we must be able to predict and mitigate likely failures. This need motivates the classic security-constrained AC optimal power flow (SCOPF) problem. SCOPF is commonly solved using adversarial optimization, where the dispatcher and an adversary take turns optimizing a robust dispatch and adversarial attack, respectively. We show that adversarial optimization is liable to severely overestimate the robustness of the optimized dispatch (when the adversary encounters a local minimum), leading the operator to falsely believe that their dispatch is secure. To prevent this overconfidence, we develop a novel adversarial sampling approach that prioritizes diversity in the predicted attacks. We find that our method not only substantially improves the robustness of the optimized dispatch but also avoids overconfidence, accurately characterizing the likelihood of voltage collapse under a given threat model. We demonstrate a proof-of-concept on small-scale transmission systems with 14 and 57 nodes.
Distilling Efficient Vision Transformers from CNNs for Semantic Segmentation

In this paper, we tackle a new problem: how to transfer knowledge from the pre-trained cumbersome yet well-performed CNN-based model to learn a compact Vision Transformer (ViT)-based model while maintaining its learning capacity? Due to the completely different characteristics of ViT and CNN and the long-existing capacity gap between teacher and student models in Knowledge Distillation (KD), directly transferring the cross-model knowledge is non-trivial. To this end, we subtly leverage the visual and linguistic-compatible feature character of ViT (i.e., student), and its capacity gap with the CNN (i.e., teacher) and propose a novel CNN-to-ViT KD framework, dubbed C2VKD. Importantly, as the teacher's features are heterogeneous to those of the student, we first propose a novel visual-linguistic feature distillation (VLFD) module that explores efficient KD among the aligned visual and linguistic-compatible representations. Moreover, due to the large capacity gap between the teacher and student and the inevitable prediction errors of the teacher, we then propose a pixel-wise decoupled distillation (PDD) module to supervise the student under the combination of labels and teacher's predictions from the decoupled target and non-target classes. Experiments on three semantic segmentation benchmark datasets consistently show that the increment of mIoU of our method is over 200% of the SoTA KD methods
A predict-and-optimize approach to profit-driven churn prevention

In this paper, we introduce a novel predict-and-optimize method for profit-driven churn prevention. We frame the task of targeting customers for a retention campaign as a regret minimization problem. The main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast, many profit-driven strategies focus on churn probabilities while considering average CLVs. This often results in significant information loss due to data aggregation. Our proposed model aligns with the guidelines of Predict-and-Optimize (PnO) frameworks and can be efficiently solved using stochastic gradient descent methods. Results from 12 churn prediction datasets underscore the effectiveness of our approach, which achieves the best average performance compared to other well-established strategies in terms of average profit.
BrainAGE revisited: Keeping up the pace with deep learning methods

Since the introduction of the BrainAGE method (Franke et al., 2010), novel machine learning methods of brain age prediction have continued to emerge. The idea of estimating the chronological age from (different modalities of) magnetic resonance images proved to be an interesting field of research due to the simplicity of its interpretation and its potential use as a biomarker of brain health. With the advancement of deep learning methods that are starting to dominate the field, it may seem like standard machine learning methods are not competitive anymore. Here we present our revised BrainAGE framework that combines multiple weighted BrainAGE models. We employed the analyses on a sample of 36840 T1-weighted images from the UK Biobank. The most accurate prediction with a mean absolute error (MAE) of 2.02 years was obtained by relevance vector regression weighted model combination. Our results are comparable to those of deep learning methods. We additionally tested the relation of different BrainAGE models and white matter hyperintensities as markers of ageing brain and found no differences in association to biomedical data between models of differing accuracies.
Fundamentals of thermoelasticity for curved beams

The purpose of this paper is twofold. Firstly, we conduct an in-depth analysis of mathematical modeling concerning thermal-mechanical curved beams, by taking into consideration three primary forces widely accepted in the literature: axial load, shear force, and bending moment. Additionally, we examine their appropriate thermal couplings, shedding light on the intricate interplay between stress-strain relationships and temperature variations. This analysis is situated within the well-recognized context of the Bresse governing model for arched beams. Secondly, drawing upon distinguished constitutive laws for heat flux of conduction, we compile a comprehensive list of thermoelastic curved beam systems in various scenarios. We introduce new categories of problems that exhibit specific features from the thermal point of view.
Sparse Universal Transformer

The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers. Empirical evidence shows that UTs have better compositional generalization than Vanilla Transformers (VTs) in formal language tasks. The parameter-sharing also affords it better parameter efficiency than VTs. Despite its many advantages, scaling UT parameters is much more compute and memory intensive than scaling up a VT. This paper proposes the Sparse Universal Transformer (SUT), which leverages Sparse Mixture of Experts (SMoE) and a new stick-breaking-based dynamic halting mechanism to reduce UT's computation complexity while retaining its parameter efficiency and generalization ability. Experiments show that SUT achieves the same performance as strong baseline models while only using half computation and parameters on WMT'14 and strong generalization results on formal language tasks (Logical inference and CFQ). The new halting mechanism also enables around 50\% reduction in computation during inference with very little performance decrease on formal language tasks.
Consistency of some sequential experimental design strategies for excursion set estimation based on vector-valued Gaussian processes

We tackle the extension to the vector-valued case of consistency results for Stepwise Uncertainty Reduction sequential experimental design strategies established in [Bect et al., A supermartingale approach to Gaussian process based sequential design of experiments, Bernoulli 25, 2019]. This lead us in the first place to clarify, assuming a compact index set, how the connection between continuous Gaussian processes and Gaussian measures on the Banach space of continuous functions carries over to vector-valued settings. From there, a number of concepts and properties from the aforementioned paper can be readily extended. However, vector-valued settings do complicate things for some results, mainly due to the lack of continuity for the pseudo-inverse mapping that affects the conditional mean and covariance function given finitely many pointwise observations. We apply obtained results to the Integrated Bernoulli Variance and the Expected Measure Variance uncertainty functionals employed in [Fossum et al., Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling, The Annals of Applied Statistics 15, 2021] for the estimation for excursion sets of vector-valued functions.
Orthogonal Random Features: Explicit Forms and Sharp Inequalities

Random features have been introduced to scale up kernel methods via randomization techniques. In particular, random Fourier features and orthogonal random features were used to approximate the popular Gaussian kernel. The former is performed by a random Gaussian matrix and leads exactly to the Gaussian kernel after averaging. In this work, we analyze the bias and the variance of the kernel approximation based on orthogonal random features which makes use of Haar orthogonal matrices. We provide explicit expressions for these quantities using normalized Bessel functions and derive sharp exponential bounds supporting the view that orthogonal random features are more informative than random Fourier features.
CrashTranslator: Automatically Reproducing Mobile Application Crashes Directly from Stack Trace

Crash reports are vital for software maintenance since they allow the developers to be informed of the problems encountered in the mobile application. Before fixing, developers need to reproduce the crash, which is an extremely time-consuming and tedious task. Existing studies conducted the automatic crash reproduction with the natural language described reproducing steps. Yet we find a non-neglectable portion of crash reports only contain the stack trace when the crash occurs. Such stack-trace-only crashes merely reveal the last GUI page when the crash occurs, and lack step-by-step guidance. Developers tend to spend more effort in understanding the problem and reproducing the crash, and existing techniques cannot work on this, thus calling for a greater need for automatic support. This paper proposes an approach named CrashTranslator to automatically reproduce mobile application crashes directly from the stack trace. It accomplishes this by leveraging a pre-trained Large Language Model to predict the exploration steps for triggering the crash, and designing a reinforcement learning based technique to mitigate the inaccurate prediction and guide the search holistically. We evaluate CrashTranslator on 75 crash reports involving 58 popular Android apps, and it successfully reproduces 61.3% of the crashes, outperforming the state-of-the-art baselines by 109% to 206%. Besides, the average reproducing time is 68.7 seconds, outperforming the baselines by 302% to 1611%. We also evaluate the usefulness of CrashTranslator with promising results.
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods

The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the result. In this review, we offer a comprehensive overview of prevailing methods proposed to quantify uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction

Obtaining accurate and effective information for drug molecules is a crucial and challenging task, which relies on high-quality chemical knowledge. However, chemical knowledge has been accumulated over the past 100 years from various regions, laboratories, and experimental purposes, which contains a lot of noise and inconsistency, leading to the out-of-distribution (OOD) problem. OOD may results in weak robustness and unsatisfied performance. In order to solve OOD learning problem with noise, a novel benchmark: ADMEOOD is proposed, which is a systematic OOD dataset curator and specifically designed for drug property prediction. ADMEOOD screens 27 Absorption, Distribution, Metabolism and Excretion (ADME) drug properties from Chembl and relevant literature. This paper explicitly make distinctions between two kinds of OOD data shifts: Noise Shift and Concept Conflict Drift (CCD). Overall, ADME contains 6 domain annotations combined with noise, CCD and no shifts, resulting in 18 different splits in total. ADMEOOD provides performance results on a variety of SOTA OOD models. The results demonstrate a significant difference performance between in-distribution and OOD data. Moreover, Empirical Risk Minimization and other models exhibit distinct trends in different domains and measurement types. The ADMEOOD benchmark can be accessed via https://github.com/qweasdzxc-wsy/ADMEOOD/.
A Branched Deep Convolutional Network for Forecasting the Occurrence of Hazes in Paris using Meteorological Maps with Different Characteristic Spatial Scales

A deep learning platform has been developed to forecast the occurrence of the low visibility events or hazes. It is trained by using multi-decadal daily regional maps of various meteorological and hydrological variables as input features and surface visibility observations as the targets. To better preserve the characteristic spatial information of different input features for training, two branched architectures have recently been developed for the case of Paris hazes. These new architectures have improved the performance of the network, producing reasonable scores in both validation and a blind forecasting evaluation using the data of 2021 and 2022 that have not been used in the training and validation.
PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a Language Model

Despite the remarkable progress in natural language understanding with pretrained Transformers, neural language models often do not handle commonsense knowledge well. Toward commonsense-aware models, there have been attempts to obtain knowledge, ranging from automatic acquisition to crowdsourcing. However, it is difficult to obtain a high-quality knowledge base at a low cost, especially from scratch. In this paper, we propose PHALM, a method of building a knowledge graph from scratch, by prompting both crowdworkers and a large language model (LLM). We used this method to build a Japanese event knowledge graph and trained Japanese commonsense generation models. Experimental results revealed the acceptability of the built graph and inferences generated by the trained models. We also report the difference in prompting humans and an LLM. Our code, data, and models are available at github.com/nlp-waseda/comet-atomic-ja.
Understanding the Formation of Galaxies with Warm Dark Matter

The formation of galaxies with warm dark matter is approximately adiabatic. The cold dark matter limit is singular and requires relaxation. In these lecture notes we develop, step-by-step, the physics of galaxies with warm dark matter, and their formation. The theory is validated with observed spiral galaxy rotation curves. These observations constrain the properties of the dark matter particles.
Halfway to the Peak: Spatially Resolved Star Formation and Kinematics in a z = 0.54 Dusty Galaxy with JWST/MIRI

We present JWST Mid-InfraRed Instrument/Medium-Resolution Spectrometer (MIRI/MRS) observations of an infrared luminous disk galaxy, FLS1, at z ∼ 0.54. With a lookback time of 5 Gyr, FLS1 is chronologically at the midpoint between the peak epoch of star formation and the present day. The MRS data provide maps of the atomic fine structure lines [Ar ii]6.99, [Ar iii]8.99, [Ne ii]12.81, and [Ne iii]15.55 μm, polycyclic aromatic hydrocarbon (PAH) features at 3.3, 6.2, and 11.3 μm, and the warm molecular gas indicators H2S(5) and H2S(3); all these emission features are spatially resolved. We find that the PAH emission is more extended along the northern side of the galaxy when compared to the well-studied star formation tracer [Ne ii]. The H2 rotational lines, which are shock indicators, are strongest and most extended on the southern side of the galaxy. [Ar ii] is the second brightest fine structure line detected in FLS1 and we show that it is a useful kinematic probe that can be detected with JWST out to z ∼ 3. Velocity maps of [Ar ii] show a rotating disk with signs of turbulence. Our results provide an example of how spatially resolved mid-infrared spectroscopy can allow us to better understand the star formation and interstellar medium conditions in a galaxy halfway back to the peak epoch of galaxy evolution.