Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle acceleration in solar flares with imaging-spectroscopy in soft X-rays

Particles are accelerated to very high, non-thermal energies during explosive energy-release phenomena in space, solar, and astrophysical plasma environments. In the case of solar flares, it has been established that magnetic reconnection plays an important role for releasing the magnetic energy, but it remains unclear if or how magnetic reconnection can further explain particle acceleration during flares. Here we argue that the key issue is the lack of understanding of the precise context of particle acceleration but it can be overcome, in the near future, by performing imaging-spectroscopy in soft X-rays (SXRs). Such observations should be complemented by observations in other wavelengths such as extreme-ultraviolets (EUVs), microwaves, hard X-rays (HXRs), and gamma-rays. Also, numerical simulations will be crucial for further narrowing down the particle acceleration mechanism in the context revealed by the observations. Of all these efforts, imaging-spectroscopy in SXRs, if successfully applied to large limb flares, will be a milestone in our challenge of understanding electron acceleration in solar flares and beyond, i.e. the Plasma Universe.
X-COBOL: A Dataset of COBOL Repositories

Despite being proposed as early as 1959, COBOL (Common Business-Oriented Language) still predominantly acts as an integral part of the majority of operations of several financial, banking, and governmental organizations. To support the inevitable modernization and maintenance of legacy systems written in COBOL, it is essential for organizations, researchers, and developers to understand the nature and source code of COBOL programs. However, to the best of our knowledge, we are unaware of any dataset that provides data on COBOL software projects, motivating the need for the dataset. Thus, to aid empirical research on comprehending COBOL in open-source repositories, we constructed a dataset of 84 COBOL repositories mined from GitHub, containing rich metadata on the development cycle of the projects. We envision that researchers can utilize our dataset to study COBOL projects' evolution, code properties and develop tools to support their development. Our dataset also provides 1255 COBOL files present inside the mined repositories. The dataset and artifacts are available at https://doi.org/10.5281/zenodo.7968845.
Implications of Atmospheric Nondetections for Trappist-1 Inner Planets on Atmospheric Retention Prospects for Outer Planets

JWST secondary eclipse observations of Trappist-1b seemingly disfavor atmospheres >∼1 bar since heat redistribution is expected to yield dayside emission temperature below the ∼500 K observed. Given the similar densities of Trappist-1 planets, and the theoretical potential for atmospheric erosion around late M dwarfs, this observation might be assumed to imply substantial atmospheres are also unlikely for the outer planets. However, the processes governing atmosphere erosion and replenishment are fundamentally different for inner and outer planets. Here, an atmosphere–interior evolution model is used to show that an airless Trappist-1b (and c) only weakly constrains stellar evolution, and that the odds of outer planets e and f retaining substantial atmospheres remain largely unchanged. This is true even if the initial volatile inventories of planets in the Trappist-1 system are highly correlated. The reason for this result is that b and c sit unambiguously interior to the runaway greenhouse limit, and so have potentially experienced ∼8 Gyr of X-ray and extreme ultraviolet–driven hydrodynamic escape; complete atmospheric erosion in this environment only weakly constrains stellar evolution and escape parameterizations. In contrast, e and f reside within the habitable zone, and likely experienced a comparatively short steam atmosphere during Trappist-1's pre-main sequence, and consequently complete atmospheric erosion remains unlikely across a broad swath of parameter space (e and f retain atmospheres in ∼98% of model runs). Naturally, it is still possible that all Trappist-1 planets formed volatile-poor and are all airless today. But the airlessness of b (and c) does not require this, and as such, JWST transit spectroscopy of e and f remains the best near-term opportunity to characterize the atmospheres of habitable zone terrestrial planets.
Characterization of Multi-Channel Denial-of-Service and Full-Scale Denial-of-Service

Over the past decades, interest in enhancing the safety of cyber-physical systems (CPSs) has risen. The systems and control research society has recognised that the embedded closed-loop in integrated systems may be damaged if attackers can execute a successful malicious attack. This article examines the resilient control problem for CPSs with numerous transmission channels under Denial-of-Service (DoS). First, a partial observer technique is developed in response to the Multi-Channel DoS (MCDoS) condition. The changing frequency of MCDoS is characterized while maintaining the Global Asymptotic Stability (GAS) of the closed loop system. The partial observer is modified then to reduce the effect of the changing frequency of MCDoS in the system. Then a resilient event-based feedback control scheme is developed to address the Full-Scale DoS (FSDoS). We depict the changing frequency of MCDoS and the frequency and duration of FSDoS, allowing the feedback system's Global Asymptotic Stability (GAS) to be maintained. We regard event-based controllers for which a minimal inter-sample time is precisely formulated in response to the existence of digital channels.
Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity

Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design. However, GNNs lack transparency as they cannot provide understandable explanations for their predictions. To address this issue, counterfactual reasoning is used. The main goal is to make minimal changes to the input graph of a GNN in order to alter its prediction. While several algorithms have been proposed for counterfactual explanations of GNNs, most of them have two main drawbacks. Firstly, they only consider edge deletions as perturbations. Secondly, the counterfactual explanation models are transductive, meaning they do not generalize to unseen data. In this study, we introduce an inductive algorithm called INDUCE, which overcomes these limitations. By conducting extensive experiments on several datasets, we demonstrate that incorporating edge additions leads to better counterfactual results compared to the existing methods. Moreover, the inductive modeling approach allows INDUCE to directly predict counterfactual perturbations without requiring instance-specific training. This results in significant computational speed improvements compared to baseline methods and enables scalable counterfactual analysis for GNNs.
A modified model for topic detection from a corpus and a new metric evaluating the understandability of topics

This paper presents a modified neural model for topic detection from a corpus and proposes a new metric to evaluate the detected topics. The new model builds upon the embedded topic model incorporating some modifications such as document clustering. Numerical experiments suggest that the new model performs favourably regardless of the document's length. The new metric, which can be computed more efficiently than widely-used metrics such as topic coherence, provides variable information regarding the understandability of the detected topics.
Active Inference in Hebbian Learning Networks

This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents. A generative model capturing the environment dynamics is learned by a network composed of two distinct Hebbian ensembles: a posterior network, which infers latent states given the observations, and a state transition network, which predicts the next expected latent state given current state-action pairs. Experimental studies are conducted using the Mountain Car environment from the OpenAI gym suite, to study the effect of the various Hebbian network parameters on the task performance. It is shown that the proposed Hebbian AIF approach outperforms the use of Q-learning, while not requiring any replay buffer, as in typical reinforcement learning systems. These results motivate further investigations of Hebbian learning for the design of AIF networks that can learn environment dynamics without the need for revisiting past buffered experiences.
Analysis, Identification and Prediction of Parkinson's disease sub-types and progression through Machine Learning

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with varying patient trajectories, yet little is understood about the underlying causes and symptom progression. The Parkinson's Progression Markers Initiative (PPMI) has collected comprehensive longitudinal data from diverse patient cohorts to identify biomarkers and aid in the development of interventions. Despite over 110 machine learning studies using the PPMI database, the majority have focused on supervised models for diagnosis prediction, which has limited impact on understanding patient variability and progression. This paper addresses this gap by combining supervised and unsupervised machine learning methods to identify subtypes that accurately predict disease progression in Parkinson's patients. Building upon previous work, we replicate and extend the study by integrating unsupervised patient clustering and prediction of present and future symptoms using 5 additional years of longitudinal data from the Progressive Parkinson's Markers Initiative (PPMI) database. Our findings demonstrate accurate prediction of disease trajectories and symptoms at baseline, offering valuable insights into patient heterogeneity and the potential for personalized interventions. The integration of supervised and unsupervised models presents a promising avenue for uncovering latent subgroups and understanding the complexity of Parkinson's disease progression.
Non-autoregressive Conditional Diffusion Models for Time Series Prediction

Recently, denoising diffusion models have led to significant breakthroughs in the generation of images, audio and text. However, it is still an open question on how to adapt their strong modeling ability to model time series. In this paper, we propose TimeDiff, a non-autoregressive diffusion model that achieves high-quality time series prediction with the introduction of two novel conditioning mechanisms: future mixup and autoregressive initialization. Similar to teacher forcing, future mixup allows parts of the ground-truth future predictions for conditioning, while autoregressive initialization helps better initialize the model with basic time series patterns such as short-term trends. Extensive experiments are performed on nine real-world datasets. Results show that TimeDiff consistently outperforms existing time series diffusion models, and also achieves the best overall performance across a variety of the existing strong baselines (including transformers and FiLM).
Sy-CON: Symmetric Contrastive Loss for Continual Self-Supervised Representation Learning

We introduce a novel and general loss function, called Symmetric Contrastive (Sy-CON) loss, for effective continual self-supervised learning (CSSL). We first argue that the conventional loss form of continual learning which consists of single task-specific loss (for plasticity) and a regularizer (for stability) may not be ideal for contrastive loss based CSSL that focus on representation learning. Our reasoning is that, in contrastive learning based methods, the task-specific loss would suffer from decreasing diversity of negative samples and the regularizer may hinder learning new distinctive representations. To that end, we propose Sy-CON that consists of two losses (one for plasticity and the other for stability) with symmetric dependence on current and past models' negative sample embeddings. We argue our model can naturally find good trade-off between the plasticity and stability without any explicit hyperparameter tuning. We validate the effectiveness of our approach through extensive experiments, demonstrating that MoCo-based implementation of Sy-CON loss achieves superior performance compared to other state-of-the-art CSSL methods.
A Tight Correlation between Millimeter and X-Ray Emission in Accreting Massive Black Holes from <100 mas Resolution ALMA Observations

Recent studies have proposed that the nuclear millimeter continuum emission observed in nearby active galactic nuclei (AGNs) could be created by the same population of electrons that gives rise to the X-ray emission that is ubiquitously observed in accreting black holes. We present the results of a dedicated high-spatial-resolution (∼60–100 mas) Atacama Large Millimeter/submillimeter Array (ALMA) campaign on a volume-limited (<50 Mpc) sample of 26 hard X-ray (>10 keV) selected radio-quiet AGNs. We find an extremely high detection rate (25/26 or 94−6+3% ), which shows that nuclear emission at millimeter wavelengths is nearly ubiquitous in accreting SMBHs. Our high-resolution observations show a tight correlation between the nuclear (1–23 pc) 100 GHz and the intrinsic X-ray emission (1σ scatter of 0.22 dex). The ratio between the 100 GHz continuum and the X-ray emission does not show any correlation with column density, black hole mass, Eddington ratio, or star formation rate, which suggests that the 100 GHz emission can be used as a proxy of SMBH accretion over a very broad range of these parameters. The strong correlation between 100 GHz and X-ray emission in radio-quiet AGNs could be used to estimate the column density based on the ratio between the observed 2–10 keV ( F2–10keVobs ) and 100 GHz (F 100 GHz) fluxes. Specifically, a ratio log(F2–10keVobs/F100GHz)≤3.5 strongly suggests that a source is heavily obscured ( log(NH/cm−2)≳23.8 ). Our work shows the potential of ALMA continuum observations to detect heavily obscured AGNs (up to an optical depth of one at 100 GHz, i.e., N H ≃ 1027 cm−2), and to identify binary SMBHs with separations <100 pc, which cannot be probed by current X-ray facilities.
Causal Fairness for Outcome Control

As society transitions towards an AI-based decision-making infrastructure, an ever-increasing number of decisions once under control of humans are now delegated to automated systems. Even though such developments make various parts of society more efficient, a large body of evidence suggests that a great deal of care needs to be taken to make such automated decision-making systems fair and equitable, namely, taking into account sensitive attributes such as gender, race, and religion. In this paper, we study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable. The interest in such a setting ranges from interventions related to criminal justice and welfare, all the way to clinical decision-making and public health. In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision, counterfactually speaking, when contrasted with an alternative, negative one. We introduce the notion of benefit fairness, which can be seen as the minimal fairness requirement in decision-making, and develop an algorithm for satisfying it. We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this. Finally, if some of the variations of the protected attribute in the benefit are considered as discriminatory, the notion of benefit fairness may need to be strengthened, which leads us to articulating a notion of causal benefit fairness. Using this notion, we develop a new optimization procedure capable of maximizing $Y$ while ascertaining causal fairness in the decision process.
AutoCharge: Autonomous Charging for Perpetual Quadrotor Missions

Battery endurance represents a key challenge for long-term autonomy and long-range operations, especially in the case of aerial robots. In this paper, we propose AutoCharge, an autonomous charging solution for quadrotors that combines a portable ground station with a flexible, lightweight charging tether and is capable of universal, highly efficient, and robust charging. We design and manufacture a pair of circular magnetic connectors to ensure a precise orientation-agnostic electrical connection between the ground station and the charging tether. Moreover, we supply the ground station with an electromagnet that largely increases the tolerance to localization and control errors during the docking maneuver, while still guaranteeing smooth un-docking once the charging process is completed. We demonstrate AutoCharge on a perpetual 10 hours quadrotor flight experiment and show that the docking and un-docking performance is solidly repeatable, enabling perpetual quadrotor flight missions.
A Semi-supervised Object Detection Algorithm for Underwater Imagery

Detection of artificial objects from underwater imagery gathered by Autonomous Underwater Vehicles (AUVs) is a key requirement for many subsea applications. Real-world AUV image datasets tend to be very large and unlabelled. Furthermore, such datasets are typically imbalanced, containing few instances of objects of interest, particularly when searching for unusual objects in a scene. It is therefore, difficult to fit models capable of reliably detecting these objects. Given these factors, we propose to treat artificial objects as anomalies and detect them through a semi-supervised framework based on Variational Autoencoders (VAEs). We develop a method which clusters image data in a learned low-dimensional latent space and extracts images that are likely to contain anomalous features. We also devise an anomaly score based on extracting poorly reconstructed regions of an image. We demonstrate that by applying both methods on large image datasets, human operators can be shown candidate anomalous samples with a low false positive rate to identify objects of interest. We apply our approach to real seafloor imagery gathered by an AUV and evaluate its sensitivity to the dimensionality of the latent representation used by the VAE. We evaluate the precision-recall tradeoff and demonstrate that by choosing an appropriate latent dimensionality and threshold, we are able to achieve an average precision of 0.64 on unlabelled datasets.
Sequential Graph Neural Networks for Source Code Vulnerability Identification

Vulnerability identification constitutes a task of high importance for cyber security. It is quite helpful for locating and fixing vulnerable functions in large applications. However, this task is rather challenging owing to the absence of reliable and adequately managed datasets and learning models. Existing solutions typically rely on human expertise to annotate datasets or specify features, which is prone to error. In addition, the learning models have a high rate of false positives. To bridge this gap, in this paper, we present a properly curated C/C++ source code vulnerability dataset, denoted as CVEFunctionGraphEmbeddings (CVEFGE), to aid in developing models. CVEFGE is automatically crawled from the CVE database, which contains authentic and publicly disclosed source code vulnerabilities. We also propose a learning framework based on graph neural networks, denoted SEquential Graph Neural Network (SEGNN) for learning a large number of code semantic representations. SEGNN consists of a sequential learning module, graph convolution, pooling, and fully connected layers. Our evaluations on two datasets and four baseline methods in a graph classification setting demonstrate state-of-the-art results.
Learned Spatial Data Partitioning

Due to the significant increase in the size of spatial data, it is essential to use distributed parallel processing systems to efficiently analyze spatial data. In this paper, we first study learned spatial data partitioning, which effectively assigns groups of big spatial data to computers based on locations of data by using machine learning techniques. We formalize spatial data partitioning in the context of reinforcement learning and develop a novel deep reinforcement learning algorithm. Our learning algorithm leverages features of spatial data partitioning and prunes ineffective learning processes to find optimal partitions efficiently. Our experimental study, which uses Apache Sedona and real-world spatial data, demonstrates that our method efficiently finds partitions for accelerating distance join queries and reduces the workload run time by up to 59.4%.
U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging

With the increasing prevalence of machine learning in critical fields like healthcare, ensuring the safety and reliability of these systems is crucial. Estimating uncertainty plays a vital role in enhancing reliability by identifying areas of high and low confidence and reducing the risk of errors. This study introduces U-PASS, a specialized human-centered machine learning pipeline tailored for clinical applications, which effectively communicates uncertainty to clinical experts and collaborates with them to improve predictions. U-PASS incorporates uncertainty estimation at every stage of the process, including data acquisition, training, and model deployment. Training is divided into a supervised pre-training step and a semi-supervised recording-wise finetuning step. We apply U-PASS to the challenging task of sleep staging and demonstrate that it systematically improves performance at every stage. By optimizing the training dataset, actively seeking feedback from domain experts for informative samples, and deferring the most uncertain samples to experts, U-PASS achieves an impressive expert-level accuracy of 85% on a challenging clinical dataset of elderly sleep apnea patients. This represents a significant improvement over the starting point at 75% accuracy. The largest improvement gain is due to the deferral of uncertain epochs to a sleep expert. U-PASS presents a promising AI approach to incorporating uncertainty estimation in machine learning pipelines, improving their reliability and unlocking their potential in clinical settings.
Bayesian Inference for $k$-Monotone Densities with Applications to Multiple Testing

Shape restriction, like monotonicity or convexity, imposed on a function of interest, such as a regression or density function, allows for its estimation without smoothness assumptions. The concept of $k$-monotonicity encompasses a family of shape restrictions, including decreasing and convex decreasing as special cases corresponding to $k=1$ and $k=2$. We consider Bayesian approaches to estimate a $k$-monotone density. By utilizing a kernel mixture representation and putting a Dirichlet process or a finite mixture prior on the mixing distribution, we show that the posterior contraction rate in the Hellinger distance is $(n/\log n)^{- k/(2k + 1)}$ for a $k$-monotone density, which is minimax optimal up to a polylogarithmic factor. When the true $k$-monotone density is a finite $J_0$-component mixture of the kernel, the contraction rate improves to the nearly parametric rate $\sqrt{(J_0 \log n)/n}$. Moreover, by putting a prior on $k$, we show that the same rates hold even when the best value of $k$ is unknown. A specific application in modeling the density of $p$-values in a large-scale multiple testing problem is considered. Simulation studies are conducted to evaluate the performance of the proposed method.