Miracle-dz/UniFAR-FTUs huggingface dataset:
Observing the Time Evolution of the Multicomponent Nucleus of 3C 84

The advent of global millimeter-band very long baseline interferometry (VLBI) in recent years has finally revealed the morphology of the base of the two most prominent nearby, bright, extragalactic radio jets in M87 and 3C 84. The images are quite surprising considering the predictions of jet theory and current numerical modeling. The jet bases are extremely wide compared to expectations, and the nucleus of 3C 84 is very complicated. It appears as a double nucleus in 86 GHz observations with 50 μas resolution and a triple nucleus with 30 μas resolution with space-based VLBI by RadioAstron at 22 GHz. What is even odder is that the double and triple nuclei are arranged along an east–west line that is approximately orthogonal to the north–south large-scale jet on 150 μas−4 mas scales. We explore the emergence of an (east–west) double nucleus in the lower-resolution 43 GHz Very Long Baseline Array (VLBA) imaging from 2018 August to 2020 April. The double nucleus is marginally resolved. We exploit the east–west resolution associated with the longest baselines, ∼0.08 mas, to track a predominantly east–west separation speed of ≈0.086 ± 0.008c. We estimate that the observed mildly relativistic speed persists over a deprojected distance of ∼1900–9800 times the central, supermassive black hole gravitational radius (∼0.3–1.5 lt-yr) from the point of origin.
Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Time

We consider combinatorial semi-bandits with uncorrelated Gaussian rewards. In this article, we propose the first method, to the best of our knowledge, that enables to compute the solution of the Graves-Lai optimization problem in polynomial time for many combinatorial structures of interest. In turn, this immediately yields the first known approach to implement asymptotically optimal algorithms in polynomial time for combinatorial semi-bandits.
Tracing projective modules over noncommutative orbifolds

For an action of a finite cyclic group $F$ on an $n$-dimensional noncommutative torus $A_\theta,$ we give sufficient conditions when the fundamental projective modules over $A_\theta$, which determine the range of the canonical trace on $A_\theta,$ extend to projective modules over the crossed product C*-algebra $A_\theta \rtimes F. $ Our results allow us to understand the range of the canonical trace on $A_\theta \rtimes F$, and determine it completely for several examples including the crossed products of 2-dimensional noncommutative tori with finite cyclic groups and the flip action of $\mathbb{Z}_2$ on any $n$-dimensional noncommutative torus. As an application, for the flip action of $\mathbb{Z}_2$ on a simple $n$-dimensional torus $A_\theta$, we determine the Morita equivalence class of $A_\theta \rtimes \mathbb{Z}_2,$ in terms of the Morita equivalence class of $A_\theta. $
Event-Chain Monte Carlo: Foundations, Applications, and Prospects

This review treats the mathematical and algorithmic foundations of non-reversible Markov chains in the context of event-chain Monte Carlo (ECMC), a continuous-time lifted Markov chain that employs the factorized Metropolis algorithm. It analyzes a number of model applications and then reviews the formulation as well as the performance of ECMC in key models in statistical physics. Finally, the review reports on an ongoing initiative to apply ECMC to the sampling problem in molecular simulation, i.e., to real-world models of peptides, proteins, and polymers in aqueous solution.
Transition region adaptive conduction (TRAC) in multidimensional magnetohydrodynamic simulations

Context. In solar physics, a severe numerical challenge for modern simulations is properly representing a transition region between the million-degree hot corona and a much cooler plasma of about 10 000 K (e.g., the upper chromosphere or a prominence). In previous 1D hydrodynamic simulations, the transition region adaptive conduction (TRAC) method has been proven to capture aspects better that are related to mass evaporation and energy exchange. Aims. We aim to extend this method to fully multidimensional magnetohydrodynamic (MHD) settings, as required for any realistic application in the solar atmosphere. Because modern MHD simulation tools efficiently exploit parallel supercomputers and can handle automated grid refinement, we design strategies for any-dimensional block grid-adaptive MHD simulations. Methods. We propose two different strategies and demonstrate their working with our open-source MPI-AMRVAC code. We benchmark both strategies on 2D prominence formation based on the evaporation–condensation scenario, where chromospheric plasma is evaporated through the transition region and then is collected and ultimately condenses in the corona. Results. A field-line-based TRACL method and a block-based TRACB method are introduced and compared in block grid-adaptive 2D MHD simulations. Both methods yield similar results and are shown to satisfactorily correct the underestimated chromospheric evaporation, which comes from a poor spatial resolution in the transition region. Conclusions. Because fully resolving the transition region in multidimensional MHD settings is virtually impossible, TRACB or TRACL methods will be needed in any 2D or 3D simulations involving transition region physics.
Urban Metric Maps for Small Unmanned Aircraft Systems Motion Planning

Low-altitude urban flight planning for small Unmanned Aircraft Systems (UAS) requires accurate vehicle, environment maps, and risk models to assure flight plans consider the urban landscape as well as airspace constraints. This paper presents a suite of motion planning metrics designed for small UAS urban flight. We define map-based and path-based metrics to holistically characterize motion plan quality. Proposed metrics are examined in the context of representative geometric, graph-based, and sampling-based motion planners applied to a multicopter small UAS. A novel multi-objective heuristic is proposed and applied for graph-based and sampling motion planners at four urban UAS flight altitude layers. Monte Carlo case studies in a New York City urban environment illustrate metric map properties and planner performance. Motion plans are evaluated as a function of planning algorithm, location, range, and flight altitude.
Data-Driven Vulnerability Detection and Repair in Java Code

Java platform provides various APIs to facilitate secure coding. However, correctly using security APIs is usually challenging for developers who lack cybersecurity training. Prior work shows that many developers misuse security APIs; such misuses can introduce vulnerabilities into software, void security protections, and present security exploits to hackers. To eliminate such API-related vulnerabilities, this paper presents SEADER -- our new approach that detects and repairs security API misuses. Given an exemplar, insecure code snippet, and its secure counterpart, SEADER compares the snippets and conducts data dependence analysis to infer the security API misuse templates and corresponding fixing operations. Based on the inferred information, given a program, SEADER performs inter-procedural static analysis to search for any security API misuse and to propose customized fixing suggestions for those vulnerabilities. To evaluate SEADER, we applied it to 25code pairs, and SEADER successfully inferred 18 unique API misuse templates and related fixes. With these vulnerability repair patterns, we further applied SEADER to 10 open-source projects that contain in total 32 known vulnerabilities. Our experiment shows that SEADER detected vulnerabilities with 100% precision, 84% recall, and 91% accuracy. Additionally, we applied SEADER to 100 Apache open-source projects and detected 988 vulnerabilities; SEADER always customized repair suggestions correctly. Based on SEADER's outputs, we filed 60 pull requests. Up till now, developers of 18 projects have offered positive feedbacks on SEADER's suggestions. Our results indicate that SEADER can effectively help developers detect and fix security API misuses. Whereas prior work either detects API misuses or suggests simple fixes, SEADER is the first tool to do both for nontrivial vulnerability repairs.
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning

Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find the proper spots for charging, because of the limited charging infrastructures and the spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve the charging experience from various aspects over a long-term horizon. In this paper, we propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning (Master), for intelligently recommending public accessible charging stations by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an individual agent, we formulate this problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with the centralized attentive critic to coordinate the recommendation between geo-distributed agents. Moreover, to quantify the influence of future potential charging competition, we introduce a delayed access strategy to exploit the knowledge of future charging competition during training. After that, to effectively optimize multiple learning objectives, we extend the centralized attentive critic to multi-critics and develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction. Finally, extensive experiments on two real-world datasets demonstrate that Master achieves the best comprehensive performance compared with nine baseline approaches.
Attention-gated convolutional neural networks for off-resonance correction of spiral real-time MRI

Spiral acquisitions are preferred in real-time MRI because of their efficiency, which has made it possible to capture vocal tract dynamics during natural speech. A fundamental limitation of spirals is blurring and signal loss due to off-resonance, which degrades image quality at air-tissue boundaries. Here, we present a new CNN-based off-resonance correction method that incorporates an attention-gate mechanism. This leverages spatial and channel relationships of filtered outputs and improves the expressiveness of the networks. We demonstrate improved performance with the attention-gate, on 1.5 Tesla spiral speech RT-MRI, compared to existing off-resonance correction methods.
Point-line-based RGB-D SLAM and Bundle Adjustment Uncertainty Analysis

Most of the state-of-the-art indirect visual SLAM methods are based on the sparse point features. However, it is hard to find enough reliable point features for state estimation in the case of low-textured scenes. Line features are abundant in urban and indoor scenes. Recent studies have shown that the combination of point and line features can provide better accuracy despite the decrease in computational efficiency. In this paper, measurements of point and line features are extracted from RGB-D data to create map features, and points on a line are treated as keypoints. We propose an extended approach to make more use of line observation information. And we prove that, in the local bundle adjustment, the estimation uncertainty of keyframe poses can be reduced when considering more landmarks with independent measurements in the optimization process. Experimental results on two public RGB-D datasets demonstrate that the proposed method has better robustness and accuracy in challenging environments.
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset

The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature of such deep black-boxed models, concerns about interpretability, fairness, and biases in healthcare scenarios where human lives are at stake call for a careful and thorough examination of both datasets and models. In this work, we focus on MIMIC-IV (Medical Information Mart for Intensive Care, version IV), the largest publicly available healthcare dataset, and conduct comprehensive analyses of dataset representation bias as well as interpretability and prediction fairness of deep learning models for in-hospital mortality prediction. In terms of interpretability, we observe that (1) the best-performing interpretability method successfully identifies critical features for mortality prediction on various prediction models; (2) demographic features are important for prediction. In terms of fairness, we observe that (1) there exists disparate treatment in prescribing mechanical ventilation among patient groups across ethnicity, gender and age; (2) all of the studied mortality predictors are generally fair while the IMV-LSTM (Interpretable Multi-Variable Long Short-Term Memory) model provides the most accurate and unbiased predictions across all protected groups. We further draw concrete connections between interpretability methods and fairness metrics by showing how feature importance from interpretability methods can be beneficial in quantifying potential disparities in mortality predictors.
Improved estimators for semi-supervised high-dimensional regression model

We study a linear high-dimensional regression model in a semi-supervised setting, where for many observations only the vector of covariates $X$ is given with no response $Y$. We do not make any sparsity assumptions on the vector of coefficients, and aim at estimating $\mathrm{Var}(Y|X)$. We propose an estimator, which is unbiased, consistent, and asymptotically normal. This estimator can be improved by adding zero-estimators arising from the unlabelled data. Adding zero-estimators does not affect the bias and potentially can reduce variance. In order to achieve optimal improvement, many zero-estimators should be used, but this raises the problem of estimating many parameters. Therefore, we introduce covariate selection algorithms that identify which zero-estimators should be used in order to improve the above estimator. We further illustrate our approach for other estimators, and present an algorithm that improves estimation for any given variance estimator. Our theoretical results are demonstrated in a simulation study.
Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study

Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendations are typically organized into categories based on their analytical actions, i.e., operations employed to transition from the current exploration state to a recommended visualization. However, despite the emergence of a plethora of VisRec systems in recent work, the utility of the categories employed by these systems in analytical workflows has not been systematically investigated. Our article explores the efficacy of recommendation categories by formalizing a taxonomy of common categories and developing a system, Frontier, that implements these categories. Using Frontier, we evaluate workflow strategies adopted by users and how categories influence those strategies. Participants found recommendations that add attributes to enhance the current visualization and recommendations that filter to sub-populations to be comparatively most useful during data exploration. Our findings pave the way for next-generation VisRec systems that are adaptive and personalized via carefully chosen, effective recommendation categories.
Towards automatic extraction and validation of on-street parking spaces using park-out events data

—This article proposes two different approaches to automatically create a map for valid on-street car parking spaces. For this, we use park-out events data from car2go. The ﬁrst one uses spatial aggregation and the second a machine learning algorithm. For the former, we chose rasterization and road sectioning; for the latter we chose decision trees. We compare the results of these approaches and discuss their advantages and disadvantages. Furthermore, we show our results for a neighborhood in the city of Berlin and report a classiﬁcation accuracy of 92% on the original imbalanced data. Finally, we discuss further work; from gathering more data over a longer period of time to ﬁtting spatial Gaussian densities to the data and the usage of apps for manual validation and annotation of parking spaces to improve ground truth data.
Geometric feature performance under downsampling for EEG classification tasks

We experimentally investigate a collection of feature engineering pipelines for use with a CNN for classifying eyes-open or eyes-closed from electroencephalogram (EEG) time-series from the Bonn dataset. Using the Takens' embedding--a geometric representation of time-series--we construct simplicial complexes from EEG data. We then compare $\epsilon$-series of Betti-numbers and $\epsilon$-series of graph spectra (a novel construction)--two topological invariants of the latent geometry from these complexes--to raw time series of the EEG to fill in a gap in the literature for benchmarking. These methods, inspired by Topological Data Analysis, are used for feature engineering to capture local geometry of the time-series. Additionally, we test these feature pipelines' robustness to downsampling and data reduction. This paper seeks to establish clearer expectations for both time-series classification via geometric features, and how CNNs for time-series respond to data of degraded resolution.
Hybrid phonetic-neural model for correction in speech recognition systems

Automatic speech recognition (ASR) is a relevant area in multiple settings because it provides a natural communication mechanism between applications and users. ASRs often fail in environments that use language specific to particular application domains. Some strategies have been explored to reduce errors in closed ASRs through post-processing, particularly automatic spell checking, and deep learning approaches. In this article, we explore using a deep neural network to refine the results of a phonetic correction algorithm applied to a telesales audio database. The results exhibit a reduction in the word error rate (WER), both in the original transcription and in the phonetic correction, which shows the viability of deep learning models together with post-processing correction strategies to reduce errors made by closed ASRs in specific language domains.
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning

Combinatorial optimization problem (COP) over graphs is a fundamental challenge in optimization. Reinforcement learning (RL) has recently emerged as a new framework to tackle these problems and has demonstrated promising results. However, most RL solutions employ a greedy manner to construct the solution incrementally, thus inevitably pose unnecessary dependency on action sequences and need a lot of problem-speciﬁc designs. We propose a general RL framework that not only exhibits state-of-the-art empirical perfor- mance but also generalizes to a variety class of COPs. Specif-ically, we deﬁne state as a solution to a problem instance and action as a perturbation to this solution. We utilize graph neural networks (GNN) to extract latent representations for given problem instances for state-action encoding, and then apply deep Q-learning to obtain a policy that gradually reﬁnes the solution by ﬂipping or swapping vertex labels. Experiments are conducted on Maximum k -Cut and Traveling Salesman Problem and performance improvement is achieved against a set of learning-based and heuristic baselines.
Applicability of Random Matrix Theory in Deep Learning

We investigate the local spectral statistics of the loss surface Hessians of artiﬁcial neural networks, where we discover excellent agreement with Gaussian Orthogonal Ensemble statistics across several network architectures and datasets. These results shed new light on the applicability of Random Matrix Theory to modelling neural networks and suggest a previously unrecognised role for it in the study of loss surfaces in deep learning. Inspired by these observations, we propose a novel model for the true loss surfaces of neural networks, consistent with our observations, which allows for Hessian spectral densities with rank degeneracy and outliers, extensively observed in practice, and predicts a growing independence of loss gradients as a function of distance in weight-space. We further investigate the importance of the true loss surface in neural networks and ﬁnd, in contrast to previous work, that the exponential hardness of locating the global minimum has practical consequences for achieving state of the art performance.
High‐resolution force microscopy of in‐plane magnetization

We have shown for the first time, in previous studies, that the force microscope fitted with a magnetic tip could be used to image magnetic domains in TbFe thin films. In this report we show that the information provided by the magnetic force microscope can also include a measurement of the component of magnetization in the sample which is parallel to the surface. Measurements were taken with a magnetized tip tilted at 45° with respect to the surface normal. In a first experiment, we imaged 1 μm diameter domains thermomagnetically written in a TbFe thin film. In a second measurement, we imaged a series of alternating domains in a thin film Co‐alloy disc which was decorated with small magnetized particles, allowing unambiguous identification of the domain boundaries. In both cases we interpret the asymmetric portion of the images to the in‐plane component of the sample magnetization.
The Wolf–Rayet + Black Hole Binary NGC 300 X-1: What is the Mass of the Black Hole?

We present new X-ray and UV observations of the Wolf–Rayet + black hole (BH) binary system NGC 300 X-1 with the Chandra X-ray Observatory and the Hubble Space Telescope Cosmic Origins Spectrograph. When combined with archival X-ray observations, our X-ray and UV observations sample the entire binary orbit, providing clues to the system geometry and interaction between the BH accretion disk and the donor star wind. We measure a binary orbital period of 32.7921 ± 0.0003 hr, in agreement with previous studies, and perform phase-resolved spectroscopy using the X-ray data. The X-ray light curve reveals a deep eclipse, consistent with inclination angles of i = 60°–75°, and a pre-eclipse excess consistent with an accretion stream impacting the disk edge. We further measure radial velocity variations for several prominent far-UV spectral lines, most notably H ii λ1640 and C iv λ1550. We find that the He ii emission lines systematically lag the expected Wolf–Rayet star orbital motion by a phase difference of Δϕ ∼ 0.3, while C iv λ1550 matches the phase of the anticipated radial velocity curve of the Wolf–Rayet donor. We assume the C iv λ1550 emission line follows a sinusoidal radial velocity curve (semi-amplitude = 250 km s−1) and infer a BH mass of 17 ± 4 M ⊙. Our observations are consistent with the presence of a wind-Roche lobe overflow accretion disk, where an accretion stream forms from gravitationally focused wind material and impacts the edge of the BH accretion disk.