Miracle-dz/UniFAR-FTUs huggingface dataset:
A fully kinetic model for orphan gamma-ray flares in blazars

Blazars emit a highly variable non-thermal spectrum. It is usually assumed that the same non-thermal electrons are responsible for the IR-optical-UV emission (via synchrotron) and the gamma-ray emission (via inverse Compton). Hence, the light curves in the two bands should be correlated. Orphan gamma-ray flares (i.e. lacking a luminous low-frequency counterpart) challenge our theoretical understanding of blazars. By means of large-scale two-dimensional radiative particle-in-cell simulations, we show that orphan gamma-ray flares may be a self-consistent by-product of particle energization in turbulent magnetically dominated pair plasmas. The energized particles produce the gamma-ray flare by inverse Compton scattering an external radiation field, while the synchrotron luminosity is heavily suppressed since the particles are accelerated nearly along the direction of the local magnetic field. The ratio of inverse Compton to synchrotron luminosity is sensitive to the initial strength of turbulent fluctuations (a larger degree of turbulent fluctuations weakens the anisotropy of the energized particles, thus increasing the synchrotron luminosity). Our results show that the anisotropy of the non-thermal particle population is key to modelling the blazar emission.
Text-to-Audio Grounding: Building Correspondence Between Captions and Sound Events

Automated Audio Captioning is a cross-modal task, generating natural language descriptions to summarize the audio clips’ sound events. However, grounding the actual sound events in the given audio based on its corresponding caption has not been investigated. This paper contributes an Audio-Grounding dataset1, which provides the correspondence be-tween sound events and the captions provided in Audiocaps, along with the location (timestamps) of each present sound event. Based on such, we propose the text-to-audio grounding (TAG) task, which interactively considers the relationship be-tween audio processing and language understanding. A base-line approach is provided, resulting in an event-F1 score of 28.3% and a Polyphonic Sound Detection Score (PSDS) score of 14.7%.
Interpret-able feedback for AutoML systems

Automated machine learning (AutoML) systems aim to enable training machine learning (ML) models for non-ML experts. A shortcoming of these systems is that when they fail to produce a model with high accuracy, the user has no path to improve the model other than hiring a data scientist or learning ML -- this defeats the purpose of AutoML and limits its adoption. We introduce an interpretable data feedback solution for AutoML. Our solution suggests new data points for the user to label (without requiring a pool of unlabeled data) to improve the model's accuracy. Our solution analyzes how features influence the prediction among all ML models in an AutoML ensemble, and we suggest more data samples from feature ranges that have high variance in such analysis. Our evaluation shows that our solution can improve the accuracy of AutoML by 7-8% and significantly outperforms popular active learning solutions in data efficiency, all the while providing the added benefit of being interpretable.
Atomic-Scale Structural Fluctuations of a Plasmonic Cavity

Optical spectromicroscopies, which can reach atomic resolution due to plasmonic enhancement, are perturbed by spontaneous intensity modifications. Here, we study such fluctuations in plasmonic electroluminescence at the single-atom limit profiting from the precision of a low-temperature scanning tunneling microscope. First, we investigate the influence of a controlled single-atom transfer from the tip to the sample on the plasmonic properties of the junction. Next, we form a well-defined atomic contact of several quanta of conductance. In contact, we observe changes of the electroluminescence intensity that can be assigned to spontaneous modifications of electronic conductance, plasmonic excitation, and optical antenna properties all originating from minute atomic rearrangements at or near the contact. Our observations are relevant for the understanding of processes leading to spontaneous intensity variations in plasmon-enhanced atomic-scale spectroscopies such as intensity blinking in picocavities.
A Quick Look at the 3 GHz Radio Sky. I. Source Statistics from the Very Large Array Sky Survey

The Very Large Array Sky Survey (VLASS) is observing the entire sky north of −40° in the S band (2 GHz < ν < 4 GHz), with the highest angular resolution (2.″5) of any all-sky radio continuum survey to date. VLASS will cover its entire footprint over three distinct epochs, the first of which has now been observed in full. Based on Quick Look images from this first epoch, we have created a catalog of 1.9 × 106 reliably detected radio components. Due to the limitations of the Quick Look images, component flux densities are underestimated by ∼15% at S peak > 3 mJy beam−1 and are often unreliable for fainter components. We use this catalog to perform statistical analyses of the ν ∼ 3 GHz radio sky. Comparisons with the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey show the typical 1.4–3 GHz spectral index, α, to be ∼−0.71. The radio color–color distribution of point and extended components is explored by matching with FIRST and the LOFAR Two-meter Sky Survey. We present the VLASS source counts, dN/dS, which are found to be consistent with previous observations at 1.4 and 3 GHz. Resolution improvements over FIRST result in excess power in the VLASS two-point correlation function at angular scales ≲7″, and in 18% of active galactic nuclei associated with a single FIRST component being split into multicomponent sources by VLASS.
Bayesian nonparametric quantile process regression and estimation of marginal quantile effects

Flexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy‐related factors on low and high birth weight. We propose a Bayesian nonparametric method to simultaneously estimate noncrossing, nonlinear quantile curves. We expand the conditional distribution function of the response in I‐spline basis functions where the covariate‐dependent coefficients are modeled using neural networks. By leveraging the approximation power of splines and neural networks, our model can approximate any continuous quantile function. Compared to existing models, our model estimates all rather than a finite subset of quantiles, scales well to high dimensions, and accounts for estimation uncertainty. While the model is arbitrarily flexible, interpretable marginal quantile effects are estimated using accumulative local effect plots and variable importance measures. A simulation study shows that our model can better recover quantiles of the response distribution when the data are sparse, and an analysis of birth weight data is presented.
A Pluto–Charon Concerto. II. Formation of a Circumbinary Disk of Debris after the Giant Impact

Using a suite of numerical calculations, we consider the long-term evolution of circumbinary debris from the Pluto–Charon giant impact. Initially, these solids have large eccentricity and pericenters near Charon’s orbit. On timescales of 100–1000 yr, dynamical interactions with Pluto and Charon lead to the ejection of most solids from the system. As the dynamics moves particles away from the barycenter, collisional damping reduces the orbital eccentricity of many particles. These solids populate a circumbinary disk in the Pluto–Charon orbital plane; a large fraction of this material lies within a “satellite zone” that encompasses the orbits of Styx, Nix, Kerberos, and Hydra. Compared to the narrow rings generated from the debris of a collision between a trans-Neptunian object and Charon, disks produced after the giant impact are much more extended and may be a less promising option for producing small circumbinary satellites.
Identifying Physical Law of Hamiltonian Systems via Meta-Learning

Hamiltonian mechanics is an effective tool to represent many physical processes with concise yet well-generalized mathematical expressions. A well-modeled Hamiltonian makes it easy for researchers to analyze and forecast many related phenomena that are governed by the same physical law. However, in general, identifying a functional or shared expression of the Hamiltonian is very difficult. It requires carefully designed experiments and the researcher's insight that comes from years of experience. We propose that meta-learning algorithms can be potentially powerful data-driven tools for identifying the physical law governing Hamiltonian systems without any mathematical assumptions on the representation, but with observations from a set of systems governed by the same physical law. We show that a well meta-trained learner can identify the shared representation of the Hamiltonian by evaluating our method on several types of physical systems with various experimental settings.
Solving high-dimensional parabolic PDEs using the tensor train format

High-dimensional partial differential equations (PDEs) are ubiquitous in economics, science and engineering. However, their numerical treatment poses formidable challenges since traditional grid-based methods tend to be frustrated by the curse of dimensionality. In this paper, we argue that tensor trains provide an appealing approximation framework for parabolic PDEs: the combination of reformulations in terms of backward stochastic differential equations and regression-type methods in the tensor format holds the promise of leveraging latent low-rank structures enabling both compression and efficient computation. Following this paradigm, we develop novel iterative schemes, involving either explicit and fast or implicit and accurate updates. We demonstrate in a number of examples that our methods achieve a favorable trade-off between accuracy and computational efficiency in comparison with state-of-the-art neural network based approaches.
Accelerating Recursive Partition-Based Causal Structure Learning

Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns and self-driving cars. This is essential to solve well-known causal decision making and prediction problems associated with those real-world applications. Recently, recursive causal discovery algorithms have gained particular attention among the research community due to their ability to provide good results by using Conditional Independent (CI) tests in smaller sub-problems. However, each of such algorithms needs a refinement function to remove undesired causal relations of the discovered graphs. Notably, with the increase of the problem size, the computation cost (i.e., the number of CI-tests) of the refinement function makes an algorithm expensive to deploy in practice. This paper proposes a generic causal structure refinement strategy that can locate the undesired relations with a small number of CI-tests, thus speeding up the algorithm for large and complex problems. We theoretically prove the correctness of our algorithm. We then empirically evaluate its performance against the state-of-the-art algorithms in terms of solution quality and completion time in synthetic and real datasets.
Scribe: Analiza statistica a parametrilor metodologici în instruirea instrumentală

REZUMAT Metodologiile de predare centrate pe student din educația instrumentală abordează nevoile generale și specifice ale studenților atât în context individual, cât și de ansamblu. Profesorii eficienți reprezintă modele semnificative de succes pentru procesul educațional; cu toate acestea, transferul aptitudinilor acestora către alți profesori poate avea o semnificație sporită în ceea ce privește procesul educațional, deoarece succesul reproductibil poate depăși în importanță succesul izolat. Soluțiile eficiente care răspund cu mare flexibilitate nevoilor studenților atât în context individual, cât și în ansamblu, se bazează nu atât pe realizarea cu succes a obiectivelor educaționale, cât pe gestionarea atentă a proceselor educaționale care rezultă în mod inevitabil în succes educațional, punând accentul mai mult pe importanța procesului decât doar pe cea a rezultatului. Prin urmare, o asemenea abordare poate beneficia din folosirea de instrumente de evaluare capabile să cuantifice procesul educațional pe parcursul desfășurării sale, cum ar fi analiza statistică, atât ca instrument în sine, cât și în conjuncție cu platforme digitale care permit colectarea și analiza de date în timp real, cum ar fi MaxMSP sau Pure Data. Folosită ca instrument complementar de investigație, analiza statistică are potențialul de a dezvălui informații detaliate însă de obicei trecute cu vederea, despre procesul educațional, precum și mecanisme potențiale pentru perfecționarea acestuia. Propunem că eficacitatea abordărilor metodologice poate fi monitorizată, evaluată și adaptată nevoilor educaționale specifice utilizând Scribe, un program de analiză a datelor dezvoltat la Universitatea din Texas. Scribe este capabil să importe înregistrarea video a unei sesiuni de predare și să marcheze elemente importante ale procesului de predare-învățare în scopul unei analize discrete.
Unsupervised Learning of Lidar Features for Use ina Probabilistic Trajectory Estimator

We present unsupervised parameter learning in a Gaussian variational inference setting that combines classic trajectory estimation for mobile robots with deep learning for rich sensor data, all under a single learning objective. The framework is an extension of an existing system identification method that optimizes for the observed data likelihood, which we improve with modern advances in batch trajectory estimation and deep learning. Though the framework is general to any form of parameter learning and sensor modality, we demonstrate application to feature and uncertainty learning with a deep network for 3D lidar odometry. Our framework learns from only the on-board lidar data, and does not require any form of groundtruth supervision. We demonstrate that our lidar odometry performs better than existing methods that learn the full estimator with a deep network, and comparable to state-of-the-art ICP-based methods on the KITTI odometry dataset. We additionally show results on lidar data from the Oxford RobotCar dataset.
Modeling Multi-Destination Trips with Sketch-Based Model

The recently proposed EMDE (Efficient Manifold Density Estimator) model achieves state of-the-art results in session-based recommendation. In this work we explore its application to Booking Data Challenge competition. The aim of the challenge is to make the best recommendation for the next destination of a user trip, based on dataset with millions of real anonymized accommodation reservations. We achieve 2nd place in this competition. First, we use Cleora - our graph embedding method - to represent cities as a directed graph and learn their vector representation. Next, we apply EMDE to predict the next user destination based on previously visited cities and some features associated with each trip. We release the source code at: https://github.com/Synerise/booking-challenge.
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning

Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy industry. We develop a new data-driven AI system, namely DeepThermal, to optimize the combustion control strategy for TPGUs. At its core, is a new model-based offline reinforcement learning (RL) framework, called MORE, which leverages historical operational data of a TGPU to solve a highly complex constrained Markov decision process problem via purely offline training. In DeepThermal, we first learn a data-driven combustion process simulator from the offline dataset. The RL agent of MORE is then trained by combining real historical data as well as carefully filtered and processed simulation data through a novel restrictive exploration scheme. DeepThermal has been successfully deployed in four large coal-fired thermal power plants in China. Real-world experiments show that DeepThermal effectively improves the combustion efficiency of TPGUs. We also report the superior performance of MORE by comparing with the state-of-the-art algorithms on the standard offline RL benchmarks.
Domain-invariant NBV Planner for Active Cross-domain Self-localization

Pole-like landmark has received increasing attention as a domain-invariant visual cue for visual robot self-localization across domains (e.g., seasons, times of day, weathers). However, self-localization using pole-like landmarks can be ill-posed for a passive observer, as many viewpoints may not provide any pole-like landmark view. To alleviate this problem, we consider an active observer and explore a novel"domain-invariant"next-best-view (NBV) planner that attains consistent performance over different domains (i.e., maintenance-free), without requiring the expensive task of training data collection and retraining. In our approach, a novel multi-encoder deep convolutional neural network enables to detect domain invariant pole-like landmarks, which are then used as the sole input to a model-free deep reinforcement learning -based domain-invariant NBV planner. Further, we develop a practical system for active self-localization using sparse invariant landmarks and dense discriminative landmarks. In experiments, we demonstrate that the proposed method is effective both in efficient landmark detection and in discriminative self-localization.
Near-Optimal Two-Pass Streaming Algorithm for Sampling Random Walks over Directed Graphs

For a directed graph G with n vertices and a start vertex ustart, we wish to (approximately) sample an L-step random walk over G starting from ustart with minimum space using an algorithm that only makes few passes over the edges of the graph. This problem found many applications, for instance, in approximating the PageRank of a webpage. If only a single pass is allowed, the space complexity of this problem was shown to be Θ̃(n · L). Prior to our work, a better space complexity was only known with Õ( √ L) passes. We essentially settle the space complexity of this random walk simulation problem for two-pass streaming algorithms, showing that it is Θ̃(n · √ L), by giving almost matching upper and lower bounds. Our lower bound argument extends to every constant number of passes p, and shows that any p-pass algorithm for this problem uses Ω̃(n · L1/p) space. In addition, we show a similar Θ̃(n · √ L) bound on the space complexity of any algorithm (with any number of passes) for the related problem of sampling an L-step random walk from every vertex in the graph. 2012 ACM Subject Classification Theory of computation → Streaming, sublinear and near linear time algorithms
Lower bounds for corner-free sets

A corner is a set of three points in Z of the form (x, y), (x+ d, y), (x, y + d) with d 6= 0. We show that for infinitely many N there is a set A ⊂ [N ] of size 2 √ log 2 N not containing any corner, where c = 2 √ 2 log2 4 3 ≈ 1.822 . Let q, d be large positive integers. For each x ∈ [qd − 1], write π(x) = (x0, . . . , xd−1) ∈ Zd for the vector of digits of its base q expansion, thus x = ∑d−1 i=0 xiq i, with 0 6 xi < q for all i. For each positive integer r, consider the set Ar of all pairs (x, y) ∈ [qd−1]2 for which ‖π(x)− π(y)‖2 = r and q 2 6 xi + yi < 3q 2 for all i. We claim that Ar is free of corners. Suppose that (x, y), (x+ d, y), (x, y+ d) ∈ Ar. Then ‖π(x) − π(y)‖2 = ‖π(x+ d)− π(y)‖2 = ‖π(x)− π(y + d)‖2 = r. (0.1)
MagNet: A Magnetic Neural Network for Directed Graphs

The prevalence of graph-based data has spurred the rapid development of graph neural networks (GNNs) and related machine learning algorithms. Yet, despite the many data sets naturally modeled as directed graphs, including citation, web-site, and trafﬁc networks, the vast majority of this research focuses on undirected graphs. In this paper, we propose MagNet , a spectral GNN for directed graphs based on a complex Hermitian matrix known as the magnetic Laplacian. This matrix encodes undirected geometric structure in the magnitude of its entries and directional information in the phase of its entries. A “charge” parameter attunes spectral information to variation among directed cycles. We show that Mag-Net’s performance exceeds other spectral GNNs on directed graph node classiﬁcation and link prediction tasks for a variety of datasets and exceeds commonly used spatial GNNs on a majority of such. The underlying principles of MagNet are such that it can be adapted to other spectral GNN architectures.
Improved Anomaly Detection using Integrated Supervised and Unsupervised Processing

There are two broad technologies of signal processing applicable to space object feature identification using nonresolved imagery: supervised processing analyzes a large set of data for common characteristics that can be then used to identify, transform, and extract information from new data taken of the same given class (e.g. support vector machine); unsupervised processing utilizes detailed physics-based models that generate comparison data that can then be used to estimate parameters presumed to be governed by the same models (e.g. estimation filters). Both processes have been used in non-resolved space object identification and yield similar results yet arrived at using vastly different processes. The goal of integrating the results of the two is to seek to achieve an even greater performance by building on the process diversity. Specifically, both supervised processing and unsupervised processing will jointly operate on the analysis of brightness (radiometric flux intensity) measurements reflected by space objects and observed by a ground station to determine whether a particular day conforms to a nominal operating mode (as determined from a training set) or exhibits anomalous behavior where a particular parameter (e.g. attitude, solar panel articulation angle) has changed in some way. It is demonstrated in a variety of different scenarios that the integrated process achieves a greater performance than each of the separate processes alone.
Bandgap widening in thermochromic Mg-doped VO2 thin films: Quantitative data based on optical absorption

Thermochromic Mg-doped VO2 films were deposited by reactive direct current magnetron sputtering onto heated glass and carbon substrates. Elemental compositions were inferred from Rutherford backscattering. Optical bandgaps were obtained from spectral transmittance and reflectance measurements—from both the film side and the back side of the samples—and ensuing determination of absorption coefficients. The bandgap of Mg-doped films was found to increase by 3.9 ± 0.5 eV per unit of atom ratio Mg/(Mg + V) for 0 < Mg/(Mg + V) < 0.21. The presence of ∼0.45 at. % Si enhanced the bandgap even more.
Ordering of intercalated water and carbonate anions in hydrotalcite. An NMR study

The ordering of interlayer water and interlayer carbonate in hydrotalcite ([Mg 3 Al(OH) 8 ] + [0.5CO 3 - . mH 2 O] - ) has been studied by using static 1 H and 13 C NMR techniques. Spectra were taken of both powders and oriented material. The results indicate that interlayer water possesses rotational freedom around the C 2 molecular axis and that the molecular symmetry axes of both carbonate (C 3 ) and water (C 2 ) are oriented parallel to the crystallographic C axis of hydrotalcite. The ordering is caused by hydrogen bonding between the hydrogen atoms of the main layer and the oxygen atoms of the interlayer molecules