Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Black Hole Binaries by Gaia

We study the prospects of the Gaia satellite to identify black hole (BH) binary systems by detecting the orbital motion of the companion stars. Taking into account the initial mass function, mass transfer, common envelope phase, interstellar absorption, and identifiability of the systems, we estimate the number of BH binaries that can be detected by Gaia and their distributions with respect to the BH mass. Considering several models with different parameters, we find that ∼200–1000 BH binaries could be detected by Gaia during its ∼5 years operation. The shape of the BH mass distribution function is affected strongly by the zero-age main sequence (ZAMS) stellar mass–BH mass relation. We show that once this distribution is established observationally, we will be able to constrain the currently unknown ZAMS mass–BH mass relation.
Discretized and covariant path integrals for stochastic processes

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Discretized and covariant path integrals for stochastic processes Thibaut Arnoulx de Pirey, Leticia F. Cugliandolo, Vivien Lecomte, Frédéric van Wijland
Data fusion techniques for fault diagnosis of industrial machines: a survey

In the Engineering discipline, predictive maintenance techniques play an essential role in improving system safety and reliability of industrial machines. Due to the adoption of crucial and emerging detection techniques and big data analytics tools, data fusion approaches are gaining popularity. This article thoroughly reviews the recent progress of data fusion techniques in predictive maintenance, focusing on their applications in machinery fault diagnosis. In this review, the primary objective is to classify existing literature and to report the latest research and directions to help researchers and professionals to acquire a clear understanding of the thematic area. This paper first summarizes fundamental data-fusion strategies for fault diagnosis. Then, a comprehensive investigation of the different levels of data fusion was conducted on fault diagnosis of industrial machines. In conclusion, a discussion of data fusion-based fault diagnosis challenges, opportunities, and future trends are presented.
Style Classification of Rabbinic Literature for Detection of Lost Midrash Tanhuma Material

Midrash collections are complex rabbinic works that consist of text in multiple languages, that evolved through long processes of instable oral and written transmission. Determining the origin of a given passage in such a compilation is not always straightforward and is often a matter disputed by scholars, yet it is essential for scholars’ understanding of the passage and its relationship to other texts in the rabbinic corpus. To help solve this problem, we propose a system for classification of rabbinic literature based on its style, leveraging recently released pretrained Transformer models for Hebrew. Additionally, we demonstrate how our method can be applied to uncover lost material from the Midrash Tanhuma.
Asymptotic behaviour of a conservative reaction-diffusion system associated with a Markov process algebra model

This paper demonstrates a lower and upper solution method to investigate the asymptotic behaviour of the conservative reaction-diffusion systems associated with Markovian process algebra models. In particular, we have proved the uniform convergence of the solution to its constant equilibrium for a case study as time tends to infinity, together with experimental results illustrations.
Designing phoretic micro- and nano-swimmers

Small objects can swim by generating around them fields or gradients which in turn induce fluid motion past their surface by phoretic surface effects. We quantify for arbitrary swimmer shapes and surface patterns, how efficient swimming requires both surface ‘activity’ to generate the fields, and surface ‘phoretic mobility. ’ We show in particular that (i) swimming requires symmetry breaking in either or both of the patterns of ‘activity’ and ‘mobility,’ and (ii) for a given geometrical shape and surface pattern, the swimming velocity is size-independent. In addition, for given available surface properties, our calculation framework provides a guide for optimizing the design of swimmers.
SPACE: Speech-driven Portrait Animation with Controllable Expression

Animating portraits using speech has received growing attention in recent years, with various creative and practical use cases. An ideal generated video should have good lip sync with the audio, natural facial expressions and head motions, and high frame quality. In this work, we present SPACE, which uses speech and a single image to generate high-resolution, and expressive videos with realistic head pose, without requiring a driving video. It uses a multi-stage approach, combining the controllability of facial landmarks with the high-quality synthesis power of a pretrained face generator. SPACE also allows for the control of emotions and their intensities. Our method outperforms prior methods in objective metrics for image quality and facial motions and is strongly preferred by users in pair-wise comparisons. Please visit the project page to view the videos and to see more results: https://research.nvidia. com/labs/dir/space/.
Graph Neural Network-based Surrogate Models for Finite Element Analysis

Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time for the simulation to complete. Computational time can be prohibitive for parametric response surface exploration tasks. In this paper, we propose as an alternative, a Graph Neural Network-based graph prediction model to act as a surrogate model for parameters search space exploration and which exhibits a time cost reduced by an order of magnitude. Numerical experiments show that this new model outperforms the Point-Net model and the Dynamic Graph Convolutional Neural Net model.
GLAST's GBM Burst Trigger

The GLAST Burst Monitor (GBM) will detect and localize bursts for the GLAST mission, and provide the spectral and temporal context in the traditional 10 keV to 25 MeV band for the high energy observations by the Large Area Telescope (LAT). The GBM will use traditional rate triggers in up to three energy bands, and on a variety of timescales between 16 ms and 16 s.
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality

Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations. In this paper, we demonstrate that current response generation (RG) models produce generic and dull responses in dialogues because they act reflexively, failing to explicitly model CG, both due to the lack of CG in training data and the standard RG training procedure. We introduce Reflect, a dataset that annotates dialogues with explicit CG (materialized as inferences approximating shared knowledge and beliefs) and solicits 9k diverse human-generated responses each following one common ground. Using Reflect, we showcase the limitations of current dialogue data and RG models: less than half of the responses in current data is rated as high quality (sensible, specific, and interesting) and models trained using this data have even lower quality, while most Reflect responses are judged high quality. Next, we analyze whether CG can help models produce better quality responses by using Reflect CG to guide RG models. Surprisingly, we find that simply prompting GPT3 to “think” about CG generates 30% more quality responses, showing promising benefits to integrating CG into the RG process.
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation

We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the manipulation policy with point cloud inputs and dexterous hands. We propose two new techniques to enable joint learning on multiple objects and sim-to-real generalization: (i) using imagined hand point clouds as augmented inputs; and (ii) designing novel contact-based rewards. We empirically evaluate our method using an Allegro Hand to grasp novel objects in both simulation and real world. To the best of our knowledge, this is the first policy learning-based framework that achieves such generalization results with dexterous hands. Our project page is available at https://yzqin.github. io/dexpoint
aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception

Autonomous driving is a popular research area within the computer vision research community. Since autonomous vehicles are highly safety-critical, ensuring robustness is essential for real-world deployment. While several public multimodal datasets are accessible, they mainly comprise two sensor modalities (camera, LiDAR) which are not well suited for adverse weather. In addition, they lack far-range annotations, making it harder to train neural networks that are the base of a highway assistant function of an autonomous vehicle. Therefore, we introduce a multimodal dataset for robust autonomous driving with long-range perception. The dataset consists of 176 scenes with synchronized and calibrated LiDAR, camera, and radar sensors covering a 360-degree field of view. The collected data was captured in highway, urban, and suburban areas during daytime, night, and rain and is annotated with 3D bounding boxes with consistent identifiers across frames. Furthermore, we trained unimodal and multimodal baseline models for 3D object detection. Data are available at \url{https://github.com/aimotive/aimotive_dataset}.
Spatial Graph Convolution Neural Networks for Water Distribution Systems

We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant, hence graph neural networks face the challenge of long-term dependencies. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain. Further, we investigate a multi-hop variation, which requires considerably less resources and opens an avenue towards big WDS graphs.
Self-modulation doping effect in the high-mobility layered semiconductor 
Bi2O2Se

Recently, an air-stable layered semiconductor ${\mathrm{Bi}}_{2}{\mathrm{O}}_{2}\mathrm{Se}$ was discovered to exhibit an ultrahigh mobility in transistors fabricated with its thin layers. In this work, we explored the mechanism that induces the high mobility and distinguishes ${\mathrm{Bi}}_{2}{\mathrm{O}}_{2}\mathrm{Se}$ from other semiconductors. We found that the electron donor states lie above the lowest conduction band. Thus, electrons get spontaneously ionized from donor sites (e.g., Se vacancies) without involving the thermal activation, different from the donor ionization in conventional semiconductors. Consequently, the resistance decreases as reducing the temperature as observed in our measurement, which is similar to a metal but contrasts to a usual semiconductor. Furthermore, the electron conduction channels locate spatially away from ionized donor defects (Se vacancies) in different van der Waals layers. Such a spatial separation can strongly suppress the scattering caused by donor sites and subsequently increase the electron mobility, especially at the low temperature. We call this high-mobility mechanism self-modulation doping, i.e., the modulation doping spontaneously happening in a single-phase material without requiring a heterojunction. Our work paves a way to design high-mobility semiconductors with layered materials.
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning

Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data. To better exploit the unlabeled data the latest SSL methods use pseudo-labels predicted from a single discriminative classifier. However, the generated pseudo-labels are inevitably linked to inherent confirmation bias and noise which greatly affects the model performance. In this work we introduce a new framework for SSL named NorMatch. Firstly, we introduce a new uncertainty estimation scheme based on normalizing flows, as an auxiliary classifier, to enforce highly certain pseudo-labels yielding a boost of the discriminative classifiers. Secondly, we introduce a threshold-free sample weighting strategy to exploit better both high and low confidence pseudo-labels. Furthermore, we utilize normalizing flows to model, in an unsupervised fashion, the distribution of unlabeled data. This modelling assumption can further improve the performance of generative classifiers via unlabeled data, and thus, implicitly contributing to training a better discriminative classifier. We demonstrate, through numerical and visual results, that NorMatch achieves state-of-the-art performance on several datasets.
Statistical hadronization phenomenology in heavy ion collisions at SPS and RHIC energies

This dissertation examines the phenomenology of statistical hadronization at ultrarelativistic energies. We start with an overview of current experimental and theoretical issues in Relativistic heavy ion physics. We then introduce statistical hadronization, and show how it gives a description of particle abundances and spectra through covariance and entropy maximization. We argue that a distinction can be made between equilibrated staged freeze-out in which post-formation hadron interactions play an important role in determining final-state observables, and non-equilibrium sudden freeze-out where spectra and abundances get determined at the same time and further interactions are negligible. We attempt to falsify sudden freeze-out by examining whether particle abundances and spectra can be described using the same formation temperature. This is done both in the equilibrium framework, and using a chemical non-equilibrium ansatz. Our fits to experimental data suggest that the sudden freeze-out model explains both the particle abundances and spectra. We then try to extract the particle formation temperature, and quantify post-freeze-out hadronic interactions using experimentally observable resonances. We discuss observed resonances and suggest further measurements that have the potential to distinguish between the possible freeze-out scenarios experimentally. Finally, we infer from experimental data how particle formation proceeds in spacetime, in particular whether freeze-out dynamics agrees with the sudden freeze-out expectation. We examine particle spectra, and show that they are not sensitive enough to pick the right freeze-out dynamics. We suggest resonances and azimuthal anisotropy as experimental probes for this task.
Iterative execution of discrete and inverse discrete Fourier transforms with applications for signal denoising via sparsification

We describe a family of iterative algorithms that involve the repeated execution of discrete and inverse discrete Fourier transforms. One interesting member of this family is motivated by the discrete Fourier transform uncertainty principle and involves the application of a sparsification operation to both the real domain and frequency domain data with convergence obtained when real domain sparsity hits a stable pattern. This sparsification variant has practical utility for signal denoising, in particular the recovery of a periodic spike signal in the presence of Gaussian noise. General convergence properties and denoising performance relative to existing methods are demonstrated using simulation studies. An R package implementing this technique and related resources can be found at https://hrfrost.host. dartmouth.edu/IterativeFT.
BERT-ASC: Implicit Aspect Representation Learning through Auxiliary-Sentence Construction for Sentiment Analysis

Aspect-based sentiment analysis (ABSA) task aim at associating a piece of text with a set of aspects and meanwhile infer their respective sentimental polarities. The state-of-the-art approaches are built upon fine-tuning of various pre-trained language models. They commonly attempt to learn aspect-specific representation from the corpus. Unfortunately, the aspect is often expressed implicitly through a set of representatives and thus renders implicit mapping process unattainable unless sufficient labeled examples are available. However, high-quality labeled examples may not be readily available in real-world scenarios. In this paper, we propose to jointly address aspect categorization and aspect-based sentiment subtasks in a unified framework. Specifically, we first introduce a simple but effective mechanism to construct an auxiliary-sentence for the implicit aspect based on the semantic information in the corpus. Then, we encourage BERT to learn the aspect-specific representation in response to the automatically constructed auxiliary-sentence instead of the aspect itself. Finally, we empirically evaluate the performance of the proposed solution by a comparative study on real benchmark datasets for both ABSA and Targeted-ABSA tasks. Our extensive experiments show that it consistently achieves state-of-the-art performance in terms of aspect categorization and aspect-based sentiment across all datasets and the improvement margins are considerable. The code of BERT-ASC is available in GitHub: https://github.com/amurtadha/BERT-ASC.
A Reinforcement Learning Approach for Process Parameter Optimization in Additive Manufacturing

Process optimization for metal additive manufacturing (AM) is crucial to ensure repeatability, control microstructure, and minimize defects. Despite efforts to address this via the traditional design of experiments and statistical process mapping, there is limited insight on an on-the-fly optimization framework that can be integrated into a metal AM system. Additionally, most of these methods, being data-intensive, cannot be supported by a metal AM alloy or system due to budget restrictions. To tackle this issue, the article introduces a Reinforcement Learning (RL) methodology transformed into an optimization problem in the realm of metal AM. An off-policy RL framework based on Q-learning is proposed to find optimal laser power ($P$) - scan velocity ($v$) combinations with the objective of maintaining steady-state melt pool depth. For this, an experimentally validated Eagar-Tsai formulation is used to emulate the Laser-Directed Energy Deposition environment, where the laser operates as the agent across the $P-v$ space such that it maximizes rewards for a melt pool depth closer to the optimum. The culmination of the training process yields a Q-table where the state ($P,v$) with the highest Q-value corresponds to the optimized process parameter. The resultant melt pool depths and the mapping of Q-values to the $P-v$ space show congruence with experimental observations. The framework, therefore, provides a model-free approach to learning without any prior.
The noncommutative Dirac oscillator with a permanent electric dipole moment in the presence of an electromagnetic field

We investigate the noncommutative Dirac oscillator (NCDO) with a permanent electric dipole moment (EDM) in the presence of an electromagnetic field. We consider a radial magnetic field generated by anti-Helmholtz coils and the uniform electric field of the Stark effect. Next, we determine the exact solutions of the system, given by the Dirac spinor and the relativistic energy spectrum. We note that such a spinor is written in terms of the generalized Laguerre polynomials, and such a spectrum is a linear function on the potential energy U, and depends on the quantum numbers n and m, spin parameter s, and of four angular frequencies: ω, ω–, ωθ, and ωη, where ω is the frequency of the DO, ω– is a type of ``cyclotron frequency'', and ωθ and ωη are the NC frequencies of position and momentum. We graphically analyze the behavior of the spectrum as a function of these frequencies for three different values of n, with and without the influence of U. Besides, as an interesting result, another interpretation can be given for the origin of the nonminimal coupling of the DO, which would be through a neutral fermion with EDM in a radial magnetic field. Finally, we also analyze the nonrelativistic limit of our results, and comparing our problem with other works, we verified that our results generalize some particular cases of the literature.
Relating Kerr SMBHs in active galactic nuclei to RADs configurations

There is strong observational evidence that many active galactic nuclei (AGNs) harbour super-massive black holes (SMBHs), demonstrating multi-accretion episodes during their life-time. In such AGNs, corotating and counterrotating tori, or strongly misaligned disks, as related to the central Kerr SMBH spin, can report traces of the AGNs evolution. Here we concentrate on aggregates of accretion disks structures, ringed accretion disks (RADs) orbiting a central Kerr SMBH, assuming that each torus of the RADs is centered in the equatorial plane of the attractor, tori are coplanar and axi-symmetric. Many of the RAD aspects are governed mostly by the spin of the Kerr geometry. We classify Kerr black holes (BHs) due to their dimensionless spin, according to possible combinations of corotating and counterrotating equilibrium or unstable (accreting) tori composing the RADs. The number of accreting tori in RADs cannot exceed n = 2. We present list of 14 characteristic values of the Kerr BH dimensionless spin a governing the classification in whole the black hole range , uniquely constrained by the RAD properties. The spin values are remarkably close providing an accurate characterization of the Kerr attractors based on the RAD properties. RAD dynamics is richer in the spacetimes of high spin values. One of the critical predictions states that a RAD tori couple formed by an outer accreting corotating and an inner accreting counterrotating torus is expected to be observed only around slowly spinning (a < 0.46M) BHs. The analysis strongly binds the fluid and BH characteristics providing indications on the situations where to search for RADs observational evidences. Obscuring and screening tori, possibly evident as traces in x-ray spectrum emission, are strongly constrained, eventually ruling out many assumptions used in the current investigations of the screening effects. We expect relevance of our classification of Kerr spacetimes in relation to astrophysical phenomena arising in different stages of AGNs life that could be observed by the planned x-ray satellite observatory ATHENA (Advanced Telescope for High ENergy Astrophysics).