Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fully Self-consistent Model for Solar Flares

The “standard solar flare model” collects all physical ingredients identified by multiwavelength observations of our Sun: magnetic reconnection, fast particle acceleration, and the resulting emission at various wavelengths, especially in soft to hard X-ray channels. Its cartoon representation is found throughout textbooks on solar and plasma astrophysics and guides interpretations of unresolved energetic flaring events on other stars, accretion disks, and jets. To date, a fully self-consistent model that reproduces the standard scenario in all its facets is lacking, since this requires the combination of a large-scale, multidimensional magnetohydrodynamic (MHD) plasma description with a realistic fast electron treatment. Here we demonstrate such a novel combination, where MHD combines with an analytic fast electron model, adjusted to handle time-evolving, reconnecting magnetic fields and particle trapping. This allows us to study (1) the role of fast electron deposition in the triggering of chromospheric evaporation flows, (2) the physical mechanisms that generate various hard X-ray sources at chromospheric footpoints or looptops, and (3) the relationship between soft X-ray and hard X-ray fluxes throughout the entire flare loop evolution. For the first time, this self-consistent solar flare model demonstrates the observationally suggested relationship between flux swept out by the hard X-ray footpoint regions and the actual reconnection rate at the X-point, which is a major unknown in flaring scenarios. We also demonstrate that a looptop hard X-ray source can result from fast electron trapping.
IMoJIE: Iterative Memory-Based Joint Open Information Extraction

While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task. Our work builds upon CopyAttention, a sequence generation OpenIE model (Cui et. al. 18). Our analysis reveals that CopyAttention produces a constant number of extractions per sentence, and its extracted tuples often express redundant information. We present IMoJIE, an extension to CopyAttention, which produces the next extraction conditioned on all previously extracted tuples. This approach overcomes both shortcomings of CopyAttention, resulting in a variable number of diverse extractions per sentence. We train IMoJIE on training data bootstrapped from extractions of several non-neural systems, which have been automatically filtered to reduce redundancy and noise. IMoJIE outperforms CopyAttention by about 18 F1 pts, and a BERT-based strong baseline by 2 F1 pts, establishing a new state of the art for the task.
APEX CO observations towards the photodissociation region of RCW 120

The edges of ionized (HII) regions are important sites for the formation of (high-mass) stars. Indeed, at least 30% of the galactic high mass star formation is observed there. The radiative and compressive impact of the HII region could induce the star formation at the border following different mechanisms such as the Collect & Collapse (C&C) or the Radiation Driven Implosion (RDI) models and change their properties. We study the properties of two zones located in the Photo Dissociation Region (PDR) of the Galactic HII region RCW120 and discussed them as a function of the physical conditions and young star contents found in both clumps. Using the APEX telescope, we mapped two regions of size 1.5'$\times$1.5' toward the most massive clump of RCW120 hosting young massive sources and toward a clump showing a protrusion inside the HII region and hosting more evolved low-mass sources. The $^{12}$CO($J=3-2$), $^{13}$CO($J=3-2$) and C$^{18}$O($J=3-2$) lines are used to derive the properties and dynamics of these clumps. We discuss their relation with the hosted star-formation. The increase of velocity dispersion and $T_{ex}$ are found toward the center of the maps, where star-formation is observed with Herschel. Furthermore, both regions show supersonic Mach number. No strong evidences have been found concerning the impact of far ultraviolet (FUV) radiation on C$^{18}$O photodissociation. The fragmentation time needed for the C&C to be at work is equivalent to the dynamical age of RCW120 and the properties of region B are in agreement with bright-rimmed clouds. It strengthens the fact that, together with evidences of compression, C&C might be at work at the edges of RCW120. Additionally, the clump located at the eastern part of the PDR is a good candidate of pre-existing clump where star-formation may be induced by the RDI mechanism.
Multimodal Target Speech Separation with Voice and Face References

Target speech separation refers to isolating target speech from a multi-speaker mixture signal by conditioning on auxiliary information about the target speaker. Different from the mainstream audio-visual approaches which usually require simultaneous visual streams as additional input, e.g. the corresponding lip movement sequences, in our approach we propose the novel use of a single face profile of the target speaker to separate expected clean speech. We exploit the fact that the image of a face contains information about the person's speech sound. Compared to using a simultaneous visual sequence, a face image is easier to obtain by pre-enrollment or on websites, which enables the system to generalize to devices without cameras. To this end, we incorporate face embeddings extracted from a pretrained model for face recognition into the speech separation, which guide the system in predicting a target speaker mask in the time-frequency domain. The experimental results show that a pre-enrolled face image is able to benefit separating expected speech signals. Additionally, face information is complementary to voice reference and we show that further improvement can be achieved when combing both face and voice embeddings.
Massive MIMO Channel Estimation With Low-Resolution Spatial Sigma-Delta ADCs

We consider channel estimation for an uplink massive multiple-input multiple-output (MIMO) system where the base station (BS) uses an array with low-resolution (1-2 bit) analog-to-digital converters and a spatial Sigma-Delta (<inline-formula> <tex-math notation="LaTeX">$\Sigma \Delta $ </tex-math></inline-formula>) architecture to shape the quantization noise away from users in some angular sector. We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. We also analyze the uplink achievable rate with maximal ratio combining (MRC), zero-forcing (ZF) and LMMSE receivers and provide a lower bound for the achievable rate with the MRC receiver. Numerical results show superior channel estimation and sum spectral efficiency performance using the <inline-formula> <tex-math notation="LaTeX">$\Sigma \Delta $ </tex-math></inline-formula> architecture compared to conventional 1- or 2-bit quantized massive MIMO systems.
A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer

Dense video captioning aims to localize and describe important events in untrimmed videos. Existing methods mainly tackle this task by exploiting only visual features, while completely neglecting the audio track. Only a few prior works have utilized both modalities, yet they show poor results or demonstrate the importance on a dataset with a specific domain. In this paper, we introduce Bi-modal Transformer which generalizes the Transformer architecture for a bi-modal input. We show the effectiveness of the proposed model with audio and visual modalities on the dense video captioning task, yet the module is capable of digesting any two modalities in a sequence-to-sequence task. We also show that the pre-trained bi-modal encoder as a part of the bi-modal transformer can be used as a feature extractor for a simple proposal generation module. The performance is demonstrated on a challenging ActivityNet Captions dataset where our model achieves outstanding performance. The code is available: this http URL
Decision Fusion in Space-Time Spreading aided Distributed MIMO WSNs

In this letter, we propose space-time spreading (STS) of local sensor decisions before reporting them over a wireless multiple access channel (MAC), in order to achieve flexible balance between diversity and multiplexing gain as well as eliminate any chance of intrinsic interference inherent in MAC scenarios. Spreading of the sensor decisions using dispersion vectors exploits the benefits of multi-slot decision to improve low-complexity diversity gain and opportunistic throughput. On the other hand, at the receive side of the reporting channel, we formulate and compare optimum and sub-optimum fusion rules for arriving at a reliable conclusion.Simulation results demonstrate gain in performance with STS aided transmission from a minimum of 3 times to a maximum of 6 times over performance without STS.
Symbolic Models for a Class of Impulsive Systems

Symbolic models have been used as the basis of a systematic framework to address control design of several classes of hybrid systems with sophisticated control objectives. However, results available in the literature are not concerned with impulsive systems which are an important modeling framework of many applications. In this letter, we provide an approach for constructing symbolic models for a class of impulsive systems possessing some stability properties. We formally relate impulsive systems and their symbolic models using a notion of so-called alternating simulation function. We show that behaviors of the constructed symbolic models are approximately equivalent to those of the impulsive systems. Finally, we illustrate the effectiveness of our results through a case study.
O gênero Diplazium Sw. (Dryopteridaceae, Pteridophyta) no Estado do Paraná, Brasil

The present research is a survey of the genus Diplazium Sw. (Dryopteridaceae) in the Parana State. The delimitation of the species was based on morphological characters and it was possible to recognize 11 species with the obtained data. Identification Key for the species, descriptions, comments, geographycal distribution and illustrations are presented.
Development of GenNav: A Generic Indoor Navigation System for any Mobile Robot

The navigation system is at the heart of any mobile robot. It consists of both the SLAM and path planning units, which the robot utilizes to generate a map of the environment, localize itself within it and generate a path to the destination . This paper describes the conceptualization, development, simulation and hardware implementation of GenNav a generic indoor navigation system for any mobile aerial or ground robot. The hardware actuators and software computation units are modularized and made independent of each other, by providing an alternate source of odometry from the Lidar. Hence the actuators used for locomotion are no longer required to carry their own source of odometry and system can be generalized to a wide variety of robots, with different type and orientation of actuators.
Intracranial Hemorrhage Detection Using Neural Network Based Methods With Federated Learning

Intracranial hemorrhage, bleeding that occurs inside the cranium, is a serious health problem requiring rapid and often intensive medical treatment. Such a condition is traditionally diagnosed by highly-trained specialists analyzing computed tomography (CT) scan of the patient and identifying the location and type of hemorrhage if one exists. We propose a neural network approach to find and classify the condition based upon the CT scan. The model architecture implements a time distributed convolutional network. We observed accuracy above 92% from such an architecture, provided enough data. We propose further extensions to our approach involving the deployment of federated learning. This would be helpful in pooling learned parameters without violating the inherent privacy of the data involved.
Inspiraling Double Compact Object Detection and Lensing Rate: Forecast for DECIGO and B-DECIGO

Emergence of gravitational wave (GW) astronomy revived the interest in exploring the low-frequency GW spectrum inaccessible from the ground. Satellite GW observatory DECihertz Interferometer Gravitational wave Observatory (DECIGO) in its original configuration and the currently proposed smaller-scale B-DECIGO are aimed to cover the decihertz part of the GW spectrum, which fills the gap between LISA millihertz and deca- to kilohertz range probed by ground-based detectors. In this paper we forecast the detection rates of inspiraling double compact objects (DCOs) and the unresolved confusion noise from these sources in DECIGO and B-DECIGO. In the context of DECIGO we use, for the first time, the population synthesis intrinsic inspiral rates of NS–NS, BH–NS and BH–BH systems. We also estimate the expected gravitational lensing rates of such sources for DECIGO and B-DECIGO. The result is that yearly detection of resolvable DCOs inspirals for the DECIGO is of the order of 102–105, while for a much smaller-scale B-DECIGO they are about 10–105 depending on the DCO population considered. Taking into account that a considerable part of these events would be detectable by ground-based GW observatories, the significance of DECIGO/B-DECIGO could be substantial. Due to contamination by unresolved sources, both DECIGO and B-DECIGO will not be able to register lensed NS–NS or BH–NS systems, but the lensed BH–BH systems could be observed at the rate of about 50 per year in DECIGO. Smaller-scale B-DECIGO will be able to detect a few lensed BH–BH systems per year. We also address the question of the magnification bias in the GW event catalogs of DECIGO and B-DECIGO.
Dynamical Modelling and Analysis of COVID-19 in India

We consider the pandemic spreading of COVID-19 in India after the outbreak of the coronavirus in Wuhan city, China. We estimate the transmission rate of the initial infecting individuals of COVID-19 in India by using the officially reported data at the early stage of the epidemic with the help of Susceptible (S), Exposed (E), Infected (I), and Removed (R) population model, the so-called SEIR dynamical model. Numerical analysis and model verification are performed to calibrate the system parameters with official public information about the number of people infected, and then to evaluate several COVID -19 scenarios potentially applicable to India. Our findings provide an estimation of disease occurrence in the near future and also demonstrate the importance of governmental and individual efforts to control the effects and time of the pandemic-related critical situations. We also give special emphasis to individual reactions in the containment process.
FA-GANs: Facial Attractiveness Enhancement with Generative Adversarial Networks on Frontal Faces

Facial attractiveness enhancement has been an interesting application in Computer Vision and Graphics over these years. It aims to generate a more attractive face via manipulations on image and geometry structure while preserving face identity. In this paper, we propose the first Generative Adversarial Networks (GANs) for enhancing facial attractiveness in both geometry and appearance aspects, which we call "FA-GANs". FA-GANs contain two branches and enhance facial attractiveness in two perspectives: facial geometry and facial appearance. Each branch consists of individual GANs with the appearance branch adjusting the facial image and the geometry branch adjusting the facial landmarks in appearance and geometry aspects, respectively. Unlike the traditional facial manipulations learning from paired faces, which are infeasible to collect before and after enhancement of the same individual, we achieve this by learning the features of attractiveness faces through unsupervised adversarial learning. The proposed FA-GANs are able to extract attractiveness features and impose them on the enhancement results. To better enhance faces, both the geometry and appearance networks are considered to refine the facial attractiveness by adjusting the geometry layout of faces and the appearance of faces independently. To the best of our knowledge, we are the first to enhance the facial attractiveness with GANs in both geometry and appearance aspects. The experimental results suggest that our FA-GANs can generate compelling perceptual results in both geometry structure and facial appearance and outperform current state-of-the-art methods.
Scaling-Up Distributed Processing of Data Streams for Machine Learning

Emerging applications of machine learning in numerous areas—including online social networks, remote sensing, Internet-of-Things (IoT) systems, smart grids, and more—involve continuous gathering of and learning from streams of data samples. Real-time incorporation of streaming data into the learned machine learning models is essential for improved inference in these applications. Furthermore, these applications often involve data that are either inherently gathered at geographically distributed entities due to physical reasons, for example, IoT systems and smart grids, or that are intentionally distributed across multiple computing machines for memory, storage, computational, and/or privacy reasons. Training of machine learning models in this distributed, streaming setting requires solving stochastic optimization (SO) problems in a collaborative manner over communication links between the physical entities. When the streaming data rate is high compared with the processing capabilities of individual computing entities and/or the rate of the communications links, this poses a challenging question: How can one best leverage the incoming data for distributed training of machine learning models under constraints on computing capabilities and/or communications rate? A large body of research in distributed online optimization has emerged in recent decades to tackle this and related problems. This article reviews recently developed methods that focus on large-scale distributed SO in the compute- and bandwidth-limited regimes, with an emphasis on convergence analysis that explicitly accounts for the mismatch between computation, communication, and streaming rates and provides sufficient conditions for order-optimum convergence. In particular, it focuses on methods that solve: 1) distributed stochastic convex problems and 2) distributed principal component analysis, which is a nonconvex problem with the geometric structure that permits global convergence. For such methods, this article discusses recent advances in terms of distributed algorithmic designs when faced with high-rate streaming data. Furthermore, it reviews theoretical guarantees underlying these methods that show that there exist regimes in which systems can learn from distributed processing of streaming data at order-optimal rates—nearly as fast as if all the data were processed at a single superpowerful machine.
Beam-Domain Secret Key Generation for Multi-User Massive MIMO Networks

Physical-layer key generation (PKG) in multi-user massive MIMO networks faces great challenges due to the large length of pilots and the high dimension of channel matrix. To tackle these problems, we propose a novel massive MIMO key generation scheme with pilot reuse based on the beam domain channel model and derive close-form expression of secret key rate. Specifically, we present two algorithms, i.e., beam-domain based channel probing (BCP) algorithm and interference neutralization based multi-user beam allocation (IMBA) algorithm for the purpose of channel dimension reduction and multi-user pilot reuse, respectively. Numerical results verify that the proposed PKG scheme can achieve the secret key rate that approximates the perfect case, and significantly reduce the dimension of the channel estimation and pilot overhead.
Bipartite graphs with close domination and k-domination numbers

Abstract Let k k be a positive integer and let G G be a graph with vertex set V ( G ) V(G) . A subset D ⊆ V ( G ) D\subseteq V(G) is a k k -dominating set if every vertex outside D D is adjacent to at least k k vertices in D D . The k k -domination number γ k ( G ) {\gamma }_{k}(G) is the minimum cardinality of a k k -dominating set in G G . For any graph G G , we know that γ k ( G ) ≥ γ ( G ) + k − 2 {\gamma }_{k}(G)\ge \gamma (G)+k-2 where Δ ( G ) ≥ k ≥ 2 \text{Δ}(G)\ge k\ge 2 and this bound is sharp for every k ≥ 2 k\ge 2 . In this paper, we characterize bipartite graphs satisfying the equality for k ≥ 3 k\ge 3 and present a necessary and sufficient condition for a bipartite graph to satisfy the equality hereditarily when k = 3 k=3 . We also prove that the problem of deciding whether a graph satisfies the given equality is NP-hard in general.
MD-Recon-Net: A Parallel Dual-Domain Convolutional Neural Network for Compressed Sensing MRI

Compressed sensing magnetic resonance imaging (CS-MRI) is a theoretical framework that can accurately reconstruct images from undersampled <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space data with a much lower sampling rate than the one set by the classical Nyquist–Shannon sampling theorem. Therefore, CS-MRI can efficiently accelerate acquisition time and relieve the psychological burden on patients while maintaining high imaging quality. The problems with traditional CS-MRI reconstruction are solved by iterative numerical solvers, which usually suffer from expensive computational cost and the lack of accurate handcrafted priori. In this article, inspired by deep learning’s (DL’s) fast inference and excellent end-to-end performance, we propose a novel cascaded convolutional neural network called MRI dual-domain reconstruction network (MD-Recon-Net) to facilitate fast and accurate magnetic resonance imaging reconstruction. Especially, different from existing DL-based methods, which operate on single domain data or both domains in a certain order, our proposed MD-Recon-Net contains two parallel and interactive branches that simultaneously perform on <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space and spatial-domain data, exploring the latent relationship between <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-space and the spatial domain. The simulated experimental results show that the proposed method not only achieves competitive visual effects to several state-of-the-art methods but also outperforms other DL-based methods in terms of model scale and computational cost.
Concept Learning in Deep Reinforcement Learning

Deep reinforcement learning techniques have shown to be a promising path to solve very complex tasks that once were thought to be out of the realm of machines. However, while humans and animals learn incrementally during their lifetimes and exploit their experience to solve new tasks, standard deep learning methods specialize to solve only one task at a time and whatever information they acquire is hardly reusable in new situations. Given that any artificial agent would need such a generalization ability to deal with the complexities of the world, it is critical to understand what mechanisms give rise to this ability. We argue that one of the mechanisms humans rely on is the use of discrete conceptual representations to encode their sensory inputs. These representations group similar inputs in such a way that combined they provide a level of abstraction that is transverse to a wide variety of tasks, filtering out irrelevant information for their solution. Here, we show that it is possible to learn such concept-like representations by self-supervision, following an information-bottleneck approach, and that these representations accelerate the transference of skills by providing a prior that guides the policy optimization process. Our method is able to learn useful concepts in locomotive tasks that significantly reduce the number of optimization steps required, opening a new path to endow artificial agents with generalization abilities.