Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposed measurement of simultaneous particle and wave properties of electric current in a superconductor

In a microscopic quantum system one cannot perform a simultaneous measurement of particle and wave properties. This, however, may not be true for macroscopic quantum systems. As a demonstration, we propose to measure the local macroscopic current passed through two slits in a superconductor. According to the theory based on the linearized Ginzburg-Landau equation for the macroscopic pseudo wave function, the streamlines of the measured current should have the same form as particle trajectories in the Bohmian interpretation of quantum mechanics. By an explicit computation we find that the streamlines should show a characteristic wiggling, which is a consequence of quantum interference.
SOFIA-EXES Observations of Betelgeuse during the Great Dimming of 2019/2020

In 2019 October Betelgeuse began a decline in V-band brightness that went beyond the minimum expected from its quasi-periodic ∼420 day cycle, becoming the faintest in recorded photometric history. Observations obtained in 2019 December with Very Large Telescope/SPHERE have shown that the southern half of the star has become markedly fainter than in 2019 January, indicating that a major change has occurred in, or near, the photosphere. We present Stratospheric Observatory for Infrared Astronomy (SOFIA) Echelon Cross Echelle Spectrograph (EXES) high spectral-resolution observations of [Fe ii] and [S i] emission lines from Betelgeuse obtained during the unprecedented 2020 February V-band brightness minimum to investigate potential changes in the circumstellar flow. These spectra are compared to observations obtained in 2015 and 2017 when the V magnitude was typical of brighter phases. We find only very small changes in the gas velocities reflected by either of the line profiles, no significant changes in the flux to continuum ratios, and hence no significant changes in the [Fe ii]/[S i] flux ratios. There is evidence that absorption features have appeared in the 2020 continuum. The Alfvén wave-crossing time from the upper photosphere is sufficiently long that one would not expect a change in the large-scale magnetic field to reach the circumstellar [Fe ii] and [S i] line-forming regions, 3 < R (R*) < 20. However, the light-crossing time is of order a few hours and a reduction in luminosity may reduce the dust-gas heating rate and [O i] emission, which has contributions from R > 20R*, where significant circumstellar oxygen-rich dust is observed.
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text

Answering questions that require reading texts in an image is challenging for current models. One key difficulty of this task is that rare, polysemous, and ambiguous words frequently appear in images, e.g., names of places, products, and sports teams. To overcome this difficulty, only resorting to pre-trained word embedding models is far from enough. A desired model should utilize the rich information in multiple modalities of the image to help understand the meaning of scene texts, e.g., the prominent text on a bottle is most likely to be the brand. Following this idea, we propose a novel VQA approach, Multi-Modal Graph Neural Network (MM-GNN). It first represents an image as a graph consisting of three sub-graphs, depicting visual, semantic, and numeric modalities respectively. Then, we introduce three aggregators which guide the message passing from one graph to another to utilize the contexts in various modalities, so as to refine the features of nodes. The updated nodes have better features for the downstream question answering module. Experimental evaluations show that our MM-GNN represents the scene texts better and obviously facilitates the performances on two VQA tasks that require reading scene texts.
Attention-based Assisted Excitation for Salient Object Segmentation

Visual attention brings significant progress for Convolution Neural Networks (CNNs) in various applications. In this paper, object-based attention in human visual cortex inspires us to introduce a mechanism for modification of activations in feature maps of CNNs. In this mechanism, the activations of object locations are excited in feature maps. This mechanism is specifically inspired by attention-based gain modulation in object-based attention in brain. It facilitates figure-ground segregation in the visual cortex. Similar to brain, we use the idea to address two challenges in salient object detection: gathering object interior parts while segregation from background with concise boundaries. We implement the object-based attention in the U-net model using different architectures in the encoder parts, including AlexNet, VGG, and ResNet. The proposed method was examined on three benchmark datasets: HKU-IS, MSRB, and PASCAL-S. Experimental results showed that our inspired method could significantly improve the results in terms of mean absolute error and F-measure. The results also showed that our proposed method better captured not only the boundary but also the object interior. Thus, it can tackle the mentioned challenges.
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources

With the increase in popularity of deep learning models for natural language processing (NLP) tasks, in the field of Pharmacovigilance, more specifically for the identification of Adverse Drug Reactions (ADRs), there is an inherent need for large-scale social-media datasets aimed at such tasks. With most researchers allocating large amounts of time to crawl Twitter or buying expensive pre-curated datasets, then manually annotating by humans, these approaches do not scale well as more and more data keeps flowing in Twitter. In this work we re-purpose a publicly available archived dataset of more than 9.4 billion Tweets with the objective of creating a very large dataset of drug usage-related tweets. Using existing manually curated datasets from the literature, we then validate our filtered tweets for relevance using machine learning methods, with the end result of a publicly available dataset of 1,181,993 million tweets for public use. We provide all code and detailed procedure on how to extract this dataset and the selected tweet ids for researchers to use.
DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

A canonical wireless communication system consists of a transmitter and a receiver. The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal. In this article, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver’s entire information recovery process. We design a one-dimensional convolution DenseNet (1D-Conv-DenseNet) structure, in which global pooling is used to improve the adaptability of the network to different input signal lengths. Multiple binary classifiers are used at the final classification layer to achieve multi-bit information stream recovery. We also exploit the DeepReceiver for unified blind reception of multiple modulation and coding schemes (MCSs) by including signal samples of corresponding MCSs in the training set. Simulation results show that the proposed DeepReceiver performs better than traditional step-by-step serial hard decision receiver in terms of bit error rate under the influence of various factors such as noise, RF impairments, multipath fading, cochannel interference, dynamic environment, and unified reception of multiple MCSs.
Stable and unstable Dp-branes in p-brane Newton–Cartan background

We formulate Dp-brane Newton–Cartan (NC) background through the limiting procedure from relativistic Dirac–Born–Infeld action and Wess–Zumino term. We also determine action for unstable D(p + 1)-brane in Dp-brane NC background and study its properties.
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.
Long-period Ap stars discovered with TESS data

The TESS space mission’s primary goal is to search for exoplanets around bright, nearby stars. Because of the high-precision photometry required for the main mission, it is also producing superb data for asteroseismology, eclipsing binary stars, gyrochronology, indeed any field of stellar astronomy where the data are variable light curves. In this work we show that the TESS data are excellent for astrophysical inference from peculiar stars that show no variability. Ap stars have the strongest magnetic fields of any main-sequence star. Some Ap stars have also been shown to have rotation periods of months, years, decades, and even centuries. The astrophysical cause of their slow rotation – the braking mechanism – is not known with certainty. These stars are rare: there are currently about three dozen with known periods. Magnetic Ap stars have long-lived spots that allow precise determination of their rotation periods. We argue and show that most Ap stars with TESS data that show no low-frequency variability must have rotation periods longer than, at least, a TESS sector of 27 d. From this we find 60 Ap stars in the southern ecliptic hemisphere TESS data with no rotational variability, of which at most a few can be pole-on, and six likely have nearly aligned magnetic and rotation axes. Of the other 54, 31 were previously known to have long rotation periods or very low projected equatorial velocities, which proves our technique; 23 are new discoveries. These are now prime targets for long-term magnetic studies. We also find that 12 of the 54 (22%) long-period Ap stars are roAp stars, versus only 3% (29 out of 960) of the other Ap stars studied with TESS in Sectors 1–13, showing that the roAp phenomenon is correlated with rotation, although this correlation is not necessarily causal. In addition to probing rotation in Ap stars, these constant stars are also excellent targets to characterise the instrumental behaviour of the TESS cameras, as well as for the CHEOPS and PLATO missions. This work demonstrates astrophysical inference from nonvariable stars – we can get “something for nothing”.
TartanAir: A Dataset to Push the Limits of Visual SLAM

We present a challenging dataset, the TartanAir, for robot navigation tasks and more. The data is collected in photo-realistic simulation environments with the presence of moving objects, changing light and various weather conditions. By collecting data in simulations, we are able to obtain multi-modal sensor data and precise ground truth labels such as the stereo RGB image, depth image, segmentation, optical flow, camera poses, and LiDAR point cloud. We set up large numbers of environments with various styles and scenes, covering challenging viewpoints and diverse motion patterns that are difficult to achieve by using physical data collection platforms. In order to enable data collection at such a large scale, we develop an automatic pipeline, including mapping, trajectory sampling, data processing, and data verification. We evaluate the impact of various factors on visual SLAM algorithms using our data. The results of state-of-the-art algorithms reveal that the visual SLAM problem is far from solved. Methods that show good performance on established datasets such as KITTI do not perform well in more difficult scenarios. Although we use the simulation, our goal is to push the limits of Visual SLAM algorithms in the real world by providing a challenging benchmark for testing new methods, while also using a large diverse training data for learning-based methods. Our dataset is available at http://theairlab.org/tartanair-dataset.
Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence

We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes. Unlike previous methods that either require extensive training data or operate on handcrafted input descriptors and thus generalize poorly across diverse datasets, our approach is both accurate and robust to changes in shape structure. Key to our method is a feature-extraction network that learns directly from raw shape geometry, combined with a novel regularized map extraction layer and loss, based on the functional map representation. We demonstrate through extensive experiments in challenging shape matching scenarios that our method can learn from less training data than existing supervised approaches and generalizes significantly better than current descriptor-based learning methods. Our source code is available at: https://github.com/LIX-shape-analysis/GeomFmaps.
Learning from Small Data Through Sampling an Implicit Conditional Generative Latent Optimization Model

We revisit the long-standing problem of \emph{learning from small sample}. In recent years major efforts have been invested into the generation of new samples from a small set of training data points. Some use classical transformations, others synthesize new examples. Our approach belongs to the second one. We propose a new model based on conditional Generative Latent Optimization (cGLO). Our model learns to synthesize completely new samples for every class just by interpolating between samples in the latent space. The proposed method samples the learned latent space using spherical interpolations (\emph{slerp}) and generates a new sample using the trained generator. Our empirical results show that the new sampled set is diverse enough, leading to improvement in image classification in comparison to the state of the art, when trained on small samples of CIFAR-100 and CUB-200.
Machine learning shadowgraph for particle size and shape characterization

Conventional image processing for a particle shadow image is usually time-consuming and suffers degraded image segmentation when dealing with images consisting of complex-shaped and clustered particles with varying backgrounds. In this paper, we introduce a robust learning-based method using a single convolution neural network for analyzing particle shadow images. Our approach employs a two-channel-output U-net model to generate a binary particle image and a particle centroid image. The binary particle image is subsequently segmented through a marker-controlled watershed approach with the particle centroid image as the marker image. The assessment of this method on both synthetic and experimental bubble images has exhibited a better performance compared to the state-of-art non-machine-learning method. The proposed machine learning shadow image processing approach provides a promising tool for real-time particle image analysis in industrial applications.
Fairness Evaluation in Presence of Biased Noisy Labels

Risk assessment tools are widely used around the country to inform decision making within the criminal justice system. Recently, considerable attention has been devoted to the question of whether such tools may suffer from racial bias. In this type of assessment, a fundamental issue is that the training and evaluation of the model is based on a variable (arrest) that may represent a noisy version of an unobserved outcome of more central interest (offense). We propose a sensitivity analysis framework for assessing how assumptions on the noise across groups affect the predictive bias properties of the risk assessment model as a predictor of reoffense. Our experimental results on two real world criminal justice data sets demonstrate how even small biases in the observed labels may call into question the conclusions of an analysis based on the noisy outcome.
CRYSPNet: Crystal structure predictions via neural networks

Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics. However, predicting crystal structure of solids remains a formidable and not fully solved problem. Standard theoretical tools for this task are computationally expensive and at times inaccurate. Here we present an alternative approach utilizing machine learning for crystal structure prediction. We developed a tool called Crystal Structure Prediction Network (CRYSPNet) that can predict the Bravais lattice, space group, and lattice parameters of an inorganic material based only on its chemical composition. CRYSPNet consists of a series of neural network models, using as inputs predictors aggregating the properties of the elements constituting the compound. It was trained and validated on more than 100,000 entries from the Inorganic Crystal Structure Database. The tool demonstrates robust predictive capability and outperforms alternative strategies by a large margin. Made available to the public (at this https URL), it can be used both as an independent prediction engine or as a method to generate candidate structures for further computational and/or experimental validation.
Understanding the dynamic effects of returning patients toward emergency department density

This paper presents the development of a dynamic hypothesis for the effect of returning patients to the emergency department (ED). A logical tree from the Theory of Constraint known as Current Reality Tree was used to identify the key variables. Then, a hypothetical framework portraying the interrelated variables and its influencing relationships was developed using causal loop diagrams (CLD). The conceptual framework was designed as the basis for the development of a system dynamics model.This paper presents the development of a dynamic hypothesis for the effect of returning patients to the emergency department (ED). A logical tree from the Theory of Constraint known as Current Reality Tree was used to identify the key variables. Then, a hypothetical framework portraying the interrelated variables and its influencing relationships was developed using causal loop diagrams (CLD). The conceptual framework was designed as the basis for the development of a system dynamics model.
Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic

Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, namely COVID-19. Many of these solutions threaten individual rights and privacy. Our goal is to break past the false dichotomy of eﬀective versus privacy-preserving contact tracing. We oﬀer an alternative approach to assess and communicate users’ risk of exposure to an infectious disease while preserving individual privacy. Our proposal uses recent GPS location histories, which are transformed and encrypted, and a private set intersection protocol.
Appraisal Theories for Emotion Classification in Text

Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable situation. This emotion reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words “I am afraid.”). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and the mental or physical effort associated with the encounter of a snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories. We make our corpus of appraisal-annotated emotion-associated event descriptions publicly available.
Information-theoretic lower bounds for zero-order stochastic gradient estimation

In this paper we analyze the necessary number of samples to estimate the gradient of any multidimensional smooth (possibly non-convex) function in a zero-order stochastic oracle model. In this model, an estimator has access to noisy values of the function, in order to produce the estimate of the gradient. We also provide an analysis on the sufficient number of samples for the finite difference method, a classical technique in numerical linear algebra. For $T$ samples and d dimensions, our information-theoretic lower bound is Ω(√d/T). We show that the finite difference method for a bounded-variance oracle has rate O(d4/3 / √T) for functions with zero third and higher order derivatives. These rates are tight for Gaussian oracles. Thus, the finite difference method is not minimax optimal, and therefore there is space for the development of better gradient estimation methods. A full version of this paper is accessible at: https://arxiv.org/pdf/2003.13881. pdf
Extended thermodynamics and microstructures of four-dimensional charged Gauss-Bonnet black hole in AdS space

The discovery of new four-dimensional black hole solutions presents a new approach to understand the Gauss-Bonnet gravity in low dimensions. In this paper, we test the Gauss-Bonnet gravity by studying the phase transition and microstructures for the four-dimensional charged anti--de Sitter black hole. In the extended phase space, where the cosmological constant and the Gauss-Bonnet coupling parameter are treated as thermodynamic variables, we find that the thermodynamic first law and the corresponding Smarr formula are satisfied. Both in the canonical ensemble and grand canonical ensemble, we observe the small-large black hole phase transition, which is similar to the case of the van der Walls fluid. This phase transition can also appear in the neutral black hole system. Furthermore, we construct the Ruppeiner geometry, and find that besides the attractive interaction, the repulsive interaction can also dominate among the microstructures for the small black hole with high temperature in a charged or neutral black hole system. This is quite different from the five-dimensional neutral black hole, for which only dominant attractive interaction can be found. The critical behaviors of the normalized scalar curvature are also examined. These results will shed new light into the characteristic property of four-dimensional Gauss-Bonnet gravity.