Miracle-dz/UniFAR-FTUs huggingface dataset:
Injecting Entity Types into Entity-Guided Text Generation

Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. We develop a novel NLG model to produce a target sequence based on a given list of entities. Our model has a multi-step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines.
Efficient inverse design and spectrum prediction for nanophotonic devices based on deep recurrent neural networks

The development of nanophotonic devices has presented a revolutionary means to manipulate light at nanoscale. How to efficiently design these devices is an active area of research. Recently, artificial neural networks (ANNs) have displayed powerful ability in the inverse design of nanophotonic devices. However, there is limited research on the inverse design for modeling and learning the sequence characteristics of a spectrum. In this work, we propose a deep learning method based on an improved recurrent neural network to extract the sequence characteristics of a spectrum and achieve inverse design and spectrum prediction. A key feature of the network is that the memory or feedback loops it comprises allow it to effectively recognize time series data. In the context of nanorods hyperbolic metamaterials, we demonstrated the high consistency between the target spectrum and the predicted spectrum, and the network learned the deep physical relationship concerning the structural parameter changes reflected on the spectrum. The effectiveness of our approach is also tested by user-drawn spectra. Moreover, the proposed model is capable of predicting an unknown spectrum based on a known spectrum with only 0.32% mean relative error. The prediction model may be helpful to predict data beyond the detection limit. We propose this versatile method as an effective and accurate alternative to the application of ANNs in nanophotonics, paving way for fast and accurate design of desired devices.
Dust emission, extinction, and scattering in LDN 1642

We study the near-infrared (NIR) scattering in LDN 1642, its correlation with the cloud structure, and the ability of dust models to simultaneously explain sub-millimetre emission, NIR extinction, and NIR scattering. We use observations from the HAWK-I instrument to measure the NIR surface brightness and extinction. These are compared with Herschel data on dust emission and, with radiative transfer modelling, with predictions calculated for different dust models. We find an optical depth ratio $\tau(250\,\mu{\rm m})/\tau(J)\approx 10^{-3}$, confirming earlier findings of high sub-millimetre emissivity. The relationships between the column density derived from dust emission and the NIR colour excesses is linear and consistent with the standard NIR extinction curve. The extinction peaks at $A_J=2.6\,$mag, the NIR surface brightness remaining correlated with $N({\rm H}_2)$ without saturation. Radiative transfer models can fit the sub-millimetre data with any of the tested dust models. However, these predict a NIR extinction that is higher and a NIR surface brightness that is lower than in observations. If the dust sub-millimetre emissivity is rescaled to the observed value of $\tau(250\,\mu{\rm m})/\tau(J)$, dust models with high NIR albedo can reach the observed level of NIR surface brightness. The NIR extinction of the models tends to be higher than directly measured, which is reflected in the shape of the NIR surface brightness spectra. The combination of emission, extinction, and scattering measurements provides strong constraints on dust models. The observations of LDN 1642 indicate clear dust evolution, including a strong increase in the sub-millimetre emissivity, not yet fully explained by the current dust models.
I Was Just Being Sarcastic! Reactive Supervision: A New Method for Collecting Sarcasm Data

Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection research. Our method can be adapted to other affective computing domains, thus opening up new research opportunities.
TOI-811b and TOI-852b: New Transiting Brown Dwarfs with Similar Masses and Very Different Radii and Ages from the TESS Mission

We report the discovery of two transiting brown dwarfs (BDs), TOI-811b and TOI-852b, from NASA’s Transiting Exoplanet Survey Satellite mission. These two transiting BDs have similar masses but very different radii and ages. Their host stars have similar masses, effective temperatures, and metallicities. The younger and larger transiting BD is TOI-811b at a mass of M b = 59.9 ± 13.0M J and radius of R b = 1.26 ± 0.06R J, and it orbits its host star in a period of P = 25.16551 ± 0.00004 days. We derive the host star’s age of Myr from an application of gyrochronology. The youth of this system, rather than external heating from its host star, is why this BD’s radius is relatively large. This constraint on the youth of TOI-811b allows us to test substellar mass–radius evolutionary models at young ages where the radius of BDs changes rapidly. TOI-852b has a similar mass at M b = 53.7 ± 1.4M J but is much older (4 or 8 Gyr, based on bimodal isochrone results of the host star) and is also smaller with a radius of R b = 0.83 ± 0.04R J. TOI-852b’s orbital period is P = 4.94561 ± 0.00008 days. TOI-852b joins the likes of other old transiting BDs that trace out the oldest substellar mass–radius evolutionary models where contraction of the BD’s radius slows and approaches a constant value. Both host stars have a mass of M ⋆ = 1.32M ⊙ ± 0.05 and differ in their radii, T eff, and [Fe/H], with TOI-811 having R ⋆ = 1.27 ± 0.09R ⊙, T eff = 6107 ± 77 K, and [Fe/H] = + 0.40 ± 0.09 and TOI-852 having R ⋆ = 1.71 ± 0.04R ⊙, T eff = 5768 ± 84 K, and [Fe/H] = + 0.33 ± 0.09. We take this opportunity to examine how TOI-811b and TOI-852b serve as test points for young and old substellar isochrones, respectively.
Constructing Large Matchings via Query Access to a Maximal Matching Oracle

Multi-pass streaming algorithm for Maximum Matching have been studied since more than 15 years and various algorithmic results are known today, including $2$-pass streaming algorithms that break the $1/2$-approximation barrier, and $(1-\epsilon)$-approximation streaming algorithms that run in $O(\text{poly} \frac{1}{\epsilon})$ passes in bipartite graphs and in $O( (\frac{1}{\epsilon})^{\frac{1}{\epsilon}})$ or $O(\text{poly} (\frac{1}{\epsilon}) \cdot \log n)$ passes in general graphs, where $n$ is the number of vertices of the input graph. However, proving impossibility results for such algorithms has so far been elusive, and, for example, even the existence of $2$-pass small space streaming algorithms with approximation factor $0.999$ has not yet been ruled out. The key building block of all multi-pass streaming algorithms for Maximum Matching is the Greedy matching algorithm. Our aim is to understand the limitations of this approach: How many passes are required if the algorithm solely relies on the invocation of the Greedy algorithm? In this paper, we initiate the study of lower bounds for restricted families of multi-pass streaming algorithms for Maximum Matching. We focus on the simple yet powerful class of algorithms that in each pass run Greedy on a vertex-induced subgraph of the input graph. In bipartite graphs, we show that $3$ passes are necessary and sufficient to improve on the trivial approximation factor of $1/2$: We give a lower bound of $0.6$ on the approximation ratio of such algorithms, which is optimal. We further show that $\Omega( \frac{1}{\epsilon})$ passes are required for computing a $(1-\epsilon)$-approximation, even in bipartite graphs. Last, the considered class of algorithms is not well-suited to general graphs: We show that $\Omega(n)$ passes are required in order to improve on the trivial approximation factor of $1/2$.
A Hybrid Intrusion Detection with Decision Tree for Feature Selection

Due to the size and nature of intrusion detection datasets, intrusion detection systems (IDS) typically take high computational complexity to examine features of data and identify intrusive patterns. Data preprocessing techniques such as feature selection can be used to reduce such complexity by eliminating irrelevant and redundant features in the dataset. The objective of this study is to analyze the efficiency and effectiveness of some feature selection approaches namely, wrapper-based and filter-based modeling approaches. To achieve that, a hybrid of feature selection algorithm in combination with wrapper and filter selection processes is designed. We propose a wrapper-based hybrid intrusion detection modeling with a decision tree algorithm to guide the selection process. Five machine learning algorithms are used on the wrapper and filter-based feature selection methods to build IDS models using the UNSW-NB15 dataset. The three filter-based methods namely, information gain, gain ratio, and relief are used for comparison to determine the efficiency and effectiveness of the proposed approach. Furthermore, a fair comparison with other state-of-the-art intrusion detection approaches is also performed. The experimental results show that our approach is quite effective in comparison to state-of-the-art works, however, it takes high computational time in comparison to the filter-based methods whilst achieves similar results. Our work also revealed unobserved issues about the conformity of the UNSW-NB15 dataset.
Incomplete Utterance Rewriting as Semantic Segmentation

Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-of-the-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.
Faster Biological Gradient Descent Learning

Back-propagation is a popular machine learning algorithm that uses gradient descent in training neural networks for supervised learning, but can be very slow. A number of algorithms have been developed to speed up convergence and improve robustness of the learning. However, they are complicated to implement biologically as they require information from previous updates. Inspired by synaptic competition in biology, we have come up with a simple and local gradient descent optimization algorithm that can reduce training time, with no demand on past details. Our algorithm, named dynamic learning rate (DLR), works similarly to the traditional gradient descent used in back-propagation, except that instead of having a uniform learning rate across all synapses, the learning rate depends on the current neuronal connection weights. Our algorithm is found to speed up learning, particularly for small networks.
In situ functionalization of graphene

While the basal plane of graphene is inert, defects in it are centers of chemical activity. An attractive application of such defects is towards controlled functionalization of graphene with foreign molecules. However, the interaction of the defects with reactive environment, such as ambient, decreases the efficiency of functionalization and makes it poorly controlled. Here, we report a novel approach to generate, monitor with time resolution, and functionalize the defects in situ without ever exposing them to the ambient. The defects are generated by an energetic argon plasma and their properties are monitored using in situ Raman spectroscopy. We find that these defects are functional, very reactive, and strongly change their density from ≈1 × 1013 cm−2 to ≈5 × 1011 cm−2 upon exposure to air. We perform the proof of principle in situ functionalization by generating defects using the argon plasma and functionalizing them in situ using ammonia functional. The functionalization induces the n-doping with a carrier density up to 5 × 1012 cm−2 in graphene and remains stable in ambient conditions.
School cohesion, speed and efficiency are modulated by the swimmers flapping motion

Abstract Fish schools are ubiquitous in marine life. Although flow interactions are thought to be beneficial for schooling, their exact effects on the speed, energetics and stability of the group remain elusive. Recent numerical simulations and experimental models suggest that flow interactions stabilize in-tandem formations of flapping foils. Here, we employ a minimal vortex sheet model that captures salient features of the flow interactions among flapping swimmers, and we study the free swimming of a pair of in-line swimmers driven with identical heaving or pitching motions. We find that, independent of the flapping mode, heaving or pitching, the follower passively stabilizes at discrete locations in the wake of the leader, consistent with the heaving foil experiments, but pitching swimmers exhibit tighter and more cohesive formations. Further, in comparison to swimming alone, pitching motions increase the energetic efficiency of the group while heaving motions result in a slight increase in the swimming speed. A deeper analysis of the wake of a single swimmer sheds light on the hydrodynamic mechanisms underlying pairwise formations. These results recapitulate that flow interactions provide a passive mechanism that promotes school cohesion, and afford novel insights into the role of the flapping mode in controlling the emergent properties of the school.
Deep Selective Combinatorial Embedding and Consistency Regularization for Light Field Super-resolution

Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited detector resolution has to be shared with the angular dimension. LF spatial super-resolution (SR) thus becomes an indispensable part of the LF camera processing pipeline. The high-dimensionality characteristic and complex geometrical structure of LF images make the problem more challenging than traditional single-image SR. The performance of existing methods is still limited as they fail to thoroughly explore the coherence among LF sub-aperture images (SAIs) and are insufficient in accurately preserving the scene's parallax structure. To tackle this challenge, we propose a novel learning-based LF spatial SR framework. Specifically, each SAI of an LF image is first coarsely and individually super-resolved by exploring the complementary information among SAIs with selective combinatorial geometry embedding. To achieve efficient and effective selection of the complementary information, we propose two novel sub-modules conducted hierarchically: the patch selector provides an option of retrieving similar image patches based on offline disparity estimation to handle large-disparity correlations; and the SAI selector adaptively and flexibly selects the most informative SAIs to improve the embedding efficiency. To preserve the parallax structure among the reconstructed SAIs, we subsequently append a consistency regularization network trained over a structure-aware loss function to refine the parallax relationships over the coarse estimation. In addition, we extend the proposed method to irregular LF data. To the best of our knowledge, this is the first learning-based SR method for irregular LF data. Experimental results over both synthetic and real-world LF datasets demonstrate the significant advantage of our approach over state-of-the-art methods.
Large-Scale Parameters of Spatio-Temporal Short-Range Indoor Backhaul Channels at 140 GHz

The use of above-100 GHz radio frequencies would be one of promising approaches to enhance the fifth-generation cellular further. Any air interface and cellular network designs require channel models, for which measured evidence of large-scale parameters such as pathloss, delay and angular spreads, is crucial. This paper provides the evidence from quasi-static spatio-temporal channel sounding campaigns at two indoor hotspot (InH) scenarios at 140 GHz band, assuming short-range backhaul connectivity. The measured two InH sites are shopping mall and airport check-in hall. Our estimated omni-directional large-scale parameters from the measurements are found in good match with those of the Third Generation Partnership Project (3GPP) for new radios (NR) channel model in InH scenario, despite the difference of assumed link types and radio frequency range. The 3GPP NR channel model is meant for access links and said to be valid up to 100 GHz, while our measurements cover short-range backhaul scenarios at 140 GHz. We found more deviation between our estimated large-scale parameters and those of the 3GPP NR channel model in the airport than in the shopping mall.
Automatic Arabic Dialect Identification Systems for Written Texts: A Survey

Arabic dialect identification is a specific task of natural language processing, aiming to automatically predict the Arabic dialect of a given text. Arabic dialect identification is the first step in various natural language processing applications such as machine translation, multilingual text-to-speech synthesis, and cross-language text generation. Therefore, in the last decade, interest has increased in addressing the problem of Arabic dialect identification. In this paper, we present a comprehensive survey of Arabic dialect identification research in written texts. We first define the problem and its challenges. Then, the survey extensively discusses in a critical manner many aspects related to Arabic dialect identification task. So, we review the traditional machine learning methods, deep learning architectures, and complex learning approaches to Arabic dialect identification. We also detail the features and techniques for feature representations used to train the proposed systems. Moreover, we illustrate the taxonomy of Arabic dialects studied in the literature, the various levels of text processing at which Arabic dialect identification are conducted (e.g., token, sentence, and document level), as well as the available annotated resources, including evaluation benchmark corpora. Open challenges and issues are discussed at the end of the survey.
On Moments of Folded and Doubly Truncated Multivariate Extended Skew-Normal Distributions

ABSTRACT This article develops recurrence relations for integrals that relate the density of multivariate extended skew-normal (ESN) distribution, including the well-known skew-normal (SN) distribution introduced by Azzalini and Dalla-Valle and the popular multivariate normal distribution. These recursions offer a fast computation of arbitrary order product moments of the multivariate truncated extended skew-normal and multivariate folded extended skew-normal distributions with the product moments as a byproduct. In addition to the recurrence approach, we realized that any arbitrary moment of the truncated multivariate extended skew-normal distribution can be computed using a corresponding moment of a truncated multivariate normal distribution, pointing the way to a faster algorithm since a less number of integrals is required for its computation which results much simpler to evaluate. Since there are several methods available to calculate the first two moments of a multivariate truncated normal distribution, we propose an optimized method that offers a better performance in terms of time and accuracy, in addition to consider extreme cases in which other methods fail. Finally, we present an application in finance where multivariate tail conditional expectation (MTCE) for SN distributed data is calculated using analytical expressions involving normal left-truncated moments. The R MomTrunc package provides these new efficient methods for practitioners. Supplementary files for this article are available online.
A Practical Method for Pupil segmentation in challenging conditions

Various methods have been proposed for authentication, including password or pattern drawing, which is clearly visible on personal electronic devices. However, these methods of authentication are more vulnerable, as passwords and cards can be forgotten, lost, or stolen. Therefore, a great curiosity has developed in individual authentication using biometric methods that are based on physical and behavioral features not possible to forget or be stolen. Authentication methods are used widely in portable devices since the lifetime of battery and time response are essential concerns in these devices. Due to the fact that these systems need to be fast and low power, designing efficient methods is still critical. We, in this paper, proposed a new low power and fast method for pupil segmentation based on approximate computing that under trading a minor level of accuracy, significant improvement in power assumption and time saving can be obtained and makes this algorithm suitable for hardware implementation. Furthermore, the experimental results of PSNR and SSIM show that the error rate in this method is negligible.
Thermophysical model for icy cometary dust particles

Context. Cometary dust particles are subjected to various forces after being lifted off the nucleus. These forces define the dynamics of dust, trajectories, alignment, and fragmentation, which, in turn, have a significant effect on the particle distribution in the coma. Aims. We develop a numerical thermophysical model that is applicable to icy cometary dust to study the forces attributed to the sublimation of ice. Methods. We extended the recently introduced synoptic model for ice-free dust particles to ice-containing dust. We introduced an additional source term to the energy balance equation accounting for the heat of sublimation and condensation. We use the direct simulation Monte Carlo approach with the dusty gas model to solve the mass balance equation and the energy balance equation simultaneously. Results. The numerical tests show that the proposed method can be applied for dust particles covering the size range from tens of microns to centimetres with a moderate computational cost. We predict that for an assumed ice volume fraction of 0.05, particles with a radius, r ≫ 1 mm, at 1.35 AU, may disintegrate into mm-sized fragments due to internal pressure build-up. Particles with r < 1 cm lose their ice content within minutes. Hence, we expect that only particles with r > 1 cm may demonstrate sustained sublimation and the resulting outgassing forces.