Miracle-dz/UniFAR-FTUs huggingface dataset:
Covid-19 Diagnosis via Voice Using Online Sequential Extreme Learning Machine

With the worldwide spreading of Coronavirus disease 2019 (Covid-19) pandemic, besides the traditional diagnosing approach, Artificial Intelligence provides additional support for the pre-diagnosis of Covid-19 by using data such as patients' images, and sounds, etc. Being able to recognize Covid-19 positive patients quickly and correctly is the key to preventing the expansion of the disease. However, the existing Covid-19 diagnosis models still face challenges due to the complex network structure and additional medical examination. It takes much time to return a diagnosis result. In this paper, a diagnostic model is proposed as an early work for Covid-19 diagnosis using sound samples. The features of sound signals are expressed by Mel Frequency Cepstral Coefficients, which are input into the Online Sequential Extreme Learning Machine for normal/abnormal detection. Data from an open-source database were used to train the proposed model, the experiments show that using vowel pronunciations the model can achieve an accuracy of 96.4% on average, with about 10 times faster for testing than the Support Vector Machine.
Alexa Feels Blue And so Do I? Conversational Agents Displaying Emotions via Light Modalities

This paper examines how conversational agents (CAs) can communicate emotions non-verbally using light as communication modality. Therefore, we manipulated the CA Alexa to demonstrate emotions (joy and sorrow) using different light modalities (Echo Dot ring, a Hue lamp and the combination) with either a congruent verbal context (party or funeral) or no verbal context. In an online study 167 participants evaluated the perceived emotion of Alexa, their own emotional state as well as the perception of Alexa after watching a video with a user interacting with Alexa. Although the perceived emotions of Alexa were not affected by the experimental conditions, the results indicate that the perception of Alexa as well as the user’s emotion is affected by the displayed communication modality. As external light can be used to manipulate the users’ perception of CAs, the findings give relevant implications for the design of CAs.
On the number density of 'direct collapse' black hole seeds

Supermassive black holes (BHs) reside in the centre of most local galaxies, but they also power active galactic nuclei and quasars, detected up to z = 7. These quasars put constraints on early BH growth and the mass of BH seeds. The scenario of ‘direct collapse’ is appealing as it leads to the formation of large mass BH seeds, 104–106 M⊙, which eases explaining how quasars at z = 6–7 are powered by BHs with masses >109 M⊙. Direct collapse, however, appears to be rare, as the conditions required by the scenario are that gas is metal-free, the presence of a strong photodissociating Lyman–Werner flux, and large inflows of gas at the centre of the halo, sustained for 10–100 Myr. We performed several cosmological hydrodynamical simulations that cover a large range of box sizes and resolutions, thus allowing us to understand the impact of several physical processes on the distribution of direct collapse BHs. We identify haloes where direct collapse can happen, and derive the number density of BHs. We also investigate the discrepancies between hydrodynamical simulations, direct or post-processed, and semi-analytical studies. Under optimistic assumptions, we find that for direct collapse to account for BHs in normal galaxies, the critical Lyman–Werner flux required for direct collapse must be about two orders of magnitude lower than predicted by 3D simulations that include detailed chemical models. However, when supernova feedback is relatively weak, enough direct collapse BHs to explain z = 6–7 quasars can be obtained for Lyman–Werner fluxes about one order of magnitude lower than found in 3D simulations.
MTGnet: Multi-Task Spatiotemporal Graph Convolutional Networks for Air Quality Prediction

Accurate air quality forecasting is essential in managing outdoor activity risk and responding to pollution emergencies. However, effectively modeling complex underlying spatiotemporal dependencies among monitoring stations remains a challenging task. Most existing methods deeply rely on local features to model dynamic spatial correlations and on RNNs to model temporal evolution. In this paper, we propose a novel multi-task deep spatiotemporal graph neural network, named MTGnet, for air quality prediction. MTGnet's main advantage is its ability to adaptively capture complex correlations among different stations, represented as an adjacency matrix, using both local features and global patterns. MTGnet consists of multiple convolutional layers for aggregating information about nearby stations and extracting essential spatial and temporal features for future air quality prediction. Using this architecture, we implement a multi-task learning scheme that trains the model to predict air quality both finely, at the station level, and coarsely, at the city level. Experiments on multiple real datasets demonstrate that MTGnet outperforms state-of-the-art methods.
GiTS: Gist-driven Text Segmentation

Text segmentation is a classical task in NLP that aims to split a document into several coherent sections. Existing work typically regards it as a binary classification task and predicts whether a paragraph or sentence is the end of a section through bottom-up paragraph content coherence modeling. However, the high-level gist, i.e., the essential meaning of a certain section, is not well utilized. In this work, we propose a gist-driven text segmentation model (GiTS), which locates section boundaries utilizing both the top-down section gist and the bottom-up paragraph contents. Specifically, GiTS consists of a gist generator and a boundary predictor. The RNN-based gist generator generates section gist in order to consider high-level content coherence and distinguishability. The boundary predictor predicts boundary through a pointer network. We also propose an auxiliary gist processor module, which can seamlessly extend GiTS to diverse NLP tasks, such as heading generation, question answering and text classification. Experimental results on text segmentation show our model outperforms the RNN-based state-of-the-art model by 6.83 % on average in terms of $P_{k}$. We also show that combining GiTS with the auxiliary gist processor outperforms the state-of-the-art heading generation models by 22.68% in accuracy and 44.20% in Rouge-1.
How Users, Facility Managers, and Bystanders Perceive and Accept a Navigation Robot for Visually Impaired People in Public Buildings

Autonomous navigation robots have a considerable potential to offer a new form of mobility aid to people with visual impairments. However, to deploy such robots in public buildings, it is imperative to receive acceptance from not only robot users but also people that use the buildings and managers of those facilities. Therefore, we conducted three studies to investigate the acceptance and concerns of our prototype robot, which looks like a regular suitcase. First, an online survey revealed that people could accept the robot navigating blind users. Second, in the interviews with facility managers, they were cautious about the robot’s camera and the privacy of their customers. Finally, focus group sessions with legally blind participants who experienced the robot navigation revealed that the robot may cause trouble when it collides with those who may not be aware of the user’s blindness. Still, many participants liked the design of the robot which assimilated into the surroundings.
Elastohydrodynamics of contact in adherent sheets

Abstract Adhesive contact between a thin elastic sheet and a substrate arises in a range of biological, physical and technological applications. By considering the dynamics of this process that naturally couples fluid flow, long-wavelength elastic deformations and microscopic adhesion, we analyse a sixth-order thin-film equation for the short-time dynamics of the onset of adhesion and the long-time dynamics of a steadily propagating adhesion front. Numerical solutions corroborate scaling laws and asymptotic analyses for the characteristic waiting time for adhesive contact and for the speed of the adhesion front. A similarity analysis of the governing partial differential equation further allows us to determine the shape of a fluid-filled blister ahead of the adhesion front. Finally, our analysis reveals a near-singular behaviour at the moving elastohydrodynamic contact line with an effective boundary condition that might be useful in other related problems.
Multivariable Causal Discovery with General Nonlinear Relationships

Today’s methods for uncovering the causal relationship(s) from observational data either constrain the function class (linearity/additive noise) or the data. We make assumptions on the data to develop a framework for Causal Discovery (CD) that works for general non-linear dependencies. Similar to previous work, we use nonlinear Independent Component Analysis (ICA) to infer the underlying sources from the observed variables. Instead of using conditional independence tests to determine the causal directions, we rely on the Jacobian of the inference function; thus, generalizing LiNGAM’s approach to the nonlinear case. We show that causal models resolve the permutation indeterminacy of ICA and prove that under strong identifiability, the inference function’s Jacobian captures the sparsity structure of the causal graph. We demonstrate that our method can infer the causal graph on multiple synthetic data sets.
Open Research Online Inﬂectional and derivational morphological spelling abilities of children with Speciﬁc Language Impairment

Children with Speciﬁc Language Impairment (SLI) are known to have difﬁculties with spelling but the factors that underpin these difﬁculties, are a matter of debate. The present study investigated the impact of oral language and literacy on the bound morpheme spelling abilities of children with SLI. Thirty-three children with SLI (9–10 years) and two control groups, one matched for chronological age (CA) and one for language and spelling age (LA) (aged 6–8 years) were given dictated spelling tasks of 24 words containing inﬂectional morphemes and 18 words containing derivational morphemes. There were no signiﬁcant differences between the SLI group and their LA matches in accuracy or error patterns for inﬂectional morphemes. By contrast when spelling derivational morphemes the SLI group was less accurate and made proportionately more omissions and phonologically implausible errors than both control groups. Spelling accuracy was associated with phonological awareness and reading; reading performance signiﬁcantly predicted the ability to spell both inﬂectional and derivational morphemes. The particular difﬁculties experienced by the children with SLI for derivational morphemes are considered in relation to reading and oral language.
End-to-end Speech Translation with Spoken-to-Written Style Conversion

End-to-end speech translation (ST), which 001 translates speech in source language directly 002 into text in target language by a single model, 003 has attracted a great deal of attention in re-004 cent years. Compared to the cascade ST, it 005 has the advantages of easier deployment, better 006 efficiency, and less error propagation. Mean-007 while, spoken-to-written style conversion has 008 been proved to be able to improve cascaded ST 009 by reducing the gap between the language style 010 of speech transcription and bilingual corpora 011 used for machine translation training. There-012 fore, it is desirable to integrate the conver-013 sion into end-to-end ST. In this paper, we pro-014 pose a joint task of speech-to-written-style-015 text conversion and end-to-end ST, as well 016 as an interactive-attention-based multi-decoder 017 model for the joint task to improve end-to-end 018 ST. Experiments on a Japanese-English lec-019 ture ST dataset and CoVoST 2 Native Japanese 020 show that our models outperform a strong base-021 line on Japanese-English ST. 022
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?

Several multi-hop reading comprehension datasets have been proposed to resolve the issue of reasoning shortcuts by which questions can be answered without performing multi-hop reasoning. However, the ability of multi-hop models to perform step-by-step reasoning when finding an answer to a comparison question remains unclear. It is also unclear how questions about the internal reasoning process are useful for training and evaluating question-answering (QA) systems. To evaluate the model precisely in a hierarchical manner, we first propose a dataset, HieraDate, with three probing tasks in addition to the main question: extraction, reasoning, and robustness. Our dataset is created by enhancing two previous multi-hop datasets, HotpotQA and 2WikiMultiHopQA, focusing on multi-hop questions on date information that involve both comparison and numerical reasoning. We then evaluate the ability of existing models to understand date information. Our experimental results reveal that the multi-hop models do not have the ability to subtract two dates even when they perform well in date comparison and number subtraction tasks. Other results reveal that our probing questions can help to improve the performance of the models (e.g., by +10.3 F1) on the main QA task and our dataset can be used for data augmentation to improve the robustness of the models.
HIGH-PRECISION DYNAMICAL MASSES OF VERY LOW MASS BINARIES

We present the results of a three year monitoring program of a sample of very low mass (VLM) field binaries using both astrometric and spectroscopic data obtained in conjunction with the laser guide star adaptive optics system on the W. M. Keck II 10 m telescope. Among the 24 systems studied, 15 have undergone sufficient orbital motion, allowing us to derive their relative orbital parameters and hence their total system mass. These measurements more than double the number of mass measurements for VLM objects, and include the most precise mass measurement to date (<2%). Among the 11 systems with both astrometric and spectroscopic measurements, six have sufficient radial velocity variations to allow us to obtain individual component masses. This is the first derivation of the component masses for five of these systems. Altogether, the orbital solutions of these low mass systems show a correlation between eccentricity and orbital period, consistent with their higher mass counterparts. In our primary analysis, we find that there are systematic discrepancies between our dynamical mass measurements and the predictions of theoretical evolutionary models (TUCSON and LYON) with both models either underpredicting or overpredicting the most precisely determined dynamical masses. These discrepancies are a function of spectral type, with late-M through mid-L systems tending to have their masses underpredicted, while one T-type system has its mass overpredicted. These discrepancies imply that either the temperatures predicted by evolutionary and atmosphere models are inconsistent for an object of a given mass, or the mass–radius relationship or cooling timescales predicted by the evolutionary models are incorrect. If these spectral-type trends are correct and hold into the planetary mass regime, the implication is that the masses of directly imaged extrasolar planets are overpredicted by the evolutionary models.
Halo Abundance Matching: accuracy and conditions for numerical convergence

Accurate predictions of the abundance and clustering of dark matter haloes play a key role in testing the standard cosmological model. Here, we investigate the accuracy of one of the leading methods of connecting the simulated dark matter haloes with observed galaxies -- the Halo Abundance Matching (HAM) technique. We show how to choose the optimal values of the mass and force resolution in large-volume N-body simulations so that they provide accurate estimates for correlation functions and circular velocities for haloes and their subhaloes -- crucial ingredients of the HAM method. At the 10% accuracy, results converge for 50 particles for haloes and 150 particles for progenitors subhaloes. In order to achieve this level of accuracy a number of conditions should be satisfied. The force resolution for the smallest resolved (sub)haloes should be in the range (0.1-0.3)rs, where rs is the scale radius of (sub)haloes. The number of particles for progenitors of subhaloes should be 150. We also demonstrate that the two-body scattering plays a minor role for the accuracy of N-body simulations thanks to the relatively small number of crossing-times of dark matter in haloes, and the limited force resolution of cosmological simulations.
Dynamic scaling at classical phase transitions approached through non-equilibrium quenching

Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215(Dated: December 16, 2013)We use Monte Carlo simulations to demonstrate generic scaling aspects of classical phase transitions ap-proached through a quench (or annealing) protocol where the temperature changes as a function of time withvelocity v. Using a generalized Kibble-Zurek ansatz, we demonstrate dynamic scaling for different types ofstochastic dynamics (Metropolis, Swendsen-Wang, and Wolff) on Ising models in two and higher dimensions.We show that there are dual scaling functions governing the dynamic scaling, which together describe the scal-ing behavior in the entire velocity range v ∈ [0,∞). These functions have asymptotics corresponding to theadiabatic and diabatic limit, and close to these limits they are perturbative in v and 1/v, respectively. Awayfrom their perturbative domains, both functions cross over into the same universal power-law scaling form gov-erned by the static and dynamic critical exponents (as well as an exponent characterizing the quench protocol). As a by-product of the scaling studies, we obtain high-precision estimates of the dynamic exponent z for thetwo-dimensional Ising model subject to the three variants of Monte Carlo dynamics; for single-spin Metropolisupdates z = 2.172(3), for Swendsen-Wang multi-cluster updatesz = 0.297(3), and for Wolff single-clusterupdates z
Rhythm analysis of second-language speech through low-frequency auditory features

Rhythm patterns play an important role in the perception of second-language (L2) speech. This paper presents a novel approach to evaluating L2 speech rhythm using low-frequency spectral features inspired by the rhythmogram auditory model. In this paper we investigate several new feature sets for use in training rhythm-centric acoustic models. By capturing information over suprasegmental linguistic units appropriate for rhythmic analysis (including syllables and prosodic feet), these novel features can outperform traditional features in detecting rhythm errors on the ISLE corpus of learner English by 5-15% absolute.
Faking Interpolation Until You Make It

Deep over-parameterized neural networks exhibit the interpolation property on many data sets. Specifically, these models can achieve approximately zero loss on all training samples simultaneously. This property has been exploited to develop optimisation algorithms for this setting. These algorithms use the fact that the optimal loss value is known to employ a variation of a Polyak step size calculated on each stochastic batch of data. We introduce a novel extension of this idea to tasks where the interpolation property does not hold. As we no longer have access to the optimal loss values a priori , we instead estimate them for each sample online. To realise this, we introduce a simple but highly effective heuristic for approximating the optimal value based on previous loss evaluations. We provide rigorous experimentation on a range of problems. From our empirical analysis we demonstrate the effectiveness of our approach, which outperforms other single hyperparameter optimisation methods.
Implementation of a discrete Immersed Boundary Method in OpenFOAM

In this paper, the Immersed Boundary Method (IBM) proposed by Pinelli is implemented for finite volume approximations of incompressible Navier-Stokes equations solutions in the open source toolbox OpenFOAM version 2.2. Solid obstacles are described using a discrete forcing approach for boundary conditions. Unlike traditional approaches encompassing the presence of a solid body using conformal meshes and imposing no-slip boundary conditions on the boundary faces of the mesh, the solid body is here represented on the Eulerian Cartesian mesh through an ad-hoc body force evaluated on a set of Lagrangian markers. The markers can move across the Eulerian mesh, hence allowing for a straightforward analysis of motion or deformation of the body. The IBM method is described and implemented in PisoFOAM, whose Pressure-Implicit Split-Operator (PISO) solver is modified accordingly. The presence of the solid body and the divergence-free of the fluid velocity are imposed simultaneously by sub-iterating between IBM and the pressure correction step. This scheme allows for the use of fast optimized Poisson solvers while granting excellent accuracy with respect to the previously mentioned constraints. Various 2D and 3D well-documented test cases of flows around fixed or moving circular cylinders are simulated and carefully validated against existing data from the literature. The capability of the new solver is discussed in terms of accuracy and numerical performances.