Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Gender-Ambiguous Text-to-Speech Voices

The gender of a voice assistant or any voice user interface is a central element of its perceived identity. While a female voice is a common choice, there is an increasing interest in alternative approaches where the gender is ambiguous rather than clearly identifying as female or male. This work addresses the task of generating gender-ambiguous text-to-speech (TTS) voices that do not correspond to any existing person. This is accomplished by sampling from a latent speaker em-beddings’ space that was formed while training a multilingual, multi-speaker TTS system on data from multiple male and female speakers. Various options are investigated regarding the sampling process. In our experiments, the effects of different sampling choices on the gender ambiguity and the naturalness of the resulting voices are evaluated. The proposed method is shown able to efﬁciently generate novel speakers that are superior to a baseline averaged speaker embedding. To our knowledge, this is the ﬁrst systematic approach that can reliably generate a range of gender-ambiguous voices to meet diverse user requirements.
Pricing for Reconfigurable Intelligent Surface Aided Wireless Networks: Models and Principles

Owing to the recent advancements of meta-materials and meta-surfaces, the concept of reconfigurable intelligent surface (RIS) has been embraced to meet the spectral and energy-efficient, as well as cost-effective solutions, for the sixth-generation (6G) wireless networks. From an operational standpoint, RISs can be easily deployed on the facades of buildings and indoor walls. Albeit promising, in the actual network operation, the deployment of RISs may face challenges because of the willingness and benefits of RIS holders from the aspect of installing RISs on their properties. Accordingly, RIS-aided wireless networks are faced with a formidable mission to balance the wireless service providers (WSPs) and RIS holders in terms of their respective interests. To alleviate this deadlock, we focus on the application of pricing models in RIS-aided wireless networks in pursuit of a win-win solution for both sides. Specifically, we commence with a comprehensive introduction of RIS pricing with its potential applications in RIS networks, meanwhile the fundamentals of pricing models are summarized in order to benefit both RIS holders and WSPs. In addition, a Stackelberg game-based model is exemplified to illustrate the operation of utility-maximization pricing. Finally, we highlight open issues and future research directions of applying pricing models to the RIS-aided wireless networks.
Photometric Properties and Stellar Parameters of the Rapidly Rotating Magnetic Early-B Star HD 345439

We first present the multicolor photometry results of the rapidly rotating magnetic star HD 345439 using the Nanshan One-meter Wide-field Telescope. From the photometric observations, we derive a rotational period of 0.7699 ± 0.0014 day. The light curves of HD 345439 are dominated by the double asymmetric S-wave feature that arises from the magnetic clouds. Pulsating behaviors are not observed in Sector 41 of the Transiting Exoplanet Survey Satellite. No evidence is found of the occurrence of centrifugal breakout events neither in the residual flux nor in the systematic variations at the extremum of the light curve. Based on the hypothesis of the Rigidly Rotating Magnetosphere model, we restrict the magnetic obliquity angle β and the rotational inclination angle i so that they satisfy the approximate relation β + i ≈ 105°. The color excess, extinction, and luminosity are determined to be E (B−V) = 0.745 ± 0.016 mag, A V = 2.31 ± 0.05 mag, and log(L/L⊙)=3.82±0.1 dex, respectively. Furthermore, we derive the effective temperature as T eff = 22 ± 1 kK and the surface gravity as log g = 4.00 ± 0.22. The mass M=7.24−1.24+1.75M⊙ , radius R=4.44−1.93+2.68R⊙ , and age τage=23.62−21.97+4.24 Myr are estimated from the Hertzsprung–Russell diagram.
COVID-19 Infection Exposure to Customers Shopping during Black Friday

The outbreak of COVID-19 within the last two years has resulted in much further investigation into the safety of large events that involve a gathering of people. This study aims to investigate how COVID-19 can spread through a large crowd of people shopping in a store with no safety precautions taken. The event being investigated is Black Friday, where hundreds or thousands of customers flood stores to hopefully receive the best deals on popular items. A mock store was created, separated into several different shopping sections, and represented using a 2-D grid where each square on the grid represented a 5’ × 5’ area of the mock store. Customers were simulated to enter the store, shop for certain items, check out, and then leave the store. A percentage of customers were chosen to be ‘infective’ when they entered the store, which means that they could spread infection quantum to other customers. Four hours of time was simulated with around 6,000 customers being included. The maximum distance exposure could be spread (2’-10’), the minimum time of exposure needed to become infected (2 - 15 minutes), and the total percentage of customers who started as infective (1% - 5%) were all changed and their effects on the number of newly infected customers were measured. It was found that increasing the maximum exposure distance by 2’ resulted in between a 20% to 250% increase in newly infected customers, depending on the distances being used. It was also found that increasing the percentage of customers who started as infective from 1% to 2% and then to 5% resulted in a 200% to 300% increase in newly infected customers.
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation

Recent approaches to empathetic response generation incorporate emotion causalities to enhance comprehension of both the user's feelings and experiences. However, these approaches suffer from two critical issues. First, they only consider causalities between the user's emotion and the user's experiences, and ignore those between the user's experiences. Second, they neglect interdependence among causalities and reason them independently. To solve the above problems, we expect to reason all plausible causalities interdependently and simultaneously, given the user's emotion, dialogue history, and future dialogue content. Then, we infuse these causalities into response generation for empathetic responses. Specifically, we design a new model, i.e., the Conditional Variational Graph Auto-Encoder (CVGAE), for the causality reasoning, and adopt a multi-source attention mechanism in the decoder for the causality infusion. We name the whole framework as CARE, abbreviated for CAusality Reasoning for Empathetic conversation. Experimental results indicate that our method achieves state-of-the-art performance.
CarbonTag: A Browser-Based Method for Approximating Energy Consumption of Online Ads

Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors’ knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.
DProtoNet: Decoupling Prototype Activation via Multiple Dynamic Masks

The interpretability of neural networks has recently received extensive attention. The previous prototype-based explainable networks involved prototype activation in both the reasoning process and interpretation process, which requires speciﬁc explainable structures for the prototype. This makes the network less accurate as it gains interpretability. To avoid this problem, we propose a new model: decoupling prototypical network (DProtoNet), which contains three modules. 1) encoder module: we pro-pose unrestricted masks to generate expressive features and prototypes. 2) inference module: we propose a multi-image prototype learning method to update prototypes so that the network can learn generalized prototypes. 3) interpretation module: we propose multiple dynamic masks (MDM) decoder to explain the network, which generates heatmaps using the consistent activation of the original image and mask image at the detection nodes of the network. It decouples the inference module and interpretation module of a prototype-based network by avoiding the use of prototype activation to explain the network’s decisions, so that the accuracy and interpretability of the network can be simultaneously improved. We test on multiple public general and medical datasets. The accuracy of our method is improved compared with the previous methods, which can be improved by up to 5%. DProtoNet achieves state-of-the-art interpretability.
AI Assistants: A Framework for Semi-Automated Data Wrangling

Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial intelligence, data wrangling remains a tedious and manual task. We introduce AI assistants, a class of semi-automatic interactive tools to streamline data wrangling. An AI assistant guides the analyst through a specific data wrangling task by recommending a suitable data transformation that respects the constraints obtained through interaction with the analyst. We formally define the structure of AI assistants and describe how existing tools that treat data cleaning as an optimization problem fit the definition. We implement AI assistants for four common data wrangling tasks and make AI assistants easily accessible to data analysts in an open-source notebook environment for data science, by leveraging the common structure they follow. We evaluate our AI assistants both quantitatively and qualitatively through three example scenarios. We show that the unified and interactive design makes it easy to perform tasks that would be difficult to do manually or with a fully automatic tool.
Tensor Regularized Total Least Squares Methods with Applications to Image and Video Deblurring

Total least squares (TLS) is an effective method for solving linear equations with the situations, when noise is not just in observation matrices but also in mapping matrices. Moreover, the Tikhonov regularization is widely used in plenty of ill-posed problems. In this paper, we extend the regularized total least squares (RTLS) method from the matrix form due to Golub, Hansen and O'Leary, to the tensor form proposing the tensor regularized total least squares (TR-TLS) method for solving ill-conditioned tensor systems of equations. Properties and algorithms about the solution of the TR-TLS problem, which might be similar to those of the RTLS, are also presented and proved. Based on this method, some applications in image and video deblurring are explored. Numerical examples illustrate the TR-TLS, compared with the existing methods.
Fast Multipole Method for Gravitational Lensing. Application to High Magnification Quasar Microlensing

We introduce the use of the Fast Multipole Method (FMM) to speed up gravitational lensing ray tracing calculations. The method allows very fast calculation of ray deflections when a large number of deflectors, $N_*$, is involved, while keeping rigorous control on the errors. In particular, we apply this method, in combination with the Inverse Polygon Mapping technique (IPM), to quasar microlensing to generate microlensing magnification maps with very high workloads (high magnification, large size and/or high resolution) that require a very large number of deflectors. Using, FMM-IPM, the computation time can be reduced by a factor $\sim 10^5$ with respect to standard Inverse Ray Shooting, making the use of this algorithm on a personal computer comparable to the use of standard IRS on GPUs. We also provide a flexible web interface for easy calculation of microlensing magnification maps using FMM-IPM\footnote{http://gloton.ugr. es/microlensing/}. We exemplify the power of this new method by applying it to some challenging interesting astrophysical scenarios, including clustered primordial black holes, or extremely magnified stars close to the giant arcs of galaxy clusters. We also show the performance/use of FMM to calculate ray deflection for a halo resulting from cosmological simulations composed by a large number ($N\gtrsim 10^7$) of elements.
Iterative algorithms for partitioned neural network approximation to partial differential equations

To enhance solution accuracy and training efficiency in neural network approximation to partial differential equations, partitioned neural networks can be used as a solution surrogate instead of a single large and deep neural network defined on the whole problem domain. In such a partitioned neural network approach, suitable interface conditions or subdomain boundary conditions are combined to obtain a convergent approximate solution. However, there has been no rigorous study on the convergence and parallel computing enhancement on the partitioned neural network approach. In this paper, iterative algorithms are proposed to address these issues. Our algorithms are based on classical additive Schwarz domain decomposition methods. Numerical results are included to show the performance of the proposed iterative algorithms.
Rumor Stance Classification in Online Social Networks: The State-of-the-Art, Prospects, and Future Challenges

The emergence of the Internet as a ubiquitous technology has facilitated the rapid evolution of social media as the leading virtual platform for communication, content sharing, and information dissemination. In spite of revolutionizing the way news is delivered to people, this technology has also brought along with itself inevitable demerits. One such drawback is the spread of rumors expedited by social media platforms, which may provoke doubt and fear. Therefore, it is essential to debunk rumors before their widespread use. Over the years, many studies have been conducted to develop effective rumor verification systems. One aspect of such studies focuses on rumor stance classification, which involves the task of utilizing user viewpoints regarding a rumorous post to better predict the veracity of a rumor. Relying on user stances in rumor verification has gained significant importance, for it has resulted in significant improvements in the model performance. In this paper, we conduct a comprehensive literature review of rumor stance classification in complex online social networks (OSNs). In particular, we present a thorough description of these approaches and compare their performances. Moreover, we introduce multiple datasets available for this purpose and highlight their limitations. Finally, challenges and future directions are discussed to stimulate further relevant research efforts.
Planets around the evolved stars 24 Booties and $\gamma$ Libra: A 30d-period planet and a double giant-planet system in possible 7:3 MMR

We report the detection of planets around two evolved giant stars from radial velocity measurements at Okayama Astrophysical observatory. 24 Boo (G3IV) has a mass of $0.99\,M_{\odot}$, a radius of $10.64\,R_{\odot}$, and a metallicity of ${\rm [Fe/H]}=-0.77$. The star hosts one planet with a minimum mass of $0.91\,M_{\rm Jup}$ and an orbital period of $30.35{\rm d}$. The planet has one of the shortest orbital periods among those ever found around evolved stars by radial-veloocity methods. The stellar radial velocities show additional periodicity with $150{\rm d}$, which are probably attributed to stellar activity. The star is one of the lowest-metallicity stars orbited by planets currently known. $\gamma$ Lib (K0III) is also a metal-poor giant with a mass of $1.47\,M_{\odot}$, a radius of $11.1\,R_{\odot}$, and ${\rm [Fe/H]}=-0.30$. The star hosts two planets with minimum masses of $1. 02M_{\rm Jup}$ and $4.58\,M_{\rm Jup}$, and periods of $415{\rm d}$ and $964{\rm d}$, respectively. The star has the second lowest metallicity among the giant stars hosting more than two planets. Dynamical stability analysis for the $\gamma$ Lib system sets a minimum orbital inclination angle to be about $70^{\circ}$ and suggests that the planets are in 7:3 mean-motion resonance, though the current best-fitted orbits to the radial-velocity data are not totally regular.
Analysis of multi-scale molecular dynamics algorithms for simulating biomolecules

Molecular dynamics simulations have provided invaluable insight into the properties of biomolecular and other systems. Unfortunately, biomolecular simulations are often computationally demanding due to the large number of particles that involve complex and long-range interactions. With the Ewald summation technique, the long-range pairwise Coulomb interactions, the dominant computational task, can be efficiently and accurately evaluated. Despite this progress, modeling most biomolecular simulations that require very long time scales is still a grand challenge that few researchers can afford to take. However, a new algorithm, the multiple time step (MTS) integration method, can dramatically reduce the computational cost. Nevertheless, the MTS methods can introduce errors and instabilities in certain circumstances. In this dissertation, we analyze the properties of a simple 1D linear system integration with the symplectic reference system propagator (r-RESPA) MTS method. A closed form expression for the time step dependent Hamiltonian which corresponds to r-RESPA integration of the model is derived. This allows us to present an analytical formula for the dependence of the integration accuracy on the short-range force cutoff. A detailed analysis of the force decompositions for the standard Ewald summation method is given. We test the analysis on a realistic system, a protein in water. Under Langevin dynamics with a weak friction coefficient to control temperature and with the SHAKE algorithm to freeze out high frequency vibrations, we show that the five femtosecond (fs) resonance barrier is postponed to approximately 20 fs. An iso-error curve in a system involving the short-range cutoff versus the multiple time step size agrees well with the analytical results. Using r-RESPA to treating long-range interactions results in a six times of increase in efficiency for the distance-based force decomposition with independent Ewald and short-range cutoffs.
Multi-scale contextual semantic enhancement network for 3D medical image segmentation

Objective. Accurate and automatic segmentation of medical images is crucial for improving the efficiency of disease diagnosis and making treatment plans. Although methods based on convolutional neural networks have achieved excellent results in numerous segmentation tasks of medical images, they still suffer from challenges including drastic scale variations of lesions, blurred boundaries of lesions and class imbalance. Our objective is to design a segmentation framework named multi-scale contextual semantic enhancement network (3D MCSE-Net) to address the above problems. Approach. The 3D MCSE-Net mainly consists of a multi-scale context pyramid fusion module (MCPFM), a triple feature adaptive enhancement module (TFAEM), and an asymmetric class correction loss (ACCL) function. Specifically, the MCPFM resolves the problem of unreliable predictions due to variable morphology and drastic scale variations of lesions by capturing the multi-scale global context of feature maps. Subsequently, the TFAEM overcomes the problem of blurred boundaries of lesions caused by the infiltrating growth and complex context of lesions by adaptively recalibrating and enhancing the multi-dimensional feature representation of suspicious regions. Moreover, the ACCL alleviates class imbalances by adjusting asy mmetric correction coefficient and weighting factor. Main results. Our method is evaluated on the nasopharyngeal cancer tumor segmentation (NPCTS) dataset, the public dataset of the MICCAI 2017 liver tumor segmentation (LiTS) challenge and the 3D image reconstruction for comparison of algorithm and DataBase (3Dircadb) dataset to verify its effectiveness and generalizability. The experimental results show the proposed components all have unique strengths and exhibit mutually reinforcing properties. More importantly, the proposed 3D MCSE-Net outperforms previous state-of-the-art methods for tumor segmentation on the NPCTS, LiTS and 3Dircadb dataset. Significance. Our method addresses the effects of drastic scale variations of lesions, blurred boundaries of lesions and class imbalance, and improves tumors segmentation accuracy, which facilitates clinical medical diagnosis and treatment planning.
CRYSTAL CHEMISTRY OF THREE-COMPONENT WHITE DWARFS AND NEUTRON STAR CRUSTS: PHASE STABILITY, PHASE STRATIFICATION, AND PHYSICAL PROPERTIES

A systematic search for multicomponent crystal structures is carried out for five different ternary systems of nuclei in a polarizable background of electrons, representative of accreted neutron star crusts and some white dwarfs. Candidate structures are “bred” by a genetic algorithm and optimized at constant pressure under the assumption of linear response (Thomas–Fermi) charge screening. Subsequent phase equilibria calculations reveal eight distinct crystal structures in the T = 0 bulk phase diagrams, five of which are complicated multinary structures not previously predicted in the context of compact object astrophysics. Frequent instances of geometrically similar but compositionally distinct phases give insight into structural preferences of systems with pairwise Yukawa interactions, including and extending to the regime of low-density colloidal suspensions made in a laboratory. As an application of these main results, we self-consistently couple the phase stability problem to the equations for a self-gravitating, hydrostatically stable white dwarf, with fixed overall composition. To our knowledge, this is the first attempt to incorporate complex multinary phases into the equilibrium phase-layering diagram and mass–radius-composition dependence, both of which are reported for He–C–O and C–O–Ne white dwarfs. Finite thickness interfacial phases (“interphases”) show up at the boundaries between single-component body-centered cubic (bcc) crystalline regions, some of which have lower lattice symmetry than cubic. A second application—quasi-static settling of heavy nuclei in white dwarfs—builds on our equilibrium phase-layering method. Tests of this nonequilibrium method reveal extra phases that play the role of transient host phases for the settling species.