Miracle-dz/UniFAR-FTUs huggingface dataset:
Talk to Kotaro: a web crowdsourcing study on the impact of phone and prosody choice for synthesized speech on human impression

During the COVID-19 pandemic, many research areas that require in person experiments with human volunteers have been impacted due to lockdowns and other activity-restricting policies. The field of robotics is no exception, and specially human-robot interaction research has been severely impacted. In order to circumvent the difficulty of gathering volunteers in person to interact with a robot, we have decided to build a novel crowdsourcing web platform for hosting our "Talk to Kotaro" experiment. The experiment consists of volunteers talking to a robot avatar and reacting to its semantic-free utterances. The developed web platform, which was built using the Python Flask framework, allows for such interactions while recording audio and video and other relevant data, which will be used for studying human impression estimation on gibberish speech. This paper describes not only the experiment and its preliminary results, but the developed platform itself; such tool is essential during pandemics and very useful for regular times, because it enables crowdsourcing data from all over the world.
A Multi-Agent Deep Reinforcement Learning Framework for VWAP Strategy Optimization

As a classical optimal trade execution algorithm, Volume Weighted Average Price (VWAP) strategy is favored by brokers. Since it is schedule-based and cannot perform well in a dynamic stock market, optimizing the traditional VWAP strategy via reinforcement learning is worth investigating. Most of the existing reinforcement learning-based execution strategies focus on formulating trading volumes or trading prices separately, ignoring the cooperation between trading volumes and trading prices. To address this issue, we propose a Multi-Agent Deep Q-Network (MADQN) trading framework to optimize the traditional VWAP strategy, which can dynamically adapt to the complex stock market and simultaneously formulate trading prices and volumes at each transaction period. Specifically, we design two different types of agents: 1) volume-driven agent for determining trading volumes at each transaction period and 2) price-driven agent for deciding trading prices. We model our stock market environment in which multiple agents participate as a fully cooperative stochastic game. The volume-driven agent and the price-driven agent take joint actions to interact with the stock market environment and then update their network respectively. We use 9-month level-2 data of eight stocks from different sectors on Shanghai Stock Exchange as experimental data. Experimental results demonstrate that our MADQN trading framework outperforms baselines in terms of several evaluation metrics.
Second Language Learning through Storytelling with a Social Robot – An Online Case Study

Social robots are applied in several fields such as education and entertainment. Using the method of storytelling, these two fields can be combined to allow for informal learning. As social robots can benefit students' motivation and learning process and storytelling is a consolidated method for vocabulary acquisition, integrating learning content into a robotic storyteller's narration might facilitate implicit learning. Focusing on language learning, we conducted an online study to compare a robotic storyteller for vocabulary learning to traditional media, e.g. text. Results show that a robotic storyteller is as effective as traditional media concerning long-term memory. While it is also perceived equally useful, participants were more satisfied when reading text. No differences were indicated for transportation into the story. This preliminary online study indicates the suitability of robotic storytelling for implicit language learning, however, future studies should examine live and co-located scenarios to investigate their full potential.
TESANet: Self-attention network for olfactory EEG classification

The olfactory system is known to be associated with emotion during odor stimulation. A well-designed computational model that can correctly recognize preference induced by odor stimulation can be vital in the food and perfume industries. Electroencephalogram (EEG) can be used to study the brain's response to odor stimulation due to its good temporal resolution and low acquisition cost. In this study, we proposed a novel self-attention deep learning framework: Temporal Segment Attention Network (TESANet) to classify the brain state of subjects when they are exposed to pleasant and unpleasant odors. Odor stimulation is a continuous process, the temporal dynamics of the EEG signal should reflect the continuous changes of the brain responses to the given odor, thus we design the model to capture the intercorrelation between time segments of the EEG by utilizing the self-attention mechanism. TESANet consists of a filter-bank layer to extract spectral features, a spatial convolution layer to extract spatial features, a temporal segmentation layer to split the data into overlapping time windows, a Long Short-Term Memory (LSTM) layer to encode the temporal segments, a self-attention layer to decode the temporal dynamics by learning the intercorrelation between time segments, and finally a fully connected layer for classification. Experiments on an olfactory EEG dataset demonstrated that the proposed method outperforms other competing deep learning methods for odor pleasantness classification.
A Lyα HALO AROUND A QUASAR AT REDSHIFT z = 6.4

We present long-slit spectroscopic data that reveal extended Lyα emission around the z = 6.417 radio-quiet quasar CFHQS J2329−0301. The Lyα emission is extended over 15 kpc and has a luminosity of >8 × 1036 W, comparable to the most luminous Lyα halos known. The emission has complex kinematics, in part due to foreground absorption, which only partly covers the extended nebula. The velocity ranges from −500 km s−1 to +500 km s−1, with a peak remarkably close to the systemic velocity identified by broad Mg ii emission of the quasar. There is no evidence for infall or outflow of the halo gas. We speculate that the Lyα emission mechanism is recombination after quasar photoionization of gas sitting within a high-mass dark matter halo. The immense Lyα luminosity indicates a higher covering factor of cold gas compared with typical radio-quiet quasars at lower redshift.
High-Throughput Multifilter VLSI Design for the AV1 Fractional Motion Estimation

A main challenge for emerging video encoders is the high complexity introduced by their new encoding tools. In the royalty-free AV1 (AOMedia Video 1) codec, a large part of this complexity is focused on the inter prediction stage. This is particularly given to fractional motion estimation (FME), where a large number of FIR (Finite Impulse Response) type filters is used in the process of interpolation that generates fractional position samples given the integer position samples as input. Therefore, strategies to mitigate this complexity, such as designing hardware accelerators, are needed. Another recurring concern is the power dissipated as many users consume video media using battery-constrained devices. Based on that, this work introduces a dedicated multifilter hardware architecture for the AV1 codec interpolation filters with a focus on the motion estimation stage. The proposal implements the Regular, Sharp and Smooth filter families, using the operand isolation technique to avoid unnecessary power consumption. The designed architecture is capable of achieving a processing throughput of 3187.5 Msamples/sec for ME (Motion Estimation) operation, and can interpolate 8k videos resolution at 60 frames per second considering the MC (Motion Compensation) scenario.
Cascaded Adversarial Learning for Speaker Independent Emotion Recognition

In contrast to traditional adversarial learning (AL) which learns speaker-invariant representations, this paper proposes cascaded adversarial learning (CAL) which learns speaker-invariant emotion data for speaker independent emotion recognition (SIER) tasks. CAL is a dual cascaded network architecture where the output of the transformation network is fed as input to the classification network. Transformation network transforms original speech emotion to speaker-invariant emotion data by implementing an AL strategy with an encoder-decoder architecture. The classification network predicts the emotion from the speaker-invariant emotion data (output of the transformation network). We argue that the speaker-invariant emotion data realized by transformation network has less variation than the original speech emotion data and therefore are conducive for SIER as it improve generalization capability. To our knowledge this is the first time a dual cascaded network has been used for SIER and demonstrate state-of-the-art performances for SIER on Emo-DB and RAVDESS datasets.
Exploring the influence of culture and gender on older adults’ perception of polite robots

This study explored the culture and gender differences among older adults interacting with polite non-humanoid robots. We have used Lakoff theory for the polite conversational maxims. A within-subjects experiment was designed with polite and non-polite, correct and erroneous behaving robots. The polite robot employs three sub-rules ("don’t impose," "give options," and "be friendly"). A user study was conducted with older adults from two cultural backgrounds: Israeli Jewish and Arab participants. The study revealed that the participants could not differentiate between the polite behaviors when the robot was correct. They were more annoyed with a polite robot making an error as compared to a non-polite erroneous robot. Whereas gender had no impact on participants’ evaluations, there were significant cultural differences between the groups: Although the Arab participants were primarily more skeptical, they enjoyed, were satisfied, and trusted the robot more than the Jewish participants. In addition, they preferred the direct non-polite behavior, whereas the Jewish participants liked the polite behavior more.
Performance Analysis of the Analog Path of Read-Out Integrated Circuit for MEMS structures under Temperature Variation

The paper presents a time-analysis of capacitive analog readout circuit properties for a range of operating temperatures. Comparison of schematic-based and post-layout extraction-based behavior is conducted, and the analysis includes and compares simulation and measurement results. The presented analysis identifies the temperature-relation of the circuit readout values as well as shows the influence of environment temperature on noise levels.
Red supergiants and stellar evolution

We review the significant role played by red supergiants (RSGs) in stellar populations, and some challenges and questions they raise for theoretical stellar evolution. We present how metallicity and rotation modify the way stars go to the red part of the Hertzsprung-Russell diagram or come back from it, and how RSGs might keep a trace of their main-sequence evolution. We compare theoretical population ratios with observed ones.
Physical embodiment vs. smartphone: which influences presence and anthropomorphism most in telecommunication?

Today, people are enjoying using teleconference systems like Zoom or Skype for social communication or for having drinks through a screen with others via the internet. In addition, some people have started using embodied systems called telepresence robots, such as the Beam robot. Some schools have started using telepresence robots so that students can attend school. However, in previous studies, systems have not been compared in terms of social presence and anthropomorphism, for example, robots compared with humans. Therefore, we wondered how the presence and anthropomorphism of such systems affect people. Therefore, we carried out a web-based experiment and conducted a one-way ANOVA (smartphone vs. telepresence robot with motion vs. without motion). Some people feel that telepresence robots bring a feeling of presence to remote places. Ironically, from the results, a video teleconference system using a smartphone and a telepresence robot did not create a feeling of presence, but regarding anthropomorphism, participants felt more of a human-likeness in the video teleconference system.
Parental Attitudes, Trust, and Comfort with Using Robots for Providing Care to Children with Developmental Disabilities

Parents of children with developmental disabilities face significantly higher workloads than parents of neurotypical children due to their higher care giving demands. Consequently, parents of children with developmental disabilities often face emotional, physical, mental, and social health declines. Currently there has been significant research and development of robots for providing care to children with developmental disabilities to address a variety of care giving scenarios. However, it is presently unclear whether parents would be comfortable with robots interacting with their children in these different child-robot interaction scenarios. In this paper, we investigate parental comfort toward robots caring for children with developmental disabilities in a variety of interaction scenarios and the influence of parental negative attitudes toward robots as well as trust on their comfort toward robots in these scenarios. Overall, our findings suggest that US parental attitudes, trust, and comfort toward robots caring for children with developmental disabilities are neutral. Parents were most comfortable with a robot serving as a teaching assistant to children with a developmental disability and least comfortable as a bus driver. Furthermore, trust for robots had a medium positive association with comfort with child-robot interactions and negative attitudes toward robots had a medium negative association with comfort with child-robot interactions.
Increasing the Stability of EEG-based Emotion Recognition with a Variant of Neural Processes

Electroencephalography (EEG) signals provide an incredible promising access to decode human emotions in affective computing. Many approaches have been applied to building EEG-based affective models and much endeavor is made to improve the performance of affective models, where test data has similar quality as training data. However, due to the strong sensitivity of EEG to external factors such as body movement and electromagnetic interference, EEG signals usually have a lot of noise and subjects have to remain as motionless as possible in a quiet environment, which is difficult to be satisfied in real applications and severely influences the user experience. To deal with this problem, a common way is to drop those channels with intensive noise. However, this results in the loss of critical information and neither existing machine learning (ML) nor deep learning (DL) approaches can handle this situation well, especially when too many channels are missed. In this paper, we propose a robust variant of the neural processes model and evaluate the stability of our model under various circumstances to simulate random data corruption in real applications. We conduct two categories of experiments by controlling the number and the places of missing channels separately and compare with classical ML and DL models. The results demonstrate that the performance of our model significantly outperforms the existing models, even using only five channels. We also explore the critical brain regions in the current EEG electrode distribution. Final performance manifests our model has extreme stability in dealing with intractable situations and sheds light on the widespread usage of portable EEG-based affective computing.
MIMO Super-Twisting Controller: A new design

We propose a novel MIMO Super-Twisting control algorithm based on passivity properties. The differences with most known MIMO algorithms are: the control terms are not of unity-type, the control matrix may be uncertain and the gain matrices can be full, instead of the usual scalar gains. A smooth Lyapunov function is used instead of the usual non-smooth one. Two examples with uncertain control matrices are presented to illustrate the unique properties of the proposed controllers, together with a comparison with other known schemes.
THE HETDEX PILOT SURVEY. V. THE PHYSICAL ORIGIN OF Lyα EMITTERS PROBED BY NEAR-INFRARED SPECTROSCOPY

We present the results from a Very Large Telescope/SINFONI and Keck/NIRSPEC near-infrared spectroscopic survey of 16 Lyα emitters (LAEs) at z = 2.1–2.5 in the COSMOS and GOODS-N fields discovered from the Hobby Eberly Telescope Dark Energy Experiment Pilot Survey. We detect rest-frame optical nebular lines (Hα and/or [O iii] λ5007) for 10 of the LAEs and measure physical properties, including the star formation rate (SFR), gas-phase metallicity, gas mass fraction, and Lyα velocity offset. We find that LAEs may lie below the mass–metallicity relation for continuum-selected star-forming galaxies at the same redshift. The LAEs all show velocity shifts of Lyα relative to the systemic redshift ranging between +85 and +296 km s−1 with a mean of +180 km s−1. This value is smaller than measured for continuum-selected star-forming galaxies at similar redshifts. The Lyα velocity offsets show a moderate correlation with the measured SFR (2.5σ), but no significant correlations are seen with the SFR surface density, specific SFR, stellar mass, or dynamical mass (≲1.5σ). Exploring the role of dust, kinematics of the interstellar medium (ISM), and geometry on the escape of Lyα photons, we find no signature of selective quenching of resonantly scattered Lyα photons. However, we also find no evidence that a clumpy ISM is enhancing the Lyα equivalent width. Our results suggest that the low metallicity in LAEs may be responsible for yielding an environment with a low neutral hydrogen column density and less dust, easing the escape of Lyα photons over that in continuum-selected star-forming galaxies.
Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and self-esteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a user-centered approach to explore how teenagers envisage a social robot "group assistant". We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers’ views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded.
Convolutional Neural Network Based Sleep Stage Classification with Class Imbalance

Accurate sleep stage classification is vital to assess sleep quality and diagnose sleep disorders. Numerous deep learning based models have been designed for accomplishing this labor automatically. However, the class imbalance problem existing in polysomnography (PSG) datasets has been barely investigated in previous studies, which is one of the most challenging obstacles for the real-world sleep staging application. To address this issue, this paper proposes novel methods with signal-driven and image-driven ways of noise addition to balance the imbalanced relationship in the training dataset samples. We evaluate the effectiveness of the proposed methods which are integrated into a convolutional neural network (CNN) based model. Experimental results evaluated on Sleep-EDF-V1, Sleep-EDF and CCSHS databases demonstrate that the proposed balancing approaches with specific tensity Gaussian white noise could enhance the overall or stage N1 recognition to some degree, especially the combination of two types of Data augmentation (DA) strategies shows the superiority of overall accuracy improvement.
MOLECULAR OUTFLOWS WITHIN THE FILAMENTARY INFRARED DARK CLOUD G34.43+0.24

We present molecular line observations, made with angular resolutions of ∼20″, toward the filamentary infrared dark cloud G34.43+0.24 using the APEX [CO(3 → 2), 13CO(3 → 2), C18O(3 → 2), and CS(7 → 6) transitions], Nobeyama 45 m [CS(2 → 1), SiO(2 → 1), C34S(2 → 1), HCO+(1 → 0), H13CO+(1 → 0), and CH3OH(2 → 1) transitions], and SEST [CS(2 → 1) and C18O(2 → 1) transitions] telescopes. We find that the spatial distribution of the molecular emission is similar to that of the dust continuum emission observed with 11″ resolution showing a filamentary structure and four cores. The cores have local thermodynamic equilibrium masses ranging from 3.3 × 102 to 1.5 × 103 M☉ and virial masses from 1.1 × 103 to 1.5 × 103 M☉, molecular hydrogen densities between 1.8 × 104 and 3.9 × 105 cm−3, and column densities >2.0 × 1022 cm−2, values characteristic of massive star-forming cores. The 13CO(3 → 2) profile observed toward the most massive core reveals a blue profile indicating that the core is undergoing large-scale inward motion with an average infall velocity of 1.3 km s−1 and a mass infall rate of 1.8 × 10−3 M☉ yr−1. We report the discovery of a molecular outflow toward the northernmost core thought to be in a very early stage of evolution. We also detect the presence of high-velocity gas toward each of the other three cores, giving support to the hypothesis that the excess 4.5 μm emission (“green fuzzies”) detected toward these cores is due to shocked gas. The molecular outflows are massive and energetic, with masses ranging from 25 to 80 M☉, momentum 2.3–6.9 × 102 M☉ km s−1, and kinetic energies 1.1–3.6 × 103 M☉ km2 s−2, indicating that they are driven by luminous, high-mass young stellar objects.
HYDRODYNAMICS OF CORE-COLLAPSE SUPERNOVAE AT THE TRANSITION TO EXPLOSION. I. SPHERICAL SYMMETRY

We study the transition to runaway expansion of an initially stalled core-collapse supernova shock. The neutrino luminosity, mass accretion rate, and neutrinospheric radius are all treated as free parameters. In spherical symmetry, this transition is mediated by a global non-adiabatic instability that develops on the advection time and reaches nonlinear amplitude. Here, we perform high-resolution, time-dependent hydrodynamic simulations of stalled supernova shocks with realistic microphysics to analyze this transition. We find that radial instability is a sufficient condition for runaway expansion if the neutrinospheric parameters do not vary with time and if heating by the accretion luminosity is neglected. For a given unstable mode, transition to runaway occurs when fluid in the gain region reaches positive specific energy. We find approximate instability criteria that accurately describe the behavior of the system over a wide region of parameter space. The threshold neutrino luminosities are in general different than the limiting value for a steady-state solution. We hypothesize that multidimensional explosions arise from the excitation of unstable large-scale modes of the turbulent background flow, at threshold luminosities that are lower than in the laminar case.