Miracle-dz/UniFAR-FTUs huggingface dataset:
Action Unit Generation through Dimensional Emotion Recognition from Text

Expressiveness is a critical feature for the communication between humans and robots, and it helps humans to better understand and accept a robot. Emotions can be expressed through a variety of modalities: kinesthetic (via facial expression), body posture and gestures, auditory, thus the acoustic features of speech, and semantic, thus the content of what is said. One of the most effective modalities to communicate emotions is through facial expressions. Social robots often show facial expressions with coded animations. However, the robot must be able to express appropriate emotional responses according to the interaction with people. In this work, we consider verbal interactions between humans and robots and propose a system composed of two modules for the generation of facial emotions by recognising the arousal and valence values of a written sentence. The first module, based on Bidirectional Encoder Representations from Transformers, is deployed for emotion recognition in a sentence. The second, an Auxiliary Classifier Generative Adversarial Network, is proposed for the generation of facial movements for expressing the recognised emotion in terms of valence and arousal.
Robot-mediated Group Instruction for Children with ASD: A Pilot Study

Children diagnosed with autism spectrum disorder (ASD) typically work towards acquiring skills to participate in a regular classroom setting such as attending and appropriately responding to an instructor’s requests. Social robots have the potential to support children with ASD in learning group-interaction skills. However, the majority of studies that target children with ASD’s interactions with social robots have been limited to one-on-one interactions. Group interaction sessions present unique challenges such as the unpredictable behaviors of the other children participating in the group intervention session and shared attention from the instructor. We present the design of a robot-mediated group interaction intervention for children with ASD to enable them to practice the skills required to participate in a classroom. We also present a study investigating differences in children’s learning behaviors during robot-led and human-led group interventions over multiple intervention sessions. Results of this study suggests that children with ASD’s learning behaviors are similar during human and robot instruction. Furthermore, preliminary results of this study suggest that a novelty effect was not observed when children interacted with the robot over multiple sessions.
Nonparametric neural topic modeling for customer insight extraction about the tire industry

In the age of social media, customers have become opinion makers that share their experience. People interested in a product can reach for these reviews on the whole Internet, thus leading to heterogeneity of data sources. As the massive amounts of data are not systematically accessible through APIs, companies frequently scrap it from several platforms and marketplaces. Data acquisition processes include some amount of ETL, yet the variability of noise contained in the data and the heterogeneity induced by different existing sources create the need for ad-hoc tools. In other words, and even if large quantities of data are accessible for virtually free, customer insight extraction is an harduous task. To circumvent this issue, we apply the Embedded Dirichlet Process (EDP) and the Embedded Hierarchical Dirichlet Process (EHDP) in a realistic setting regarding industrial practices. These neural topic models simultaneously learn the number of topics, representations of documents, topic embeddings and word embeddings from data. These properties allow for semantic units' disambiguation and for deeper text exploration than other approaches. As such, they can also serve as a way to refine industrial ETL processes. When not higher, both the EDP and the EHDP models achieve similar likelihood than other methods in our experimental settings, while providing with more analytical levels. Last but not least, we could achieve our results without having to perform costly reruns to find the number of topics.
A novel finite-time first-order sliding-mode unknown input observer with certain fixed-time convergence characteristics

For uncertain linear system subjected to uniformly bounded unknown inputs it is presented a novel finite-time unknown input first-order sliding-mode observer. The strategy involves the design of a pair of quadratically stable first-order sliding-mode observers, aimed to guarantee the robustness to non-smooth unknown inputs and the finite-time convergence to zero of the output estimation errors. Then, the exact estimation of the unmeasured states is achieved in a fixed-time since the sliding motion is established through the design of an auxiliary output consisting of a delayed linear-combination of the estimations provided by the above-mentioned pair of observers. To confirm the effectiveness of the proposed scheme, along with a sketch of the proof of its convergence characteristics, a detailed numerical comparison analysis with the existing first-order sliding-mode competitor observers is illustrated.
THE METALLICITY PROFILE OF M31 FROM SPECTROSCOPY OF HUNDREDS OF H ii REGIONS AND PNe

The oxygen abundance gradients among nebular emission line regions in spiral galaxies have been used as important constraints for models of chemical evolution. We present the largest-ever full-wavelength optical spectroscopic sample of emission line nebulae in a spiral galaxy (M31). We have collected spectra of 253 H ii regions and 407 planetary nebulae (PNe) with the Hectospec multi-fiber spectrograph of the MMT. We measure the line-of-sight extinction for 199 H ii regions and 333 PNe; we derive oxygen abundance directly, based on the electron temperature, for 51 PNe; and we use strong-line methods to estimate oxygen abundance for 192 H ii regions and nitrogen abundance for 52 H ii regions. The relatively shallow oxygen abundance gradient of the more extended H ii regions in our sample is generally in agreement with the result of Zaritsky et al., based on only 19 M31 H ii regions, but varies with the strong-line diagnostic employed. Our large sample size demonstrates that there is significant intrinsic scatter around this abundance gradient, as much as ∼3 times the systematic uncertainty in the strong-line diagnostics. The intrinsic scatter is similar in the nitrogen abundances, although the gradient is significantly steeper. On small scales (deprojected distance <0.5 kpc), H ii regions exhibit local variations in oxygen abundance that are larger than 0.3 dex in 33% of neighboring pairs. We do not identify a significant oxygen abundance gradient among PNe, but we do find a significant gradient in the [N ii] ratio that varies systematically with surface brightness. Our results underscore the complex and inhomogeneous nature of the interstellar medium of M31, and our data set illustrates systematic effects relevant to future studies of the metallicity gradients in nearby spiral galaxies.
Acceptance and Perception of Covid-19 Vaccination for Children

Covid-19 vaccine hesitancy and acceptance delay is an unprecedented challenge for concerned authorities. Existing studies lack the investigation about public vaccination acceptance, specifically for children. In this study, we surveyed the adult population in the UK to determine the diversity in public perception and acceptance of Covid-19 vaccination specifically for the children, among different sociodemographic groups. Statistical results and intelligent clustering outcomes indicate significant relationships between sociodemographic diversity and vaccination acceptance for children and their families. Acceptability for children is significantly dependent on ethnicity $(\mathrm{p}=3.7\mathrm{e}-05)$, age group, and gender, where only 47% of participants show willingness towards children's vaccination. Primary dataset in this study, along with the experimental outcomes, might be useful for public awareness and policy makers towards better preparation for future epidemics as well as working globally to combat the ongoing Covid-19 variations while running effective vaccination campaigns in the identified sociodemographic groups.
Enhance Chest X-ray Classification with Multi-image Fusion and Pseudo-3D Reconstruction

Chest radiography (X-ray) is a critical imaging modality for the diagnosis of thorax diseases. Automated classification of chest X-rays has enormous potential to benefit diagnostic decision making. Most existing models are designed to consider a single-view chest X-ray image, mostly the frontal view, failing to utilize the complementary information provided by the lateral view when available. In clinical practice, radiologists often examine both frontal and lateral views to gather more information for diagnosis. Thus, a well-designed model should take advantage of the paired views to enhance the classification results. To this end, we present a novel dual-view deep learning framework to enhance the classification that uses an intermediate bi-directional fusion architecture to exploit the intrinsic spatial correlation registered between the two views. In particular, we design two different modules, a vertical alignment fusion and a 3D reconstruction fusion, that can effectively extract and fuse important features from both input streams to generate a pair of recalibrating signals, which are then used to amplify the pertinent parts while suppressing the irrelevant segments of the feature map in each stream. We evaluate our proposed methods extensively on two large public chest X-ray datasets, CheXpert and MIMIC-CXR-JPG. Experiment results show that our methods can effectively improve the classification results over fine-tuned baselines and other state-of-the-art methods, demonstrating the benefit of exploiting the spatial correlation between the paired views in the model. We also find that our fusion methods significantly outperform the early fusion and late fusion models, highlighting the need to capture the spatial correlation across different intermediate feature layers.
Two-phase thermofluidic modeling and validation of a multi-zone microchannel evaporator

In this work we present a numerical modeling framework to predict the thermofluidic performance of a multi-zone microchannel evaporator operating under forced-convection (pumped) and thermosyphon (gravity-driven) modes. The base model assumes uniform refrigerant flow and heater power to each evaporator zone and solves for coupled heat transfer and fluid flow within an individual microchannel evaporator zone, including two-dimensional conduction in the evaporator base plate. The approach utilizes established correlations for heat transfer and pressure drop within the microchannels and solves the resulting system of coupled differential equations numerically. Frictional pressure drops associated with the inlet and outlet plenums and the T-junction formed by the outlet plenum and riser tube are also estimated and included in the base model. The modeling results are validated with an experimental database for an 18-zone microchannel evaporator designed for cooling a telecommunications equipment shelf, with each zone targeting heat removal from a single ½ U circuit pack card and using R134a as the working fluid. The base model is then extended to account for zone-to-zone variations in refrigerant mass flow rate by developing a local pressure drop model that includes the effects of the inlet and outlet plenums that, respectively, supply refrigerant to and remove refrigerant from the individual evaporator zones. This model enables investigation of the thermofluidic characteristics of the multizone evaporator for uniform and spatially varying heat load profiles. We compare flow maldistribution effects of different evaporator designs under different operating conditions, and the corresponding predicted refrigerant flow and temperature distributions. The model provides insight into the design of the microchannel evaporator to prevent flow maldistribution.
SPITZER SPECTROSCOPY OF INFRARED-LUMINOUS GALAXIES: DIAGNOSTICS OF ACTIVE GALACTIC NUCLEI AND STAR FORMATION AND CONTRIBUTION TO TOTAL INFRARED LUMINOSITY

We use mid-infrared (MIR) spectroscopy from the Spitzer Infrared Spectrograph to study the nature of star-formation and supermassive black hole accretion for a sample of 65 IR-luminous galaxies at 0.02 < z < 0.6 with F(24 μm) > 1.2 mJy. The MIR spectra cover wavelengths 5–38 μm, spanning the polycyclic aromatic hydrocarbon (PAH) features and important atomic diagnostic lines. Our sample of galaxies corresponds to a range of total IR luminosity, LIR = L(8–1000 μm) = 1010–1012 L☉ (median LIR of 3.0 × 1011 L☉). We divide our sample into a subsample of galaxies with Spitzer Infrared Array Camera 3.6–8.0 μm colors indicative of warm dust heated by an active galactic nucleus (AGN; IRAGN) and those galaxies whose colors indicate star-formation processes (non-IRAGN). Compared to the non-IRAGN, the IRAGN show smaller PAH emission equivalent widths, which we attribute to an increase in mid-IR continuum from the AGN. We find that in both the IRAGN and star-forming samples, the luminosity in the PAH features correlates strongly with [Ne ii] λ12. 8 μm emission line, from which we conclude that the PAH luminosity directly traces the instantaneous star-formation rate (SFR) in both the IRAGN and star-forming galaxies. We compare the ratio of PAH luminosity to the total IR luminosity, and we show that for most IRAGN star-formation accounts for 10%–50% of the total IR luminosity. We also find no measurable difference between the PAH luminosity ratios of L11.3/L7.7 and L6.2/L7.7 for the IRAGN and non-IRAGN, suggesting that AGN do not significantly excite or destroy PAH molecules on galaxy-wide scales. Interestingly, a small subset of galaxies (8 of 65 galaxies) show a strong excess of [O iv] λ25. 9 μm emission compared to their PAH emission, which indicates the presence of heavily-obscured AGN, including 3 galaxies that are not otherwise selected as IRAGN. The low PAH emission and low [Ne ii] emission of the IRAGN and [O iv]-excess objects imply the IR luminosity of these objects is dominated by processes associated with the AGN. Because these galaxies lie in the “green valley” of the optical color–magnitude relation and have low implied SFRs, we argue their hosts have declining SFRs and these objects will transition to the red sequence unless some process restarts their star-formation.
The Effects of Positive Technology Readiness in Smart Airports on Integrated Technology Acceptance Theories and Information-Sharing Intentions

Smart airports have been emerged due to the fourth industrial revolution and the development of the aviation industry. The advent of smart airports makes it possible for the airports to accommodate more and more flight travelers than ever. The researcher conducted a study to investigate users' information sharing intentions according to the technology readiness and integrated technology acceptance theories due to the expansion of unmanned services resulting from the COVID-19 pandemic. Targeting airplane travelers through smart airports over the last three years, the researcher collected 370 survey responses. Among them, 354 answers were valid with the exclusion of 16 responses to improve the validity of the research analysis. The researcher used statistics programs such as SPSS (version 22) and AMOS (version 22) to measure frequency, validity, reliability analysis, and route analysis. Sub-variables of positive technology readiness, such as optimism and innovativeness, significantly affect performance expectation and attempt expectation, social status, and facilitating conditions, which are sub-variables of UTAUT. In contrast, optimism does not affect facilitating conditions. Besides, the research findings imply that UTAUT does affect information-sharing intentions. The researcher clarified the effects of positive technology readiness and expanded technology acceptance intentions and their relationships to information sharing intentions based on research results.
TESTING A DYNAMICAL EQUILIBRIUM MODEL OF THE EXTRAPLANAR DIFFUSE IONIZED GAS IN NGC 891

The observed scale heights of extraplanar diffuse ionized gas (eDIG) layers exceed their thermal scale heights by a factor of a few in the Milky Way and other nearby edge-on disk galaxies. Here, we test a dynamical equilibrium model of the eDIG layer in NGC 891, where we ask whether the thermal, turbulent, magnetic field, and cosmic-ray pressure gradients are sufficient to support the layer. In optical emission-line spectroscopy from the SparsePak integral field unit on the WIYN 3.5 m telescope, the Hα emission in position–velocity space suggests that the eDIG is found in a ring between galactocentric radii of , where . We find that the thermal ( km s−1) and turbulent ( km s−1) velocity dispersions are insufficient to satisfy the hydrostatic equilibrium equation given an exponential electron scale height of . Using a literature analysis of radio continuum observations from the CHANG-ES survey, we demonstrate that the magnetic field and cosmic-ray pressure gradients are sufficient to stably support the gas at kpc if the cosmic rays are sufficiently coupled to the system ( ). Thus, a stable dynamical equilibrium model is viable only if the eDIG is found in a thin ring around R = 8 kpc, and nonequilibrium models such as a galactic fountain flow are of interest for further study.
Formation of Tidal Captures and Gravitational Wave Inspirals in Binary-single Interactions

We perform the first systematic study of how dynamical stellar tides and general relativistic (GR) effects affect the dynamics and outcomes of binary-single interactions. For this, we have constructed an N-body code that includes tides in the affine approximation, where stars are modeled as self-similar ellipsoidal polytropes, and GR corrections using the commonly used post-Newtonian formalism. Using this numerical formalism, we are able resolve the leading effect from tides and GR across several orders of magnitude in both stellar radius and initial target binary separation. We find that the main effect from tides is the formation of two-body tidal captures that form during the chaotic and resonant evolution of the triple system. The two stars undergoing the capture spiral in and merge. The inclusion of tides can thus lead to an increase in the stellar coalescence rate. We also develop an analytical framework for calculating the cross section of tidal inspirals between any pair of objects with similar mass. From our analytical and numerical estimates, we find that the rate of tidal inspirals relative to collisions increases as the initial semimajor axis of the target binary increases and the radius of the interacting tidal objects decreases. The largest effect is therefore found for triple systems hosting white dwarfs and neutron stars (NSs). In this case, we find the rate of highly eccentric white dwarf—NS mergers to likely be dominated by tidal inspirals. While tidal inspirals occur rarely, we note that they can give rise to a plethora of thermonuclear transients, such as Ca-rich transients.
Holographic RG-flows and Boundary CFTs

Solutions of (d+1)-dimensional gravity coupled to a scalar field are obtained which holographically realize interface and boundary CFTs. The solution utilizes a Januslike AdSd slicing ansatz and corresponds to a deformation of the CFT by a spatiallydependent coupling of a relevant operator. The BCFT solutions are singular in the bulk, but physical quantities such as the holographic entanglement entropy can be calculated. ar X iv :1 20 7. 73 25 v1 [ he pth ] 3 1 Ju l 2 01 2
DISSECTING THE POWER SOURCES OF LOW-LUMINOSITY EMISSION-LINE GALAXY NUCLEI VIA COMPARISON OF HST-STIS AND GROUND-BASED SPECTRA

Using a sample of ∼100 nearby line-emitting galaxy nuclei, we have built the currently definitive atlas of spectroscopic measurements of Hα and neighboring emission lines at subarcsecond scales. We employ these data in a quantitative comparison of the nebular emission in Hubble Space Telescope (HST) and ground-based apertures, which offer an order-of-magnitude difference in contrast, and provide new statistical constraints on the degree to which transition objects and low-ionization nuclear emission-line regions (LINERs) are powered by an accreting black hole at ≲10 pc. We show that while the small-aperture observations clearly resolve the nebular emission, the aperture dependence in the line ratios is generally weak, and this can be explained by gradients in the density of the line-emitting gas: the higher densities in the more nuclear regions potentially flatten the excitation gradients, suppressing the forbidden emission. The transition objects show a threefold increase in the incidence of broad Hα emission in the high-resolution data, as well as the strongest density gradients, supporting the composite model for these systems as accreting sources surrounded by star-forming activity. The narrow-line LINERs appear to be the weaker counterparts of the Type 1 LINERs, where the low accretion rates cause the disappearance of the broad-line component. The enhanced sensitivity of the HST observations reveals a 30% increase in the incidence of accretion-powered systems at z ≈ 0. A comparison of the strength of the broad-line emission detected at different epochs implies potential broad-line variability on a decade-long timescale, with at least a factor of three in amplitude.
Teacher-free Distillation via Regularizing Intermediate Representation

Feature distillation always leads to significant performance improvements, but requires extra training budgets. To address the problem, we propose TFD, a simple and effective Teacher-Free Distillation framework, which seeks to reuse the privileged features within the student network itself. Specifically, TFD squeezes feature knowledge in the deeper layers into the shallow ones by minimizing feature loss. Thanks to the narrow gap of these self-features, TFD only needs to adopt a simple l2 loss without complex transformations. Extensive experiments on recognition benchmarks show that our framework can achieve superior performance than teacher-based feature distillation methods. On the ImageNet dataset, our approach achieves 0.8% gains for ResNet18, which surpasses other state-of-the-art training techniques.
Competitive adsorption of a two-component gas on a deformable adsorbent

We investigate the competitive adsorption of a two-component gas on the surface of an adsorbent whose adsorption properties vary due to the adsorbent deformation. The essential difference of adsorption isotherms for a deformable adsorbent both from the classical Langmuir adsorption isotherms of a two-component gas and from the adsorption isotherms of a one-component gas is obtained, taking into account variations in the adsorption properties of the adsorbent in adsorption. We establish bistability and tristability of the system caused by variations in adsorption properties of the adsorbent in competitive adsorption of gas particles on it. We derive conditions under which adsorption isotherms of a binary gas mixture have two stable asymptotes. It is shown that the specific features of the behavior of the system under study can be described in terms of a potential of the known explicit form.