Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Expressive Speaking Styles with Limited Training Data in a Multi-Speaker, Prosody-Controllable Sequence-to-Sequence Architecture

Although Sequence-to-Sequence (S2S) architectures have become state-of-the-art in speech synthesis, the best models ben-eﬁt from access to moderate-to-large amounts of training data, posing a resource bottleneck when we are interested in generating speech in a variety of expressive styles. In this work we explore a S2S architecture variant that is capable of generating a variety of stylistic expressive variations observed in a limited amount of training data, and of transplanting that style to a neutral target speaker for whom no labeled expressive resources exist. The architecture is furthermore controllable, allowing the user to select an operating point that conveys a desired level of expressiveness. We evaluate this proposal against a classically supervised baseline via perceptual listening tests, and demonstrate that i) it is able to outperform the baseline in terms of its generalizability to neutral speakers, ii) it is strongly preferred in terms of its ability to convey expressiveness, and iii) it provides a reasonable trade-off between expressiveness and naturalness, allowing the user to tune it to the particular demands of a given application.
Globalization and GHG Emissions in the EU: Do We Need a New Development Paradigm?

The European Union (EU) has adopted a new development strategy based on “green” growth and announced carbon neutrality by 2050. Still, the EU’s previous development path was mainly based on trade openness and globalization, with positive economic and negative climate impacts. The aim of this paper was to test the hypothesis of globalization-induced carbon emissions in order to evaluate a possible future development path. The Arellano–Bond estimator was employed for dynamic panel analysis in 26 EU countries over the period 2000–2018. A significant and positive relationship was found between economic globalization and passenger mobility and greenhouse gas (GHG) emissions, while environmental taxes can correct the negative climate effect. On the other hand, social and political dimensions of globalization reduce negative climate impacts. To achieve net zero emissions, the EU needs to continue its global climate leadership, extend the use of environmental taxes, and stimulate economic growth based on low-carbon technologies such as hydrogen, energy storage, and CCUS.
Impact of Stratospheric Intrusions on Ozone Enhancement in the Lower Troposphere and Implication to Air Quality in Hong Kong and Other South China Regions

Stratospheric intrusions are an important source of ozone (O3) in the troposphere. In this study, 17 years of O3 sounding data from the Hong Kong Observatory with a weekly sampling frequency are analyzed to identify stratospheric intrusions and quantify their impact on O3 enhancement events in springtime from 2004 to 2020. 24.7% of O3 enhancement events are related to stratospheric intrusion whereas 31.7% of intrusions lead to the enhancement of O3 in the lower troposphere. Occurrences of stratospheric intrusion in springtime are closely related to tropopause folding and tropospheric convective activities in subtropical regions. The Weather Research and Forecasting model coupled with Chemistry simulations are conducted with the integrated process analysis to quantify the impact of stratospheric intrusions on the enhancement of O3 in the lower free troposphere as well as near the surface under different synoptic patterns. Synoptic patterns associated with stratospheric intrusion‐driving O3 enhancements are classified into two categories: Saddle Point (“S”) and Cold Front (“F”). While downward transport of O3‐enriched air from the enhancement layer exerts an important impact on surface O3 for synoptic pattern “S,” the impact on surface O3 is limited for surface pattern “F. ” The impact of stratospheric intrusions together with entrainment on surface O3 may have a strong indication on the development of effective emission control strategies on surface O3 reduction in Hong Kong.
Effects of anxiety and sleep on academic engagement among university students

ABSTRACT Objective Poor sleep quality resulting from experiencing anxiety, and low adherence to sleep hygiene behaviours, may lead to poorer academic engagement. The aim of the current study was to elucidate the associations between anxiety, sleep hygiene, sleep quality, and academic engagement of university students. Methods Participants (N = 614) comprised students attending a major university in Australia (n = 329) and Hong Kong (n = 285). A two-wave correlational design was utilised. At Time 1 (T1), anxiety was measured using the anxiety subscale of the Depression Anxiety Stress Scale 21 (DASS-21) and sleep hygiene behaviour was assessed with the Sleep Hygiene Index (SHI). One month later, at Time (T2), sleep quality was measured with the self-report Pittsburgh Sleep Quality Index (PSQI) global sleep quality score and academic engagement was measured using the shortened Utrecht Work Engagement Scale – Student version (UWES-S). Structural equation modelling was used to analyse the associations among study constructs. Results Analyses found that in both the Australia and Hong Kong samples the model showed good fit to data for the observed effect sizes. In both samples, anxiety predicted sleep quality and effects were mediated by sleep hygiene. Also, the indirect effect of anxiety on academic engagement via sleep hygiene and sleep quality was significant, and fully mediated the effects of anxiety on academic engagement in both samples. Conclusions Results indicate that students with higher anxiety tend to have lower adherence to sleep hygiene behaviours and experience poorer sleep quality which, in turn, negatively affects their academic engagement. Key Points What is already known about this topic: (1) Students are expected to spend large amounts of time studying and display a high level of engagement with their academic programme. (2) Academic engagement is linked to students’ academic achievement and wellness. (3) Academic disengagement has been linked to poor sleep hygiene behaviours and high levels of anxiety. What this topic adds: (1) The impact of anxiety on sleep quality was associated with sleep hygiene behaviours, and the impact of anxiety on academic engagement was associated with sleep hygiene behaviours and sleep quality. (2) The model predicting academic engagement from anxiety mediated by sleep hygiene and sleep quality was similar in both Australian and Hong Kong students. (3) Findings may signpost possible avenues for the development of interventions aimed at improving university students’ sleep quality and academic engagement.
Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts, data on the spread of the COVID-19 virus in Lithuania is used, the forecasts of epidemic dynamics were examined, and the results were presented in the study. Nevertheless, the approach presented might be applied to any country and other pandemic situations. The COVID-19 outbreak started at different times in different countries, hence some countries have a longer history of the disease with more historical data than others. The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks. © 2021 Tech Science Press. All rights reserved.
On the Potential of Rate Adaptive Point Cloud Streaming on the Point Level

In many robotic applications as well as in the area of immersive multimedia, large point clouds need to be transmitted over a network preferably in real-time. Typically, the point cloud is first compressed to a significantly smaller size, before it is transmitted as a simple file transfer. A drawback of this method is that it requires a point cloud to be fully available for the compression process to begin, inducing additional delay. In this paper, we explore the potential of streaming incoming points directly from an ongoing laser scan. Therefore, we propose a compression algorithm that operates on an ongoing point stream. Furthermore, we propose and compare different rate adaptation methods that dynamically adapt the compression rate to the currently available network data rate. Our evaluations show promising results in terms of rate fluctuations around the available network data rate and the achieved quality of our solution.
Multi-Directional Fixed Crack Model Extended to Masonry Structures

The mechanical nonlinearity of a masonry structure depends on debonding and slip of masonry joints and the fracture of blocks like bricks and concrete. The present study proposes a behavioral simulation to idealize masonry joints by allo-cating three orthogonal planes, which are governed by the existing multi-directional crack model, and to represent the damage of blocks by using another three crack planes. The shear stiffness of the joint changes due to the disintegration of the infilling mortar caused by the shear slip of the joint, as well as the confining pressure dependence of the shear strength. Shear response analysis of a masonry structure considering this disintegration was carried out, and the applicability of the analysis model was validated. The following three types of structures were selected for validation: 1) masonry structures in which both joints and masonry blocks are damaged, 2) structures in which masonry blocks are primarily damaged but deformation is not concentrated in the joints, and 3) structures in which mortar joints are exclusively damaged but the masonry blocks are exempt of damage. It was confirmed that the proposed analysis model can analyze the damage mode of masonry structures. It evaluates yield strength as well and deformability is estimated on the safe side of an engineering viewpoint.
IoT-Based Intrusion Detection Systems: A Review

ABSTRACT Internet of Things (IoT) is a modern prototype that merges physical entities affiliated with a variety of fields, like industrial tasks, firm automation, mortal fitness, and habitat observance with the internet. It intensifies the occupancy of Internet-linked objects in everyday tasks, resulting in issues linked to security besides usefulness. For many years, Intrusion Detection Systems (IDS) have proven to be advantageous for guarding information systems and networks. Conversely, enacting old IDS procedures on IoT is unrealistic owing to some specific elements. For instance, specific protocol stacks, strained-asset gadgets, and measures. This study furnishes inspection of IDS inquiry achievements for IoT. The objective entails the establishment of key biases, general drawbacks, and inquiry guidelines. Grouping of the suggested IDSs was undertaken in the state-of-art regarding the subsequent qualities: discernment technique, IDS placement procedure, security peril, and confirmation procedure. The paper further considered every attribute shortcoming, exploring views of material with suggestions on certain IDS procedures for IoT or inaugurating attack distinguishing procedures for IoT menaces with the possibility of being fixed in IDSs. Graphical abstract
CMOS‐Inspired Complementary Fluidic Circuits for Soft Robots

The latest efforts in digital fluidic circuits’ research aim at being electronics‐free, light‐weight, and compliant controllers for soft robots; however, challenges arise to adjust the fluidic circuit's digital logic operations. Currently there is no other way to modulate the amplitude or frequency but to structurally redesign the entire fluidic circuitry. This is mainly because there is currently no method to create an analog circuit‐like behavior in the digital fluidic circuits using conventional digitized fluidic gates. In this work, a new approach is presented to designing a circuit with digitized fluidic gates that is comparable to an analog circuit capable of actively tuning the circuit's fluidic characteristics, such as pressure gain, amplitude of output, and time response. For the first time, a pressure‐controlled oscillator is modeled, designed, and prototyped that not only controls the fluidic oscillation, but also modulates its frequency using only a single, quasi‐static pressure input. It can also demonstrate the circuit's performance for the control of a soft robotic system by actively modulating the motion of a soft earthworm robot up to twice of crawling speeds. This work has distinct contributions to designing and building intelligent pneumatic controllers toward truly comprehensive soft robotic systems.
Semantic Human Face Analysis for Multi-level Age Estimation

Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training process. Computer-vision is characterized by its superior ability to extract traditional facial features such as shape, size, texture, and deep features. However, it is still difficult for computers to extract and deal with semantic features inferred by human-vision. Therefore, we need somehow to bridge the semantic gap between machines and humans to enable utilization of the human brain capabilities of perceiving and processing visual information in semantic space. Our research aims to exploit human-vision in semantic facial feature extraction and fusion with traditional computer-vision features to obtain integrated and more informative features as an initial study paving the way to further augment the outperforming state-of-the-art age estimation models. A hierarchical automatic age estimation is achieved upon two consecutive stages: classification to predict (high-level) age group, followed by regression to estimate (low-level) exact age. The results showed noticeable performance improvements, when fusing semantic-based features with traditional vision-based features, surpassing the performance of traditional features alone.
Recognizing local artifacts in two-photon imaging of dendrites beneath blood vessels in vivo

Localized wavefront aberrations would introduce artifacts in biomedical imaging, which, however, are often neglected, as their compensations are at the cost of the field-of-view. Here, we show rarely reported local artifacts in two-photon imaging of dendrites beneath blood vessels in a mouse brain in vivo and interpret the phenomena via numerical simulations. The artifacts of divided parallel structures are found to be induced by coma and astigmatism, resulting from sample tilting and the cylinder shape of vasculatures, respectively. Different from that in single-photon microscopy, such artifacts in nonlinear microscopy show unique characteristics and should be recognized for proper interpretation of the images.
Sequence-to-Sequence Learning for Deep Gaussian Process Based Speech Synthesis Using Self-Attention GP Layer

This paper presents a speech synthesis method based on deep Gaussian process (DGP) and sequence-to-sequence (Seq2Seq) learning toward high-quality end-to-end speech synthesis. Feed-forward and recurrent models using DGP are known to produce more natural synthetic speech than deep neural networks (DNNs) because of Bayesian learning and kernel regression. However, such DGP models consist of a pipeline architecture of independent models, acoustic and duration models, and require a high level of expertise in text processing. The proposed model is based on Seq2Seq learning, which enables a uniﬁed training of acoustic and duration models. The encoder and decoder layers are represented by Gaussian process regressions (GPRs) and the parameters are trained as a Bayesian model. We also propose a self-attention mechanism with Gaussian processes to effectively model character-level input in the encoder. The subjective evaluation results show that the proposed Seq2Seq-SA-DGP can synthesize more natural speech than DNNs with self-attention and recurrent structures. Besides, Seq2Seq-SA-DGP reduces the smoothing problems of recurrent structures and is effective when a simple input for an end-to-end system is given.
Imagery Rescripting Helps Victims Cope With Experienced Injustice

Abstract. In clinical psychology, imagery rescripting (ImRs) is a promising intervention to target negative emotional memories after victimization experiences. The present research tested the effects of ImRs in “minor” cases of experienced injustice. After imagining being the victim of an injustice, participants ( N = 272) were randomly assigned to one of four conditions in a univariate design (treatment: active ImRs, passive ImRs; control: pleasant imagery, imagery rehearsal). Dependent variables were positive and negative affect; mediator variables were justice-related satisfaction and empowerment. Both justice-related satisfaction and empowerment increased positive and decreased negative affect, but empowerment was higher in the active than in the passive ImRs condition (while justice-related satisfaction was increased by both ImRs conditions). These results suggest that ImRs can be beneficial even in minor victimization experiences and that these beneficial effects are mediated by both empowerment and justice-related satisfaction.
Helix Inspired 28 GHz Broadband Antenna with End-Fire Radiation Pattern

: This paper presents the design and characterization of a via free planar single turn helix for 28 GHz broadband applications. The proposed antenna is designed using ROGERS RO4003 material, having a simple structure and end-fire radiation pattern. The antenna comprises of a compact dimension of 1.36 λ 0 × 0.9 λ 0 with a thickness of 0.0189 λ 0 (where λ 0 is the free-space wavelength at the central frequency of 28 GHz). Parametric study has been carried out to investigate the impact of key design parameters and to achieve an optimum design. Results show a good agreement between the simulated and measured results. A single turn helical inspired antenna covers − 10 dB impedance bandwidth of 26.25–30.14 GHz having a peak gain of 5.83 dB and radiation efficiency up to 85%. Moreover, linear array configurations with 2 and 4 elements have been analyzed for applications with higher gain and space constraints. Presented array configurations are suitable for applications having space constraints in one dimension. Results show that peak gain up to 8.2 dB and 11.1 dB can be achieved with 2 and 4 elements, respectively. Due to its simple planar and via free structure, this antenna is suitable for 5G communications and for sensing, imaging, IoT and tracking applications at 28 GHz band spectrum.
Results from a quasi-controlled trial of a “Coping with Voices” group. Part 2

ABSTRACT Background This is the second of two papers that investigated the effectiveness of a group-based program known as ‘Coping with Voices’. The first paper reported on the group facilitators experience of delivering the program. This second paper reports the findings from the participants. Method Of the 61 participants that gave informed consent to participate in the study 19 did not attend a group, so this provided a natural quasi-control group. The study compared pre and post primary measures for the 42 participants who attended a group. 29 group attendees also provided qualitative data. Secondary measures were measured longitudinally for all 61 participants. Individuals who participated in the program were compared to those individuals who did not. Results For the 42 voice hearers who attended Coping with Voices, the quantifiable data showed a significant reduction in negative beliefs about voices, voice severity, distress about voices and the perceived power of the voices. Participant feedback was overwhelmingly positive. The secondary measures show a significant reduction in the number of hospital admissions and an improvement in basis life skills for group attendees versus non-attendees at 12 months follow up. Discussion Overall, these findings suggest ‘Coping with Voices’ is feasible, acceptable and worthwhile.
Diurnal variation in surface latent heat flux and the effect of diurnal variability on the climatological latent heat flux over the tropical oceans

Diurnal variation in surface latent heat flux (LHF) and the effects of diurnal variations in LHF-related variables on the climatological LHF are examined using observations from the Global Tropical Moored Buoy Array. The estimated amplitude of the climatological diurnal LHF over the Indo-Pacific warm pool and the equatorial Pacific and Atlantic cold tongues is remarkable, with maximum values exceeding 20.0 W m−2. Diurnal variability of sea surface skin temperature (SSTskin) is the primary contributor to the diurnal LHF amplitude. Because the diurnal SSTskin amplitude has an inverse relationship with surface wind speed over the tropical oceans, an inverse spatial pattern between the diurnal LHF amplitude and surface wind speed results. Resolving diurnal variations in the SSTskin and wind improves the estimate of the climatological LHF by properly capturing the daytime SSTskin and daily mean wind speed, respectively. The diurnal SSTskin-associated contribution is large over the warm pool and equatorial cold tongues where low wind speeds tend to cause strong diurnal SSTskin warming, while the magnitude associated with the diurnal winds is large over the highly dynamic environment of the Inter-Tropical Convergence Zone. The total diurnal contribution is about 9.0 W m−2 on average over the buoy sites. There appears to be a power function (linear) relationship between the diurnal SSTskin-associated (wind-associated) contribution and surface mean wind speed (wind speed enhancement from diurnal variability). The total contribution from diurnal variability can be estimated accurately from high-frequency surface wind measurements using these relationships.