Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech Synthesis from Articulatory Movements Recorded by Real-time MRI

Previous speech synthesis models from articulatory movements recorded using real-time MRI (rtMRI) only predicted vocal tract shape parameters and required additional pitch information to generate a speech waveform. This study proposes a two-stage deep learning model composed of CNN-BiLSTM that predicts a mel-spectrogram from a rtMRI video and a HiFi-GAN vocoder that synthesizes a speech waveform. We evaluated our model on two databases: the ATR 503 sentences rtMRI database and the USC-TIMIT database. The experimental results on the ATR 503 sentences rtMRI database show that the PESQ score and the RMSE of F 0 are 1.64 and 26.7 Hz. This demonstrates that all acoustic parameters, including fundamental frequency, can be estimated from the rtMRI videos. In the experiment on the USC-TIMIT database, we obtained a good PESQ score and RMSE for F 0 . However, the synthesized speech is unclear, indicating that the quality of the datasets affects the intelligibility of the synthesized speech.
Background-aware Modeling for Weakly Supervised Sound Event Detection

Nowadays, a common framework for weakly supervised sound event detection (WSSED) is multiple instance learning (MIL). However, MIL directly optimizes the clip-level classification re-sults, so it tends to localize the most distinct part rather than the entire sound event, making the indiscriminating parts of sound events mistakenly identified as background sounds. In this paper, we focus on adding background awareness for WSSED by proposing a learning structure called BA-WSSED. Our BA-WSSED first introduces a pseudo separator with softmax activation and two aggregators to purify and aggregate the event feature and the background feature, respectively. Then, with the help of the proposed background-aware staggered (BAS) loss, both the event classifier and the background classifier are learned to generate staggered classification scores for discerning and suppressing background sounds. Experiments show that our BA-WSSED significantly improves the performance of the general MIL-based WSSED method on multiple datasets and can be employed on various baseline models.
Student Interview-Informed Behavior Contracts for High School Students Identified as at Risk

Addressing the challenging behavior of students requires evidence-based interventions that can be implemented in school settings; however, there is a relative lack of literature investigating effective strategies for high school students in secondary settings. Behavior contracts have been used to address challenging behavior in elementary and middle settings but less so in high school settings. Furthermore, the extent to which they have involved student input at the high school level has been unclear despite research indicating that collaborative intervention development processes may be associated with several additional benefits beyond effectiveness. Despite these empirical shortcomings, behavior contracts may be well suited to address individual high school students’ challenging behavior given their flexibility, collaborative nature, and use of goal setting and clearly stated contingencies (i.e., rules) to promote behavior change. Thus, the purpose of this study was to evaluate the acceptability and effectiveness of a structured interview-informed behavior contract intervention to address the disruptive behavior of high school students. Specifically, we used a multiple baseline design across three students and found that the interview-informed behavior contracts were effective in increasing their academically engaged behavior (Tau = .62) and decreasing disruptive and passive off-task behavior. Limitations and future directions are discussed.
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Image registration is an active research topic in the fields of remote sensing, photogrammetry, computer vision, pattern recognition, and medical image processing. Both corner and geometric center points are used for image registration, but not much attention has been paid to date to the differences in the outcome of image registration when these different types of points are used. Such differences have the potential to directly affect the success of multiresolution or multimodal image registration. The goal of this paper is to compare the characteristics of corner and center points in image registration and to quantify the differences in the registration accuracy when different features are used. Corners and geometric centers were compared in two cases: registration of images with the same resolution and registration of images with different resolutions. The results show that geometric centers provide more accurate results for image registration in both cases. Quantitative analysis revealed that the position error of the centers is only about 47% 63% of the position error of the corners. Experiments confirmed this finding. Résumé. Le recalage d’images est un sujet de recherche actif dans les divers domaines de la télédétection dont la photogrammétrie, la vision artificielle, la reconnaissance de formes et le traitement d’imagerie médicale. Les points de coin et les points géométriques centraux sont utilisés pour le recalage d’images mais, jusqu’à présent, peu d’attention a été portée aux différences dans les résultats du recalage lorsque ces différents types de points sont utilisés. De telles différences peuvent affecter directement le succès du recalage d’images multirésolutions ou multimodales. L’objectif de cet article est de comparer les caractéristiques des points de coin et des points centraux dans le recalage d’images et de quantifier les différences dans la précision du recalage lorsque différentes caractéristiques sont utilisées. Les coins et les centres géométriques ont été comparés dans deux cas : le recalage d’images ayant la même résolution et le recalage d’images ayant des résolutions différentes. Les résultats montrent que les centres géométriques donnent des résultats plus précis de recalage dans les deux cas. Une analyse quantitative a révélé que l’erreur de positionnement des centres est seulement d’environ 47 à 63% comparativement à l’erreur de positionnement des coins. Les expériences ont confirmé ces résultats. [Traduit par la Rédaction]
Split Learning Assisted Multi-UAV System for Image Classification Task

Due to its ease of deployment and high mobility, unmanned aerial vehicles (UAVs) have gained great popularity for a variety of applications. To conduct high-level and complicated tasks such as search/rescue missions and target identification, deep learning functions at UAVs are required. To this end, distributed learning methods such as federated learning (FL) and split learning (SL) have been proposed. In this paper, we investigate the SL assisted image classification task in a multi-UAV system for applications such as area exploration and object detection. Specifically, the whole deep learning model is cut into the UAV-side model and BS (base station)-side model. Each UAV performs forward propagation on UAV-side model by using the locally gathered images, and sends the smashed data to the BS. The BS performs forward and backward propagation based on the smashed data, and sends back the gradients of the cut layer to the UAVs, which is used for the backward propagation of the UAV-side model. The performance was evaluated using an aerial perspective geographic dataset, and the effectiveness of the proposed system was validated by comparing with FL-based and centralized learning methods. It was found that SL can significantly reduce computation time at UAV compared with FL, and is particularly effective with non-IID (independent and identically distributed) dataset. SL also requires less data during the training initial phase and has a faster convergence speed compared to centralized learning.
New zooarchaeological evidence from Pictish sites in Scotland: implications for early medieval economies and animal-human relationships

Our knowledge of the use of livestock in early medieval Scotland is fragmentary and relies on a handful of well-studied faunal assemblages, with few from Pictland (north-east Scotland), an important and enigmatic group and latterly kingdom in Northern Britain that existed between the 3rd and 9th centuries AD. The assemblages that have been recovered and studied mainly occur at the limits of this territory, beyond the heartland of the Picts in the northern and eastern mainland. Recent archaeological excavations at three high-status sites in eastern Scotland have unexpectedly yielded well-preserved faunal remains providing a unique and long-awaited opportunity to explore further human-animal relationships and the use of animals in Pictish society. This paper presents new data from the initial study of these assemblages. It discusses the implications in terms of animal economy in Pictland, the potential of these sites to yield larger faunal assemblages and the directions of future research. Results show that cattle were a pivotal element of the economy, playing a multi-faceted role (beef and secondary products), pigs ranked second in frequency which likely reflects the high status of these sites and sheep appear as a marginal resource and were primarily raised for consumption. Results also suggest that these sites may have operated within an integrated network rather than functioning solely as self-sufficient entities.
Improving End-to-End Modeling For Mandarin-English Code-Switching Using Lightweight Switch-Routing Mixture-of-Experts

Code-switching is a common phenomenon in multilingual communities. In this paper, we study end-to-end model for Mandarin-English intra-sentential code-switching speech recognition. A lightweight Switch-Routing network is proposed, which includes two experts and a switch router. Two experts, representing Mandarin and English learners, implicitly provide language identification information and skillfully use monolingual data to assist code-switching task training, which solves the problem of data sparsity. In addition, our network is a lightweight structure, which makes use of the advantages of Switch Transformer and discards its weakness of increasing model capacity. Finally, we study the effect of using lightweight Switch Routing in different blocks of encoder and decoder. Compared with Bi-Encoder, proposed model has a better performance on the ASRU code-switching test set, and the most important thing is that it requires much less inference time with RTF decreasing by 31.39 % .
Geodesic Distance Based Scattering Power Decomposition for Compact Polarimetric SAR Data

We propose a geodesic distance (GD)-based scattering power decomposition for compact polarimetric (CP) synthetic aperture radar (SAR) data acquired over agricultural landscapes. The proposed technique decomposes the polarized portion of the total backscattered power in proportion to the normalized target similarity measures. The measures are derived from the GDs, which are computed between the Kennaugh matrices of observed and canonical targets (dihedral or trihedral). We observed a pseudo-power component in the double bounce power, which can be attributed to target irregularities. To compensate for the pseudo-power component, we proposed a compensation strategy by utilizing the CP radar vegetation index (CpRVI). The compensation factor assists in readjusting the polarized power components. The proposed approach was tested with real (RADARSAT Constellation Mission: RCM) and simulated (RADARSAT-2: RS2) hybrid CP data over agricultural sites in Canada. The effectiveness of the approach was demonstrated by comparing the decomposed powers with a recently proposed CP scattering power decomposition.
Noise-Robust Bandwidth Expansion for 8K Speech Recordings

Speech recordings in call centers are narrowband and mixed with various noises. Developing a bandwidth expansion (BWE) model is important to mitigate the automated speech recognition (ASR) performance gap between the low and high sampling rate speech data. To further address the in-the-wild noise in call center settings, we propose an Embedding-Polished Wave-U-Net (EP-WUN) that includes an additional speech quality classifier to handle the noise and bandwidth expansion of 8k audio simultaneously. Our framework shows improved speech quality metrics on a well-known BWE dataset (Valentini-Botinhao corpus) when comparing to the current state-of-the-art noise-robust BWE model with 33% fewer parameters. It also achieves an 11.71% word error rate reduction when evaluating on a real-world interactive voice response system from the E.SUN bank.
A More Accurate Internal Language Model Score Estimation for the Hybrid Autoregressive Transducer

We present a novel constrained learning method for hybrid autoregressive transducer (HAT) models that results in more validated language model (LM) adaptation. LM adaptation in HAT is justified only when the transducer logits and the sum of speech and text logits in the label estimation sub-networks are approximately the same. The mean squared error (MSE) between the two logits was added to the HAT loss to encourage the HAT models to satisfy the required condition. The proposed method exhibited significantly lower and more stable internal language model perplexities than those of HAT. Consequently, it attained lower word error rates (WERs) compared to HAT in various model architecture settings and in both cases with and without LM adaptation. In the television content task, the proposed method achieved a relative reduction in WERs of up to 28.60% compared to HAT. In most cases, the accuracy of pre-trained HAT models also improved upon training with the additional MSE loss.
Ultra-Wideband High-Efficiency Solar Absorber and Thermal Emitter Based on Semiconductor InAs Microstructures

Since the use of chemical fuels is permanently damaging the environment, the need for new energy sources is urgent for mankind. Given that solar energy is a clean and sustainable energy source, this study investigates and proposes a six-layer composite ultra-wideband high-efficiency solar absorber with an annular microstructure. It achieves this by using a combination of the properties of metamaterials and the quantum confinement effects of semiconductor materials. The substrate is W–Ti–Al2O3, and the microstructure is an annular InAs-square InAs film–Ti film combination. We used Lumerical Solutions’ FDTD solution program to simulate the absorber and calculate the model’s absorption, field distribution, and thermal radiation efficiency (when it is used as a thermal emitter), and further explored the physical mechanism of the model’s ultra-broadband absorption. Our model has an average absorption of 95.80% in the 283–3615 nm band, 95.66% in the 280–4000 nm band, and a weighted average absorption efficiency of 95.78% under AM1.5 illumination. Meanwhile, the reflectance of the model in the 5586–20,000 nm band is all higher than 80%, with an average reflectance of 94.52%, which has a good thermal infrared suppression performance. It is 95.42% under thermal radiation at 1000 K. It has outstanding performance when employed as a thermal emitter as well. Additionally, simulation results show that the absorber has good polarization and incidence angle insensitivity. The model may be applied to photodetection, thermophotovoltaics, bio-detection, imaging, thermal ion emission, and solar water evaporation for water purification.
Calibrating linear continuous-time dynamical systems via perturbation analysis

This work considered the continuous-time linear dynamical systems described by the matrix differential equations, and aimed at studying the perturbation analysis via solving perturbed linear dynamical systems. In specific, we solved Riccati differential equations and continuous-time algebraic Riccati equations with finite and infinite times respectively. Moreover, we stated some assumptions on the existence and uniqueness of the solutions of the perturbed Riccati equations. Similar techniques were applied to the discrete-time linear dynamical systems. Two numerical examples illustrated the efficiency and accuracy.
Characterization of SOD1-DT, a Divergent Long Non-Coding RNA in the Locus of the SOD1 Human Gene

Researchers studying Amyotrophic Lateral Sclerosis (ALS) have made significant efforts to find a unique mechanism to explain the etiopathology of the different forms of the disease. However, despite several mutations associated with ALS having been discovered in recent years, the link between the mutated genes and its onset has not yet been fully elucidated. Among the genes associated with ALS, superoxide dismutase 1 (SOD1) was the first to be identified, but its role in the etiopathogenesis of the disease is still unclear. In recent years, research has been focused on the non-coding part of the genome to fully understand the mechanisms underlying gene regulation. Non-coding RNAs are conserved molecules and are not usually translated in protein. A total of 98% of the human genome is composed of non-protein coding sequences with roles in the transcriptional and post-transcriptional regulation of gene expression. In this study, we characterized a divergent nuclear lncRNA (SOD1-DT) transcribed in the antisense direction from the 5′ region of the SOD1 coding gene in both the SH-SY5Y cell line and fibroblasts derived from ALS patients. Interestingly, this lncRNA seems to regulate gene expression, since its inhibition leads to the upregulation of surrounding genes including SOD1. SOD1-DT represents a very complex molecule, displaying allelic and transcriptional variability concerning transposable elements (TEs) included in its sequence, widening the scenario of gene expression regulation in ALS disease.
6G Wireless Channel Scenario Extensions and Characteristics Analysis for Urban Environment

Urban wireless communications are one of most important scenarios in the sixth generation (6G) global communication networks. To provide customized high-quality services for all users in various 6G urban wireless communication scenarios, it is necessary and fundamental to study all kinds of 6G urban wireless channel scenarios and establish corresponding channel models for each scenario. However, existing standardized channel models are insufficient to cover all 6G urban communication scenarios. This paper aims to extend the conventional urban communication scenarios with detailed definitions and environmental parameters to accurately establish corresponding channel models. Specifically, the statistical properties of high speed train (HST) scenario are simulated and compared with those of measurement data. The channel model simulation results match well with the channel measurement data, which demonstrates the correctness of the channel model. Then, by applying the proposed precise scenarios and the corresponding model parameters to the channel model, corresponding channel characteristics can be quickly provided. Finally, the channel characteristics of several urban scenarios are simulated and analyzed.
Variations in Reference Evapotranspiration and Associated Driving Forces in the Pearl River Delta of China during 1960–2016

Recent climate warming and rapid urban development in the Pearl River Delta (PRD) of China exerted great impacts on the reference evapotranspiration (RET), which in turn affects the management of water resources and the quality of the urban environment. The objectives of this study are to examine (i) the temporal variability of the RET in the PRD and (ii) the underlying causes responsible for the temporal variation in the RET across space inside the PRD. The results indicate the following. (1) The RET in the PRD had an overall increasing trend caused by the increase of construction land during 1960 – 2016. (2) The increase of surface albedo caused by land cover conversion from woodland to grassland played an important role in the noticeable decline of the RET in Guangzhou and Zengcheng. (3) The dominant factors triggering RET variation varied across space in the PRD. In detail, the decline of sunshine duration (SD) decreasing Rn and the decline of wind speed (WS) weakening energy exchange were the dominant factors in decreasing RET in Guangzhou and Zengcheng. In contrast, the daily maximum temperature, daily minimum temperature, and relative humidity (RH), which were the factors causing the increase of vapor pressure deficit (VPD), were responsible for the RET increase in Taishan, Zhongshan, and Shenzhen. Overall, our results indicated that the RET in PRD exhibited a strong spatial heterogeneity due to differences in land use change and climatic conditions. Therefore, the improvement of water resources management and urban environment in the PRD should consider the spatial variation and underlying forces of RET changes.
Design of Ultra-miniaturized Wearable Antenna for Bio-telemetry Applications

|In this paper, an ultra-miniaturized, planar dual-band wearable antenna is proposed for bio-telemetry applications. The proposed antenna covers the 433 MHz and 915 MHz Industrial, Scienti(cid:12)c, and Medical (ISM) bands with a compact volume of 0 : 000000384 (cid:21) 30 . The antenna consists of a meander line on the top side of the substrate, while the backside is loaded with an inductive grid structure to achieve miniaturization. Moreover, the absence of vias in the design of the antenna oﬀers a signi(cid:12)cant bene(cid:12)t in terms of simplifying the fabrication process. The design approach considers the integration of other components for device-level architecture. The antenna exhibits stable performance when being placed on diﬀerent human body parts, such as the head and hand. The evaluated speci(cid:12)c absorption rate (SAR) complies with the regulated human safety standard. Additionally, the link margin (LM) calculation shows that the antenna could establish a biotelemetry communication link at a distance of 20 meters.
Microservice Application Scheduling in Multi-Tiered Fog-Computing-Enabled IoT

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively.