Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficent Large-Scale Multi-Unimodular Waveform Design with Good Correlation Properties via Direct Phase Optimizations

In this paper, we propose an efficient algorithm for designing large-scale multi-unimodular waveforms with low correlations. Different from existing approaches that commonly involve repetitive projections of complex values into their constant-modulus approximations, we conduct optimizations directly on the phase values of waveform elements. Specifically, we optimize the weighted integrated sidelobe level of waveforms, and formulate such design into an unconstrained optimization problem with respect to phase values of waveform elements. Then, we derive the gradient of the newly formulated objective function, through which we subsequently elaborate its majorant with the support of a properly designed Lipschitz-constant related quantity. Our major contributions also lie in obtaining a closed-form update of phase values that boils down to a gradient-descent regime, and calculating the update with fast implementations. Simulation results verify the superiority of our algorithm over existing state-of-the-art methods.
Study on the mycorrhizal structure of common plants and rhizosphere AMF diversity of different plant communities in Central Province, Mongolia

With the dual destruction cased by man-made activities and natural causes, the biodiversity and ecosystem function of the prairie are reducing rapidly, which are manifest in such phenomenon as grassland desertification, sharp reduction in wetland, soil quality degradation, erosion of soil by wind, rain and watersheds. This condition restricts the development level of Mongolia's financial status and production forces, and so the protection and utilization of biodiversity resources are extremely important and harbor no delay. Arbuscular mycorrhizal fungi (AMF) has a broad distribution and species diversity, it also has very important functions of maintaining material circulation in ecosystems, improving ecosystem productivity, and ensuring ecological restoration. We selected different plant communities and the common plants in the Tuv aimag (Central province) of Mongolia to study the correlation between species diversity, genetic diversity and AM fungi distribution with physical and chemical properties of soil.
Achieving Fair Speech Emotion Recognition via Perceptual Fairness

Speech emotion recognition (SER) is a key technological module to be integrated into many voice-based solutions. One of the unique fairness issues in SER is caused by the inherently biased emotion perception given by the raters as ground truth labels. Mitigating rater biases are at core for SER to move toward optimizing both recognition and fairness performance. In this work, we proposed a two-stage framework, which produces debiased representations by using a fairness constraint adversarial framework in the first stage. Then, users are endued with the right to toggle between specified gender-wise perceptions on-demand after the gender-wise perceptual learning in the second stage. We further evaluate our results on two important fairness metrics to show that the distributions and predictions across different gender are fair.
DNS and LES of wall-bounded compressible turbulent flows in narrow cooling channels

This paper describes the numerical study of a compressible turbulent channel flow with isothermal walls using direct numerical and large eddy simulations. Computations are carried out using a high-order low-dissipation bandwidth-optimized WENO method to describe the hyperbolic terms of Navier-Stokes equations. The DNS results are compared to another DNS data in order to validate the procedure. Coherent structures of the flow field is analyzed with the Q criterion, the vorticity field, and scatter-plots. The Strong Reynolds Analogy is not valid for such non-adiabatic flows. In the case of large eddy simulations, the impact of a dynamic procedure for the turbulent Prandtl number modeling is evaluated. Results are found more accurate and computations are more stable.
Retinal Biomarkers for Detecting Diabetic Retinopaty Using Smartphone-Based Deep Learning Frameworks

Convolutional neural networks (CNNs) have shown success in detecting Diabetic Retinopathy (DR) from retinal images captured by high-quality fundus cameras. As an alternate solution, smartphone-based systems with limited field-of-view (FoV) are recently proposed for DR screening. This paper investigates where to focus on the retina when using such smartphone-based devices. After training a CNN on original fundus images from diverse datasets, we evaluate the trained model on various regions of the retina (the fovea, the optic disc, the center of fovea and optic disc, the center of lower fovea and optic disc, and center of upper fovea and optic disc) that could be most effective to determine DR. Our experiments show that retinal images from smartphone-based systems with a narrower FoV (40%) that covers around fovea provided very close performance to original images with 0.963 AUC (within 0.99 of the optimum).
Fast Robust Principle Component Analysis Using Gauss-Newton Iterations

Robust Principal Component Analysis (RPCA) is an optimization problem that decomposes a data matrix into a low-rank and a sparse matrix. However, solving this problem using alternating procedures requires sequentially computing singular value decompositions (SVDs) of large matrices, which is computationally expensive. In this work, we propose a computation protocol that leverages Gauss-Newton iterations to speed up the sequential computation of SVDs and accelerate the entire RPCA process. Our method is validated on synthetic and video data, benchmarked against established RPCA algorithms, and analyzed for stability with respect to hyperparameters. Our proposed protocol can also be applied to problems that require repeated computation of the proximal of functions that solely depend on singular values of the input matrix.
The Moderation Role of Innovation and Infrastructure on the Relationship Between COVID-19 Crises and Health Care Performance: Evidence from Jordan

This study examines the moderation role of innovation and infrastructure on the relationship between covid-19 crisis and healthcare performance in Jordan. The methodology of this study includes a literature review to identify relevant studies and theories related to the topic and identify gaps in the existing research. Attention is paid to human behavior and personnel interaction in the hospitals that receive Covid-19 cases. The findings of this study will be used to identify areas where improvements can be made in healthcare infrastructure and innovative practices to support healthcare performance during future crises better. This paper identifies specific innovative and most effective infrastructure supporting healthcare performance during a crisis, such as telemedicine, remote monitoring, or emergency medical service (EMS) systems. Also, the paper informs policy-making by providing insights into the impact of innovative practices and infrastructure on healthcare performance and how these factors can mitigate future crises' impact on healthcare systems. The healthcare industry needs ideas and strategies as the Covid-19 pandemic grips the world. These strategies will help the industry deal with the unstable and continuously changing environment. © 2023 WITPress. All rights reserved.
The frictionless axial interaction of a rigid disk with a two-layered functionally graded transversely isotropic medium

A rigorous formulation is presented for the frictionless axisymmetric interaction of a rigid disk with a two-layered inhomogeneous medium. The materials are considered to be linearly elastic transversely isotropic materials and the exponential variation of properties along the depth of each layer is assumed in order to model the effect of inhomogeneity. The disk is considered to be at the top of the coating layer or embedded at the interface of the two media. By satisfying the boundary conditions, the problem leads to a dual integral equation and is reducible to a Fredholm integral equation of the second kind which is solved numerically. The numerical solutions survey the effect of material inhomogeneity through two special cases: a functionally graded coating on a homogeneous transversely isotropic half-space and a homogeneous transversely isotropic coating on an inhomogeneous half-space. The accuracy of the numerical solutions is verified by comparison with the corresponding solution for homogeneous material in the literature.
Diagnostic and prognostic performance of renal compartment volume and the apparent diffusion coefficient obtained from magnetic resonance imaging in mild, moderate and severe diabetic kidney disease

Background Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD). There are unmet needs for noninvasive diagnosis and prognosis prediction of DKD in clinical practice. This study examines the diagnostic and prognostic value of magnetic resonance (MR) markers of renal compartment volume and the apparent diffusion coefficient (ADC) for mild, moderate, and severe DKD. Methods This study was registered at the Chinese Clinical Trial Registry Center (registration number: ChiCTR-RRC-17012687). Sixty-seven DKD patients were prospectively randomly enrolled and underwent clinical examination and diffusion-weighted magnetic resonance imaging (DW-MRI). Patients with comorbidities that affected renal volumes or components were excluded. Ultimately, 52 DKD patients were included in the cross-sectional analysis. The ADC in the renal cortex (ADCcortex), ADC in the renal medulla (ADCmedulla) and difference between ADCcortex and ADCmedulla (ΔADC) were measured using a twelve-layer concentric objects (TLCO) approach. Renal compartment volumes of the parenchyma and pelvis were derived from T2-weighted MRI. Due to lost contact or ESRD diagnosed before follow-up (n=14), only 38 DKD patients remained for follow-up (median period =8.25 years) to investigate the correlations between MR markers and renal outcomes. The primary outcomes were the composite of doubling of the primary serum creatinine concentration or ESRD. Results ADCcortex presented superior performance in discriminating DKD with normal and declined estimated glomerular filtration rate (eGFR) over ADCmedulla, ΔADC and renal compartment volumes with an AUC of 0.904 (sensitivity of 83% and specificity of 91%) and was moderately correlated with the clinical biomarkers eGFR and proteinuria (P<0.05). The Cox survival analysis demonstrated that ADCcortex rather than ΔADC is a predictor of renal outcomes with a hazard ratio of 3.4 (95% CI: 1.1–10.2, P<0.05) independent of baseline eGFR and proteinuria. Conclusions ADCcortex is a valuable imaging marker for the diagnosis and prediction of renal function decline in DKD.
Low Skeletal Muscle Mass and the Incidence of Delirium in Hospitalized Older Patients: A Systematic Review and Meta-Analysis of Observational Studies

Background Both low skeletal muscle mass and delirium are prevalent in older hospitalized patients, while their associations are unclear. This systematic review and meta-analysis aim to investigate the associations between low skeletal muscle mass and the incidence of delirium in hospitalized patients. Methods The PubMed, Web of Science, and Embase were searched for relevant studies published before May 2022, and we conducted this systematic review and meta-analysis according to the PRISMA and MOOSE guidelines. The summary odds ratios (OR) and 95% confidence intervals (CI) were estimated, and subgroup analyses were also conducted according to the age and major surgeries. Results Finally, nine studies with 3 828 patients were included. The pooled result showed no significant association between low skeletal muscle mass and the incidence of delirium (OR 1.69, 95% CI 0.85 to 2.52). However, sensitivity analysis suggested that one study caused a significant alteration of the summary result, and the meta-analysis of the remaining 8 studies showed that low skeletal muscle mass was significantly associated with an 88% increased incidence of delirium (OR 1.88, 95% CI 1.43 to 2.33). Furthermore, subgroup analyses indicated that low skeletal muscle mass was associated with a higher incidence of delirium in patients ≥75 years old or undergoing major surgeries instead of those <75 years old or without surgeries, respectively. Conclusions Hospitalized patients with low skeletal muscle mass might have higher incidence of delirium, particularly in those of older age and undergoing major surgeries. Therefore, great attention should be paid to these patients.
Accurate and Efficient Prediction of PhotonicCrystal Waveguide Bandstructures using NeuralNetworks

: We demonstrate the use of neural networks to predict the optical properties of photonic crystal waveguides (PhCWs) with high accuracy and significantly faster computation times compared to traditional simulation methods. Using 100,000 PhCW designs and their simulated bandstructures, we trained a neural network to achieve a test set relative error of 0.103% in predicting gap guided bands. We use pre-training to improve neural network performance, and numerical differentiation to accurately predict group index curves. Our approach allows for rapid, application-specific tailoring of PhCWs with a runtime of sub-milliseconds per design, a significant improvement over conventional simulation techniques.
Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus

The emotion detection technology to enhance human decision-making is an important research issue for real-world applications, but real-life emotion datasets are relatively rare and small. The experiments conducted in this paper use the CEMO, which was collected in a French emergency call center. Two pre-trained models based on speech and text were fine-tuned for speech emotion recognition. Using pre-trained Transformer encoders mitigates our data’s limited and sparse nature. This paper explores the different fusion strategies of these modality-specific models. In particular, fusions with and without cross-attention mechanisms were tested to gather the most relevant information from both the speech and text encoders. We show that multimodal fusion brings an absolute gain of 4-9% with respect to either single modality and that the Symmetric multi-headed cross-attention mechanism performed better than late classical fusion approaches. Our experiments also suggest that for the real-life CEMO corpus, the audio component encodes more emotive information than the textual one.
The experiences of Mexican language teachers in transnational contexts

ABSTRACT The article examines the transnational mobility experienced by two language teachers of Mexican origin, one who migrated to the United States and the other to the UK. Drawing on autobiographies and in-depth interview data, the analysis shows the complex relationship of different factors in shaping how the participants experienced their transnational mobility processes. These factors include differences in their sense of agency, the link between their bilingualism and their professional identities, and the emergence of different positionings regarding their previous and current work contexts. The article concludes by outlining the implications that the findings have for policy and practice.
Biomolecules Involved in Both Metastasis and Placenta Accreta Spectrum—Does the Common Pathophysiological Pathway Exist?

Simple Summary Both invasive placentation and cancer metastasis involve the intricate migration and invasion of cells into the surrounding tissues, while also creating new blood vessels to support their growth. The complex interplay of molecular signals that regulate cell behavior, including growth factors, cytokines, and extracellular matrix components, plays a crucial role in both processes. Despite their differing physiological contexts and evolutionary origins, invasive placentation and cancer metastasis share many molecular and cellular mechanisms that allow them to invade and proliferate in the surrounding tissues. In both pathological conditions, cells need to overcome the local mechanisms of immunological defense, activate invasion, and induce angiogenesis. This article focuses on describing the biomolecules that link these pathological processes. Abstract The process of epithelial-to-mesenchymal transition (EMT) is crucial in the implantation of the blastocyst and subsequent placental development. The trophoblast, consisting of villous and extravillous zones, plays different roles in these processes. Pathological states, such as placenta accreta spectrum (PAS), can arise due to dysfunction of the trophoblast or defective decidualization, leading to maternal and fetal morbidity and mortality. Studies have drawn parallels between placentation and carcinogenesis, with both processes involving EMT and the establishment of a microenvironment that facilitates invasion and infiltration. This article presents a review of molecular biomarkers involved in both the microenvironment of tumors and placental cells, including placental growth factor (PlGF), vascular endothelial growth factor (VEGF), E-cadherin (CDH1), laminin γ2 (LAMC2), the zinc finger E-box-binding homeobox (ZEB) proteins, αVβ3 integrin, transforming growth factor β (TGF-β), β-catenin, cofilin-1 (CFL-1), and interleukin-35 (IL-35). Understanding the similarities and differences in these processes may provide insights into the development of therapeutic options for both PAS and metastatic cancer.
Improving Seq2Seq TTS Frontends With Transcribed Speech Audio

Due to the data inefficiency and low speech quality of grapheme-based end-to-end text-to-speech (TTS), having a separate high-performance TTS linguistic frontend is still commonly regarded as necessary. However, a TTS frontend is itself difficult to build and maintain, since it requires abundant linguistic knowledge for its construction. In this article, we start by bootstrapping an integrated sequence-to-sequence (Seq2Seq) TTS frontend using a pre-existing pipeline-based frontend and large amounts of unlabelled normalized text, achieving promising memorization and generalisation abilities. To overcome the performance limitation imposed by the pipeline-based frontend, this work proposes a Forced Alignment (FA) method to decode the pronunciations from transcribed speech audio and then use them to update the Seq2Seq frontend. Our experiments demonstrate the effectiveness of our proposed FA method, which can significantly improve the word token accuracy from 52.6% to 91.2% for out-of-dictionary words. In addition, it can also correct the pronunciation of homographs from transcribed speech audio and potentially improve the homograph disambiguation performance of the Seq2Seq frontend.
On Crowdsourcing-Design with Comparison Category Rating for Evaluating Speech Enhancement Algorithms

Speech enhancement techniques improve the quality or the intelligibility of an audio signal by removing unwanted noise. It is used as preprocessing in numerous applications such as speech recognition, hearing aids, broadcasting and telephony. The evaluation of such algorithms often relies on reference-based objective metrics that are shown to correlate poorly with human perception. In order to evaluate audio quality as perceived by human observers it is thus fundamental to resort to subjective quality assessment and in doing so we identify subgroups of users where the subjective assessments correlate better to objective metrics. In this paper, a user evaluation based on crowdsourcing (subjective) and the Comparison Category Rating (CCR) method is compared against the DNS-MOS, ViSQOL and 3QUEST (objective) metrics. The overall quality scores of three speech enhancement algorithms from real time communications (RTC) are used in the comparison using the P.808 toolkit. Results indicate that while the CCR scale allows participants to identify differences between processed and unprocessed audio samples, two groups of preferences emerge: some users rate positively by focusing on noise suppression processing, while others rate negatively by focusing mainly on speech quality. We further present results on the parameters, size considerations and speaker variations that are critical and should be considered when designing the CCR-based crowdsourcing evaluation1.
IR-ECG: Invertible Reconstruction of ECG

Electrocardiogram(ECG) reconstruction is studied to rebuild ECG from heterogeneous biosignal, such as Photoplethys-mography(PPG), to combine the diagnosis experience of ECG and the convenient collection of PPG. Heterogeneous biosignals are information-lost channels compared with ECG, so simple mapping from the heterogeneous biosignals to ECG leads to unsatisfactory performance. In this paper, we present an invertible neural network, called IR-ECG(invertible reconstruction of ECG), to model the processes of ECG reconstruction. We deliberately design the invertible block, called TimeFlow, to construct the flow-based model. In the forward process, IR-ECG produces the heterogeneous biosignal and captures the distribution of lost information from ECG. In the backward process, ECG reconstruction is finished using a randomly-drawn latent vector and heterogeneous biosignal. Experimental results show that IR-ECG outperforms existing works in quantitative, visual and semantic evaluations.
Disentangling Speech from Surroundings with Neural Embeddings

We present a method to separate speech signals from noisy environments in the embedding space of a neural audio codec. We introduce a new training procedure that allows our model to produce structured encodings of audio waveforms given by embedding vectors, where one part of the embedding vector represents the speech signal, and the rest represent the environment. We achieve this by partitioning the embeddings of different input waveforms and training the model to faithfully reconstruct audio from mixed partitions, thereby ensuring each partition encodes a separate audio attribute. As use cases, we demonstrate the separation of speech from background noise or from reverberation characteristics. Our method also allows for targeted adjustments of the audio output characteristics.
Expectation Propagation on Factor Graphs Based on Matrix Decomposition

In the context of the Gaussian linear model, recent works have studied factor graph modification using QR decomposition that enables the derivation of scalar Expectation Propagation (EP) based detectors. In this paper, we investigate on new factor graph representations induced by the use of the Golub-Kahan bi-diagonal Decomposition (GKD) and of the Singular Value Decomposition (SVD). New EP messages induced by the GKD or SVD underlying graphs are derived, that can be both scalar or vector messages. Complexity and performance of the resulting algorithms are studied for digital communications applications.