Miracle-dz/UniFAR-FTUs huggingface dataset:
Trinomial: Return-Risk and Sustainability: Is Sustainability Valued by Investors? A Choice Experiment for Spanish Investors Applied to SDG 12

Traditionally, finance has paid attention to the risk-return trade-off. Recently, given the incorporation of the 2030 Agenda and climate change, a third pillar has been incorporated into the investment decision: sustainability. Socially responsible investment is an instrument that can incorporate all three pillars. This paper aims to assess sustainability by Spanish investors using a choice experiment by applying the Bayesian approach with Markov chain Monte Carlo sampling and obtain the willingness to pay (invest) for each attribute. The results show that profitability remains the most important factor, although risk is at the same level as sustainability.
Transductive Feature Space Regularization for Few-shot Bioacoustic Event Detection

In few-shot bioacoustic event detection, besides interested target events, background noises and various uninterested sound events lead to complex decision boundaries, which require regularized feature distributions in feature space. Due to the low label availability of uncertain noise events, existing few-shot learning methods with entropy-based regularizers suffer from overfitting during optimization. In this paper, we propose a transductive inference model with a prior knowledge based reg-ularizer (PKR) to overcome the overfitting problem. We use a task-adaptive feature extractor to reconstruct a regularized feature space. A PKR is proposed to minimize the divergence be-tween the original and reconstructed feature space. The development set of DCASE 2022 Task 5 is adopted as the experimental dataset. With the increasing iterations, the proposed model performs with long-lasting results around 55.43 F -measure, and well solves the overfitting problem in transductive inference.
Precoding and Gain Adjustment Scheme for Block Low-Resolution DACs in Massive MIMO Downlink

In this paper, a nonlinear quantized precoding and gain adjustment scheme for low-resolution digital-analog converters (DACs) in a massive multiple-input multiple-output (MIMO) system is proposed. The nonlinear quantized precoding determines transmit antenna outputs with a transmit symbol and channel state information. In a full-digital massive MIMO system, low-resolution DACs are used to suppress power consumption. Conventional precoding algorithms for low-resolution DACs do not optimize transmit antenna gains individually. Thus, in this paper, a gain adjustment scheme that optimizes individual transmit antenna gains and the DAC outputs of a virtual block antenna formed by multiple antennas is proposed. Numerical results obtained through computer simulation show that the proposed precoding scheme achieves bit error rate performance close to that of the conventional Gibbs sampling based algorithm with much smaller antenna gains on a i.i. d Rayleigh fading channel.
Thai Dialect Corpus and Transfer-based Curriculum Learning Investigation for Dialect Automatic Speech Recognition

We release 840 hours of read speech multi-dialect ASR corpora consisting of 700 hours of main Thai dialect, named Thai-central, and 40 hours for each local dialect , named Thai-dialect, with transcripts and their translations to Thai. The di-alects, selected to represent different regions of Thailand, are Khummuang, Korat, and Pattani. We also release the baseline dialectal ASR models trained using the curriculum learning approach. We found that the pre-training with the high-resource main dialect and target dialect generally yields the best performance. We believe that the availability of our corpora would contribute to the problem of low-resource Thai dialects. The corpus data will be available on Github 1 .
Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet

An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications.
Estimation of PN Sequence for Spread Spectrum Pilot Signals in Grant-Free Access System

For the grant-free random access system in the Internet of Thing (IoT) scenario, the recovery of the pilot sequence and the identification of the IoT device is a crucial issue. Contrapose the problem that the existing grant-free access schemes cannot accurately recover the pilot sequence in the intensive industrial zone with impulse noise, this paper proposes to use spread spectrum signal as pilot signal and proposes an estimation algorithm based on joint k-means and M estimation accordingly. This algorithm dynamically suppresses the influence of noise with adaptive weighted function according to the estimated noise energy in the iterative process. First, the received signal is segmented to obtain samples. Second, the samples are clustered using the K-means algorithm. In the iterative process of the algorithm, cluster centers are used to estimate the energy of signal noise. According to the estimation result of the noise energy, the adaptive weighted function is used to dynamically update the cluster centers and the similarity between samples and cluster centers. Finally, assigning +1 or −1 to the samples according to the clustering results, and then the estimation of pseudo-code sequence (PN sequence) is realized while impulse noise is suppressed. Simulation results show that the proposed algorithm can greatly improve the performance of PN sequence estimation under impulse noise channel. The bit error ratio (BER) of the proposed algorithm valued 0.008 outperformed that of EVD valued 0.3 when the generalized signal-to-noise ratio (GSNR) is −4dB. In particular, the proposed algorithm has better performance when the noise distribution has heavier tails, which is different from traditional algorithms.
Expression of Claudins in Preneoplastic Conditions of the Gastrointestinal Tract: A Review

Simple Summary Recent evidence has emerged highlighting the role of claudins in the development of cancers of gastrointestinal organs. Accordingly, studies have further analyzed the role and expression of claudin in preneoplastic gastrointestinal conditions, including Barrett’s esophagus, sessile serrated polyps, inflammatory bowel disease, and others. Several claudins, mainly -1, -2, -3, -4, and -18, have been identified as aberrantly expressed in these conditions. Furthermore, studies have highlighted their prognostic role in patients. Identifying how claudin expression changes in preneoplastic conditions may allow for early diagnosis and treatment. Additionally, it may unlock the door for potential therapeutic molecular targets. Abstract Premalignant lesions of the gastrointestinal tract are a group of disorders which act as the harbinger of malignant tumors. They are the ground-zero of neoplastic transformation, and their identification and management offer patients the best opportunity of blocking the progress of cancer. However, diagnoses of some of these conditions are hard to make, and their clinical importance is difficult to assess. Recent reports indicated that several claudin proteins have altered expressions in many cancers, including esophageal, gastric, colon, liver, and pancreatic cancers. The early identification of the aberrant expression of these proteins could lead to the early diagnosis and management of gastrointestinal tumors. Specifically, claudins -1, -2, -3, -4, and -18 are frequently overexpressed in gastrointestinal preneoplastic lesions. These altered expressions have shown clinical value in several tumors, providing diagnostic and prognostic information. In this article, we review the literature on the aberrant expression of claudins in preneoplastic lesions of the gastrointestinal tract. Additionally, we summarize their diagnostic and prognostic implications.
Multi-Level Knowledge Distillation for Speech Emotion Recognition in Noisy Conditions

Speech emotion recognition (SER) performance deteriorates significantly in the presence of noise, making it challenging to achieve competitive performance in noisy conditions. To this end, we propose a multi-level knowledge distillation (MLKD) method, which aims to transfer the knowledge from a teacher model trained on clean speech to a simpler student model trained on noisy speech. Specifically, we use clean speech features extracted by the wav2vec-2.0 as the learning goal and train the distil wav2vec-2.0 to approximate the feature extraction ability of the original wav2vec-2.0 under noisy conditions. Furthermore, we leverage the multi-level knowledge of the original wav2vec-2.0 to supervise the single-level output of the distil wav2vec-2.0. We evaluate the effectiveness of our proposed method by conducting extensive experiments using five types of noise-contaminated speech on the IEMOCAP dataset, which show promising results compared to state-of-the-art models.
Predictive Network Configuration with Hierarchical Spectral Clustering for Software Defined Vehicles

The increasing connectivity and autonomy of vehicles has led to a growing need for dynamic and real-time adjustments to software and network configurations. Software Defined Vehicles (SDV) have emerged as a potential solution to adapt to changing user needs with continuous updates and onboard reconfigurations to offer infotainment, connected, and background services such as cooperative driving. However, network configuration management in SDVs remains a significant challenge, particularly in the context of shared Ethernet-based in-vehicle networks. Traditional worst-case static configuration methods cannot efficiently allocate network resources while ensuring Quality of Service (QoS) guarantees for each network flow within the physical topology capabilities. In this work, we propose a configuration generation methodology that addresses these limitations by dynamically switching between pre-computed offboard configurations downloaded to the vehicle. Simulation results are presented and future work is discussed.
Physiological Stress, Bone Growth and Development in Imperial Rome

This study investigates bone maintenance and loss within a life course perspective in the Imperial Roman population of Velia. The use of biocultural factors, such as diet, reproductive history and physical activity are emphasized in the interpretive process. A total of 135 individuals were examined. Bone loss is assessed using three methods: radiogrammetry of the 2nd metacarpal, rib cortical histomorphometry, and the analysis of trabecular architecture in L4 vertebrae. Physiological stress in adults is explored using porotic hyperostosis, cribra orbitalia and periostitis. Stress during growth and development is investigated through dental enamel hypoplasias, vertebral neural canal sizes and skeletal growth profiles of juvenile skeletons. Analyses of bone of maintenance and loss reveal that bone loss in the Velia population did not follow similar patterns to modern communities. The most meaningful difference was that no sex differences were observed between males and females for all three measures of bone maintenance and loss. In addition, the analyses of radiogrammetry, histomorphometry and trabecular architecture all support a hypothesis that strenuous physical activity over the life course helped mediate bone loss with age, particularly in females. The reproductive history of females in the Roman period is also hypothesized to have played a protective role in mediating bone loss. Physiological stress was common in the juvenile and adult stages of life, but this stress did not seemingly affect bone remodeling and loss with age or between the sexes. The approaches used in this dissertation advance bioarchaeological theory by implementing a life course perspective that emphasize developmental plasticity to investigating bone loss. This study also contributes methodologically by demonstrating the importance of using multiple lines of evidence when exploring bone loss in past populations.
Which aspects of motor speech disorder are captured by Mel Frequency Cepstral Coefficients? Evidence from the change in STN-DBS conditions in Parkinson’s disease

One of the most popular speech parametrizations for dysarthria has been Mel Frequency Cepstral Coefficients (MFCCs). Although the MFCCs ability to capture vocal tract characteristics is known, the reflected dysarthria aspects are primarily undis-closed. Thus, we investigated the relationship between key acoustic variables in Parkinson’s disease (PD) and the MFCCs. 23 PD patients were recruited with ON and OFF conditions of Deep Brain Stimulation of the Subthalamic Nucleus (STN-DBS) and examined via a reading passage. The changes in dysarthria aspects were compared to changes in a global MFCC measure and individual MFCCs. A similarity was found in 2nd to 3rd MFCCs changes and voice quality. Changes in 4th to 9th MFCCs reflected articulation clarity. The global MFCC parameter outperformed individual MFCCs and acoustical measures in capturing STN-DBS conditions changes. The findings may assist in interpreting outcomes from clinical trials and improve the monitoring of disease progression.
The Combined Use of Optical and SAR Data for Large Area Impervious Surface Mapping

One of the megatrends marking our societies today is the rapid growth of urban agglomerations which is accompanied by a continuous increase of impervious surface (IS) cover. In light of this, accurate measurement of urban IS cover as an indicator for both, urban growth and environmental quality is essential for a wide range of urban ecosystems studies. The aim of this work is to present an approach based on both optical and SAR data in order to quantify urban impervious surface as a continuous variable on regional scales. The method starts with the identification of relevant areas by a semi automated detection of settlement areas on the basis of single-polarized TerraSAR-X data. Thereby the distinct texture and the high density of dihedral corner reflectors prevailing in build-up areas are utilized to automatically delineate settlement areas by the use of an object-based image classification method. The settlement footprints then serve as reference area for the impervious surface estimation based on a Support Vector Regression (SVR) model which relates percent IS to spectral reflectance values. The training procedure is based on IS values derived from high resolution QuickBird data. The developed method is applied to SPOT HRG data from 2005 and 2009 covering almost the whole are of Can Tho Province in the Mekong Delta, Vietnam. In addition, a change detection analysis was applied in order to test the suitability of the modelled IS results for the automated detection of constructional developments within urban environments. Overall accuracies between 84 % and 91% for the derived settlement footprints and absolute mean errors below 15% for the predicted versus training percent IS values prove the suitability of the approach for an area-wide mapping of impervious surfaces thereby exclusively focusing on settlement areas on the basis of remotely sensed image data.
Active Parental Mediation and Adolescent Problematic Internet Use: The Mediating Role of Parent–Child Relationships and Hiding Online Behavior

In today’s information society, with the growing integration of the Internet into individuals’ lives, problematic Internet use (PIU) among adolescents has become more prevalent. Therefore, we conducted this study to investigate the correlation between active parental mediation and PIU in adolescents, as well as the potential mediating roles of parent–child relationship and adolescents’ hiding online behavior. A total of 539 middle school students (mean age = 13.384) were recruited for this study and participated by completing a series of paper-and-pencil questionnaires. The findings indicated a significant negative relationship between active parental mediation and PIU. Furthermore, both the mediating role of the parent–child relationship and the role of hiding online behavior were found to be significant. Specifically, the mediating role is comprised of two paths: the independent mediating role of the parent–child relationship, and the sequential mediating role involving both the parent–child relationship and hiding online behavior. The study contributes an innovative theoretical perspective to deepen the understanding of the formation mechanism of PIU. Moreover, it offers practical empirical insights for the prevention and intervention of PIU among adolescents.
Towards Multi-Lingual Audio Question Answering

Audio Question Answering (AQA) is a multi-modal translation task where a system analyzes an audio signal and a natu-ral language question to generate a desirable natural language answer. AQA has been primarily studied through the lens of the English language. However, addressing AQA in other languages, in the same manner, would require a considerable amount of resources. This paper proposes scalable solutions to multi-lingual audio question answering on both data and modeling fronts. We propose mClothoAQA, a translation-based multi-lingual AQA dataset in eight languages. The dataset consists of 1991 audio files and nearly 0.3 million question-answer pairs. Finally, we introduce a multi-lingual AQA model and demonstrate its strong performance in eight languages. The dataset and code can be accessed at https://github. com/swarupbehera/mAQA .
Antibiotic Resistance Pattern of Escherichia coli Derived from Retail Raw Meat in North Sumatera, Indonesia

Bacteria from animals raised for food that have developed antimicrobial resistance (AMR) may serve as a vehicle for its transmission from animal to human. Nevertheless, the antibiotic resistance profile of Escherichia coli from raw meat is scarcely reported, especially in North Sumatra. The aim of this investigation was to investigate the retail raw meat for the presence of E. coli and its antimicrobial resistance pattern. We purchased 80 packages of fresh meat products from four traditional markets located in Deli Serdang and Medan. Escherichia coli was isolated by an established microbiology test and a phenotypic antimicrobial susceptibility test was conducted by the method of disk diffusion. The data analysis of antibiotic resistance and multi-drug resistance (MDR) levels was performed by using WHONET 5.6. The antibiotic resistance dissimilarity between the two regencies was analyzed by using chi-square analysis. This study observed about 19 E coli isolates (23.7%) were obtained from raw meat. Resistance toward the antibiotics listed as critically important for veterinary and human therapy is increasing concern for public health. The isolates were highly resistant to ampicillin and tetracycline. Meanwhile gentamycin is susceptible. The MDR level of E. coli in both regencies of North Sumatra was about 21.05%. This finding contribute essential information on AMR in food animals and can be used to improve local and regional plans for the administration of antibiotics to livestock.
Effective Artificial Neural Network Framework for Time-Modulated Arrays Synthesis

The antenna array synthesis problem has long been known as a tough issue, which attracts considerable interest to explore high-performance low-complexity optimization techniques. In this article, an efficient artificial neural network (ANN) for time-modulated arrays (TMAs) synthesis is proposed. By defining the equivalent excitation properly, TMA synthesis can be first transformed to a generalized phased array optimization. Next, a two-stage ANN framework composed of two encoders and a universal decoder is established to optimize the equivalent excitation, and then a single-input single-output (SISO) sinc−1-ANN is proposed to solve inverse of $\text {sinc}\left ({\cdot }\right)$ efficiently. To achieve fast and accurate pattern prediction, the decoder is pretrained to be a real-time array analyzer, while the encoder is designed as an array synthesizer to develop online training. By minimizing the loss function related to radiation pattern and equivalent excitation, the desired pattern can be achieved. Then, with the help of the pretrained SISO sinc−1-ANN, the static excitation coefficient, switch-on duration, and starting time are acquired. Simulation results of different types of desired TMA patterns are provided to verify the superiority, effectiveness, and efficiency of the proposed approach.
On the Diversity and Formation Modes of Martian Minerals

A systematic survey of 161 known and postulated minerals originating on Mars points to 20 different mineral‐forming processes (paragenetic modes), which are a subset of formation modes observed on Earth. The earliest martian minerals, as on Earth, were primary phases from mafic igneous rocks and their ultramafic cumulates. Subsequent primary igneous minerals were associated with products of limited fractional crystallization, including alkaline and quartz‐normative lithologies. Significant mineral diversification occurred via precipitation of primary phases from aqueous and atmospheric fluids, including authigenesis, hydrothermal and cryogenic precipitation, and evaporites, including freeze drying during eras of low atmospheric pressures. In particular, hydrothermal mineral formation associated with both volcanic fluids and sustained hydrothermal activity in impact fracture zones may have triggered significant mineral diversification, though as yet undocumented. At least 65 such primary minerals have been identified by flown missions to Mars and from martian meteorites. A host of secondary martian minerals were produced by near‐surface processes related to water/rock interactions, including hydration/dehydration, oxidation/reduction, serpentinization, metasomatism, and a variety of diagenetic alterations. Additional mineral diversity resulted from metamorphic events, including thermal and shock metamorphism, lightning, and bolide impacts. However, several dominant sources of mineral diversity on Earth, including (a) extensive fluid/rock interactions and element concentration associated with plate tectonics; (b) high‐pressure regional metamorphism associated with plate tectonics; and (c) biologically mediated mineralization—are not known to be in play on Mars. Consequently, we estimate the total mineral diversity of Mars to be an order of magnitude smaller than on Earth.