Miracle-dz/UniFAR-FTUs huggingface dataset:
Remixed2Remixed: Domain adaptation for speech enhancement by Noise2Noise learning with Remixing

This paper proposes Remixed2Remixed, a domain adaptation method for speech enhancement, which adopts Noise2Noise (N2N) learning to adapt models trained on artificially generated (out-of-domain: OOD) noisy-clean pair data to better separate real-world recorded (in-domain) noisy data. The proposed method uses a teacher model trained on OOD data to acquire pseudo-in-domain speech and noise signals, which are shuffled and remixed twice in each batch to generate two bootstrapped mixtures. The student model is then trained by optimizing an N2N-based cost function computed using these two bootstrapped mixtures. As the training strategy is similar to the recently proposed RemixIT, we also investigate the effectiveness of N2N-based loss as a regularization of RemixIT. Experimental results on the CHiME-7 unsupervised domain adaptation for conversational speech enhancement (UDASE) task revealed that the proposed method outperformed the challenge baseline system, RemixIT, and reduced the blurring of performance caused by teacher models.
Monitoring of nanoplasmonics-assisted deuterium production in a polymer seeded with resonant Au nanorods using in situ femtosecond laser induced breakdown spectroscopy

In this brief report, we present laser induced breakdown spectroscopy (LIBS) evidence of deuterium (D) production in a 3:1 urethane dimethacrylate (UDMA) and triethylene glycol dimethacrylate (TEGDMA) polymer doped with resonant gold nanorods, induced by intense, 40 fs laser pulses. The in situ recorded LIBS spectra revealed that the D/(2D+H) increased to 4-8% in the polymer samples in selected events. The extent of transmutation was found to linearly increase with the laser pulse energy (intensity) between 2 and 25 mJ (up to 3x10^17 W/cm). The observed effect is attributed only to the field enhancing effects due to excited localized surface plasmons on the gold nanoparticles.
Symplectic realizations and Lie groupoids in Poisson Electrodynamics

We define the gauge potentials of Poisson electrodynamics as sections of a symplectic realization of the spacetime manifold and infinitesimal gauge transformations as a representation of the associated Lie algebroid acting on the symplectic realization. Finite gauge transformations are obtained by integrating the sections of the Lie algebroid to bisections of a symplectic groupoid, which form a one-parameter group of transformations, whose action on the fields of the theory is realized in terms of an action groupoid. A covariant electromagnetic two-form is obtained, together with a dual two-form, invariant under gauge transformations. The duality appearing in the picture originates from the existence of a pair of orthogonal foliations of the symplectic realization, which produce dual quotient manifolds, one related with space-time, the other with momenta.
Identification of Opinion Leaders in a Telegram Network of Forwarded Messages

Unraveling the role of opinion leaders in the digital realm, this study investigates the influence of key actors on Telegram, a hybrid platform that combines messaging app features with social network dynamics, where channel administrators gain a unique authoritative role. This research aims to create a method to identify opinion leaders in a network of forwarded messages on Telegram, adapting a method originally developed to be applied to Twitter. The adapted method is showcased through a case study during the 2022 Brazilian Presidential Election, involving the monitoring of 25 pro-Bolsonaro groups. The findings contribute to understanding the dynamics of digital opinion leadership, particularly in politically charged environments.
Duality defects in $D_n$-type Niemeier lattice CFTs

We discuss the construction of duality defects in $c=24$ meromorphic CFTs that correspond to Niemeier lattices. We will illustrate our constructions for the $D_n$-type lattices. We will identify non-anomalous $\mathbb{Z}_2$ symmetries of these theories, and we show that on orbifolding with respect to these symmetries, these theories map to each other. We investigate this map, and in the case of self-dual orbifolds, we provide the duality defect partition functions. We show that exchange automorphisms in some CFTs give rise to a new class of defect partition functions.
Alternate Training of Shared and Task-Specific Parameters for Multi-Task Neural Networks

This paper introduces novel alternate training procedures for hard-parameter sharing Multi-Task Neural Networks (MTNNs). Traditional MTNN training faces challenges in managing conflicting loss gradients, often yielding sub-optimal performance. The proposed alternate training method updates shared and task-specific weights alternately, exploiting the multi-head architecture of the model. This approach reduces computational costs, enhances training regularization, and improves generalization. Convergence properties similar to those of the classical stochastic gradient method are established. Empirical experiments demonstrate delayed overfitting, improved prediction, and reduced computational demands. In summary, our alternate training procedures offer a promising advancement for the training of hard-parameter sharing MTNNs.
Endo-Hepatology: The Buzz Goes Much beyond Liver Biopsy—A Narrative Review

Abstract The word endo-hepatology evokes many perceptions. Some believe it to be the emerging new interventional branch and some believe it to be an extension of endoscopy. Nonetheless, it has continued to evolve into an exciting area of active work. With the advent of endoscopic ultrasound (EUS) based procedures, new vistas have opened up. EUS-guided liver biopsies, portal hypertension management, and multiple new procedures are being described. EUS also plays a large role in the management of patients in the peritransplant period. With this review, we present an overview of the role of EUS in the field of hepatology as well as during the peritransplant period and its applications.
Systematic Review and Meta-analysis of Randomized Controlled Trials on Manual Therapy for the Treatment of Gastroesophageal Reflux Disease

Abstract Objective The objective of our study was to systematically evaluate the clinical efficacy and safety of manual therapy in the treatment of gastroesophageal reflux disease (GERD), providing a reference value for clinical decision-making. Method Studies of randomized controlled trials (RCTs) evaluating the efficacy of manual therapy in patients with GERD were searched through Chinese National Knowledge Infrastructure, Wanfang, VIP China Science and Technology Journal Database, China Biology Medicine Database, PubMed, The Cochrane Library, OVID Medline, and Embase. Two researchers independently reviewed the literature, extracted data, and performed a risk of bias analysis using the Cochrane risk of bias assessment tool, and conducted meta-analysis analysis and publication bias evaluation, the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) tool 26 was used to rate the caliber of the data in this meta-analysis. Results This study included 11 RCTs. Meta-analysis showed that the manual therapy group had a higher total effective rate (odds ratio [OR] = 4.63, 95% confidence interval [CI; 3.01, 7.14], p < 0.00001) and better Reflux Disease Questionnaire scores {weighted mean difference (WMD) = −1.59, 95% CI [−2.85, −0.33], p = 0.01} than the control group. The subgroup analysis showed significant differences in improving the total effective rate in manual therapy versus Western medicine, manual therapy versus Chinese medicine decoction, manual therapy + Western medicine versus Western medicine, and manual therapy + conventional treatment versus conventional treatment groups. Among the 11 trials, 5 reported adverse events, and all RCTs had the possibility of publication bias. Subgroup analysis shows that the differences in age could significantly influence heterogeneity; The GRADE analysis revealed that the overall quality of evidence for all outcome indicators was low and did not support our recommendation for the outcome. Conclusion Manual therapy is more effective than medication therapy alone in relieving GERD symptoms. Furthermore, conventional therapy combined with manual therapy was found to be even more effective. Hence, it is crucial to consider these findings when applying manual therapy to GERD patients to enhance treatment outcomes. Future studies must address issues such as study quality, treatment duration, and generalizability.
Improving the Detection of Multilingual South African Abusive Language via Skip-gram using Joint Multilevel Domain Adaptation

The distinctiveness and sparsity of low-resource multilingual South African abusive language necessitate the development of a novel solution to automatically detect different classes of abusive language instances using machine learning. Skip-gram has been used to address sparsity in machine learning classification problems but is inadequate in detecting South African abusive language due to the considerable amount of rare features and class imbalance. Joint Domain Adaptation has been used to enlarge features of a low-resource target domain for improved classification outcomes by jointly learning from the target domain and large-resource source domain. This paper, therefore, builds a Skip-gram model based on Joint Domain Adaptation to improve the detection of multilingual South African abusive language. Contrary to the existing Joint Domain Adaptation approaches, a Joint Multilevel Domain Adaptation model involving adaptation of monolingual source domain instances and multilingual target domain instances with high frequency of rare features was executed at the first level, and adaptation of target-domain features and first-level features at the next level. Both surface-level and embedding word features were used to evaluate the proposed model. In the evaluation of surface-level features, the Joint Multilevel Domain Adaptation model outperformed the state-of-the-art models with accuracy of 0.92 and F1-score of 0.68. In the evaluation of embedding features, the proposed model outperformed the state-of-the-art models with accuracy of 0.88 and F1-score of 0.64. The Joint Multilevel Domain Adaptation model significantly improved the average information gain of the rare features in different language categories and reduced class imbalance.
Echolation as Modulation: A Case Study of Chus Pato and Erín Moure’s Secession/Insecession, Accompanied by a Fan Fiction of Moure’s Work More Generally

ABSTRACT This article takes a creative-critical approach to reading Erín Moure’s Secession/Insecession, a translation and echolation of Chus Pato Secesión, originally written in Galician. It subversively adopts the different species of Jean Vinay and Jean-Paul Darbelnet’s procedure of modulation from their 1958 comparative treatise Stylistique Comparée du français et de l’anglais to look at the way translator’s voice, somatic experience and political situation are shifted in Moure’s translation and creative rewriting. Across the different species of modulation, it charts the way that Moure’s echolation is a metacommentary as well as an archive of her translational experience and upon translation itself, modulating author with translator, writing with translation, but also modulating time and space between Pato’s realities and her own. Woven throughout these readings are connections to Moure’s work as a whole, in particular to the provocative heteronymic figure of Elisa Sampedrín, her intertextuality and translation techniques, and how they tell another story for translation. Finally, the body of the article is interrupted with prose-poetical passages in French that carry this reflection into the fan-fictional.
Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks

Background Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. Methods We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. Results Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. Conclusions In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.
Pair-EGRET: enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models

Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein-protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs. Here, we present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pre-trained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the three-dimensional structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we find that our method outperforms other state-of-the-art methods for predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix. Pair-EGRET is freely available in open source form at (https://github.com/1705004/Pair-EGRET).
Functional characterization of eQTLs and asthma risk loci with scATAC-seq across immune cell types and contexts

Cis-regulatory elements (CREs) control gene transcription dynamics across cell types and in response to the environment. In asthma, multiple immune cell types play an important role in the inflammatory process. Genetic variants in CREs can also affect gene expression response dynamics and contribute to asthma risk. However, the regulatory mechanisms underlying control of transcriptional dynamics across different environ-mental contexts and cell-types at single cell resolution remains to be elucidated. To resolve this question, we performed scATAC-seq in activated peripheral blood mononuclear cells (PBMC) from children with asthma with phytohemagglutinin (PHA) or lipopolysaccharide (LPS), and treated with dexamethasone (DEX), an anti-inflammatory glucocorticoid. We analyzed changes in chromatin accessibility, measured transcription factor motif activity, and identified treatment and cell-type specific transcription factors that drive changes in both gene expression mean and variability. We observed strong positive linear dependence between motif response and their target gene expression changes, but negative in variability changes. This result suggests that an in-crease of transcription factor binding tightens the variability of gene expression around the mean. We then annotated genetic variants in chromatin accessibility peaks and response motifs followed by computational fine-mapping of eQTL signals from a pediatric asthma cohort. We found that eQTLs were highly enriched in peaks with response motifs and refined the credible set of several asthma risk genes. In conclusion, scATAC-seq enhances the understanding of molecular mechanisms for asthma risk variants mediated by gene expression.
Natural disaster is a wakeup call before it becomes social disaster and tourophobia of eco-destinations

ABSTRACT This paper summarizes the current state of crisis and disaster at the eco-destination of Thailand and investigates its impact on tourists’ preferences. The study authenticates the claim of the inevitability of disasters at eco-destinations, explains the effects of timely response by relevant authorities, and provides inputs to diffuse the chaotic post-disaster situation and the development of tourophobia. It further notes the contribution of destination reputation and crisis communication to maintaining tourists’ trust and making Thailand the first choice for eco-tourism enthusiasts. This paper provides insights into effective post-disaster management before it turns eco-destinations into tourophobic destinations.
The EEG Signal Encryption Scheme Based on Hyperchaotic System and Identity Authentication

With the rapid development of brain-computer interfaces (BCI), their applications between the human brain and external devices is becoming more common, but with this comes the exposure of the existence of privacy protection issues. EEG signals contain sensitive information about an individual's thoughts, emotions, and health status, and there is a security risk of leakage or tampering when they are transmitted over open networks. To prevent the illegal acquisition and misuse of EEG signals, an EEG signal encryption scheme that combines the hyperchaotic system and identity authentication is proposed. First, a partial key is generated by hashing algorithm on the basis of identity and EEG signals. Then, the hyperchaotic system uses it as an initial value to generate a chaotic sequence. Finally, the generated random sequence is utilized for confusion and diffusion operations to achieve encryption of EEG signals. The experimental results show that the encryption scheme has high security, strong key sensitivity and anti-attack capability.
Formal Characterization of Malicious Code in Power Information Systems Based on Multidimensional Feature Fusion

Malicious code in power systems exhibits a wide variety of features, diverse origins, and complex forms compared to malicious code in other information systems. Analyzing malicious code based on a single dimension of features is inadequate to meet the research demands of such a complex spectrum. In this paper, we research into the multidimensional feature fusion of malicious code is beneficial for the comprehensive and accurate identification of potential threats and attacks, as well as improving the efficiency of malicious code analysis. To closely integrate the business operations and network structure of power information systems and organically combine multidimensional features such as assembly, byte, and image of malicious code within the environment, this paper proposes a method for feature fusion in power information systems based on multidimensional features. Furthermore, we experimentally validate this method for malicious code lineage analysis using graph convolutional neural networks.
Understanding interplay between destination psychological ownership and community citizenship behavior to support sustainable tourism development

ABSTRACT Local residents play a significant role in establishing the destination’s strong brand. This study intends to weigh the support of community residents for sustainable tourism development (STD) using the latent variables of community citizenship behavior (CCB), place attachment, and destination psychological ownership (DPO). Primary data gathered through an offline survey from 367 inhabitants in 15 municipal wards in Hyderabad, India, were analyzed using structural equation modeling and the bootstrap method. The empirical results of the study show that DPO had a significant effect on place identification, place dependence, and social bonding. Barring involving behavior, rest four dimensions of CCB significantly impacted residents’ support for STD. This research provides theoretical and managerial insights on inhabitants’ roles in the development of a popular location. Study results are based on 367 sample data.
Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement

Understanding neurodegenerative disease pathology depends on a close examination of neurons and their processes. However, image-based single-cell analyses of neurons often require laborious and time-consuming manual classification tasks. Here, we present a machine learning approach leveraging convolutional neural network (CNN) classifiers that have the capability to accurately identify various classes of neuronal images, including single neurons. We developed the Single Neuron Identification Model 20-Class (SNIM20) which was trained on a dataset of induced pluripotent stem cell (iPSC)-derived motor neurons, containing over 12,000 images from 20 distinct classes. SNIM20 is built in TensorFlow and trained on images of differentiated iPSC cultures stained for nuclei and microtubules. This classifier demonstrated high predictive accuracy (AUC = 0.99) for distinguishing single neurons. Additionally, the 2-stage training framework can be used more broadly for cellular classification tasks. A variation was successfully trained on images of a human osteosarcoma cell line (U2OS) for single-cell classification (AUC = 0.99). While this framework was primarily designed for single-cell microraft-based identification and capture, it also works with cells in standard plate formats. We additionally explore the impact of specific fluorescent channels and brightfield images, class groupings, and transfer learning on the quality of the classification. This framework can both assist in high throughput neuronal or cellular identification and be used to train a custom classifier for the user’s needs.
Decoding contextual influences on auditory perception from primary auditory cortex

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair’s perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that pitch-change selective cells are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the distances between the pitch representations predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch and pitch-direction selective cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.