Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychopathological Symptoms Among Chronically Ill Patients During SARS-CoV-2 Pandemic in Poland

Introduction The global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic had a drastic psychological and economic impact on the global population. Having a chronic disease during the pandemic is associated with numerous limitations and challenges like regular hospital visits, access to health-care units and getting specialized treatment. In addition, chronically ill patients are at great risk of acquiring the SARS-CoV-2 virus and at experiencing a more severe course of illness, due to comorbid conditions as well as more frequent encounters with health-care workers and other patients in medical facilities. The aim of this study was to examine the psychological disturbances, during the pandemic in chronically ill patients. Methods During the cross-sectional survey conducted between May and October 2020, 398 patients with four different chronic conditions (psoriasis, multiple sclerosis and patients who have undergone a kidney transplant or received dialysis). Study sample was examined regarding the occurrence of psychopathological symptoms (General Health Questionnaire 28) and their perceived stress levels (Perceived Stress Scale). Results The highest scores were found in the MS group and the lowest scores were found in the kidney transplantation group in every subscale of the GHQ-28. Close to half of the studied population (48.74%, n = 193) patients scored above the cut-off for psychopathology. Conclusion As the study was conducted during the SARS-CoV-2 pandemic in Poland, it stands to reason that the pandemic affected the psychological wellbeing of chronically ill patients. A COVID-19 infection, being quarantined and having had contact with a person who was infected, did not significantly affect the outcome measures; however, further research is needed to explore this topic.
Association between internal training load and muscle injuries in Brazilian professional soccer players

The training load is associated with injury risk in a variety of sports. This study aimed to evaluate the association between the internal training load and injury risk in Brazilian professional soccer players. The data were collected from 32 soccer players across two full seasons (2017 and 2018). The rating of perceived exertion (RPE) for every training/match session was used as an internal load variable. The cumulative training load from 3 and 4 weeks (C3 and C4) and the acute:chronic workload ratio (ACWR) were calculated. A generalized estimating equation analysis was applied to examine associations of non-contact muscle injuries with C3, C4 and ACWR. A total of 33 injuries were recorded across the two full seasons. A significant association was found between cumulative training load for three (C3, p = 0.003) and four weeks (C4, p = 0.023) and the occurrence of injuries. Players in the “high load” group presented greater injury risk in relation to the “moderate load” group (C4: OR = 4.5; IC 95% 1.5–13.3; C3: OR = 3.7; IC 95% 1.7–8.1). There was no association between ACWR and injury occurrence. The athletes exposed to a high cumulative load in a period of 3 to 4 weeks presented higher injury risk in comparison to those who had moderate cumulative training loads. Besides that, there was no association between ACWR and injury occurrence.
Approximate Prediction Intervals for Generalized Linear Mixed Models Having a Single Random Factor

Three methods to construct prediction intervals in a generalized linear mixed model (GLMM) are the methods based on pseudo-likelihood, Laplace, and Quadrature approximations. All three of these methods are available in the SAS procedure GLIMMIX. The pseudo-likelihood method involves approximate linearization of the GLMM into a linear mixed model (LMM) framework, and the other two methods utilize approximate conditional mean squared error (MSE) formulas for the empirical best predictor (eBP). A new method has been proposed based on the unconditional MSE of the eBP, working entirely within the GLMM context; then inherent computational challenges were confronted by proposing a Monte Carlo algorithm to evaluate the plug-in estimators of the unconditional MSE. For three illustrated examples, the negative binomial, the Poisson and the Bernoulli GLMMs, numerical results showed that our prediction interval methodology improves the coverage probability over the three methods available in GLIMMIX. Moreover, our results showed that with bootstrap adjustments, our method achieves coverage probabilities satisfactorily close to the nominal level.
Thermally Stable Single-Transistor Neuron for Reliable Neuromorphic System

A thermally stable single-transistor neuron (1T-neuron) that uses a vertical nanowire Si MOSFET is demonstrated. The concept of a single-transistor latch (STL) in a MOSFET, which is used to mimic neural firing in a biological neuron, is sensitive to the temperature (<inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula>) because it generally occurs due to impact ionization (I.I.). As an alternative to the thermally sensitive I.I. , thermally insensitive band-to-band tunneling (BTBT) is used to enable the STL to function. Neuronal operation with thermal stability is thereby achieved. It is verified in spiking neural network (SNN) simulations that the recognition ability of handwritten digits in the MNIST dataset was not sensitive to <inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula>.
BOOK REVIEW: Multipole Theory in Electromagnetism: Classical, Quantum and Symmetry Aspects, with Applications

1. Classical multipole theory 2. Quantum theory of multipole moments and polarizabilities 3. Space and time properties 4. Linear constitutive relations from multipole theory 5. Transmission and scattering effects: direct multipole results 6. Reflection effects: direct multipole results 7. Transformations of the response fields and the constitutive sensor 8. Applications of the gauge and Faraday transformations 9. Transmission and reflection effects: transformed multipole results
Isochronous is beautiful? Syllabic event detection in a neuro-inspired oscillatory model is facilitated by isochrony in speech

Oscillation-based neuro-computational models of speech perception are grounded in the capacity of human brain oscillations to track the speech signal. Consequently, one would expect this tracking to be more efficient for more regular signals. In this paper, we address the question of the contribution of isochrony to event detection by neuro-computational models of speech perception. We consider a simple model of event detection proposed in the literature, based on oscillatory processes driven by the acoustic envelope, that was previously shown to efficiently detect syllabic events in various languages. We first evaluate its performance in the detection of syllabic events for French, and show that “perceptual centers” associated to vowel onsets are more robustly detected than syllable onsets. Then we show that isochrony in natural speech improves the performance of event detection in the oscillatory model. We also evaluate the model’s robustness to acoustic noise. Overall, these results show the importance of bottom-up resonance mechanism for event detection; however, they suggest that bottom-up processing of acoustic envelope is not able to perfectly detect events relevant to speech temporal segmentation, highlighting the potential and complementary role of top-down, predictive knowledge.
Speech imitation skills predict automatic phonetic convergence: a GMM-UBM study on L2

Phonetic convergence is the observation that two interlocutors adapt their speech towards one another on an acoustic-phonetic level. It happens automatically and unconsciously, but people can also deliberately imitate others when asked to do so. Here, we investigate to what degree people converge to their interlocutor in a scripted dialogue when they are and when they are not explicitly requested to imitate their interlocutor. More specifically, we collected two separate data sets, where Italianand French-native participants read English sentences aloud in alternating speaking turns. The results of both groups with different language backgrounds were compared against each other. We used a Gaussian mixture model – universal background model (GMM-UBM) to assess phonetic convergence on the sentence level. The GMM-UBM configuration was optimized to make the best distinction between speakers on validation data. We found that people start to converge to one another while interacting compared to the baseline and even more substantially when explicitly asked to do so. Results are robust across data sets. More importantly, the degree of implicit convergence people display is related to how good of an explicit imitator they are, supporting the claim that the two phenomena are based on the same neurocognitive process.
Perceptual Characteristics Based Multi-objective Model for Speech Enhancement

Deep learning has been widely adopted for speech applica-tions. Many studies have shown that using the multiple objective framework and learned deep features is effective for improving system performance. In this paper, we propose a perceptual characteristics based multi-objective speech enhancement (SE) algorithm that combines the conventional loss and objective losses of pitch and timbre related features. Timbre related features include frequency modulation (encoded by the pitch contour), amplitude modulation (encoded by the energy contour), and speaker identity. For the speaker identity loss, we consider the deep features derived in a speaker identification system. The proposed algorithm consists of two parts, a LSTM based SE model and CNN based multi-objective models. The objective losses are derived between speech enhanced by the SE model and clean speech and combined with the SE loss for updating the SE model. The proposed algorithm is evaluated using the corpus of Taiwan Mandarin hearing in noise test (TMHINT). Experimental results show the proposed algorithm evidently outperforms the original SE model in all objective scores, including speech quality, speech intelligibility and signal distortion.
iDeepMMSE: An improved deep learning approach to MMSE speech and noise power spectrum estimation for speech enhancement

Deep learning approaches have been successfully applied to single channel speech enhancement exhibiting significant performance improvement. Recently, approaches unifying deep learning techniques into a statistical speech enhancement framework were proposed, including Deep Xi and DeepMMSE in which a priori signal-to-noise ratios (SNRs) were estimated by deep neural networks (DNNs) and noise power spectral density (PSD) and spectral gain functions were computed with estimated parameters. In this paper, we propose an improved Deep-MMSE (iDeepMMSE) which estimates the speech PSD and speech presence probability as well as the a priori SNR using a DNN for MMSE estimation of the speech and noise PSDs. The a priori and a posteriori SNRs are refined with the estimated PSDs, which in turn are used to compute spectral gain function. We also replaced the DNN architecture with the Conformer which efficiently captures the local and global sequential information. Experimental results on the Voice Bank-DEMAND dataset and Deep Xi dataset showed the proposed iDeepMMSE outperformed the DeepMMSE in terms of the perceptual evaluation of speech quality (PESQ) scores and composite objective measures.
Optimal models with maximizing probability of first achieving target value in the preceding stages

Decision makers often face the need of performance guarantee with some sufficiently high probability. Such problems can be modelled using a discrete time Markov decision process (MDP) with a probability criterion for the first achieving target value. The objective is to find a policy that maximizes the probability of the total discounted reward exceeding a target value in the preceding stages. We show that our formulation cannot be described by former models with standard criteria. We provide the properties of the objective functions, optimal value functions and optimal policies. An algorithm for computing the optimal policies for the finite horizon case is given. In this stochastic stopping model, we prove that there exists an optimal deterministic and stationary policy and the optimality equation has a unique solution. Using perturbation analysis, we approximate general models and prove the existence of ε-optimal policy for finite state space. We give an example for the reliability of the satellite systems using the above theory. Finally, we extend these results to more general cases.
A sufficient condition for nonsingularity of two-dimensional finite-gap Schr\"{o}dinger operator discretization of a quad graph

We construct a two-dimensional Schr\"{o}dinger operator on a quad-graph using finite-gap approach. A guad-graph is a planar graph with only 4-sided faces. We define the non-singularity of the operator as the positivity of all of his coefficients. At last, we prove some sufficient conditions of such positivity on the spectral curve and the graph.
Empirical Analysis of Customer Risk and Corporate Financing Constraints Based on Supply Chain Networks

A supply chain's risk spillover effect will affect the customer's risk on the financing constraints of suppliers. This paper builds on the evaluation of customer risk by fuzzy mathematics, combines with the A-share listed companies in Shanghai and Shenzhen from 2007 to 2019 as a study sample, and empirically inspects the influence of customer risk on the level of corporate financing constraints. According to the study, it shows that the customer risk is currently at a moderate level, which will notably impair the supplier's external financing ability. This phenomenon is more remarkable when the monetary policy is tightened with fierce competition in the industry. This paper unveils the economic consequences of customer risk spillovers from a supply chain, enriches the study of the generation mechanism of corporate financing constraints, and provides investors and regulators with empirical evidence to appreciate corporate financing constraints.
Sentiment Analysis on Massive Open Online Courses (MOOCs): Multi-Factor Analysis, and Machine Learning Approach

Massive open online courses (MOOCs) have evolved rapidly in recent years due to their open and massive nature. However, MOOCs suffer from a high dropout rate, since learners struggle to stay cognitively and emotionally engaged. Learner feedback is an excellent way to understand learner behaviour and model early decision making. In the presented study, the authors aim to explore learner sentiment expressed in their comments using machine learning and multi-factor analysis methods. They address several research questions on sentiment analysis on educational data. A total of 3311 messages, posted on a MOOC discussion forum, were analysed and categorized using machine learning and data analysis. The results obtained in this study show that it is possible to perform sentiment analysis with very high accuracy (94.1%), and it is also possible to periodically supervise the variations in learners' sentiments. The results of this study are very useful. In the context of online learning, it is very beneficial to have information about learner sentiment.
PISA: PoIncaré Saliency-Aware Interpolative Augmentation

Saliency-aware portion-wise mixup has proven to be an effective data augmentation technique for different modalities and tasks. However, it involves calculating the saliency over gradient vectors in the Euclidean space, representations that often possess complicated geometries and inherent hierarchical structure. We propose PISA, saliency-aware interpolative regularization operating in the hyperbolic space, to better capture the complex geometries of representations. To this end, we also formulate a saliency-aware mixup for speech signals. PISA outperforms existing state-of-the-art interpolative augmentation methods on 7 benchmark and low-resource datasets from the domains of speech signal processing and computer vision. PISA results in more stable training than existing data augmentation methods while being robust to adversarial attacks. It can be generalized across modalities, models and downstream tasks.
Characterization of the complete chloroplast genome of Homalocladium platycladum (Polygonaceae) and its phylogenetic analysis

Abstract Homalocladium platycladum is a fascinating ornamental plant that has long been used in Chinese medicine. Here, we characterize the complete chloroplast genome sequence of this plant (GenBank: NC_062330). This circular genome has a total length of 163,202 bp containing a large single-copy region (87,820bp), a small single-copy region (13,538bp), and a pair of inverted repeat regions (30,922bp). A total of 130 predicted genes were identified, including 85 protein-coding genes, 37 transfer RNA genes, and 8 ribosomal RNA genes. Phylogenetic analysis demonstrated that H. platycladum belongs to the Polygonaceae family and is highly analogous with Homalocladium and Muehlenbeckia families.
Multicenter Retrospective Analysis of Intradiscal Condoliase Injection Therapy for Lumbar Disc Herniation

Background and Objectives: Intradiscal injection of Condoliase (chondroitin sulfate ABC endolyase), a glycosaminoglycan-degrading enzyme, is employed as a minimally invasive treatment for lumbar disc herniation (LDH) and represents a promising option between conservative treatment and surgical intervention. Since its 2018 approval in Japan, multiple single-site trails have highlighted its effectiveness, however, the effect of LDH types, and influences of patient age, sex, etc., on treatment success remains unclear. Moreover, data on teenagers and elderly patients has not been reported. In this retrospective multi-center study, we sought to classify prognostic factors for successful condoliase treatment for LDH and assess its effect on patients < 20 and ≥70 years old. Materials and Methods: We reviewed the records of 137 LDH patients treated through condoliase at four Japanese institutions and assessed its effectiveness among different age categories on alleviation of visual analog scale (VAS) of leg pain, low back pain and numbness, as well as ODI and JOA scores. Moreover, we divided them into either a “group-A” category if a ≥50% improvement in baseline leg pain VAS was observed or “group-N” if VAS leg pain improved <50%. Next, we assessed the differences in clinical and demographic distribution between group-A and group-N. Results: Fifty-five patients were classified as group-A (77.5%) and 16 patients were allocated to group-N (22.5%). A significant difference in Pfirrmann classification was found between both cohorts, with grade IV suggested to be most receptive. A posterior disc angle > 5° was also found to approach statical significance. In all age groups, average VAS scores showed improvement. However, 75% of adolescent patients showed deterioration in Pfirrmann classification following treatment. Conclusions: Intradiscal condoliase injection is an effective treatment for LDH, even in patients with large vertebral translation and posterior disc angles, regardless of age. However, since condoliase imposes a risk of progressing disc degeneration, its indication for younger patients remains controversial.
A robust RGB-D SLAM based on multiple geometric features and semantic segmentation in dynamic environments

In indoor dynamic scenes, traditional visual simultaneous localization and mapping (SLAM) algorithms based on RGB-D cameras incorrectly use dynamic features to estimate the poses of the cameras, and do not fully utilize the geometric information in the scenes, resulting in the low positioning accuracy and robustness of SLAM systems. To solve this problem, this paper proposes an RGB-D SLAM algorithm based on multiple geometric features and semantic segmentation. The core of our SLAM system is the proposed robust exclusion method of dynamic point and line features. The method consists of the following three steps: (a) identify potential dynamic point features using motion consistency checks; (b) obtain potential motion regions via semantic segmentation, and then determine dynamic regions by combining these with dynamic point features; (c) remove point and line features in dynamic regions. The exclusion method of dynamic point and line features can be easily integrated into RGB-D SLAM systems for improving the accuracy and robustness of SLAM systems in dynamic scenes. Experimental results on the Technische Universität München (TUM) dataset demonstrate that the proposed algorithm has better positioning accuracy and stability than the original dynamic semantic (DS-SLAM) algorithm in dynamic environments. The effectiveness of the proposed algorithm is verified by comparison with other classical visual SLAM algorithms. Better mapping performance is achieved by this proposed algorithm in actual indoor scenes.
DAA Treatment Failure in a HIV/HBV/HCV Co-Infected Patient Carrying a Chimeric HCV Genotype 4/1b

Approved direct antiviral agent (DAA) combinations are associated with high rates of sustained virological response (SVR) and the absence of a detectable hepatitis C viral load 12–24 weeks after treatment discontinuation. However, a low percentage of individuals fail DAA therapy. Here, we report the case of a HIV/HBV/HCV co-infected patient who failed to respond to DAA pangenotypic combination therapy. The sequencing of NS5a, NS5b, NS3 and core regions evidenced a recombinant intergenotypic strain 4/1b with a recombination crossover point located inside the NS3 region. The identification of this natural recombinant virus underlines the concept that HCV recombination, even if it occurs rarely, may play a key role in the virus fitness and evolution.
Nonlinear Bubble Interactions in Acoustic Pressure Fields

The systems consisting of a two-phase mixture, as clouds of bubbles or drops, have shown many common features in their responses to different external force fields. One of particular interest is the effect of an unsteady pressure field applied to these systems, case in which the coupling of the vibrations induced in two neighboring components (two drops or two bubbles) may result in an interaction force between them. This behavior was explained by Bjerknes by postulating that every body that is moving in an accelerating fluid is subjected to a 'kinetic buoyancy' equal with the product of the acceleration of the fluid multiplied by the mass of the fluid displaced by the body. The external sound wave applied to a system of drops/bubbles triggers secondary sound waves from each component of the system. These secondary pressure fields integrated over the surface of the neighboring drop/bubble may result in a force additional to the effect of the primary sound wave on each component of the system. In certain conditions, the magnitude of these secondary forces may result in significant changes in the dynamics of each component, thus in the behavior of the entire system. In a system containing bubbles, the sound wave radiated by one bubble at the location of a neighboring one is dominated by the volume oscillation mode and its effects can be important for a large range of frequencies. The interaction forces in a system consisting of drops are much smaller than those consisting of bubbles. Therefore, as a first step towards the understanding of the drop-drop interaction subject to external pressure fluctuations, it is more convenient to study the bubble interactions. This paper presents experimental results and theoretical predictions concerning the interaction and the motion of two levitated air bubbles in water in the presence of an acoustic field at high frequencies (22-23 KHz).