Miracle-dz/UniFAR-FTUs huggingface dataset:
VoiceFixer: Toward General Speech Restoration With Neural Vocoder

Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on single-task speech restoration (SSR), such as speech denoising or speech declipping. However, SSR systems only focus on one task and do not address the general speech restoration problem. In addition, previous SSR systems show limited performance in some speech restoration tasks such as speech super-resolution. To overcome those limitations, we propose a general speech restoration (GSR) task that attempts to remove multiple distortions simultaneously. Furthermore, we propose VoiceFixer, a generative framework to address the GSR task. VoiceFixer consists of an analysis stage and a synthesis stage to mimic the speech analysis and comprehension of the human auditory system. We employ a ResUNet to model the analysis stage and a neural vocoder to model the synthesis stage. We evaluate VoiceFixer with additive noise, room reverberation, low-resolution, and clipping distortions. Our baseline GSR model achieves a 0.499 higher mean opinion score (MOS) than the speech enhancement SSR model. VoiceFixer further surpasses the GSR baseline model on the MOS score by 0.256. Moreover, we observe that VoiceFixer generalizes well to severely degraded real speech recordings, indicating its potential in restoring old movies and historical speeches. The source code is available at https://github.com/haoheliu/voicefixer_main.
Introduce the Result Into Self-Attention

Chengcheng Ye Jilin University netyuki@163.com Abstract——Traditional self-attention mechanisms in convolutional networks tend to use only the output of the previous layer as input to the attention network, such as SENet, CBAM, etc. In this paper, we propose a new attention modification method that tries to get the output of the classification network in advance and use it as a part of the input of the attention network. We used the auxiliary classifier proposed in GoogLeNet to obtain the results in advance and pass them into attention networks. we added this mechanism to SE-ResNet for our experiments and achieved a classification accuracy improvement of at most 1.94% on cifar100.
Freeze-in from preheating

We consider the production of dark matter during the process of reheating after inflation. The relic density of dark matter from freeze-in depends on both the energy density and energy distribution of the inflaton scattering or decay products composing the radiation bath. We compare the perturbative and non-perturbative calculations of the energy density in radiation. We also consider the (likely) possibility that the final state scalar products are unstable. Assuming either thermal or non-thermal energy distribution functions, we compare the resulting relic density based on these different approaches. We show that the present-day cold dark matter density can be obtained through freeze-in from preheating for a large range of dark matter masses.
LAGRANGIAN CLASSES AND SEMISTABLE MUKAI VECTORS FOR K3 SURFACES

Motivated by the study of growth rate of the number of geodesics in ﬂat surfaces with bounded lengths, we study generalizations of such problems for K3 surfaces. In one generalization, we give an upper bound on the asymptotics of the number of irreducible special Lagrangian classes in K3 surfaces with bounded period integrals. In another generalization, we give the leading term of the counting function of the number of semistable classes with bounded central charges for generic Bridgeland stability conditions on algebraic K3 surfaces.
A multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method for bearing fault diagnosis under the situation of insufficient labeled samples

Intelligent data-driven fault diagnosis methods have been widely applied, but most of these methods need a large number of high-quality labeled samples. It costs a lot of labor and time to label data in actual industrial processes, which challenges the application of intelligent fault diagnosis methods. To solve this problem, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method is proposed for the bearing fault diagnosis under the insu ﬃ cient labeled samples situation. This method includes three stages: pre-training, deep clustering and enhanced supervised learning. In the ﬁrst stage, a skip-connection based convolutional auto-encoder (SCCAE) is proposed and pre-trained to automatically learn low-dimensional representations. In the second stage, a semi-supervised improved deep embedded clustering (SSIDEC) model that integrates the pre-trained auto-encoder with a clustering layer is proposed for deep clustering. Additionally, virtual adversarial training (VAT) is introduced as a regularization term to overcome the overﬁtting in the model’s training. In the third stage, high-quality clustering results obtained in the second stage are assigned to unlabeled samples as pseudo labels. The labeled dataset is augmented by those pseudo-labeled samples and used to train a bearing fault discriminative model. The e ﬀ ectiveness of the method is evaluated on the Case Western Reserve University (CWRU) bearing dataset. The results show that the method can not only satisfy the semi-supervised learning under a small number of labeled samples, but also solve the problem of unsupervised learning, and has achieved better results than traditional diagnosis methods. This method provides a new research idea for fault diagnosis with limited labeled samples by e ﬀ ectively using unsupervised data.
FastMVAE2: On Improving and Accelerating the Fast Variational Autoencoder-Based Source Separation Algorithm for Determined Mixtures

This article proposes a new source model and training scheme to improve the accuracy and speed of the multichannel variational autoencoder (MVAE) method. The MVAE method is a recently proposed powerful multichannel source separation method. It consists of pretraining a source model represented by a conditional VAE (CVAE) and then estimating separation matrices along with other unknown parameters so that the log-likelihood is non-decreasing given an observed mixture signal. Although the MVAE method has been shown to provide high source separation performance, one drawback is the computational cost of the backpropagation steps in the separation-matrix estimation algorithm. To overcome this drawback, a method called “FastMVAE” was subsequently proposed, which uses an auxiliary classifier VAE (ACVAE) to train the source model. By using the classifier and encoder trained in this way, the optimal parameters of the source model can be inferred efficiently, albeit approximately, in each step of the algorithm. However, the generalization capability of the trained ACVAE source model was not satisfactory, which led to poor performance in situations with unseen data. To improve the generalization capability, this article proposes a new model architecture (called the “ChimeraACVAE” model) and a training scheme based on knowledge distillation. The experimental results revealed that the proposed source model trained with the proposed loss function achieved better source separation performance with less computation time than FastMVAE. We also confirmed that our methods were able to separate 18 sources with a reasonably good accuracy.
Exploring the Role of Local and Global Explanations in Recommender Systems

Explanations are well-known to improve recommender systems’ transparency. These explanations may be local, explaining individual recommendations, or global, explaining the recommender model overall. Despite their widespread use, there has been little investigation into the relative benefits of the two explanation approaches. We conducted a 30-participant exploratory study and a 30-participant controlled user study with a research-paper recommender to analyze how providing local, global, or both explanations influences user understanding of system behavior. Our results provide evidence suggesting that both are more helpful than either alone for explaining how to improve recommendations, yet both appeared less helpful than global alone for efficiently identifying false positive and negative recommendations. However, we note that the two explanation approaches may be better compared in a higher-stakes or more opaque domain.
JFB: Jacobian-Free Backpropagation for Implicit Networks

A promising trend in deep learning replaces traditional feedforward networks with implicit networks. Unlike traditional networks, implicit networks solve a fixed point equation to compute inferences. Solving for the fixed point varies in complexity, depending on provided data and an error tolerance. Importantly, implicit networks may be trained with fixed memory costs in stark contrast to feedforward networks, whose memory requirements scale linearly with depth. However, there is no free lunch --- backpropagation through implicit networks often requires solving a costly Jacobian-based equation arising from the implicit function theorem. We propose Jacobian-Free Backpropagation (JFB), a fixed-memory approach that circumvents the need to solve Jacobian-based equations. JFB makes implicit networks faster to train and significantly easier to implement, without sacrificing test accuracy. Our experiments show implicit networks trained with JFB are competitive with feedforward networks and prior implicit networks given the same number of parameters.
Quantitative mean-field limit for interacting branching diffusions

We establish an explicit rate of convergence for some systems of meaneld interacting di usions with logistic binary branching towards the solutions of nonlinear evolution equations with non-local self-di usion and logistic mass growth, shown to describe their large population limits in [11]. The proof relies on a novel coupling argument for binary branching di usions based on optimal transport, which allows us to sharply mimic the trajectory of the interacting binary branching population by certain system of independent particles with suitably distributed random space-time births. We are thus able to derive an optimal convergence rate, in the dual bounded-Lipschitz distance on nite measures, for the empirical measure of the population, from the convergence rate in 2-Wasserstein distance of empirical distributions of i.i. d. samples. Our approach and results extend techniques and ideas on propagation of chaos from kinetic models to stochastic systems of interacting branching populations, and appear to be new in this setting, even in the simple case of pure binary branching di usions. 2020 Mathematics Subject Classi cation: 92D25, 60J85, 60H30, 35Q92.
A Conserved Hydrophobic Moiety and Helix–Helix Interactions Drive the Self-Assembly of the Incretin Analog Exendin-4

Exendin-4 is a pharmaceutical peptide used in the control of insulin secretion. Structural information on exendin-4 and related peptides especially on the level of quaternary structure is scarce. We present the first published association equilibria of exendin-4 directly measured by static and dynamic light scattering. We show that exendin-4 oligomerization is pH dependent and that these oligomers are of low compactness. We relate our experimental results to a structural hypothesis to describe molecular details of exendin-4 oligomers. Discussion of the validity of this hypothesis is based on NMR, circular dichroism and fluorescence spectroscopy, and light scattering data on exendin-4 and a set of exendin-4 derived peptides. The essential forces driving oligomerization of exendin-4 are helix–helix interactions and interactions of a conserved hydrophobic moiety. Our structural hypothesis suggests that key interactions of exendin-4 monomers in the experimentally supported trimer take place between a defined helical segment and a hydrophobic triangle constituted by the Phe22 residues of the three monomeric subunits. Our data rationalize that Val19 might function as an anchor in the N-terminus of the interacting helix-region and that Trp25 is partially shielded in the oligomer by C-terminal amino acids of the same monomer. Our structural hypothesis suggests that the Trp25 residues do not interact with each other, but with C-terminal Pro residues of their own monomers.
Saliva and Plasma Reflect Metabolism Altered by Diabetes and Periodontitis

Periodontitis is an inflammatory disorder caused by disintegration of the balance between the periodontal microbiome and host response. While growing evidence suggests links between periodontitis and various metabolic disorders including type 2 diabetes (T2D), non-alcoholic liver disease, and cardiovascular disease (CVD), which often coexist in individuals with abdominal obesity, factors linking periodontal inflammation to common metabolic alterations remain to be fully elucidated. More detailed characterization of metabolomic profiles associated with multiple oral and cardiometabolic traits may provide better understanding of the complexity of oral-systemic crosstalk and its underlying mechanism. We performed comprehensive profiling of plasma and salivary metabolomes using untargeted gas chromatography/mass spectrometry to investigate multivariate covariation with clinical markers of oral and systemic health in 31 T2D patients with metabolic comorbidities and 30 control subjects. Orthogonal partial least squares (OPLS) results enabled more accurate characterization of associations among 11 oral and 25 systemic clinical outcomes, and 143 salivary and 78 plasma metabolites. In particular, metabolites that reflect cardiometabolic changes were identified in both plasma and saliva, with plasma and salivary ratios of (mannose + allose):1,5-anhydroglucitol achieving areas under the curve of 0.99 and 0.92, respectively, for T2D diagnosis. Additionally, OPLS analysis of periodontal inflamed surface area (PISA) as the numerical response variable revealed shared and unique responses of metabolomic and clinical markers to PISA between healthy and T2D groups. When combined with linear regression models, we found a significant correlation between PISA and multiple metabolites in both groups, including threonate, cadaverine and hydrocinnamate in saliva, as well as lactate and pentadecanoic acid in plasma, of which plasma lactate showed a predominant trend in the healthy group. Unique metabolites associated with PISA in the T2D group included plasma phosphate and salivary malate, while those in the healthy group included plasma gluconate and salivary adenosine. Remarkably, higher PISA was correlated with altered hepatic lipid metabolism in both groups, including higher levels of triglycerides, aspartate aminotransferase and alanine aminotransferase, leading to increased risk of cardiometabolic disease based on a score summarizing levels of CVD-related biomarkers. These findings revealed the potential utility of saliva for evaluating the risk of metabolic disorders without need for a blood test, and provide evidence that disrupted liver lipid metabolism may underlie the link between periodontitis and cardiometabolic disease.
Male and Female Rats Have Different Physiological Response to High-Fat High-Sucrose Diet but Similar Myocardial Sensitivity to Ischemia-Reperfusion Injury

Prediabetes is a strong predictor of type 2 diabetes and its associated cardiovascular complications, but few studies explore sexual dimorphism in this context. Here, we aim to determine whether sex influences physiological response to high-fat high-sucrose diet (HFS) and myocardial tolerance to ischemia-reperfusion injury. Male and female Wistar rats were subjected to standard (CTRL) or HFS diet for 5 months. Then, ex-vivo experiments on isolated perfused heart model were performed to evaluate tolerance to ischemia-reperfusion injury. HFS diet induced fasting hyperglycemia and increased body fat percent to a similar level in both sexes. However, glucose intolerance was more pronounced in female HFS. Cholesterol was increased only in female while male displayed higher level of plasmatic leptin. We observed increased heart weight to tibia length ratio only in males, but we showed a similar decrease in tolerance to ischemia-reperfusion injury in female and male HFS compared with respective controls, characterized by impaired cardiac function, energy metabolism and coronary flow during reperfusion. In conclusion, as soon as glucose intolerance and hyperglycemia develop, we observe higher sensitivity of hearts to ischemia-reperfusion injury without difference between males and females.
A Review on the Enhancement of Calcium Phosphate Cement with Biological Materials in Bone Defect Healing

Calcium phosphate cement (CPC) is a promising material used in the treatment of bone defects due to its profitable features of self-setting capability, osteoconductivity, injectability, mouldability, and biocompatibility. However, the major limitations of CPC, such as the brittleness, lack of osteogenic property, and poor washout resistance, remain to be resolved. Thus, significant research effort has been committed to modify and reinforce CPC. The mixture of CPC with various biological materials, defined as the materials produced by living organisms, have been fabricated by researchers and their characteristics have been investigated in vitro and in vivo. This present review aimed to provide a comprehensive overview enabling the readers to compare the physical, mechanical, and biological properties of CPC upon the incorporation of different biological materials. By mixing the bone-related transcription factors, proteins, and/or polysaccharides with CPC, researchers have demonstrated that these combinations not only resolved the lack of mechanical strength and osteogenic effects of CPC but also further improve its own functional properties. However, exceptions were seen in CPC incorporated with certain proteins (such as elastin-like polypeptide and calcitonin gene-related peptide) as well as blood components. In conclusion, the addition of biological materials potentially improves CPC features, which vary depending on the types of materials embedded into it. The significant enhancement of CPC seen in vitro and in vivo requires further verification in human trials for its clinical application.
In-Feed Supplementation of Resin Acid-Enriched Composition Modulates Gut Microbiota, Improves Growth Performance, and Reduces Post-Weaning Diarrhea and Gut Inflammation in Piglets

Simple Summary The weaning process can be detrimental to piglets and often leads to low feed intake, low weight gain, diarrhea, and eventually increased mortality. Resin acid-enriched composition (RAC) is an innovative natural feed ingredient that contains 90% fatty acids, mostly linoleic and oleic, pinolenic acids, and conjugated linoleic acid (CLA). It also contains 9% active component resin acid (RA), mostly abietic and dehydroabietic, and pimaric acids. It possesses antimicrobial, anti-inflammatory, and immunomodulatory properties and has been used in feed to improve performance in pigs. We hypothesized that RAC supplementation to sow and piglet diets might modulate intestinal microbiota, and improve post-weaning growth and reduce post-weaning diarrhea (PWD) and gut inflammation. Sow diets were supplemented with RAC from the last six weeks of gestation until weaning, and piglets were fed RAC trough creep feed and post weaning feed until seven weeks of age. Results indicated that supplementing sow and piglet diets with RAC during late gestation, lactation and at post weaning modulates gut microbiota, reduces inflammatory biomarkers, improves growth performance of post-weaning piglets, and prevents PWD in weaned piglets. Considering these results, RAC supplementation represents a potential strategy to improve piglet growth performance, and is a natural alternative to antibiotics for preventing PWD. Abstract The weaning process represents a delicate phase for piglets, and is often characterized by lower feed intake, lower weight gain, diarrhea, and ultimately increased mortality. We aimed to determine the effects of RAC supplementation in diets on improving piglet growth and vitality, reducing post-weaning diarrhea, and enhancing gut health. In a 2 × 2 × 2 factorial experiment, we selected forty sows and their piglets. Piglets were followed until seven weeks of age. There were no significant differences found between RAC treated and control piglets until weaning (p = 0.26). However, three weeks after weaning, RAC treated piglets had higher body weight and average daily growth (ADG) than the control piglets (p = 0.003). In addition, the piglets that received RAC after weaning, irrespective of mother or prior creep feed treatment, had lower post-weaning diarrhea (PWD) and fecal myeloperoxidase (MPO) level than control piglets. Gut microbiota analysis in post-weaning piglets revealed that RAC supplementation significantly increased Lachnospiraceae_unclassified, Blautia, Butyricicoccus, Gemmiger and Holdemanella, and decreased Bacteroidales_unclassified. Overall, RAC supplementation to piglets modulated post-weaning gut microbiota, improved growth performance after weaning, reduced post-weaning diarrhea and reduced fecal myeloperoxidase levels. We therefore consider RAC to be a potential natural feed supplement to prevent enteric infections and improve growth performance in weaning piglets.
Genotype Diversity and Spread of White Spot Syndrome Virus (WSSV) in Madagascar (2012–2016)

White Spot Disease (WSD) caused by the White Spot Syndrome Virus (WSSV) is the most devastating viral disease threatening the shrimp culture industry worldwide, including Madagascar. WDS was first reported on the island in 2012; however, little is known about the circulation of the virus and its genetic diversity. Our study aimed at describing the molecular diversity and the spread of WSSV in the populations of Madagascan crustaceans. Farmed and wild shrimps were collected from various locations in Madagascar from 2012 to 2016 and were tested for WSSV. Amplicons from positive specimens targeting five molecular markers (ORF75, ORF94, ORF125, VR14/15 and VR23/24) were sequenced for genotyping characterizations. Four genotypes were found in Madagascar. The type-I genotype was observed in the south-west of Madagascar in April 2012, causing a disastrous epidemic, then spread to the North-West coast. Type-II strains were detected in October 2012 causing an outbreak in another Penaeus monodon farm. In 2014 and 2015, types II and III were observed in shrimp farms. Finally, in 2016, types II and IV were found in wild species including Fenneropenaeus indicus, Metapenaeus monoceros, Marsupenaeus japonicus and Macrobrachium rosenbergii. Considering the economic importance of the shrimp industry for Madagascar, our study highlights the need to maintain WSSV surveillance to quickly take appropriate countermeasures in case of outbreak and to sustain this industry.